Significant Implication of Optimal Expansion Planning in Wind Turbines Toward Composite Generating System Reliability

Significant Implication of Optimal Expansion Planning in Wind Turbines toward Composite Generating System Reliability

Muhammad Murtadha Othman1,2*, Nadia Hassin1, Ismail Musirin1,2 & Nur Ashida Salim1,2 1Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia; 2Committee of Research (CORE), Advanced Computing & Communication (ACC), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

This paper presents the stochastic approach deployed to perform expansion planning of the wind turbine generating (WTG) units in a power system. The expansion planning of WTG requires the information of its forced outage rate (FOR) determined by using the continuous-time Markov Chain. The FOR represents as an outage probability of a component emerged as it is taken out intentionally from a system customarily for the purpose of preventive maintenance or repair. In contrast with the conventional power plant, for the case study of WTG, the determination of FOR is utterly unique since it depends on the information of uncertain weather condition. Hence, the loss of load expectation (LOLE) and expected unserved energy (EUE) could be estimated so that it will not infringe its respective predetermined limits indispensable for the expansion planning of WTG using the stochastic approach. The modified IEEE RTS-79 system is used as a case study to verify effectiveness of the proposed method in the determination of WTG expansion planning. The results have shown improvement on the EUE and LOLE in relation with optimal expansion of the WTG units determined by using the proposed method.

Keywords: Optimal Expansion Planning; Wind Turbine Generating Units; Continuous-Time Markov Chain; Loss of Load Expectation; Expected Unserved Energy.

1. INTRODUCTION In particular, the inconsistency of WTG The Global Wind Energy Council availability is usually incurred due to uncertain prognosticate that wind power could supply one generation of the electric supply attributed by third of the world’s electricity by 20501. Wind unpredictable and intermittent situations of wind energy has many advantages to offer pertaining speed at the same site throughout the years. with energy generation that evokes a faster Recently, there are enormous numbers of development of wind turbine generator (WTG) research study have been carried out to discuss in future years. There are more than 50 countries on several factors rendering to an adverse effect around the world that have large progress in the reliability of WTG farm. It is worthwhile pertinent with energy supply generated by the to mention that the uncertain weather condition WTG and is well accepted by the end users1. In has become upheavals to the maintenance spite of several advantages that could be activities conducted to the WTG system2. In provided by the WTG, there is also a conjunction with this matter, high wind seasons disadvantage attributed by the WTG pertaining and harsh or adverse weather conditions will with its inconsistency or less reliable in delay the maintenance process of WTG and generating electric power supplied to a system unplanned failure of WTG may also emerge. as compared with the conventional power plant. These uncertain environments will dwindle the performance and reliability of the WTG system. *Email Address: [email protected] The time-sequential simulation technique used to analyse the reliability of generation system connected with the WTG system has

3 1 been introduced in . The three-state Markov 휆 = (푓푎푖푙푢푟푒 / 푦푒푎푟) model of WTG has been deployed to determine 푀푇퐵퐹 its forced outage rate (FOR) required for the (1) reliability analysis of the whole generation The µ may occur for every hour is system. Moreover, a scheme of assessing the calculated using equation (2). 1 reliability of generation system for a Brazilian 휇 = (푟푒푝푎푖푟/ 푦푒푎푟) 푀푇푇푅 region by employing the Markov model taking (2) into account the FOR of its WTG system has 4 The information of λ and µ is then used in been discussed in . the continuous-time Markov Chain to determine This paper presents the expansion planning the FOR of a WTG unit for a given year as of WTG system using the stochastic approach. depicted in Figure 1. The continuous-time The expansion of WTG system requires the information of its FOR in a given year Markov Chain is emanating from a three-state determined based on the continuous-time model designed specifically for the WTG Markov Chain of WTG entailed with the repair operation influenced by uncertain weather rate, µ, and failure rate, λ, influenced by the conditions. The uncertain weather conditions is wind speed. The continuous-time Markov Chain one of the known factors instigate to a common- is basically constructed based on the three-state cause failure of WTG operation. Therefore, the Markov model. The FOR can be referred to as a three-state model is composed with the two up level of security risk encountered by the WTG states and one down state. There is no output unit. The amalgamation of FOR obtained from power (Pt) produced during the particular hours both WTG and others generation systems are of exorbitant wind speed (sWt) that courses the used to determine reliability indices of expected down state (DOWN3) of WTG operation. The unserved energy (EUE) and loss-of-load WTG will restore back to its operational expectation (LOLE) for the whole generation condition when there is enough and acceptable system that will be an imperative information sWt at a given time. Hence, it is represented by for expansion planning. Robustness of the the transition state from DOWN3 to UP1 or stochastic approach used to undertake the from DOWN3 to UP2. On one hand, the WTG optimal expansion of WTG system is verified on also stops operating for preventive maintenance an IEEE RTS-79 system. and restore back after repair. Most of the wind

farm customarily contains several numbers of 2. METHODOLOGY WTG unit. The sW for each WTG unit is This section explicates on the application t assumed to be the same. As a result, the of continuous-time Markov Chain for summation of all electric power generated by the determining the FOR of a WTG unit for a given WTG units will become P produced by the year. The subsequent subsection will explain in- t WTG farm. The cut-in wind speed (V ) that is depth on the stochastic approach used to ci less than 3 m/s to 5 m/s will not shove the WTG perform the optimal expansion of WTG system to operate and produce electricity. It is whilst ensuring there is no infringement on the preferable to have a lower value of V due to the specified limitation of EUE and LOLE. ci fact that the WTG will operate and produce 2.1 ESTIMATION OF FORCED OUTAGE electricity albeit the sWt is low. The cut-off wind RATE USING THE CONTINUOUS- speed (Vco) of 20 (m/s) is formerly specified by TIME MARKOV CHAIN the manufacturer to protect the WTG from any Initially, the mean time between failure damage during its operation at exorbitant wind (MTBF) and mean time to repair (MTTR) is speed and Pt will not be generated. estimated for determining the respective values of failure rate (λ) and repair rate (µ)1. The MTBF is defined as the total hours of observation over the total number of random failures. On the overleaf, the MTTR can be expressed as the sum of all corrective maintenance divided by the total number of failures during observation interval. Hence, the number of failures that may occur for every hour is,

푏 (푒+ µ) P1 = (푎+푏)(푒+휆+µ+푓) (8) 푎 (µ+푑) P2 = (푎+푏)(휆+µ+푐+푑) (9) (휆+푓) (휆+ 푐) P3 = (푒+휆+µ+푓)(휆+µ+푐+푑) (10) The second method utilizes the transition diagram that is rather ominous in contrast to the stochastic transitional probability matrix. Nevertheless, both of the methods exert to the same results.

Fig. 1. Three-state model of WTG. Table I. State transition of three-state model By referring to Figure 1, the assigned of WTG. UP1 UP2 DOWN3 parameters resemble as given below. State (State 1) (State 2) (State 3) a = sWt < Vr ; b = sWt > Vr ; c = sWt < Vci ; d = sWt > Vci.; e = sWt < Vco ; f = sWt > Vco . WTG Operable Operable Failed where, Pr : rated power. Vr : rated wind speed. There are two methods used to compute Fig. 2. The transition diagram between UP1 and the FOR acquired from the three-state model of UP2. continuous-time Markov Chain. The first Figure 2 elucidates that the wind speed method uses the stochastic transitional (sWt) higher than the rated wind speed (Vr) is probability matrix to derive the transition delineated as the transition from UP1 to UP2 parameters of a, b, c, d, e, f, µ and λ imminent and vice versa. WTG are operable for both between the two up states, or between the up states to generate the electricity. states and down state, or vice versa5. The − 푎 푎 P = [ ] [P1 P2] (11) formation of stochastic transitional probability 푏 −푏 푏 matrix issuances from the three-state model of P1= (12) 푎+푏 푎 continuous-time Markov Chain can be obtained P2 = (13) from equation (3). 푎+푏 푃1̇ [푃2̇ ] = 푃3̇ −(푎 + 푓 + 휆) 푎 푓 + 휆 [ 푏 −(푏 + 푐 + 휆) 푐 + 휆 ] 푒 + µ 푑 + µ −(푒 + 푑 + 2µ) × [P1 P2 P3] (3) Detail derivation from the matrix may Fig. 3. The transition diagram between UP1 and divulge into, DOWN3. −(푎 + 푓 + 휆)푃1 + 푏푃2 + (푒 + µ)P3 = 0 In Figure 3, the WTG system is still (4) operable to generate electricity attributed by the aP1 + −(b + c + λ)P2 + (d + µ)P3 = 0 sW t that is lower than the cut-out wind speed (5) (Vco). Besides that, the WTG will not generate (f +λ )P1 +( c + λ)P2 + −(e + d + 2µ)P3 = 0 electricity in conjunction with the sWt higher (6) than the cut-out wind speed (Vco). P1 + P2 + P3 = 1 (7) P By solving the matrix equation using the − (휆 + 푓) 휆 + 푓 = [ ] [P1 P3] (14) substitution method, simultaneously the 푒 + µ −(푒 + µ) (푒+µ) probability of state equations can be expounded P1 = (15) as, (푒+µ+휆+푓) (휆+푓) P3 = (푒+µ+휆+푓)

(16) influenced by wind speed. Availability, A = P1 + P2 (29) Unavailability (FOR), U = P3 = 1 – A (30)

Fig. 4. The transition diagram between UP2 and 2.2 Wind turbine generation expansion DOWN3. planning determined by using the By referring to Figure 4, the WTG system stochastic approach is inhibited from generating the electricity due It is perspicuous in previous chapter that to the sWt lower than the cut-in wind speed the application of continuous-time Markov (Vci). The WTG system will restore back to an Chain to acquire the FOR value is essential in operable state in generating electricity abreast estimating the reliability indices of expected with the sWt higher than the Vci. unserved energy (EUE) and loss-of-load P expectation (LOLE) for the whole generation − (휆 + 푐) 휆 + 푐 system. Both of the reliability indices used as a = [ ] [P2 P3] (17) µ + 푑 −(µ + 푑) reference are vital to rein from excessive (µ+푑) numbers of WTG unit attained during the P2 = (18) (휆+µ+푐+푑) expansion planning. (휆+푐) P3 = The calculation of EUE and LOLE begins (휆+µ+푐+푑) with the determination of mean capacity outage (19) using equation (31). The steady state probabilities computed H(ki) = ∑푁푐 푋(푘)푃(푘) (31) from the transition diagram are given in the 푘=푘푖 where, ensuing equations. ki : minimum rated capacity. State UP1 = WTG operable. 푏 (푒+µ) Nc : maximum rated capacity. P1 = × (20) 푎+푏 (푒+µ+휆+푓) X(k) : capacity outage. 푏(푒+µ) P1 = (21) P(k) : incremental probability or FOR. (푎+푏)(푒+µ+휆+푓) The expected value of unserved load, State of UP2 = WTG operable. U(Li), is determined by, 푎 (µ+푑) P2 = × (22) U(Li) = 퐻(푘푖) + (퐿푖 − 퐶)푃́ (푘푖) 푎+푏 (휆+µ+푐+푑) 푎 (µ+푑) (32) P2 = (23) (푎+푏)(휆+µ+푐+푑) where, State of DOWN3 = WTG failed. Li : hourly peak load. (휆+푓) (휆+푐) P3 = × (24) C : total capacity. 푒+휆+µ+푓 (휆+µ+푐+푑) 푃́ (푘푖) : cumulative probability. (휆+푓) (휆+ 푐) P3 = (25) (푒+휆+µ+푓)(휆+µ+푐+푑) EUE (MWh) is defined as the capacity Generally, the frequency of each state is deficiency that is the expected amount of calculated by multiplying the state probability electricity not supplied by the generation system 6,7,8 (Pn) with the sum of departure rate from the in a given year . 푁푡 state as explicated below. EUE = ∆푇 ∑푖=1[ 퐻(푘푖) + (퐿푖 − f1 = P1 (푎 + 푓 + 휆) 퐶 ) 푃́ (푘푖)] (33) (26) where, f2 = P2 (푏 + 푐 + 휆) Nt : total hours in a year. (27) LOLE is an important reliability index in f3 = P3 (푑 + 푒 + 2µ) (28) power system used to indicate the risk of The availability represents as the customers’ demand disruption due to random probability of operable WTG system and the failure imminent in a system for several hours unavailability is probability of WTG system per year. In other words, the total loading failed to operate. This signifies that the WTG system transcends the availability of generation system is operating successfully during state 1 capacity can be construed in a reliability index (UP1) or state 2 (UP2). State 3 (DOWN3) is of LOLE written in equation (34)6,7,8. deciphered as the unavailability, wherein the ∆푇 푁푡 LOLE = ∑푖=1 푃푖 WTG system is failed to operate heuristically 푇 (34)

4 where, verify the effectiveness of the proposed method Pi = 푃́ (푘푖), represent as a deficient capacity used in the expansion of WTG system. The test probability during time interval i. system is configured with 32 existing generating The procedure of stochastic approach used units ranging from 12 MW to 400 MW and 24 to determine the expansion of WTG system buses having the total load demand of 2850 subject to the specified limit of LOLE and EUE MW. The base case generation capacity (MW) is explicated in the following steps. and FOR of each unit are tabulated in Tables II a) Calculate the failure rate, λ, and repair rate, and III, respectively. µ, using equations (1) and (2), respectively Table II. Existing generating units for the for each type of WTG unit available in the IEEE RTS-79 test system. market. Unit Unit Unit Unit Unit Unit b) Upload the wind speed, sWt, information. Are Bu 1 2 3 4 5 6 a s (M (M (M (M (M (M c) Specify the rated wind speed (Vr), cut-in W) W) W) W) W) W) wind speed (Vci) and cut-out wind speed 3 1 20 20 76 76 - - (Vco) for each type of WTG unit. 3 2 20 20 76 76 - - d) Determine the probability condition of wind 3 7 100 100 100 - - - speed for each state of P1, P2 and DOWN3 2 13 197 197 197 - - - using equations (8), (9) and (10), or 1 15 12 12 12 12 12 155 1 16 155 - - - - - equations (21), (23) and (25), respectively in 1 18 400 - - - - - tandem with every type of WTG unit. 1 21 400 - - - - - e) Use equations (29) and (30) to determine the 2 22 50 50 50 50 50 50 availability and unavailability (FOR) for 2 23 155 155 350 - - - every WTG unit, respectively. f) Generate randomly the number of WTG Table III. The force outage rate (FOR) of units available in step (a). Then, arrange the existing generating units. randomly generated WTG units with the Capacity Unit(s) FOR generating units inherently existed in the (MW) power system. 12 5 0.02 g) Determine the incremental probability P(ki) 20 4 0.1 ́ and cumulative probability 푃(푘푖) for each 50 6 0.01 generator outage X(ki) in equations (31) and 76 4 0.02 (32), respectively. 100 3 0.04 h) Calculate the mean capacity outage, H(ki), using equation (31). 155 4 0.04 i) Determine the expected value of unserved 197 3 0.05 load, U(Li), using equation (32) for every 350 1 0.08 hourly load at time interval i. 400 2 0.12 j) Use equations (33) and (34) to determine the In Table III, the existing generating units EUE and LOLE for the whole generation with its FOR will be arranged accordingly with system. the new WTGs having FOR during the k) Repeat steps (f)-(j) for several times and expansion planning undertaken by the record the list of WTG units, EUE and stochastic approach. The EUE and LOLE LOLE obtained from every iteration. prescribed during the base case condition of l) Retrieve the list of WTG units having the existing generating system are depicted in Table lowest value of EUE and LOLE. The IV. The EUE brings to an indicative meaning selected list of WTG units represent as the that the energy could not be delivered to a finest expansion planning of WTG system customer subject to the capacity limitation in a that will improve reliability condition of the generation system. The EUE with a large whole generation system. amount of 1,595 MWh obtained during the base case condition of existing generating units 3. RESULTS AND DISCUSSION signifies that there is a substantial amount of This section explicate on the expansion energy not served to the customer. This planning results of WTG system obtained by quandary is also indicated by the LOLE value using the stochastic approach and is affected by of 9.3446 hours/year transcends the standard the hourly wind speed information in one year. limit of 2.4 hours/year. Hence, this unearthly A case study of IEEE RTS-79 system is used to situation requires the expansion of WTG units

to overcome the quandary related to the reasonably enough to improve the reliability of substantial values of EUE and LOLE. generation system and serves with less In the expansion of WTG system using the interruption in producing electricity to the stochastic approach, the rated wind speed of customer. Vr=30 km/h, cut-in wind speed of Vci=20 km/h and cut-out wind speed of Vco=45 km/h are the 4. CONCLUSIONS parameters specified in the continuous-times The implementation of continuous-time Markov Markov Chain for every WTG unit. Each WTG Chain used to determine the forced outage rate unit is having the failure rate of λ=0.0417 (FOR) of wind turbine generating (WTG) units failure/year, repair rate of µ=0.125 repair/year, has been discussed in this paper. The FOR is availability of 0.9997 and unavailability of important for reliability studies in the optimal FOR=0.0003 determined by using equations expansion planning of WTG units undertaken (1), (2), (29) and (30), respectively in the by the stochastic approach. Effectiveness of the continuous-times Markov Chain. proposed method used in the WTG expansion Table IV. Base case EUE and LOLE for the planning has been verified in a test system of existing generating units. IEEE RTS-79. Compendium of the results Number Capacity EUE LOLE construe that the WTG expansion units of 365 of units (MW) (MWh) (hours/year) MW with a new total generation capacity of 5 12 3770 MW have attained to its optimal solution 4 20 in juncture with the expected unserved energy 6 50 (EUE) of 22.56 MWh and loss-of-load 4 76 expectation (LOLE) of 0.2426 hours/year that is 3 100 1,595 9.3446 below than its standard limitation. 4 155 3 197 ACKNOWLEDGEMENTS 1 350 The authors would like to thank the Research 2 400 Management Institute (RMI), Universiti Several runs of stochastic approach have Teknologi MARA, through research grant 600- been carried out in such a way that an in-depth IRMI/DANA5/3/BESTARI(0001/2016) and; analysis can be done thoroughly on the the Ministry of Education (MOE), Malaysia, expansion planning of WTG system. through research grant Table VI. Finest EUE and LOLE results for FRGS/2/2014/TK03/UITM/02/1 for the the expansion of WTG units. financial support of this research. Existing Wind turbine Generating system generator EUE LOLE REFERENCES Numbe Capacit Numbe Capacit (MWh (hours/yea [1] H.-J. Wagner, J. Mahtur, Introduction to wind energy r of y r of y ) r) systems, Springer-Verlag, Berlin Heidelberg (2009). units (MW) units (MW) [2] E. Byon, L. Ntaimo, C. Singh, Y. Ding, Handbook of 5 12 4 10 Wind Power Systems, Springer-Verlag, Berlin 4 20 5 15 Heidelberg (2013). 6 50 5 20 [3] P. Wang, R. Billinton, IEE Proceedings of 4 76 6 25 22.561 Generation,Transmission and Distribution 148, 4 3 100 - - 0.2426 6 (2002). 4 155 - - [4] A. P. Leite, C. L. T. Borges, IEEE Transactions on 3 197 - - Power Systems 21, 4 (2006). 1 350 - - [5] R. Ramakumar, Engineering reliability fundamentals 2 400 - - and applications, Prentice-Hall International Editions Hence, several runs of stochastic approach (1993). is carried out that eventually exert to the [6] M. M. Othman, N. Abd Rahman, I. Musirin, M. F. Firuzabad, A. R. Ghahnavieh, The Scientific World optimal WTG expansion solution of 365 MW Journal 2015 (2015). with a new total generation capacity of 3770 [7] M. M. Othman, N. Abd Rahman, and I. Musirin, MW as shown in Table VI. Simultaneously, Applied Mechanics and Materials 785 (2015). improvisation in the generation system [8] M.M. Othman, N. Abd Rahman, D.S. Sasi, and I. Musirin. Inter-area transfer capability assessment reliability is obtained with the EUE of 22.56 considering optimal flexible solutions of capacity MWh and LOLE of 0.2426 hours/year that is benefit margin. Power Engineering and Optimization less than the standard limit of 2.4 hours/year. Conference (PEOCO2012), (2012) June 6-7; Melaka, Malaysia. The results imply that the 20 units of WTG are

A Time-varying Finite Memory Structure Estimation Filter using Quadratic Programming

Pyung Soo Kim and Junsu Kim System Software Solution Lab., Dept. of Electronic Engineering, Korea Polytechnic University, 237 Sangidaehak-ro, Siheung-si, Gyeonggi-do, 429-793, KOREA . [email protected]

Abstract— In this paper, a discrete time-varying finite varying systems are quite often used for many memory structure estimation filter is proposed to practical applications such as detection, tracking and incorporate inequality constraints using a quadratic guidance in the aerospace industry, a discrete time- programming strategy. Since the past all observation varying finite memory structure estimation filter is data outside the most recent window is discarded, also very necessary to incorporate inequality solving the optimization does not consider the arrival cost for every windows. It is shown that the proposed constraints. time-varying estimation filter can be represented in the Therefore, in this paper, a discrete time-varying simple matrix form for the unconstrained case and has finite memory structure estimation filter is proposed unbiasedness and deadbeat properties. Computer to incorporate inequality constraints using a quadratic simulations for a sinusoidal signal model shows that the programming strategy of [4][5][8]. The past all proposed time-varying estimation filter gives a better observation data outside the most recent window is estimate compared with the existing infinite memory discarded and thus the arrival cost is not considered in structure estimation filter when there is a temporary solving the optimization for every windows. It is model uncertainty. shown that the proposed time-varying finite memory

Index Terms— Time-varying system; Quadratic structure estimation filter can be represented in the programming; Finite memory structure; Inequality simple matrix form for the unconstrained case and constraint. have good inherent properties such as unbiasedness and deadbeat. Via computer simulations for a I. INTRODUCTION sinusoidal signal model, it is shown that the proposed estimation filter gives a better estimate compared with As shown in [1]-[3], inequality constraints can be the existing infinite memory structure estimation filter easily incorporated within the Kalman filter using a such as Kalman filter for the temporarily uncertain quadratic programming if the data are processed in system. In addition, the window length can be batch fashion. However, due to the infinite memory considered as useful parameters to make the structure of the Kalman filter, the problem size grows performance of the proposed estimation filter as good with time as more data becomes available, which can as possible. be limited for real-time application. Therefore, a This paper is organized as follows. In Section II, a moving window formulation with incorporating time-varying finite memory structure estimation filter inequality constraints was applied to resolve this using quadratic programming is proposed and its issue[4]-[7]. Among them, the least squares inherent good properties are shown. In Section III, optimization is performed over a fixed length window computer simulations are performed. Finally, to bound the size of the quadratic program as shown concluding remarks are presented in Section IV. in [4][5]. However, this moving window estimation filtering still has the infinite memory structure. Hence, II. TIME-VARYING FINITE MEMORY STRUCTURE an alternative moving window estimation filter was ESTIMATION FILTER USING QUADRATIC proposed for discrete time-invariant systems using the PROGRAMMING quadratic program[8], which has the finite memory structure. The finite memory structure in estimation A discrete time-varying state-space model is filters has been known to have inherent properties represented by such as unbiasedness and deadbeat, and be robust against temporary modeling uncertainties and round- off errors[9]-[13]. However, since discrete time-

xi+1 = i xi + Gi wi ,  zi   vi   wi  (1)  M   M   M  zi = Hi xi + vi , z v w Z   iM +1, V   iM +1, W   iM +1 i    i    i    n q       where x  R is the unknown state vector, z  R i i  zi−1   vi−1   wi−1  is the measured observation vector. The system noise p q wi  R and the observation noise vi  R are and zero-mean white Gaussian whose covariances Qi and  H  −1  R are assumed to be positive definite matrix. The iM i,M i  −1  H   random variable w typically models unmeasured iM +1 i,M −1 i   i   , i, j  i−1i−2 i− j , (4) disturbances and model inaccuracies, while the  −1   Hi−2i,2  random variable vi is measurement noise.  −1  Usually, the range of unknown variables such as  Hi−1i,1  states xi and disturbances wi can be known, and thus they can be formed as following inequality H  −1G H  −1G  H  −1 G  i−1 i,1 i−1 i−2 i,2 i−2 iM i,M iM  −1 −1  constraints:  0 H  G  H  G    i−1 i,1 i−1 iM +1 i,M −1 iM +1 . i         xi X  {xi : Di xi  di},  0 0  H  −1G  (2)  i−1 i,1 i−1  wi W  {wi : Eiwi  ei}, On the most recent window [iM ,i] for the current n n n n x w nx time i , a finite memory structure estimation filter is where Di  R , Ei  R , di  R and obtained from the solution of the following quadratic nw ei  R . If these constraints (2) can be incorporated program in estimating states xi and disturbances wi , better * estimates can be obtained as shown in [4]-[8]. In order J M = minJ M(xi ,Wi ) x ,W to solve the least squares problem, optimization i i software such as quadratic programming or nonlinear programming is used. When the quadratic subject to (1) and inequality constraints (2) where programming incorporates inequality constraints of (2), these inequality constraints can be placed on the J M (xi ,Wi ) T unknown variables. However, on-line computation of   xi  −1   xi  Z −    Ri 0  Z −    =  i i i    i i i   the quadratic programming is limited in actual  Wi     Wi   0 Qi  applications because the size of the problem grows as  Wi   Wi  T (5) more data become available. Hence, a moving   x    x   i  −1 i  window formulation strategy can be applied for a =  Zi − i i   Ri  Zi − i i    Wi   Wi  fixed dimension quadratic program as shown in T −1 [4][5][8]. That is, the optimization problem is +Wi Qi Wi T  T −1 T −1  formulated on the most recent window [i − M ,i] with  xi  i Ri i i Ri i  xi  =   W  T −1 T −1 −1 W  fixed window length M . The window initial time  i  i Ri i i Ri i + Qi  i   xi  i − M will be denoted by iM hereafter for simplicity. − 2Z TR −1   + Z TR −1Z i i i i W  i i i The discrete time-varying system (1) can be  i  represented in a batch form on the most recent with weighting matrices given by window [i ,i] . The finite number of observations M T Qi  diag(Qi Qi +1  Qi−1) , Z i is expressed in terms of the state xi at the current M M T time i as follows: R  diag(R R  R ) . i iM iM +1 i−1

 xi  Then, the optimal state and disturbance estimates are Zi = i xi + iWi +Vi = i i   +Vi (3) ˆ Wi  denoted by xˆi and Wi given finite number of

observations Zi. Particularly, if where ˆ (xˆi ,Wi ) = arg min J M(xi ,Wi ) x ,W M iM i

then the proposed time-varying finite memory the observations Z i is determined by the current state structure estimation filter xî denotes the solution to xi as Zi = i xi. Therefore, the following is obtained (5) at current time i . The obtained time-varying directly from the proof of the unbiasedness property: estimation filter xî has the finite memory structure ˆ since only the finite number of observations Z i on the xi = xi most recent window [iM ,i] is utilized. This completes the proof of the deadbeat property. It is shown that the proposed time-varying ■ estimation filter can be represented in a simple matrix The deadbeat property in Theorem 1 means that the form when there are no constraints. Taking a proposed time-varying finite memory structure derivation of (5) with respect to xi and setting it to estimation filter provides exact state when in actual zero, the proposed time-varying estimation filter xî there are no noises as in (7) although it is designed for is given by following simple matrix form: the system (1) with noises. Note that the deadbeat property indicates the finite convergence time and the −1 fast estimation ability of the proposed time-varying ˆ T T −1 T T −1 xi = (i (iQii +Ri ) i ) i (iQii +Ri ) Zi . estimation filter. Thus, it can be expected that the (6) proposed estimation filter would be appropriate for fast estimation and detection of signals with unknown T T −1 It is noted that the matrix i (iQii +Ri ) i is times of occurrence. In addition, these good inherent nonsingular if and only if the matrix  is of full rank, properties such as unbiasedness and deadbeat in the i proposed approach cannot be obtained by existing T since the matrix iQii +Ri is positive definite. The infinite memory structure estimation filter such as matrix i is of full rank if i , Hi is observable for Kalman filter.

ˆ M  n . For the unconstrained case, xi can be shown III. NUMERICAL SIMULATIONS to have an unbiasedness property when there are noises and a deadbeat property when there are no Computer simulations are performed for a following noises. The unbiasedness of the estimate means that sinusoid signal model with an uncertain model its mean value tracks the mean value of the state at parameter  i to compare with the existing infinite every time. The deadbeat of the estimate means that memory structure estimation filter such as Kalman its value tracks exactly the state at every time. filter:

Theorem 1. When there are no constraints, the cos( / 32) +  sin( / 32)  1 proposed time-varying finite memory structure i i =  , Gi =   , (8)  − sin( / 32) cos( / 32) + i  1 estimation filter xˆi is unbiased for noisy and disturbed systems and exact for noise-free and Hi = 1 1. disturbance-free systems. In this simulation, to reflect the knowledge of the Proof. When there are noise and disturbance on the random variable wi , the inequality constraints wi  0 window is assumed. That is, the system noise wi = i where [iM ,i] , Since Vi is zero-mean in (3), i is zero mean and normally distributed random E[Zi ] = i E[xi ] . Therefore, the following is true: 0.12 I. variable with covariance The observation T T −1 −1 T T −1 noise vi is zero mean and normally distributed Exˆi = (i (iQii +Ri ) i ) i (iQii +Ri ) EZi  random variable with covariance 0.22 I. T T −1 −1 T T −1 = (i (iQii +Ri ) i ) i (iQii +Ri ) i Exi  The proposed time-varying finite memory structure = Exi  estimation filtering and the Kalman filtering are compared for the temporarily uncertain system. The This completes the proof of the unbiasedness uncertain model parameter is taken by two cases, property.  i = 0.08 and  i = 0.1 for the interval 100  i 150 . When there are no noise and disturbance on the In addition, the window length is taken by two cases, window [iM ,i] as M =10 and M =15 for the proposed estimation filtering. As shown in Figure 1 and 2, the estimation xi+1 = i xi , zi = Hi xi , (7) error of the proposed estimation filtering is smaller than that of the Kalman filtering on the interval where modeling uncertainty exist for all cases. In addition, the convergence of estimation error is much faster

than that of the Kalman filtering after temporary modeling uncertainty disappears. This phenomenon is more remarkable when the model is more uncertain, that is, i = 0.1. Of course, the Kalman filtering can outperform the proposed estimation filtering after the effect of temporary modeling uncertainty completely disappears. Therefore, the proposed estimation filtering can be more robust than the Kalman filtering when applied to temporarily uncertain systems, although the proposed estimation filtering is designed with no consideration for robustness. Moreover, it can be known that a large window length may yield the long convergence time of the estimation error and thus degrades the performance of the proposed estimation filtering. Therefore, it can be stated that the Figure 2: Estimation error when M =15 window length M can be considered as an useful parameter to make the performance of the proposed ACKNOWLEDGMENT estimation filtering as good as possible. This research was supported by Basic Science IV. CONCLUDING REMARKS Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of This paper has proposed a time-varying finite memory Education (NRF-2017R1D1A1B03033024). structure estimation filter to incorporate inequality constraints using a quadratic programming strategy. REFERENCES The arrival cost has not been considered when the optimization is solved for every windows. The [1] N. Gupta and R. Hauser, “Kalman filtering with equality and inequality state constraints,” ArXiv e-prints, 2007. proposed time-varying estimation filter has been [2] D. Simon, “Kalman filtering with state constraints: a survey shown to be represented in the simple matrix form for of linear and nonlinear algorithms,” IET Control Theory & the unconstrained case and have good inherent Applications, vol. 4, no. 8, pp. 1303~1318, 2010. properties such as unbiasedness and deadbeat. [3] X. Xinjilefu, S. Feng, and C. G. Atkeson, “Dynamic state estimation using quadratic programming,” in Proc. of 2014 Computer simulations has shown that the proposed IEEE Conference on Intelligent Robots and Systems, 2014, time varying estimation filter gives a better estimate pp. 990~994. compared with the existing Kalman filter with infinite [4] C. V. Rao, J. B. Rawlings, and J. H. Lee, “Constrained linear memory structure for the temporarily uncertain state estimation - a moving horizon approach,” Automatica, vol. 37, no. 10, pp. 1619~1628, 2001. system. Moreover, the window length can be [5] Z. Wang, Z. Liu, X Ban, R. Pei, “A study on recursive considered as useful parameters to make the algorithm of moving horizon estimation,” in Proc. of the 2003 performance of the proposed time-varying estimation IEEE Conference on Control Applications, Istanbul, Turkey, filtering as good as possible. Turkey, June 2003. [6] H. Qin and W. Chen, “Application of the constrained moving horizon estimation method for the ultra-short baseline attitude determination,” Acta Geodaetica et Geophysica, vol. 48, no. 1, pp. 27~38, 2013. [7] X. Zhao, H. Qin, L. Cong, D. Yang, “Application of the constraint moving horizon estimation method for GPS/SINS integrated navigation system,” in Proc. of the 2014 International Technical Meeting of the Institute of Navigation, San Diego, CA, USA, pp. 568~573, Jan 2014. [8] P. S. Kim and Y. S. Lee, “A constrained receding horizon estimator with FIR structures,” Transactions on Control, Automation and Systems Engineering, vol. 3, no. 4, pp. 289~292, 2001. [9] P. S. Kim, “An alternative FIR filter for state estimation in discrete-time systems,” Digital Signal Processing, vol. 20, no. 3, pp. 935~943, 2010. [10] S. Zhao, Y. S. Shmaliy, B. Huang, and F. Liu, “Minimum variance unbiased FIR filter for discrete time-variant systems,” Automatica, vol. 53, no. 2, pp. 355~361, 2015. Figure 1: Estimation error when M =10 [11] P. S. Kim, M. S. Jang, S. Y. Kang, and E. H. Lee, “A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay,” International Journal of Distributed Sensor Networks, vol. 13, no. 1, pp. 1~8, 2017. [12] J. M. Pak, P. S. Kim, S. H. You, S. S. Lee, and M. K. Song, “Extended least square unbiased FIR filter for target tracking using the constant velocity motion model,” International

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Surface roughness prediction in minimum quantity lubrication grinding of Al6061-T6: ANFIS modelling

M Sayuti 1, *, Yusuf S. Dambatta1, Ahmed A. D. Sarhan2, 3, M. Hamdi1, SM. Manladan1, 4

1 Centre of Advanced Manufacturing and Material Processing, Department of Mechanical Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. 2Department of Mechanical Engineering, College of Engineering Sciences and Applied Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Kingdom of Saudi Arabia. 3Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut 71516, Egypt 4Department of Mechanical Engineering, Bayero University Kano, Nigeria. [email protected]

Abstract— Aluminium alloys have recently seen makes them suitable candidates to substitute steel and increased applications in various engineering fields such cast iron in different components of the auto vehicles. as automotive and aerospace engineering due to their Also, the low weightiness of these alloys greatly reduces light weight, high strength, high stiffness to weight ratio, environmental hazards and emission of greenhouse improved crashworthiness, excellent corrosion gasses arising from excessive burning of fuels [2]. The resistance, and high heat resistance. In this work, an Al6061 alloy is mostly used to manufacture car wheels, experimental investigation of surface grinding was panels, brake discs and brake drums [1]. performed during minimum quantity nanolubrication Aluminium alloys are normally fabricated into various grinding of aluminium alloy Al6061-T6 using silicon automotive components using either high speed steel, dioxide nanoparticle. Adaptive neuro fuzzy inference diamond or carbide tools. However, silicon nitride based system (ANFIS) prediction modelling was used to tools are mostly unsuitable while machining aluminium analyze and predict the effect of four grinding due to the high solubility of the silicon in aluminium. parameters i.e. nano-particle concentration, depth of cut, The main objective of using any machining process to feed rate and nozzle stand-off distance, on the surface fabricate the aluminium alloys is to obtain reduced roughness. The ANFIS model shows that smaller values chatter, low surface roughness and high efficiency [3]. of feed rate, amount of the nanoparticle concentrations, Surface grinding is one of the most utilized secondary and depth of cut produces better surface roughness. manufacturing system that is used to achieve high surface Moreover, an optimal range for stand-off distance of 50- finish in products with high degree of accuracy and 55 µm was found for lower surface roughness. In tolerance. The grinding process is characterized by high addition, the experiments conducted for verification of specific energy, which could be attributed to the the surface roughness shows that the constructed ANFIS kinematics of the process, and also process specifications model has an accuracy of 96.8%. such as abrasive shape, abrasive size, direction of grinding, and concentration of the abrasive grains. In Keywords: Grinding, MQL; ANFIS; Al6061-T6 alloy; addition, this high energy expended in the grinding Surface roughness; SiO2 Nanofluid. process results in increased temperature around the grinding region. The high temperature aids material I. INTRODUCTION removal (via softening action), but it is often detrimental to the workpiece surface quality [4]. As such appropriate Aluminium alloys have been used to manufacture evacuation of the heat produced during the grinding various parts of aircraft and auto vehicles since early operation is extremely important. Flood cooling is the 1930s. The alloys are classified based on series i.e. 2xxx, traditional method often utilized as either lubricant or 7xxx, and 6xxx series [1]. The aluminium alloys are coolant during grinding operations. Although it performs characterized by high strength-to-weight ratios, which 12

well in cooling and debris evacuation, the flood coolant’s II. EQUIPMENT & METHODOLOGY performance in terms of lubrication is very bad. Also, the The work material utilized in this work is the aerospace issues of disposal, recycling, and health risks to the aluminium tempered grade 6061 of tensile strength operator is a major limitation of the flood cooling process between 260-310MPa and Vickers hardness of 105HV. [5,6]. The arrangement of the apparatus used in this study is Recently, there has been increase in the campaign for shown in Fig. 1a & b. Also, the range of parameters and the need to minimize the use of cutting fluids due to their their symbols used in this experiment are tabulated in negative impact on the environment. Also, the lubrication Table 1. techniques accounts for the most of the cost of manufacturing. As such, excessive utilization of the MQL-nozzle cutting fluids should be limited. Moreover, green Grinding manufacturing techniques have shown to be viable wheel options to achieving a near dry machining process, with lower deformations [7]. The MQL have been found to be a suitable candidate in replacing the flood cooling, thereby achieving near dry machining. Also, the cost of manufacturing is significantly decreased by the MQL process, because the rate of consumption of lubricant in the MQL system is about 10,000 times less than the flood cooling method [5,8]. Sample The MQL process involves spraying a minute amount Work-table of oil with the aid of high pressured air into the grinding region. Studies have shown that the MQL process (a) enhances surface integrity, reduces tool wear and grinding forces. Recently, there has been usage of vegetable oil as the MQL lubricant during machining of Grinding Machine cast aluminium alloys [3]. Also, previous research have indicated that the nozzle stand-off distance, flow rate and extrusion pressure are the main parameters that affect the performance of the MQL machining. The use of nanofluids as coolants is also a major area of research. The improved tribological effects presented by the nano particles (such as graphite, molybdenum disulphide, silicon dioxide etc.) during the grinding operation is also being studied extensively [8]. The tribological effects is MQL- considered as the main advantage of the nanolubrication Equipment system due to increased evacuation of heat via convection by the nanoparticles in the fluid [9]. Review of previous literatures have shown that there is (b) limited work done in investigating the effects of lubricating and grinding process parameters on the Figure 1: Illustration of apparatus used (a) Workpiece surface quality of aluminium alloys during MQL and MQL nozzle (b) Experimental setup. conventional grinding operations. In this paper, we have presented an experimental analysis of the conventional Table 1: Experimental parameters and their levels grinding of tempered grade aluminium 6061-T6 alloy S/N Parameters Sym Levels with silicon carbide grinding wheel. We studied the 1 2 3 effect of nanoparticle concentration, depth of cut, feed 1 Nano-particle A 0.2 3 6 rate and stand-off distance on the surface roughness. The concentration MQL nano lubricant which consists of silicon dioxide nanoparticle mixed with vegetable oil was used as the (% wt.) lubricant in the grinding operations. In addition, ANFIS 2 Depth of cut B 5 10 15 modelling was used to predict and analyze the (µm) relationship between the input and output parameters. 3 Feed rate C 10 15 18 (m/min)

4 Nozzle stand- D 50 60 70 3 1 3 3 3 0.99 off distance 4 2 1 2 3 0.95 (mm) 5 2 2 3 1 0.86 The Naga Ichi model NI 450AV2 grinding machine 6 2 3 1 2 1.38 was used in this experimental investigation. Also, the 7 3 1 3 2 1.24 Saint-Gobain Norton’s silicon carbide grinding wheel 8 3 2 1 3 0.77 (25A100-JVBE) with dimension of 150 × 13 ×31.75mm 9 3 3 2 1 0.46 was used as the tool for material removal. The MQL nano lubrication system was used to supply the solid particle IV. ANFIS MODELLING nano-lubrication into the grinding zone. Silicon dioxide Adaptive network based fuzzy inference system nanoparticle was atomized in vegetable oil and utilized (ANFIS) is an artificial intelligent technique that is used as solid nanolubricant during the grinding operations. to obtain solutions for variable exhibiting non-linear The nanolubricant was supplied at a constant flow rate of relationships. The ANFIS models presents a mapped 1ml/min and pressure of 9MPa into the grinding zone relationship of the input and output variables, and using industrial based MQL system shown in Fig. 1b. subsequently obtaining the optimum range of the The surface roughness was obtained using the Mitutoyo membership functions (MF) using the hybrid learning SJ-210 roughness tester. The experimental runs was method [10]. The main advantage of the ANFIS structure designed based on Taguchi design of experiments. is that its architecture is built using both the artificial neural network (ANN) and fuzzy logic (FL) [11]. The III. RESULTS combined influence of the ANN and FL gives the ANFIS models a higher logical performance even with smaller The experimental results according to the Taguchi knowledge base [12]. The architecture of the ANFIS design of experiments for the surface roughness Ra is model is made up of fuzzy inference system which is shown in Table 2. In addition, Fig. 2a & b shows the set subdivided into five layers. Each of the ANFIS layer up used to obtain the surface roughness. contains numerous nodes which corresponds to a specified node function. The first layer consists of the input fuzzification, while the second layer involves construction of the knowledge base. The third layer is the region of building the fuzzified rules base while the fourth layer is the region of decision making. Finally, the fifth layer which is also the output layer, is the region for the defuzzification. These five different layers shown in Fig. 3 comprises of a Sugeno-type training model for the fuzzy inference system (FIS), which involves learning and adaptation processes. The adaptive nodes (represented by squares) are the variable parameters of the model.

(a) (b)

Figure 1: Set-up showing the SJ-210 profilometer used for measuring surface roughness.

Table 1: Experimental runs based on Taguchi design of Figure 3: Architecture of a two-input ANFIS Sugeno experiments and results for measured surface roughness. model.

Exp. no. A B C D Ra The ANFIS could be easily understood if it is assumed 1 1 1 1 1 0.84 to consist of two inputs say (x, y) and a single output z. it 2 1 2 2 2 0.60 is mainly guided by a set of If-Then rules of Sugeno type 14

fuzzy. An example of the rule in the Sugeno FIS as is illustrated in equation 1 &2 [10,12,11]. Rule 1: If x is A1 and y is B1 and w is C1 and u is D¬1, then z is f1 (x, y, w, u) (1) Rule 2: If x is A2 and y is B2 and w is C2 and u is D¬2, then z is f2 (x, y, w, u) (2) Where x and y are the input variables of the ANFIS model, whereas A & B are polynomials which represents the fuzzified sets of (x, y) and output of the Sugeno FIS. Furthermore, an ANFIS model was constructed for the surface roughness with the aid of the ANFIS editor in (a) MATLAB R2015a software, using the experimental results as the training dataset. The fuzzy rule designer of the ANFIS model is made up of nine fuzzy rules obtained from the training data according to the Sugeno fuzzy model based on the ANFIS model construction explained by [13]. The ANFIS model was trained based on the sets of rules for 100-epochs. In addition, the model for surface roughness was found to have an average training error of 1.0164 ×10-6.

V. DISCUSSION Figure 4a shows the variation of the surface roughness with feed rate and nanoparticle concentration in the lubricant. The ANFIS model illustrates that at lower feed rates, smaller amount of the nanoparticle concentrations (b) will yield lower roughness values as shown in Fig. 4c. However, at higher feed rate, an increase in the nanoparticle concentration will result in higher surface roughness. This could be ascribed to the increased scratching action of the hard silicon dioxide nanoparticles, which causes higher rough edges and surface roughness. Since it is established that higher concentration leads to deterioration of surface quality, a lower amount of the nanoparticle is henceforth recommended. In addition, it could be seen from Fig. 4b that higher feed rate and lower depth of cut, produces a better surface quality. As such a combination of higher feed rate and lower grinding depths is recommended for better surface quality. The stand-off distance was (c) observed to have little influence on the surface quality, but the 60 µm was found to give better roughness values at nanoparticle concentrations between 0 to 3 weight percent. However, at higher concentrations, the stand-off distance between 50-55µm gives better performance as shown in Fig. 4d.

1 푁 푉푒푥푝.−푉푝푟푒푑. Accuracy A = ∑푖=1 (1 − ) × 100% 푁 푉푒푥푝. (4) Where N is the total number of experiments conducted for the verification.

Table 2: Error and accuracy of ANFIS model for surface roughness Ra. Mea Pred. Err Accur s/ A B C D s. Ra Ra (% acy n (µm) (µm) ) (%)

1 0.0 1 2.8 8 55 0.91 0.94 0 3 97.03 (d) 1 0.0 Figure 2: Variation of ANFIS predicted surface 2 2.6 5 50 0.82 0.84 5 3 96.81 roughness with (a) ) Feed rate and nanoparticle concentration (b) Feed rate and depth of cut (c) Depth of 1 0.0 cut and Nanoparticle concentration (d) Stand-off distance 3 1.5 12 65 0.90 0.93 and nanoparticle concentration. 8 3 96.67

VI. VERIFICATION The accuracy of the developed ANFIS model was investigated using three sets of experiments. The 1.1 parametric settings selected for the verification 0.9 experiment were within the range of the developed ANFIS model. The settings of the process parameters and 0.7 their corresponding levels for the experimentation are as 0.5 shown in Table 3 and 4. The errors in the readings

between the measured and predicted values as obtained 0.3 Surface Surface roughness by equation 3 for the surface roughness Ra, is presented 1 2 3 in Tables 3. Experimental run no.

Error e = 푅푎푒푥푝.−푅푎푝푟푒푑. ×100% Ra meas. Ra pred. 푅푎푒푥푝. (3) Figure 3: Contrast between ANFIS predicted and The accuracy of the AFIS models was obtained using equation 4. The ANFIS model for the surface roughness experimentally obtained responses for surface roughness. was found to have an average accuracy of about 96.8%. From Fig. 5 it could be seen that the constructed prediction model is characterized by high accuracy, which indicates its effectiveness for predicting the surface roughness by utilization of the grinding parameters only. As such, within the utilized range of experimental parameters, the models could be reliably used to predict the responses even prior to the machining operations, and a suitable value for the desired response could be selected appropriately. Figure 6 shows an Figure 4: Example of the measure surface roughness for Example of the measure surface roughness for experimental run with machine settings 2.8% experimental run with machine settings 2.8% nanoparticle concentration, 8µm depth of cut, 10 m/min nanoparticle concentration, 8µm depth of cut, 10 m/min feed rate and 55mm stand-off distance. feed rate and 55mm stand-off distance.

VII. CONCLUSION 9. Najiha M, Rahman M, Kadirgama K (2016) Performance of water-based TiO 2 nanofluid during the minimum quantity lubrication machining of aluminium In this work, an ANFIS prediction model was developed alloy, AA6061-T6. Journal of Cleaner Production 135:1623-1636 10. Ying L-C, Pan M-C (2008) Using adaptive network based fuzzy inference in other to predict the surface roughness in the grinding system to forecast regional electricity loads. Energy Conversion and Management 49 (2):205-211 of Al6061-T6 aerospace alloy. The grinding operation 11. Çaydaş U, Hasçalık A, Ekici S (2009) An adaptive neuro-fuzzy inference was performed using a nanolubricant formed by mixing system (ANFIS) model for wire-EDM. Expert Systems with Applications 36 (3):6135-6139 silicon dioxide nanoparticles with vegetable oil. The 12. Sengur A (2008) Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification. Expert Systems with Applications 34 Taguchi design of experiment was used to conduct the (3):2120-2128 experimental analysis using four grinding parameters i.e. 13. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence. depth of cut, feed-rate, stand-off distance, and nanoparticle concentration. The developed ANFIS model was characterized by high accuracy, i.e. about 96.8%. This shows that the ANFIS model could be used to provide a highly accurate estimation of the surface roughness during the grinding of Al6061-T6 material using minimum quantity nano lubrication. It was also found that an optimum stand-off distance of 50-55µm can be used to obtain lower surface roughness. However, when there is a higher amount of the silicon dioxide nanoparticle in the lubricant, it was found to cause higher surface roughness. As such, smaller concentration should be used in machining the ductile materials. However, a higher concentration of the silicon dioxide nanoparticles in the nanolubricant could perform better when machining brittle materials as a result of its high load bearing capability.

VIII. ACKNOWLEDGEMENTS The authors would like to acknowledge the University of Malaya for providing Postgraduate Research Grant-no. PG065-2016A.

IX. REFERENCES 1. Santos MC, Machado AR, Sales WF, Barrozo MA, Ezugwu EO (2016) Machining of aluminum alloys: a review. The International Journal of Advanced Manufacturing Technology 86 (9-12):3067-3080 2. Manladan S, Yusof F, Ramesh S, Fadzil M, Luo Z, Ao S (2016) A review on resistance spot welding of aluminum alloys. The International Journal of Advanced Manufacturing Technology:1-30 3. Kelly J, Cotterell M (2002) Minimal lubrication machining of aluminium alloys. Journal of Materials Processing Technology 120 (1):327-334 4. Hadad M, Sadeghi B (2013) Minimum quantity lubrication-MQL turning of AISI 4140 steel alloy. Journal of Cleaner Production 54:332-343 5. Lawal SA, Choudhury IA, Nukman Y (2013) A critical assessment of lubrication techniques in machining processes: a case for minimum quantity lubrication using vegetable oil-based lubricant. Journal of Cleaner Production 41:210-221 6. Dambatta YS, Sarhan AA, Sayuti M, Hamdi M (2017) Ultrasonic assisted grinding of advanced materials for biomedical and aerospace applications—a review. The International Journal of Advanced Manufacturing Technology:1-34 7. Hadad M, Hadi M (2013) An investigation on surface grinding of hardened stainless steel S34700 and aluminum alloy AA6061 using minimum quantity of lubrication (MQL) technique. The International Journal of Advanced Manufacturing Technology 68 (9-12):2145-2158 8. Rao SN, Satyanarayana B, Venkatasubbaiah K (2011) Experimental estimation of tool wear and cutting temperatures in MQL using cutting fluids with CNT inclusion. International Journal of Engineering Science and Technology 3 (4)

Effects of Process Parameters on Surface Quality of Stainless Steel 316L Parts Produced by Selective Laser Melting

D. Aqilah1, M. Sayuti1,2, F. Yusof 1,2, Yusuf S. Dambatta1, A. Amirah1, W. N. Izzati1 1Department of Mechanical, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia 2Centre of Advanced Manufacturing and Materials Processing (AMMP), University of Malaya, 50603 Kuala Lumpur, Malaysia [email protected]

Abstract—Selective Laser Melting is an additive metallic powders directly from 3D CAD data into dense manufacturing that is used to fabricate different types of solid parts without additional post processing process [3]. complex components using layer-by-layer approach. The The focused laser beam will consolidate the powder process is used to produce the solid parts straightly from a metal to manufacture the solid part through layer-by- computer-aided design 3D-data, by melting the powdered layer which specifically scans the surface of the powder material layer-by-layer with the aid of laser power. This work entails experimental investigations of the effects of bed [4]. It is a continuous part building process which laser power, scanning speed and hatching distance on the happens either through diffusion bonding or by actual surface roughness of stainless steel 316L specimens using fusion of the powder particles, which is done through the machine. The design of experiment was conducted using melting of the metallic powders at controlled Taguchi’s L16 orthogonal array. Furthermore, statistical temperatures (to avoid evaporation and re-solidification). analysis using signal-to-noise response and analysis of After a trace of layer powder is completely melted, it then variance was used to obtain the optimal parameter undergoes rapid solidification to generate a new profile combinations of the SLM process. According to the [5]. Although the SLM has many benefits in terms of experimental results, laser power was found to have the versatility of materials and production of complex most significant effect on the surface roughness in relation to the scanning speed and hatching distance. By using shapes, it is yet associated with limitations such as high regression analysis, the predicted value of surface internal stress or part-distortions, densification ratio roughness and signal-to-noise ratios was calculated. The during the process happen, high temperature gradients, predicted values were compared with the experimental and poor surface quality due to formation of balling and values. Lastly, shot-peening technique was used to improve dross [6]. the surface roughness of the manufactured part. It was Previous researchers investigated the surface quality found that shot- peening process offers about 33.28% of the parts built using SLM. Calignano [3] investigated reduction to the surface roughness of the manufactured surface quality of aluminium on the built part DMLS by parts. controlling the process parameters while Song et al.[2] Index Terms— Surface roughness; Selective laser melting; Shot-Peening; Taguchi. studied the influence of the process parameters in SLM on the surface roughness of Titanium built part by SLM. I. INTRODUCTION From their studies, it was found that some of the input parameters that can be controlled to get a better surface Selective laser melting (SLM) has been a preferred finish of part produced. Among these parameters, it is choice to build or assemble complex parts with found that the surface quality is highly depends on the significant reduction manufacturing times in comparison scanning speed, laser power and hatching distance with the traditional manufacturing processes. It can be because they decide the amount of energy input expended used to produce end-use parts with minimal production during melting process. of waste material by fusing metallic powders into solid Also, some improvement on the surface quality have parts through melting process using a focused laser beam investigated by using shot-peening process where it [1]. enhancing a better surface quality of the SLM parts. The The desirability of the SLM process is due to the shot peening is a cold working process that is often used advantage of the high performance of its produced parts as a finishing technique for metallic parts. It involves the in terms of physical and mechanical properties, coupled use of small spherical shots which are bombarded onto with accessibility of the powder materials [2]. The the surface of the part in other to obtain a better surface process works in a unique way of transforming alloy and finish and mechanical characteristics of the built part [7].

This research involves investigating the optimum Scanning mm/ B 250 510 760 900 process parameters in SLM machining of metal-based Speed s stainless steel (SS316L) material in other to achieve the Hatching mm C 0.0 0.1 0.1 0.1 best quality of surface finish. The parameters studied distance 8 0 2 4 include the hatching distance, scanning speed and laser power. In order to ascertain the significant process Range of the parameters for laser power, scanning parameters which affects the surface quality of the built speed and hatching distance used in this work is shown part, a Taguchi based design using the L16 orthogonal in Table 1. Moreover, layer thickness and laser focus array experimental was utilized. The Taguchi diameter were kept constant as 50µm and 80µm optimization method was used for the design of respectively. The Taguchi experimental design and experiments due to its efficiency, effectiveness, analysis were prepared to determine the effectiveness of reliability and ease of use [8]. Also, Analysis of variance the selected parameters on the surface roughness of the (ANOVA) and Signal-to-noise ratios (S/N) were used on built part by using MINITAB software. To assure the the Taguchi design to obtain the optimum parameters. accuracy of the results obtained during the experiments, Afterwards, confirmation tests were conducted using the each of the experiments was repeated three times, and the optimal parametric setting. Finally, the top surface of the average of the results was taken. Furthermore, ANOVA manufactured sample was analysed before and after the statistical analysis and S/N ratios were applied to shot peening process, to determine the effect of the post evaluate the factors that influence surface roughness, Ra. processing activity on the surface roughness.

II. METHODOLOGY

In SLM, a laser moves with a set of scanning speed value (v), and selectively melts the powder material layer-by-layer. The diameter of the physical beam is usually smaller than the diameter of the area where the Fig. 1 shows the drawing of the test parts in this research particles are melted. The SLM 280HL machine was used work. in this study where the system of the machine is equipped with 400 W fiber laser and the 280 x 280 x 365 mm3 reduced by substrate plate thickness. Figure 2: Drawing of the sample To conduct the experiment, the design of experiment need to be set up. For this study, Taguchi method have been used to conduct the experiment where it is based on the orthogonal array experiments which provides reduced The surface roughness of each sample was obtained variance for the experimentation process with optimum using the standard ISO 468:1982 Mitutoyo Surftest SJ- settings of the control parameters. The S/N ratios are the 210 surface roughness tester as shown in Fig. 2. The log functions that are used to find the optimum parameter value of the surface roughness defined as Ra, was settings. It also helps for data analysis and prediction of measured three times at three different places. In order to the optimum results. In this research work, the S/N ratios obtain an improved surface finish, the shot peening was are calculated using the smaller the better given by the conducted on no. 6 parameters setting using the logarithmic relationship shown in equation 1. Peenmatic 620s machine at a pressure of 4 Bars. Then, the value of Ra was analysed and compared between the 1 n = −10log [( )∑y2][dB] samples before and after shot peened. s 10 n i (1) Figure 1: Figure 2: Mitutoyo SJ-210 surface roughness tester

Where: n = S/N ratio calculated from the result values, s yi = Value of the ith experiment, n = Number of repetition of each experiment.

Table 1: Process Parameter Parameter Unit Sy Levels s s m 1 2 3 4 Laser Watt A 120 200 275 360 Power (W)

3 120 760 0.12 1.32 10.1 - 3 20.11 4 120 900 0.14 0.95 10.1 - 1 20.10 5 200 250 0.1 8.00 12.9 - 7 22.26 6 200 510 0.08 4.90 12.5 - 0 21.94 7 200 760 0.14 1.88 11.7 - 2 21.38 8 200 900 0.12 1.85 11.2 - 1 20.99 9 275 250 0.12 9.17 18.5 - 5 25.37 III. RESULTS & DISCUSSION 10 275 510 0.14 3.85 20.0 - 8 26.06 11 275 760 0.08 4.52 14.4 - The parts were manufactured by using the different 3 23.18 process parameter settings according to the Taguchi L16 12 275 900 0.1 3.06 11.3 - arrays. The objective of the investigation was to obtain 0 21.06 the optimum parameters for a better surface roughness in 13 360 250 0.14 10.29 35.7 - the manufactured SLM specimens. As such, the lower the 5 31.07 better problem was utilized in the analysis so as to obtain 14 360 510 0.12 5.88 24.0 - a better surface finish. Analysis of variance (ANOVA), 6 27.62 coefficients for each factor at low level and their p-values 15 360 760 0.1 4.74 28.2 - were analysed. The values of S/N ratio in the Ra were 0 29.01 measured from the experiments and listed in Table 2. The 16 360 900 0.08 5.00 20.1 - number of S/N rely on whether mean-squared deviation 3 26.08 is a number it is a positive or negative3. In addition, a sufficient amount of energy is needed to deliver the To determine which parameters are the most powder on the platform in order to construct a better significant on the surface quality of the built part, the p- functional melted specimen. When the amount of energy value of the parameters was compared in ANOVA is insufficient, it might cause a poor bond neck between analysis as shown in Table 3. From the table, it is found the powder particles. Thus, by increasing the energy that the laser power factor is the highest influence on the density of the process, there could be improvements of surface quality because it has the least p-value which is the surface finish of the produced parts. The energy less than 0.05 significance level. density, Ed, is measured by using the equation 2. Table 3:Analysis of variance (ANOVA) for S/N ratios, R2= 95.64% P E = (2) Source D Adj Adj F-Value P-Value d h × v d F SS MS Where: P = Laser power, Laser 3 151.99 50.6 38.11 0.00 power 7 hd = Hatching distance v = Scanning speed. Scannin 3 15.61 5.20 3.91 0.07 g speed Hatchin 3 7.28 2.43 1.83 0.24 g distance

Table 2: Results of the experiment, Ra and S/N ratios based on Error 6 7.98 1.33 combination of process parameters by using L16 array Total 15 182.86 N P v hd E Ra S/N o (W (mm/s (mm (J/mm2 (µm) ratio Table 4: Response Table for S/N ratios ) ) ) ) s Level Smaller the better 1 120 250 0.08 6.00 9.92 - Laser Scanning Hatching 19.93 Power Speed Distance 2 120 510 0.1 2.35 11.5 - 1 -20.34 -24.65 -22.78 3 21.24 2 -21.64 -24.21 -23.39

3 -23.92 -23.42 -23.52 4 -28.44 -22.06 -24.65 Delta 8.10 2.60 1.87 Rank 1 2 3

Table 4 shows the response table for S/N ratios in each factor level. It involved ranks based on delta measurements which compares the relative magnitude of impacts. To get delta value, the highest average value of each factor is subtracted from the lowest average value. As shown in the table, the rank 1 is seen to have the highest delta value, followed by rank 2 and rank 3. As such, based on the results of the S/N ratio, it could be said that the laser power has the major influence on the surface quality of the built part, followed by scanning speed and hatching distance. Figure 4: Interaction plot for surface roughness

Based on Fig. 3, the plot shows the response means for each factor level and directly verify these results. From the figure, at the grand mean, horizontal line was drawn and the slope of the line which relates the levels of the process parameters shown the effects of each process parameters. Also, it could be seen that the optimum surface roughness was achieved when the laser power is 120 W, at a scanning speed of 900 mm/s and a hatching distance of 0.08 mm.

The interaction between each variable and the surface quality of specimens can be seen in Fig. 4. When the lines are parallel in the interactions plot, it shows that there is no interaction between the parameters. The greater the Since, there is a temperature gradient between the degree of intersection of the lines and less parallel from solidifying zone and the laser beam when the laser one another gives the higher degree of interaction. Based moves, the increase in temperature creates a shear force on the graph, when the values of laser power and hatching on the liquid surface that is contrasted by the surface distance are low and value of scanning speed is high, tension of the melted liquid [9]. The interlayer there is a high possibility of obtaining a low Ra value. It connection and wettability of the melt could be improved could be seen that the scanning speed and hatching when a higher laser power is applied due to its ability to distance have a good interaction in achieving a good flatten the melted pool and balling effect could be surface finish. Moreover, it could be seen that there is reduced when the wettability is improved due to the strong relationship and interaction between the laser decrease in surface tension variations. Nevertheless, power and scanning speeds when the laser power is less large amounts of material vaporization can occur with than 200W. draw back pressures that cause disruptions on the surface of the melt pool thereby increasing the Ra [10] when the values were set too high. Similarly, when a low scan Figure 3: Main effects plot for S/N ratios speed is utilized, the top surface finish could be improved because the melt pools will take longer time to flatten From Equation 2, it could be seen that there is a directly before solidification, with the help of gravity and surface proportional relationship between the energy density and curvature forces that counteract the external shear forces. laser power, whereas there is an inversely proportional Moreover, when low scan speed is utilized, it could cause variation between the hatching distance and scanning increase in the volume of liquid produced within the melt speed with the energy density. As such, whenever the pool because the increase in liquid volume has a tendency laser power is increased and the other two factors were to widen the melt pool, thereby causing huge thermal decreased, there tend to be imminent increase in the differences across it, and consequently varying the temperature of the powders. surface tensions. In addition, the use of low scanning speed causes the non-melted core of particles to bake together into coarsened balls with a diameter almost the

same as that of the beam. To minimize these changes, 33.28% reduction in the surface roughness as a result of small entities known as “balling” can occur due to the the application of the shot peening process. Also, it could melt pool may break off where it solidifies at the edge of be seen clearly that the surface roughness was greatly the melt pool, thus increasing the surface roughness, Ra. reduced by the shot peening as shown in Fig. 5a & b by Optimal parameters allows the stabilization of the using Dino-Lite digital microscope with 100x thermal processes, consequently avoiding the balling magnification. effect. As observed from these figures, the shot peeing process Further analysis using the regression analysis method proffers significant improvement to the surface finish of was done to ensure the response values fits the model the sample. This could be attributed to the removal of data. The regression analysis used in this work involved the burrs and imperfections in the parts during the shot using mathematical models to see the relationship peening process with the presence of voids or necks on between the control parameters and the performance of the surface have been reduced as shown in fig 5b. surface roughness Ra, based on the robust design of L16 orthogonal array. The preceding analysis developed the response equation by considering the influences of laser power (P), scanning speed (v) and hatching distance (hd). The mathematical model developed relating the surface roughness Ra and the process parameters is shown in equation 3; Ra [μm] = −3.27 + 0.06815 (P) − 0.00845 (v) + 2 77.5 (hd)[R = 82.56 %] (3)

The analysis of residuals involved obtaining deviations between observed and fitted values versus the corresponding fitted values. The result shows that there is a nice horizontal band around the residual line with a zero value, suggesting that it fits the data well. From the regression analysis, the predicted value of surface roughness Ra, when using 120 W of laser power, 900 mm/s of scanning speed and 0.08 mm of hatching distance was obtained.

Table 5: Predicted and experimental values of surface roughness

Based on the S/N ratio curves analysis, the optimum parameter combination for SS316L parts manufactured using the SLM technique was obtained based on the surface with the lowest value of surface finish Ra. The optimum data obtained from the graph is 120 W of laser Control factors Predicted Experiment power, 900 mm/s of scanning speed and 0.08 mm of value al value hatching distance. In addition, these setups were tabulated in Table 5, including the value of S/N values P v hd Ra S/N Ra S/N predicted by regression analysis method and compared (W (mm/s (mm (um ratio (um ratio with the experimental values. ) ) ) ) ) 12 900 0.08 5.0 - 5.53 - To complete the analysis of surface quality tests, the 0 3 18.0 15.2 shot 1 1 peening method was applied in order to investigate the possible improvement produced in the surface quality of the components. It produces a high velocity blast stream which produces fine dimples that allow for smoother finishing of the parts without change in the dimension or originality of the parts. The values of surface roughness obtained before and after shot peening are 12.50 µm and 8.34 µm. From the results obtained, there is about

Mechanics and Materials in Design, vol. 11, pp. 463-478, 2015. IV. CONCLUSIONS [8] B. Rahmati, A. A. D. Sarhan, and M. Sayuti, "Investigating the optimum molybdenum disulfide (MoS2) The effect of three process parameters on the surface nanolubrication parameters in Computer Numerical Control roughness was investigated and analysed. From the milling of Aluminium (AL6061-T6) alloy," The International Journal of Advanced Manufacturing analysis, the laser power was found to have the most Technology, vol. 70, pp. 1143-1155, 2014. significant effect on the surface roughness of the parts [9] S. Price, K. Cooper, and K. Chou, "Evaluations of produced using the SLM technique. Furthermore, from temperature measurements in powder-based electron beam the confirmation tests, it was found that the Ra was additive manufacturing by near-infrared thermography," International Journal of Rapid Manufacturing 23, vol. 4, pp. reduced to 5.53 µm when the machine setting of laser 1-13, 2014. power (120 W), scanning speed of (900 mm/s) and [10] A. Stwora and G. Skrabalak, "Influence of selected hatching distance of (0.08 mm) were used. In addition, parameters of Selective Laser Sintering process on from the analysed results, it was found that the value of properties of sintered materials," Journal of achievements in materials and manufacturing engineering, vol. 61, pp. 375- surface roughness could be decreased by about 33.28% 380, 2013. Figure 5: a) Before and b) after shot-peening with the aid of the shot-peening post processing method. This post processing technique is there highly recommended in other to obtain better overall surface quality.

V. ACKNOWLEDGMENTS The authors would like to acknowledge the University of Malaya for providing the necessary facilities and resources for this research. This research was fully funded by the Ministry of Higher Education, Malaysia, with University of Malaya Research Grant (UMRG) No: RG161-15AET.

VI. REFERENCES

[1] W. E. Frazier, "Metal additive manufacturing: a review," Journal of Materials Engineering and Performance, vol. 23, pp. 1917-1928, 2014. [2] B. Song, S. Dong, B. Zhang, H. Liao, and C. Coddet, "Effects of processing parameters on microstructure and mechanical property of selective laser melted Titanium," Materials & Design, vol. 35, pp. 120-125, 2012. [3] F. Calignano, D. Manfredi, E. P. Ambrosio, L. Iuliano, and P. Fino, "Influence of process parameters on surface roughness of aluminum parts produced by Direct Metal Laser Sintering," The International Journal of Advanced Manufacturing Technology, vol. 67, pp. 2743-2751, 2012. [4] X. Zhao, Q. Wei, B. Song, Y. Liu, X. Luo, S. Wen, et al., "Fabrication and characterization of AISI 420 stainless steel using selective laser melting," Materials and Manufacturing Processes, vol. 30, pp. 1283-1289, 2015. [5] E. Louvis, P. Fox, and C. J. Sutcliffe, "Selective laser melting of aluminium components," Journal of Materials Processing Technology, vol. 211, pp. 275-284, 2011. [6] K. Alrbaey, D. Wimpenny, R. Tosi, W. Manning, and A. Moroz, "On Optimization of Surface Roughness of Selective Laser Melted Stainless Steel Parts: A Statistical Study," Journal of Materials Engineering and Performance, vol. 23, pp. 2139-2148, 2014. [7] F. Yang, Z. Chen, and S. Meguid, "Effect of initial surface finish on effectiveness of shot peening treatment using enhanced periodic cell model," International Journal of

Comparison of VAR and GSTAR Models for Forecasting Tourist Arrivals Data in Indonesia

Rismal1, Mukhammad Solikhin2, Marvin Jecson Pandu3, Suhartono3 and Dedy Dwi Prastyo3 1Department of Information System, Institut Teknologi Del, Sumatera Utara 22381, Indonesia 2Department of Informatics Engineering, Institut Teknologi Del, Sumatera Utara 22381, Indonesia 3Department of Statistics, Institut Teknologi Sepuluh Nopember 60111, Indonesia [email protected]

Abstract—Forecasting the number of foreign tourist employment and many other fields [3]. arrivals is recently crucial for the government and tourism Several studies have associated with STAR. For businesses as guidance for decision making and planning instance, it is implemented to the data of crime rate in in the development of Indonesia tourism sector. The the Boston and Sao Paulo ares1. STAR models have a objective of this research is to apply and compare the weakness on the parameter exibility describing the accuracy between Vector Autoregressive (VAR) and Generalized Space Time Autoregressive (GSTAR) models distinct locations and time dependencies in the time for forecasting the number of foreign tourist arrivals series and location. This weakness covered by a model through three main gates in Indonesia, i.e. Soekarno known as Generalized Space-Time Autoregressive or Hatta, Juanda, and Adisumarno airports. This research GSTAR. The fundamental difference between GSTAR used monthly data about the number of tourist arrivals in and STAR is located in the condition of the model of those three airports that be observed from January 2003 to parameters. STAR model assumes that the locations December 2015. Root Mean Square Error (RMSE) is used used in the study are homogenous, so that this model to select the best model. The results showed that GSTAR can only be applied in uniform locations. While on model yield more accurate forecast than VAR method for GSTAR, there is an assumption stating that the research at least one year ahead forecasting location are not only for homogeneous, then the Index Terms—Foreign Tourists; GSTAR; RMSE; VAR. difference between these locations are shown in the form of a weighted matrix. Several studies related to I. INTRODUCTION GSTAR discussed the data for forecasting oil production in the three wells [4], [5], the update One of multivariate time series model examining the application to GDP Data in West European Countries relationship among variables is Vector Autoregressive [6] and to consumer price index [7]. Besides, the model (VAR). VAR models are built to be able to capture the has been widely developed [8] on forecasting seasonal phenomenon. In its development, we often encounter multivariate time series on tourism data with VAR- multivariate time series which does not only relate to GSTAR model [9]. the events in previous times, but also effect on location One applied field widely studied regarding to the of other places. It is called the space-time data. Model forecast research is the number of foreign tourists. Java space-time plays an important role in the field of Island is one of the objectives of foreign tourists who geology, ecology and many other fields. Model of come to Indonesia through the entrances of the space-time was first introduced by developing a model Soekarno-Hatta (Soeta), Juanda, and Adisumarmo of space-time that had been derived by the Box-Jenkins. (Adisu) airports. Tourist arrivals would effect on [1] developed space-time modeling in a procedure economy of the destinations. It includes consists of three stages, namely the space-time model accommodation, transportation, industry and trade. identification, parameter estimation, and diagnostic Those impacts certainly absorb a lot of labors. In checking in modeling Space-Time Autoregressive addition to foreign exchange earner, it is also a driver of (STAR). STAR model is actually the combination of other sectors. Then, the proper planning is necessary, as autoregressive models (AR) with order p of Box- an example, it is how many tourists will come. By that Jenkins and spatial models. It is well developed a space- information, it can be previously figured other sectors time geostatistics model which was often used to predict planning, even as guidelines to see the opportunities the problems linked to the geographical conditions, such exist. In this paper, we will forecast international as pollution, crime and population [2]. Development of tourist’s arrivals that come to Indonesia through the the space-time Econometrics were widely applied to the entrance of the Soeta, Juanda, and Adisu airport by problems of economic growth, unemployment, using VAR and GSTAR model.

written as follows: II. GENERAL FORM OF VAR AND GSTAR  p s (k) Vector autoregressions (VARs) constitute a special of YtYtst( ) =+−+ΦΦW()e()ssk0 (3) the more general class of Vector Autoregressive sk==11 Moving Average (VARMA) models. In essence, a VAR 1 N model is a fairly unrestricted or flexible approximation where Φsss000= diag,,() , to the reduced form of a wide variety [10]. A Study 1 N showed the effect on VAR forecast of changes in the Φsksksk= diag,,() , and et( ) is i.i.d. with mean model structure. The general Vector AR ( p) process and covariance E e( t) e( t) = 0 . [11]:  In order to identify GSTAR, spatial order is generally p ()I −−−=ΦΦYa1BBptt (1) limited to the first order because higher order will be difficult to interpret [13]. The order of time (autoregressive) can be determined by using Akaike It is obviously invertible. For the process to be Information Criterion (AIC)4. But the determination of stationary, it is required that the zero of the model order based on the AIC cannot capture I −−−ΦΦBBp lies outside the unit circle or seasonal patterns, therefore it is possibly made based on 1 p a plot of MCCF and MPCCF [13]. equivalently means that the roots pp−1 III. DATA AND METHODOLOGY I −−−= ΦΦ1 p 0 are inside the circle. In  −1 such case, we can write  The data used in this paper is the number of foreign Ytptst== sΦaΨ()B a  − s=0 tourist arrival in Soeta, Juanda and Adisu airport from January 2003 to August 2015 with data testing six and where the weight Ψs are summable. eighteen months ahead. It is conducted in several stages, which are: −1 1  + ΦΦpp()()BB= (2) 1. Checking whether the data stationary or not Φ ()B p 2. Creating MCCF and MPCCF plot of the initial data 3. Identifying VAR models where ++ is the adjoin matrix of ΦΦpij()()BB=  4. Assessing parameter estimation of VAR model Φ ()B  + 5. Forecasting by testing 6 months and 18 months p , we have ΦYΦaptpt()()BB= The 6. Determining the weight of the location of GSTAR covariance matrix function is obtained by left- 7. Identifying GSTAR Model multiplying Y on both side of the transposed Equation tk− 8. Assessing parameter estimation of GSTAR model (1) and then taking expectation. Then we 9. Forecasting by testing 6 months and 18 months have EYYYEY Ya−−−=ΦΦ  . t−−−− kttt( ppt k t11 ) 10. Comparing the accuracy of VAR and GSTAR by GSTAR is a generalization of the model Space Time RMSE Autoregressive (STAR) which is also the specification of the model Vector Autoregressive (VAR) model. The IV. VAR MODEL FOR FORECASTING THE FOREIGN fundamental difference between GSTAR and STAR TOURISTS DATA models lies in the assumption parameters. STAR model assumes the locations used in the study are same, thus The forecasting starts up by finding the correlation this model can only be applied in uniform locations. among airports. It can be seen that the highest While on GSTAR, they have to be heterogeneous, so correlation among them is between Soeta and Juanda the difference between these locations is shown in the which is 0.891. While the least correlation coefficient form of a weighted matrix [12]. By recognizing spatial score is between Soeta and Adisu, which is 0.466. neighborhood relationships, we can use the spatial Correlation coefficient score between Juanda and Adisu dimension to achieve further parsimony by [2]. is 0.553. If known series of multivariate time series of n Based on the results of descriptive statistics shown in components, then GSTAR model of order p Table 1, it tells that the minimum value of the three autoregressive with spatial order 12,,,  p , variables are 53411 for Soeta, 2960 for Juanda, and 143 for Adisu, while the maximum value for these three GSTAR( p ;12 ,  , , p ) in matrix notation, can be variables are 252914, 22986, and 3325 respectively.

Based on those data, it can be said that these three variables are likely to increase in each period. It is also reinforced by a time series plot so that these three variables tend to be non-stationary.

Table.1 Descriptive Statistics

VAR N MEAN STDEV MIN MAX Soeta 152 131422 43582 53411 252914 Juanda 152 12420 4782 2960 22986

Adisu 152 1204.6 690 143 3325

2003 2007 2011 2015 Jan Jan Jan Jan soekarno-hatta juanda 250000 20000 200000 15000 150000 10000

100000 5000

50000 0 adisumarmo

3000 2000 1000 0 Figure 2. ACF for Soeta, Juanda, and Adisu Data Respectively Month Jan Jan Jan Jan Year 2003 2007 2011 2015 After the data meet the assumption of stationary, it is Figure 1: Time Series Plot identified to determine the order of Vector Autoregressive Integrated Moving Average (VARIMA) Time series plot, Figure 1, shows that the pattern of model by MPCCF Plots. The Schematic Representation the data from all three airports tends to be seasonally of Partial Cross Correlation is cut off or significant on trend. The Autocorrelation Function (ACF), Figure 2, lag 1 and seasonal lag 12. shows the data of foreign tourists following a particular It can be seen from the three negative signs appearing pattern. It provides the information that the data is not on the lag 1 and the negative signs appearing on stationary on the mean and variance, which means we seasonal lag 12. In addition to plot of MPCC in Figure need to commit transformation and differencing. From 7, order of VARIMA model also can be seen from the ACF and Partial Autocorrelation Function (PACF) smallest AIC value. The smallest AIC value lies in AR Plots, Figure 3, it is obtained that it the ACF is still (2) and MA (0) as shown in Table 2. So that the going down slowly which implies that it is still difficult VARIMA model formed is VARIMA([1,2, 12] , 1, 0) to identify the model, then it is necessary to do regular (0, 1, 0)12. differencing 1 and Seasonal 12. The results of Box-Cox transformation in Figure 5, tends to approach the estimated value of 0.00. Therefore, the data need transformation of logarithm. After differencing and transformation, the data has been stationary in mean and variance as shown in Figure 4. The stationary identification process on the foreign tourists’ data can be seen from the plot of MCCF in Figure 6. The schematic representation of cross correlation tells that the data of foreign tourists in three locations already stationary after regular transformation and differencing 1 and seasonal 12, So does the schematic representation of partial cross correlation as shown in Figure 7.

22 value of d Jn  ;0,5 is more than 50%. That is 80.4%, which means that the residuals are normally multivariate distributed.

Table 3 AIC Score of Residual Model

Lag MA (0) MA (1) AR (0) -10.690 -10.444 AR (1) -10.540 -10.316 AR (2) -10.410 -10.201 AR (3) -10.325 -10.126 AR (4) -10.226 -10.102 AR ( 5) -10.159 -10.070 AR (6) -10.107 -9.896 AR (7) -9.957 -9.730 AR (8) -9.782 -9.537 AR (9) -9.629 -9.364 AR (10) -9.621 -9.333

From the parameter estimation of VARIMA([1; 2; 12] ; 1; 0) (0; 1; 0)12. Model can be written as follows: Figure 3. PACF for Soeta, Juanda, and Adisu Data Respectively

YY11,*0,5540,1870*00,2560 1 −−− t−  YY*00,3230*000=−+ 22, 1  t− The testing of white noise residual assumption is done   YY33,*000*000 1  t − by re-modeling the residuals of the model. Furthermore, (4) YY1,tt−− 21,*000* 12  a1,t we check the smallest AIC value, Table 3. If the   YY*00,4270*+−+ a 2,tt−− 22, 12  2,t location of the smallest AIC is between AR lag (0) and YY*000,289*−  a MA (0), then the residual can be said already meet the 3,tt−− 23, 12  3,t assumption of white noise.

Table 2 AIC Score of VARIMA Model

Lag MA (0) MA (1) AR (0) -9.500 -9.758 AR (1) -9.950 -9.841 AR (2) -9.970 -9.812 AR (3) -9.864 -9.729 AR (4) -9.875 -9.771 AR ( 5) -9.802 -9.747

AR (6) -9.729 -9.543 Figure 4. Stationer Time Series Plot AR (7) -9.554 -9.355

AR (8) -9.435 -9.224 Due to Y * the transformation data, the model for AR (9) -9.292 -9.065 it, AR (10) -9.135 -8.891 each location as Equation (5), (6), and (7).

According to the Table 3, it is able to see that the

YYYYYYYY1,1,11,121,131,11,21,131,14tttttttt=+−−−−+−−−−−−− 0,554( )

−−−+0,187(YYYY2,12,22,132,14tttt−−−− ) (5) −−−++0,256(YYYYa2,22,32,142,151,ttttt−−−− )

YYYYYYYY2,2,tttttttt=+−−−−+ 12, 122,−−−−−−− 132, 12,22, 132, 14 0,323( )

−−−++0,427(YYYYa2,ttttt−−−− 122, 132,242,252, ) (6)

YYYY3,3,13,123,13tttt=+− −−− (7) −−−++0,289 YYYYa ( 3,123,133,243,253,ttttt−−−− ) Figure 6. Schematic Representation of Cross Correlation V. GSTAR-OLS MODEL FOR FORECASTING THE FOREIGN TOURIST DATA USING INVERSE DISTANCE WEIGHT

Order of GSTAR-OLS model used in this analysis is the same as the order of VARIMA in previous model, where there are three significant lags. They are the lag 1, 2 and 12 for each location. While the order of the spatial limited to the order of 1. Therefore, GSTAR- OLS used in this analysis is GSTAR 1,2,12− I 1 1 12 . ( 1 ) ( )( )

Figure 7. Schematic Representation of Partial Cross Correlation Figure 5. Box Cox Transformation for Soeta, Juanda, and Adisu Data conversely Based on the Figure 8, the weighted inverse distance involved will be: Based on VAR model for each location, it is known that the arrival of foreign tourists to Soeta influenced by 0 0,41 0,6 itself at 1 month, 12 months, 13 months and 14 months W = 0,245 0 0,754 (8) earlier. As well as Influenced by the arrival of foreign ij   tourists in Juanda at 1 month, 2 months, 3 months, 13 0,32 0,68 0 months, 14 months and 15 months earlier. Juanda is influenced by itself at 1 month, 2 months, 12 months, 13 The parameter estimation model is provided by Table months, 14 months, 24 months and 25 months earlier. 4. While Adisu is influenced itself at 1 month, 12 months, 13 months, 24 months and 25 months earlier. Table 4 Parameter Estimation Model with Weighted Inverse Distance

Location Parameter Estimate p-value 1 Soeta 10 -0.501 <.0001

1 YYYY3,3,13,123,13tttt =+− −−− 20 -0.232 0.0023 Juanda −−−+0,096(YYYY1,21,31,141,15tttt−−−− ) 2 (12) 21 -0.372 <.0001 −−−++0,204(YYYYa2,22,32,142,153,ttttt−−−− ) 2 Adisu 31 -0.300 0.0002 Equation (10), (11), and (12) are GSTAR model for for each location, it is known that the arrival of foreign tourists to Soeta influenced by itself at 1 month, 12 months, 13 months and 14 months earlier. Juanda is influenced by itself at 1 month, 2 months, 12 months, 13 months and 14 months earlier, influenced by Soeta at 1 month, 2 months, 13 months and 14 months earlier, and influenced by Adisu at 2 months, 3 months, 14 months and 15 months earlier. Adisu is influenced by itself at 1 month, 12 months, and 13 months earlier, affected by

Soeta and Juanda at 2 months, 3 months, 14 months and 15 months Figure 8. Distance Map among Locations

VI. COMPARING VAR AND GSTAR Order of GSTAR-Ordinary Least Square (GSTAR- OLS) model used in this analysis is the same as the The model obtained for forecasting with VAR method order of VARIMA in previous model, where there are is Equation (5), (6), and (7). By GSTAR method, it is three significant lags. They are the lag 1, 2 and 12 for yielded Equation (10), (11), and (12). The models are each location. While the order of the spatial limited to used to work on data testing six and eighteen months the order of 1. Therefore, GSTAR-OLS used in this ahead. As the comparison, Table 5 is provided. analysis is GSTAR1,2,1211 − I 12 . ( 1 ) ( )( ) Table 5 The parameter Estimate of RMSE Score for each location GSTAR1,2,1211 − I 12 is significant in 1%. The ( 1 ) ( )( ) model can be written as follows based on the Table 4. Testing Soeta Juanda Adisu Total 6 VAR 13548.4 2398.9 362.2 16309.5 GSTAR 27499.3 3487.2 2384.8 33371.3

YY11, 1 −0.501 00000 t− 18 VAR 235287.4 21214.7 982.1 257484.2  YY=+0000 −0.2320. − 372 GSTAR 22275.1 3212.2 2458.9 27946.2 22, 1 t−   YY33, 1 00000 t− −0.300 Total 298610.2 30313 6188 (9) 00,410,6 Ya1,tt− 21,  0,24500,754 Ya+ From the Table 5 it can be seen that the VAR method 2,tt− 22,    and GSTAR give varying results in forecasting the  0,320,680  Ya3,tt− 23, Based on the models described in Equation (9), it can arrival of tourist. The accuracy testing uses 6-month be mathematically obtained the model of VAR method giving better results than the GSTAR method. However, GSTAR method gives better results for each location as than the VAR forecasting for testing 18 months. follows: VII. CONCLUSION

YYYY1,1,tttt 1 1,=+− 12−−− 1, 13 (10) In forecasting the number of foreign tourist arrivals −−−++0,501 YYYYa ( 1,ttttt−−−− 1 1, 2 1, 13 1, 141,) through Soeta, Juanda, and Adisu airport, the method should be used is GSTAR method. It provides better

YYYY2,t= 2,1 t− + 2,12 t − − 2,13 t − forecast accuracy than the VAR method for at least one year ahead forecasting. Due to the use of testing 18 −0,232(YYYY2,1t− − 2,2 t − − 2,13 t − + 2,14 t − ) months GSTAR generate the total value of RMSE

−0,091(YYYY1,1t− − 1,2 t − − 1,13 t − + 1,14 t − ) (11) which smaller than the VAR. That is 27946.2.

−0,28(Y2,2t− − Y 2,3 t − − Y 2,14 t − + Y 2,15 t − ) + a 2, t

ACKNOWLEDGMENT [7] H. Bonar, B. N. Ruchjana, and G. Darmawan, “Development of generalized space time autoregressive integrated with ARCH error (GSTARI–ARCH) model based on consumer price index This work was supported by LPPM Institut Teknologi phenomenon at several cities in North Sumatera province,” AIP Del, Indonesia. Conference Proc., vol. 1827, 2017, pp. 020009-1–020009-8. REFERENCES [8] N. Nainggolan and J. Titaley, “Developtment of Generalized Space Time Autoregressive (GSTAR) Model”. AIP Conference Proc., vol. 1827, 2017, pp. 020034-1-020034-5. [1] P. E. Pfeifer and S. J. Deutsch, “A Three Stage Iterative [9] D. U. Wutsqa, Suhartono, B. Sutijo, “Generalized Space-Time Procedure for Space-Time Modeling,” Technometrics, vol. pp. Autoregressive Modelling,” Proc. 6th IMT-GT Conference on 22, 35-47, 1980a. Mathematics, Statistics and its Applications (ICMSA2010), [2] N. Cressie and C. K. Wikle, Statistics for Spatio-temporal data. Kuala Lumpur, Malaysia, 2010, pp. 752 – 761. Singapore: A John Wiley & Sons, inc., Publication, 2011. [10] J. G. Gooijer, Hyndman, “25 years of time series forecasting,” [3] J. P. LeSage and R. K. Pace, Introduction, Advances in International Journal of Forecasting, vol. 22, 2006, pp. 443- Econometrics: Spatial and Spatial Temporal Econometrics. 473. Oxford: Elsevier Ltd, vol. 18 pp. 1-32, 2004a. [11] R. W. Hafer, R. G. Sheehan, “The sensitivity of VAR forecasts [4] Suhartono, and R. M. Atok, “Comparison between GSTAR and to alternative lag structures,” International Journal of VARIMA models for forecasting data with time series and Forecasting, vol. 5, 1989, pp. 399 – 408. location,” Department of Statistics, ITS, Surabaya, 2006. [12] S. Borovkova, H. P. Lopuhaä, and B. N. Ruchjana, “Consistency [5] B. N. Ruchjana, “An Autoregressive Space-Time Generalization and Asymptotic Normality of Least Squares Estimators in Model and Its Application to Petroleum Production,” Generalized STAR Models,” Statistica Neerlandica, 2008. Dissertation, Institut Teknologi Bandung, 2002. [13] W. W. S. Wei, “Time Series Analisis: Univarite and Multivariate [6] N. Nurhayati, U. S. Pasaribu, and O. Neswan, “Application of Method,” Pearson Education, Inc., 2nd Ed., USA, 2006. Generalized Space-Time Autoregressive Model on GDP Data in

West European Countries,” Journal of Probability and Statistics, pp. 1-16, 2012.

Impact of Inter-UE Interference on Full Duplex Cellular System

Junsu Kim, Su Min Kim, and 1Sukhyun Seo Department of Electronics Engineering, Korea Polytechnic University, Siheung, Korea 1 [email protected]

Abstract— Future wireless communication systems obtained by the HD operation. Hence, many existing require extremely high throughput compared to the works try to manage the self-interference. existing systems. For this goal, full-duplex (FD) operation Meanwhile, if the FD operation is adopted in a time is getting more interests as a method to improve system division duplex (TDD) cellular system, in which a throughput. Main limitation of FD operation is a self- NodeB operates in FD mode and many uplink and interference caused by the same device. In a cellular system, another type of interference occurs to degrade downlink user equipment (UEs) are competing for performance gain of the FD operation, which is inter-UE communication chance, another type of interference interference (IUI). IUI is also an important factor which occurs to degrade performance gain of the FD operation. can limit the FD performance as well as self-interference. This type of interference is inter-UE interference (IUI) Most previous works focus on handling of self- which is caused between the UEs. For example, when a interference. In this paper, we first evaluate the impact of NodeB in FD mode is receiving from the UE-A while IUI on the throughput performance. Moreover, we the NodeB is transmitting to the UE-B, the signal propose a practical FD scheduling algorithm which transmitted by the UE-A may reach to the UE-B. Hence, achieves close-to-optimal throughput with limited IUI is also an important factor which can limit the FD feedback overhead. performance as well as self-interference. Index Terms— Full duplex, cellular system, scheduling On the other hand, most previous works focus on algorithm, inter-UE interference. handling of self-interference to improve FD performance. In this paper, we first evaluate the impact I. INTRODUCTION of IUI on the throughput performance to show that IUI can negatively dominate the FD system. Moreover, we Because of the nature of radio propagation, separation propose a practical FD scheduling algorithm which between reception and transmission in time, frequency achieves close-to-optimal throughput with limited or any types of radio resources has been considered to feedback overhead. The proposed algorithm is justified be a reasonable way for radio communications. This numerically through simulations. approach is called as half-duplex (HD) operation. While This paper is organized as follows. In section 2, we the HD avoids interference problem between reception provide system model considered in this paper first, and and transmission parts in the same transceiver, it scheduling strategies are described. In section 3, a reduces reception and transmission chances, which may practical FD scheduling algorithm is proposed. The severely limit the system throughput. numerical results which evaluate the impact of IUI and Future wireless communication systems such as the performance of the proposed algorithm are beyond-4G (B4G) or 5G require extremely high presented in section 4. Finally, our work of this paper is throughput compared to the existing systems. For this concluded in the last section. goal, all types of traditional techniques for communication should be re-considered and re- II. PROBLEM STATEMENT designed. The HD operation is also an important object for the improvement. In this background, full-duplex A. System Model (FD) operation is getting more interests as a method to improve system throughput. Main limitation of FD operation is a self-interference which is caused by the transmission part to the reception part of the same device. If we are able to control the self-interference perfectly, the throughput obtained by the FD operation will be twice of the throughput

NodeB selects single UE at each time slot. When the UEs j and k are selected for uplink and downlink transmission, in the first time slot, the UE j transmits to the NodeB first. Then, the received SINR at NodeB can be formulated as HD 2  up, j =  j UE , (1)

where UE is the transmit power of UE normalized by noise power. In the next time slot, the NodeB transmits to the UE k in downlink direction. The received SINR at the UE k becomes HD 2 Fig. 1. System model.  dn,k = k NB , (2)

where  denotes the transmit power of the NodeB Fig.1 illustrates the system model considered in this NB paper, in which M uplink UEs and N downlink UEs are normalized by noise power. It is worthy to note that, trying to obtain communication opportunities from the because of FD operation, there are no interference terms NodeB. The NodeB selects one or more UEs to in (1) and (2). Finally, the total throughput in bps/Hz participate in communication at each time slot based on can be presented as a certain selection criterion. We assume the NodeB 1 1 RHD = log (1+  HD )+ log (1+  HD ). operates in FD mode, i.e. it transmits to one downlink j,k 2 2 up, j 2 2 dn,k UE while it receives from another uplink UE in the (3) same time slot. Hence, the NodeB selects one UE out of The optimal scheduling rule for HD operation which M uplink UEs and one UE among the N downlink UEs. maximizes (3) can be formulated as For example, as shown in Fig.1, if we assume that an * * HD uplink UE j and a downlink UE k are selected, then the (j ,k )= arg max Rj,k . (4) j1,,M  UE j and the NodeB transmit simultaneously. Because k{1,,N} of the characteristics of radio propagation, two types of interference occur: self-interference at the NodeB and IUI between the uplink and downlink UEs. In the figure, C. Scheduling for Full-Duplex Operation without while the solid lines denote desired signal paths, the Inter-UE Interference dotted lines represent the two types of interference. Now, we formulate the scheduling rule for FD Please note that these interferences are induced by FD operation case when the IUI is not considered, e.i. only operation. Most of the previous studies on FD operation the self-interference is assumed to be dominant. only focused on the self-interference. However, in a When an uplink UE j and a downlink UE k is selected cellular scenario where there are many uplink and and the NodeB is operated in FD mode, then the downlink UEs in the same cell, the IUI may severely received SINR of NodeB can be dominate the performance degradation of the FD 2   operation. FDwoIUI j UE  up, j = . (5) In Fig.1,  and  represent the complex channel 2 j k 1+ g NB coefficients of the desired signal paths, g and hjk At the same time, since the IUI is ignored in this case, the received SINR at the downlink UE k becomes denote the complex channel coefficients of the FDwoIUI 2 interference channels, respectively. In this paper, we  dn,k = k NB . (6) assume that all the channels are independent Rayleigh Then, the total throughput is presented as fading channels. FDwoIUI FDwoIUI FDwoIUI In the following subsections, we formulate Rj,k = log2 (1+  up, j )+ log2 (1+  dn,k ). instantaneous signal to interference and noise ratios (7) (SINRs) and scheduling criteria for HD operation, FD To maximize (7), the scheduler should select UEs which operation without IUI and FD operation with IUI. satisfy the following rule: * * FDwoIUI (j ,k )= arg max Rj,k (8) j1,,M  B. Scheduling for Half-Duplex Operation k{1,,N} As a reference system, we consider a conventional TDD system which operates in HD mode where the

D. Scheduling for Full-Duplex Operation with Inter- It is worthy to note that, among the four channel UE Interference values  j ,  k , g and hjk in Fig.1, only hjk is not If we consider IUI, the received SINR at NodeB becomes known to the NodeB. Value of can be measured by 2 the NodeB directly, is measured by the UE k and FDwIUI  j UE  up, j = , (9) reported to the NodeB, and is also possible to be 1+ g 2  NB measured by the NodeB. However, any UEs cannot and the received SINR at downlink UE turns to be measure because there are no ways to investigate 2   complicated IUI channel structure. This is the main FDwIUI k NB . (10)  dn,k = 2 reason for the optimal scheduling in (12) is not possible 1+ hjk UE to be implemented in practice. Hence, the important Hence, the total throughput in bps/Hz is goal of the proposed algorithm is to estimate the FDwIUI FDwIUI FDwIUI channel correlation structure among the uplink and Rj,k = log2 (1+  up, j )+ log2 (1+  dn,k ) downlink UEs. (11) and optimal scheduling rule for maximal A. Initial Set-Up of Correlation Table throughput is At initial state, the NodeB does not have any * * FDwIUI (j ,k )= arg max Rj,k . (12) information about IUI. To build up this information, it j1,,M  k{1,,N} sets up an UE correlation table, which tracks mutual In summary, the FD system suffers from two influences among the uplink and downlink UEs. Table 2 types of interference in spite of its efficient shows an example correlation table when M=2 and N=4. As shown in the table, total sum of entries utilization of time slots. SINR expressions for becomes one. above three different scheduling schemes can be summarized in Table 1. Table 2. Example correlation table with 2 uplink UEs and 4 downlink UEs. Downlink III. PRACTICAL UPLINK-DOWNLINK UE SCHEDULING UE 1 2 3 4 ALGORITHM FOR FD NODEB Uplink UE

As mentioned in section 2, two types of interference 1 0.125 0.125 0.125 0.125 are not avoidable in practice. Moreover, without any 2 0.125 0.125 0.125 0.125 efficient handling of the interference, it is not possible to obtain benefits from FD operation. Therefore, it is required to design a practical scheduling algorithm to B. Uplink and Downlink UE Scheduling realize the benefits of FD operation in real cellular 2 systems. In this section, we propose a For scheduling in (12), the information | h jk | is Table 1. SINR expression for each case. missing in the NodeB. The proposed scheduling Full-Duplex algorithm uses the values in the correlation table instead Half-Duplex w/o IUI w/ IUI 2 of | h jk | . Hence, (10) can be modified as 2 2  j UE |  |  Uplink 2 Prop k NB 2  dn,k = , (13)  j UE SINR 1+ g  NB 1+ C j,k UE

where C j,k denotes the element (j, k) in the correlation 2 k NB table such as Table 2. 2 Downlink   2 SINR k NB 1+ hjk UE C. SINR Measurement and Feedback of UE After scheduling, the uplink UE j transmits to the practical scheduling algorithm to obtain near-optimal NodeB and the NodeB transmits to the downlink UE k throughput with minimum overhead. simultaneously. Because the downlink UE k does not

know IUI, it expects to receive the SINR such as evaluation, we assume that all channels are independent Exp 2 Rayleigh fading channels, such that | |2 ~ E x p( ) ,  dn,k = k NB . (14) k  2 2 However, the downlink UE k experiences IUI | k | ~ Exp( ) , | g | ~ Exp(g ) , and caused by uplink UE j, then the actual SINR obtained 2 by the UE k becomes | hjk | ~ E x p(h ) , where Exp() denotes the 2 exponential distribution with mean  . Act k NB  dn,k = 2 . (15) 1+ h  jk UE A. Impact of IUI By comparing (14) and (15), the UE k can estimate the amount of IUI. Practically, it is not preferable to feedback the value of IUI because of the feedback bandwidth limitation. Moreover, the relative strength of IUI among UEs is enough for scheduling in the NodeB. Hence, we design an 1-bit feedback information rule as shown in Fig.2.

Fig. 3. Impact of IUI when N=2,  =  = 0 dB, and

g = -3 dB. Fig. 2. 1-bit feedback value mapping function. Fig.3 investigates the impact of IUI on the throughput For feedback, the UE k compares the ratio of (14) and performance by comparing the spectral efficiency (15) and the threshold  . The feedback value of 1 obtained by (4), (8), and (12) when there are two uplink denotes that the UE k is strongly interfered by unknown UEs and =  = 0 dB, and g = -3 dB, uplink UE. By feedback this 1-bit information to the NodeB, the UE k can let the NodeB know whether the respectively. In the figure, it can be shown that FD scheduled UEs in this time slot are mutually correlated. without IUI from (8) and HD from (4) provide upper bound and lower bound performance, respectively. As expected, the spectral efficiency degrades when IUI is D. Correlation Table Update in the NodeB induced. Higher average interference channel gain  h Based on Fig.2, the downlink UE k generates 1-bit yields more degradation in spectral efficiency. Hence, it feedback information to report to the NodeB. Then the is clear that the effect of IUI in the multi-UE FD NodeB updates the correlation table in Table 2. Note systems is not negligible and it should be controlled that total sum of the entries in the table should be kept carefully to obtain the advantage of FD operation. one. Therefore, as the time proceeds, the entries for closely related UE pairs have greater values than others. After updating the table, the NodeB repeats the B. Performance of the Proposed Scheduling Algorithm scheduling procedure from subsection 3.B to 3.D. Fig.4 evaluates the spectral efficiency performance of the proposed scheduling scheme in section 3 when there are two uplink UEs and = = 0 dB, and = -3 IV. NUMERICAL EVALUATION dB, respectively. In this section, we evaluate the impact of IUI on the Solid line with triangle marker denotes the spectral total throughput performance first. Then the efficiency obtained by (12) which is an ideal case where performance of the proposed practical FD scheduling all IUI information is known to the NodeB for algorithm is evaluated numerically. For numerical

scheduling. This feedback threshold means that the operation only focused on self-interference at the feedback value ‘1’ is reported when the actually receiving part of the NodeB induced by itself. On the received SINR is less than half of the expected SINR. other hand, this paper shows that interference between the uplink and downlink UEs may dominate the overall system performance. Second, we proposed a practical FD scheduling algorithm with limited feedback overhead, which provides near-optimal spectral efficiency. The performance gap shown by the proposed scheme mainly induced simple feedback structure, e.i., spectral efficiency and feedback information amount are in trade-off relation. For further work, it is needed to investigate the optimal rule for feedback information generation, which is another parameter varies system performance.

ACKNOWLEDGMENT

This work was supported by the National Research Fig. 4. Performance of the proposed FD scheduling Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2017R1A2B1004474). scheme when N=2,  =  = 0 dB, and g = -3 dB.

REFERENCES Then, the NodeB updates the correlation table such that currently selected uplink and downlink UEs are less [1] R.N.Mitra, and D.P.Agrawal, 5G mobile technology: A survey, likely to be selected together next time. ICT Express, Vol.1, No.3, (2015) 132-137 From Fig.4, it is possible to show that the proposed [2] A. Al-Dulaimi, S. Al-Rubaye, Q. Ni and E. Sousa, 5G scheme achieves the spectral efficiency close to the Communications Race: Pursuit of More Capacity Triggers LTE in Unlicensed Band, IEEE Vehicular Technology Magazine, vol. optimal FD scheme. The performance gap mainly 10, no. 1, (2015) 43-51 comes from extremely small feedback information. If [3] D.Zarbouti, G.Tsoulos, G.Athanasiadou, Effects of antenna we have enough bandwidth for feedback, entire array characteristics on in-band full-duplex relays for broadband estimated IUI can be reported and almost the same cellular communications, ICT Express, Vol.1, No.3, (2015) 121- 126 spectral efficiency with the optimal FD scheme will also [4] Y. Liu, X. G. Xia, Z. Zhang and H. Zhang, Distributed Space- be achieved. Time Coding Based on the Self-Coding of RLI for Full-Duplex Two-Way Relay Cooperative Networks, IEEE Transactions on V. CONCLUSION Signal Processing, vol. 65, no. 12, (2017) 3036-3047 [5] Y. Jiang, F. C. M. Lau, I. W. H. Ho, H. Chen and Y. Huang, Max–Min Weighted Downlink SINR With Uplink SINR In this paper, we first evaluated the impact of IUI on Constraints for Full-Duplex MIMO Systems, IEEE Transactions the system performance of a cellular system with on Signal Processing, vol. 65, no. 12, (2017) 3277-3292 NodeB operated in FD mode. The NodeB selects one [6] R. Li, Y. Chen, G. Y. Li and G. Liu, Full-Duplex Cellular Networks, IEEE Communications Magazine, vol. 55, no. 4, uplink UE and one downlink UE to receive and to (2017) 184-191 transmit simultaneously. Most previous works about FD

Image Analysis of Periorbital Region to Classify Affective States of Children

Rusli. N1, Sidek. S.N1, and Md. Yusof. H1 1Mechatronics Engineering Department, Kulliyyah of Engineering, International Islamic University Malaysia [email protected]

Abstract—Problem in classification of affective states in Spectrum Disorder (ASD). autistic children based on image analysis has created new Hence, the other perspective of looking at the challenge since majority of the research works are still cutaneous blood perfusion under the skin on facial based on observations or the use of invasive equipment for surface for any cues of different affective states is data collections. In this study we use facial thermal image investigated. There are two biological mechanisms that analysis to classify the affective states based on the distribution of thermal cues in the image pixels. This paper enable thermal observation due to affective nature as in reports on our preliminary findings on the analysis done example of subcutaneous vasoconstriction and on the data obtained from healthy kids as focused subjects emotional sweating [2-3]. These mechanisms are in order to form a reference model of affective states. A activated by epinephrine released in the blood stream series of experiments done on the subjects whose range of and they happen when a person is in danger or threaten ages of six to nine years old have been conducted by using causing the triggering of subcutaneous vasoconstriction a set of stimuli and the results depicted distinct patterns on [4-5]. The adjustments in blood flow change the emitted different classes of affective states. The images captured thermal print patterns. during the experiments were treated as responses from In this paper, the words Affective States and emotions subjects towards the stimuli which were adopted from International Affective Pictures System (IAPS). The region are used interchangeably. The emotions have been of interest was tapered to a periorbital area. Texture stated as a subclass of affective states and it can be analysis was then conducted by using first order statistics represented as a complex model of affects [6]. The from gray level pixel intensity defined and second order of model is developed from a combination of valence and gray level co-occurrence matrices to govern the weighty arousal where valence is measured based on how the and distinguishable characteristic patterns between person perceived the emotion as positive and negative affective states. The first order and second order statistical emotions whereby arousal measures the strength of the analysis were combined for the, MANOVA test to be person feels the emotion. It is reported that there are two conducted to choose for the significant contributed groups of categorical emotions namely positive and features in the analysis. The conclusive result from SVM classifier suggests that these features are able to negative emotions based on valence or pleasant that can distinguish between affective states. Thermal image be identified in the model [7]. Hence, we also analysis thus could be used as non-verbal means to help concentrate into the two categorical emotions; positive identify affective states of special group of children such as emotions and negative emotions in this study. children with Autism Spectrum Disorder. The average intensity value is chosen as the first feature derived from the first order statistical value. The Index Terms—Affective states, ASD, Thermal Image, first-order statistics is the simplest and the most Periorbital. common feature used in grey image analysis. It studies

the distributions of the grey intensity values in the I. INTRODUCTION image pixels. However, the first order statistics only

depend on the individual pixel intensities and not on the Affective states are also called moods, are defined as neighboring pixel values or co-occurrence. emotion-like states [1]. It is common for the change of Hence, the second-order statistics are considered to affective states in individual to be recognized through observe a pair of grey values and determine the the level of engagements or group of emotions either information from the co-occurrence matrix. The positive or negative emotions. Engagement is occurrence of some gray-level conformation can be epitomized in a behavioral feedback meanwhile defined by a matrix of relative frequencies P(θ,d,I1,I2) emotions normally refer to the speech or facial where P is the probability of all pairwise of pixel expression. However, speech and facial expression are intensities (I1 and I2) combinations at a particular not reliable to be used for children with Autism 36

displacement distance (d) and at a particular orientation set between 30ºC to 40 ºC as to maintain the same scale (θ) [8]. In this paper, we studied on the performance of for temperature readings for every subject. The camera the combination of first order and second order based has been set to the speed of 30 frames per second (fps) features in affective states classifications. The statistical throughout the experiment. During the experiment, tools of SPSS software have been used to identify the subject was advised not to wear any glasses and they most significant and weighty contributed features to the were informed to avoid any meal 1-hour before the good performance of the classifier’s accuracy. experiment to avoid any metabolic effect form digestion This paper is presented as follows. In section 2, the process. The experimental setup is shown in Figure 2. experiment procedure is discussed. In section 3, the technique used to automatically generate the selected region of interest (ROI) is explained. The experimental 1 meter results were presented in section 4. Finally, the major Digital contribution to this paper is summarized in the last Camera Thermal Stimuli Subject section. Camera Processing Unit

II. EXPERIMENTAL PROCEDURES

The following rudiments as shown in Figure 1 are Figure 2: Experimental setup needed for data collections procedure. It depicts the At the beginning of the experiments, the subjects three main elements involved namely, equipment, were explained on the objective of the study which is to subject and stimuli selections. investigate the response and expression in emotions of the children towards the video. Then, they were further explained on the process of the experiment. Thermal Image B. Stimuli Selection Process The audio-visual type of stimuli is selected due to its

Image Acquisition efficacy for emotion induction and has been testified in Equipment Subject selection several studies [11-13]. A short movie with a time span Region of Interest (ROIs) selection Stimuli selection of 80 seconds was developed by combining still images

Feature Extraction obtained from International Affective Picture System

Figure 1: Environment element for thermal image process (IAPS [14]) and affect-congruent music because recent study has found that the combination of IAPS images with music to be predominantly effective affect A. Equipment Selection induction technique [15-16]. The IAPS images were The FLIR thermal camera model T420 was chosen to used as induction stimuli due to its efficacy for emotion capture and record thermal facial image of subjects induction for affect in younger and has been proven in during experiment. The thermal camera used a passive studies [17,18]. As in typical picture induction study, band infrared of the electromagnetic wave spectrum the duration of an image is presented in two to seven where it may be used in observing anatomical features seconds. Meanwhile, the interval of 50ms is set between such as vein patterns and blood perfusion [9]. stimuli of different affective states [19]. However, we Furthermore, thermal image analysis can be applied in have set an extra time which is 15 seconds per image. credibility assessment where the increase in blood flow The still images were selected and grouped to form a causes increase in skin temperature [10]. short video for a specific discrete affective state The image acquisitions by thermal camera are needed according to IAPS standard rating. The scale rate of to be performed under controlled environment. valence which is above five is considered as positive Generally, under the ambient environment, the body valence whilst below five is dedicated to the negative controls body temperature for homeostasis between valence. 29ºC to 31ºC with light clothing and 25ºC to 29ºC There are five videos developed to form the set of without clothing. Hence, the data collection processes stimuli to induce happy, disgust, surprise, fear and were conducted in well ventilated room with angry affective states which later classified into two temperature maintained at 24ºC throughout the categorical emotions that are positive and negative experiment. Meanwhile, the emissivity constant valence of emotions. In order to avoid misreading or parameter for the thermal camera was set to 0.98 mixing emotions in the data, the sequence of video according to FLIR standard value for human skin. shown was arranged from positive to negative valences Meanwhile, the range of temperature on the camera was as shown in the Complex Affect Model [6]. In 37

summary, 80 seconds of thermal video for a single 30fps, thus, total number of frames will be 2400 frames affective state was recorded where the first 10 secs was per person per emotion. Every frame has a different allocated to form the baseline. It was followed by 60 maximum intensity; therefore, a threshold value is secs video of affective state’s induction and the last 10 defined from the maximum intensity across all the secs was again treated as baseline. The same concept is sequences of frames. The excerpt of the code to identify applied for all class of the affective states. The timeline the threshold value is shown as follows of the stimuli was represented in Figure 3.

2400 푁 푀 푡ℎ푟푒푠ℎ표푙푑ℎ표푡 = max( ∑ 푚푎푥 ∑ ∑ 퐼푡(x, y)) 푡=1 푥=1 푦=1 (1)

Where t is the frame’s sequential and MxN is the frame’s size of the image. Later, the image was Before During After transformed to a binary image through thresholding technique where the hottest area was calculated from 95 10 sec 60 sec 10 sec percent of the threshold value: Figure 3: Timeline of the stimuli 𝑖푓 (퐼(푥, 푦) > 퐶 ∗ 푡ℎ푟푒푠ℎ표푙푑ℎ표푡)

푚푎푠푘(푥, 푦) = 1 % 푐표푛푣푒푟푡 푡표 푤ℎ𝑖푡푒 푝𝑖푥푒푙 C. Subject Selection There are 16 kids ranging age from six to nine years 푒푙푠푒 old were volunteered to be as subjects for the experiment. Ethical clearances for the involved subjects 푚푎푠푘(푥, 푦) = 0 % 푐표푛푣푒푟푡 푡표 푏푙푎푐푘 푝𝑖푥푒푙 have been granted in prior. They are claimed healthy as (2) they do not have any difficulties in social interaction development as autistic children do. All the subjects where C is a constant value used to get the hottest area were fully briefed and have got their parents’ closest to the periorbital boundary. In this study, C was permission and signed consented form prior to their set to 0.95. participation in the experiment. Biographical data were Then, based on the white area covered in the binary documented at the beginning of the experiment. image either on the right or left pre-orbital region, the average of true intensity values is computed. Subjected to the natural attitude of children of the selected age III. FEATURE EXTRACTIONS FROM FIRST ORDER AND where they tend to move their heads around during the SECOND ORDER STATISTICS experiment, disregard of frontal facial images only, one side of periorbital region was also considered in The image analysis was done by analyzing the texture analysis. The criterion of the marked boundary is the of the image where the intensity values were maximum of white area of connected object in binary considered. Features are then calculated over the results image. In our observation, this technique is applicable of sector of the image band upon image segmentation. for all frames where the boundary felt into the area In this work, the texture analysis is implemented where between nose and eyes as shown in Figure 4 and it was an image is observed from the spatial deviation in pixel not constrained to either left or right. intensities. There are two main features extracted from the selected ROI; average intensity as the first order statistic and the second order statistical analysis of Gray Level Co-Occurrence Matrix (GLCM). Both features are based on the image analysis and not the temperature analysis. The average intensity values were determined from the hottest area surround the periorbital area. Initially, the hottest area is identified through the thresholding process where the constant value was set Figure 4: ROI marked in frames of thermal images and transformed the image into a binary image. Since, there are 80 seconds of captured videos and have been The average of intensity values within the boundary is converted to sequences of images at camera speed of targeted instead of the size of area and calculated as in 38

Equation 3. graph represents the average intensity values versus event where y values are epitomized for event at ∑퐴 ∑퐵 푚푎푠푘(푥, 푦) ∗ 퐼(푥, 푦) baseline, induction and rest respectively. 푎푣푒푟푎푔푒 = 푥=1 푦=1 퐴푥퐵

(3)

The average value will be calculated separately for every frame. Hence, there will be 2400 average values were stored. On the other hand, the second feature was based on the GLCM parameters. Haralick et al.[8] was the first person to introduce the GLCM technique which is becoming most common and extensively used in texture feature extraction. The GLCM depicts second order statistic of an image by calculating the frequency for combination of pairs of pixel happens in a definite spatial relationship to a pixel in the whole image. The image is devoted to the whole segmented image at pre- orbital region as described in Figure 4. In this paper we focus on four main extracted features from GLCM which are correlation, contrast, energy and homogeneity. This was the finalized parameters that Fig.5. Graph of average intensity at three events have been made in our previous work [20].

A unique pattern was clearly distinguishable between IV. RESULTS AND DISCUSSION the events and emotions. However, as shown in Figure 5, the graphs only depicted the pattern for two emotions; In order to significantly view the difference in Disgust and Happy which are randomly selected to changes of affective states, the results were calculated represent two opposite emotions in valence. Thus, these based on these three different slots following the observations steered us to choose the three events as stimuli’s timeline; baseline (before video starts), during significant dependent variables to look into. In order to induction (video plays) and rest time (after video is select the optimum features from fifteen extracted ended). The average intensity values were then stored features as to contribute to the best classification in individually for every subject and carried by three affective states, a MANOVA statistical analysis was different variable names which are average intensity applied. The independent variables of two categorical baseline, average intensity induction and average affective states were tested with respect to the fifteen intensity rest time accordingly. These data were dependent variables of feature extractions. Table 1 combined with the GLCM features for the same ROI shows the result of all fifteen dependent variables for and the same considerations were counted for the average intensity value within the maximum connected GLCM features in which each of the GLCM parameter region at pre-orbital. was divided into three different event categories namely baseline_GLCM, induction_GLCM and rest_GLCM. In Table 1. Result from MANOVA test the second-order analysis, it is assumed that two Dependent variables textures are not exclusively distinguishable if their p-values second order statistics are alike [21, 22]. Thus, in our VARIABLES case, the disparities between images are our interest due Baseline average intensity 0.64 Induction average intensity 0.028 to determine the changes in affective states. There are five feature extractions were counted in the After average intensity 0.002 analysis where four features are the GLCM parameters Baseline GLCM contrast 0.020 (correlation, contrast, homogeneity and energy) and one Induction GLCM contrast 0.774 for average intensity value and each of them was After GLCM contrast 0.441 considered at three different events. Thus, in total, there Baseline GLCM correlation 0.000 are fifteen features to be analyzed. The reason to compute in three different events is due to the pattern Induction GLCM correlation 0.214 that can be seen from the graph shown in Figure 5. The After GLCM correlation 0.012 39

Baseline GLCM energy 0.231 Induction GLCM energy 0.166 The row and the column of this table represent the After GLCM energy 0.078 actual and the predicted class. Since, the five discrete emotions have been collected and combined into two Baseline GLCM homogeneity 0.001 categorical emotions thus only two classifications were Induction GLCM homogeneity 0.204 tested and defined. Class 0 represents the negative After GLCM homogeneity 0.018 valence of emotions considered of 16 samples for 3 discrete emotions (FEAR, ANGRY and DISGUST) meanwhile class 2 represents of positive valence of The rejection region was set at p < 0.05. The emotions considered of 16 samples for 2 discrete breakdowns of interactions used analyses of simple emotions (HAPPY and SURPRISE). main effects. The p-value is calculated to find a significant difference in dependent variables. Hence, based on the result above, there are seven significant V. CONCLUSIONS variables selected based on the p-values generated from MANOVA test. These seven variables were then used The performance of linear SVM classifier suggests a for classification of the emotions. By using Matlab convincing result to use significant features extracted software, the common algorithms for classification were from thermal image in affective states classification. tested and the results are shown in Table 2. The However, the accuracy is expected to be increased by classification process is performed using a standard revising the stimuli used. The suitable and high impact toolbox in MATLAB, Classification Learner in Matlab still images from IAPS need further investigation. This 2015a. We have tested for the same dataset by using is due to some IAPS images that seem to be subjective three different classifiers. From Table 2, it is clearly where some children experience them as negative and seen that linear Support Vector Machine (SVM) accords others as positive. There is considerable idiographic the highest performance. variation across individuals in their affective reactions to the images, where mean ratings across individuals Table 2. Result from MANOVA test had large standard deviations. Since, cutaneous blood perfusion is also providing grounds for observations of emotional reading; hence, the ability of thermal imaging CLASSIFIER Linear SVM k-NN Decision camera to capture the thermal imprint from blood flow Tree is an advantage. This method aspires to explore the thermal imaging system in affective states in children Accuracy 82.5 77.5 68.8 with ASD that is labelled as do not have correct and proper facial expression.

ACKNOWLEDGMENT The confusion matrix shown in Figure 7 depicted the values of True Positives Rate (TPR) for both classes. We wish to express our gratitude to the Ministry of The accuracy describes the number of correct Higher Education (MOHE) for funding the project predictions made out of the total number of predictions under the Fundamental Research Grant Scheme represented in percentage form. (FRGS), Grant no: FRGS16-030-0529

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Design of novel highly nonlinear photonic crystal fiber for ultrahigh-resolution optical coherence tomography

Feroza Begum1,*, Abul Kalam Azad1, Emeroylariffion Abas1, and Nianyu Zou2 1Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong BE 1410, Brunei Darussalam 2School of Information Science and Engineering, Dalian Polytechnic University, Dalian, China *[email protected]

Abstract—We represent a novel photonic crystal fiber Commonly investigated broadband light sources like with high nonlinearity for optical coherence tomography semiconductor-based superluminescent diodes (SLDs) application. The proposed highly nonlinear photonic [4, 5], femtosecond pulse laser sources [6-13] and crystal fibers different properties are computed based on picosecond pulse laser source [14]. The present OCT finite difference method. Ultraflattened dispersion, small chromatic dispersion slope, large nonlinear coefficients, systems longitudinal resolution is depend on the light and very small confinement loss property are obtained for source optical bandwidth. Generally the SLDs optical this designed highly nonlinear photonic crystal fiber. bandwidth is 20 to 80 nm, longitudinal resolution is Moreover, the high power wideband super continuum about 10 to 20 μm and low output power of 2 mW to 15 spectrum and high longitudinal resolution of living tissue mW, which providing more detailed structural are achieved. Longitudinal resolution of living tissue is information [4, 5]. However, this achieved longitudinal achieved 1.3 μm at center wavelengths 1.1 μm as well as 1.0 μm at center wavelengths 1.31 μm by applying resolution of SLD is not enough to detect single cells. picosecond pulse. Furthermore, the output power of 64.0 Up to 372 nm spectra centered at about 1.1 μm, 1.3 μm W at 1.1 μm center wavelength and 67 W at 1.31 μm longitudinal resolution, about 50 mW of output power center wavelength is demonstrated. were generated by 110 fs Kerr-lens modelocked (KLM) Ti:sapphire oscillator [6]. A compact femtosecond Index Terms— Photonic Crystal Fiber; supercontinuum Neodymium doped glass (Nd:glass) laser that provide spectrum; effective area; optical coherence tomography. ultrahigh resolution of 3.6 μm at 1.0 μm center I. INTRODUCTION wavelength [7]. A fiber-based, compact mode-locked ytterbium-doped (Yb-doped) laser permits longitudinal The photonic crystal fibers (PCFs) have brought an resolution of less than 1.6 μm at center wavelength 1.04 advanced range of fibers with wider design space and μm for ultrahigh-resolution OCT imaging [8]. An OCT manageable dispersion properties [1]. The highly light source connecting a LED and a near-infrared nonlinear PCFs low dispersion wavelengths is possible emitting glass (1.0Yb2O3–4.0Nd2O3–47.0Bi2O3– to move from visible to near infrared for getting 47.0B2O3–1.0Sb2O3) phosphor is used for center wideband supercontinuum (SC) with high peak power wavelength around 1.0 μm and obtained longitudinal femtosecond lasers source [2]. It is possible to achieve resolution of 4.6 μm [9]. Neodymium-doped Y3Al5O12- extremely broad bandwidth SC sources from PCFs. The crystals (Nd:YAG) were investigated at around 1.0 μm broad bandwidth SC spectrum is essential for optical center wavelength and analyze spectrum bandwidth is coherence tomography (OCT) systems. Huang D, et al. approximately 0.2 nm and output power of 1.3 W [10]. was established OCT [3] at first and it is noninvasive Generation a SC spectrum pumped with 200 fs Yb- medical imaging procedure. The internal cross-sectional doped fiber laser at a central wavelength of 1.07 μm is images of biological tissue are obtained from high- reported [11]. A FWHM bandwidth of 210 nm, resolution OCT. OCT imaging yields less dispersion, longitudinal resolution of 3.7 μm and on sample output broad insertion and enhanced sensitivity around 1.0 and power of 10 mW are obtained by using a Cr4+:forsterite 1.3 μm wavelength. Around 1.0 μm wavelength is laser source [12] at center wavelength 1.25 μm. SC attractive in ophthalmology. Also, the wavelength generated single photonic crystal fiber with 85 fs pulse around 1.3 μm is interesting in dentistry. train compact Nd:Glass oscillator is demonstrated at

center wavelength 1.3 μm in ultrahigh resolution OCT and it is reported 4.7 μm longitudinal resolution [13]. A Ge-doped PCF picosecond pulse laser were investigated at 1.31 μm center wavelength and obtained 6.1 μm of tooth enamel longitudinal resolution and 6.5 μm of dentin longitudinal resolution [14]. It is pointed out that still SLDs have suffered with low output power and narrow spectral bandwidth. Furthermore, femtosecond pulse based laser SC sources maintains better d performance than that of SLDs. However, the cost of a d1 femtosecond laser source is a big issue. Therefore, the low-cost, high power ultrabroadband light source is an essential issue for high performance OCT practically. The inexpensive picosecond SC laser light source which permits to implement economical wideband light source Λ than that of expensive femtosecond SC light source. Air hole In this research, we propose picosecond pulse based Silica HN-PCF in around 1.0–1.4 μm wavelength OCT bands. It is obtaining wide bandwidth spectra, immense axial resolution of living tissue and large input power. According to numerically simulated result we have seen Figure 1: Architecture of the proposed six-ring highly nonlinear that the proposed highly nonlinear PCF exhibits PCF nonlinear coefficients greater than 85 [Wkm]-1 at 1.1

μm, 62 [Wkm]-1 at 1.31 μm. The dispersion value is from zero to – 7.0 ps/(nm.km) remained between 1.06 III. SIMULATION METHOD μm to 1.4 μm wavelength. The dispersion slope value is ± 0.07 ps/(nm2.km) changed within the wavelength region 1.06 μm to 1.40 μm. The lower confinement loss The proposed HN-PCF different properties are is 0.1 dB/km attained in the targeted wavelength spaces. computed by using full-vector finite difference method On the other hand, longitudinal resolution is obtained with anisotropic perfectly matched layer [15, 16]. The 1.3 μm at 1.1 μm center wavelength and 1.0 μm at different properties include chromatic dispersion D(λ), center wavelength 1.31 μm which are better than [4-14]. chromatic dispersion slope, confinement loss Lc, mode Moreover, power of 64 W at 1.1 μm center wavelength effective area Aeff. Sellmeier equation is provided the and 67 W at 1.31 μm center wavelength are achieved material dispersion which is straight added in the which are higher than [4-14]. chromatic dispersion calculation [15, 16]. Therefore, chromatic dispersion D(λ) correlate to the entire chromatic dispersion of the PCFs. Nonlinear coefficient γ is calculated with [17]. II. ARCHITECTURE OF THE PROPOSED PCF

In Fig. 1, the architecture of the proposed highly IV. RESULTS AND DISCUSSION nonlinear PCF (HN-PCF) is depicted. The 1st ring and 3rd ring air hole diameter is set d1, 2nd, 4th to 6th ring air hole diameter is set d, Λ is the center-to-center Fig. 2(a) exhibits the chromatic dispersion and distance of two adjacent air holes, in this architecture. chromatic dispersion slope parameters of the proposed The cladding region periodicity in index-guiding PCF is highly nonlinear PCF. And Fig. 2(b) represents the unimportant to focus the light inside the core area. This effective mode area and confinement loss properties of designed principle is persuade the proposed HN-PCF to the proposed HN-PCF. In this case, the air hole manage the chromatic dispersion slope and chromatic diameters are d1 and d, and pitch is Λ. The optimum dispersion in broad range of wavelength. The cladding chromatic dispersion is 0.0 – 7.0 ps/(nm.km) for the region regularity is changed by reducing the 1st and 3rd ring air hole diameter. The sizes of air hole are increased of remaining rings which are providing low confinement loss.

15 0.12 1.00

(a) .km)] 10 2 0.06 5 0.75

0 0.00

0.50 -5 -0.06 FWHM = 291 nm -10

Intensity [a.u.] Intensity 0.25

Dispersion [ps/(nm.km)] Dispersion d =0.29,d=0.78,=0.87 1

-15 -0.12 [ps/(nm Slope Dispersion  = 1.1 m 1.0 1.1 1.2 1.3 1.4 c Wavelength [m] 0.00 0.90 1.05 1.20 1.35 (a) Wavelength [m]

3 101 (a) (b)

] 2 -1

m 10 1.00  2

-3

10 0.75 1 10-5

0.50 Effective Area [ Area Effective d =0.29,d=0.78,=0.87 1 -7 [dB/km] Loss Confinement FWHM = 435 nm 0 10

1.0 1.1 1.2 1.3 1.4 [a.u.] Intensity 0.25 Wavelength [m]  = 1.31 m c (b) 0.00 1.0 1.2 1.4 1.6 1.8 Figure 2: (a) Dispersion property and property of dispersion slope Wavelength [m] and (b) Property of effective area and property of confinement loss

(b) proposed highly nonlinear PCF between the wavelength range of 1.06 μm to 1.4 μm. The dispersion slope value Figure 3: (a) SC spectrum at center wavelengths 1.1 μm and (b) SC is changed ± 0.07 ps/(nm2.km) in the wavelength range spectrum at center wavelengths 1.31 μm of 1.06 to 1.40 μm. In the targeted ranges wavelength, the low confinement loss of < 0.1 dB/km is achieved. The large nonlinear coefficient is obtained > 85 [Wkm]- 1.31 μm center wavelength λc and at 1.1 μm center 1 at 1.1 μm and > 62 [Wkm]-1 at 1.31 μm. wavelength, respectively. From Table 1, it is seen that For SC spectrum numerical calculation, nonlinear the power are 64 W and 67 W in two different center Schrödinger equation (NLSE) is applied [18]. The wavelengths; one is 1.1 μm and another one is 1.31 μm, respectively. This input powers of the applicable pulse proposed HN-PCF numerically computed SC generation are higher than [4-14]. spectra is shown in Figs. 3(a) and (b). In the proposed The air coherence length l is expressed for a HN-PCF, the full width at half maximum (FWHM) c Gaussian spectrum by the formula [18]. In air, living sech2 input pulse 1.0 ps is considered for propagating tissue’s longitudinal resolution, l can be estimated by through the fiber. In Fig. 3, Raman scattering parameter r [18] after calculating coherence length l . Because of l T is 3.0 fs. The incident pulse input power P is 64.0 c r R in is proportional with l therefore the coherence length l W at 1.1 μm center wavelength λ and at 1.31 μm center c c c should be low value for ultrahigh-resolution OCT wavelength λ 67.0 W. The computed β and β values c 2 3 imaging. In our simulations, it is found that l is 1.8 μm are shown in table 1 at 1.1 μm center wavelength and at c at 1.1 μm center wavelength as well as 1.7 μm at 1.31 1.31 μm center wavelength. It is seen from Fig. 3 that μm center wavelength for the proposed HN-PCF. wide FWHM of SC spectrum of 435 nm and 291nm are Therefore, the longitudinal resolution is obtained about obtained at 1.0 μm for dentin and 1.3 μm for ophthalmology. In

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Design of Spatial Model of Poverty Index Prediction Using Triple Exponential Smoothing Method

Kristoko Dwi Hartomo, Sri Yulianto, Asni Valentina Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50711, Indonesia [email protected]

Abstract—High poverty rate has been a main problem of II. THE PROPOSED METHOD Indonesia, especially in the Central Java Province. The province itself set a target to reduce its poverty rate by The first step of our research is identifying poverty 6.84% or 1.36% per year. However, it only managed to problems, government tend to pay less attention to reduce poverty rate less than 1% per year. Based on this potentially poor regions because there is no spatial problem, this research predicts poverty index using Triple Exponential Smoothing: Brown's One – Parameter prediction model of poverty index in the Central Java Quadratic. The spatial model of this research aims to map Province. Next, the second stage is the literature review the actual and predicted figures of poverty index in the and data analysis. We conduct short-term poverty index Central Java Province. Our prediction results of poverty prediction (one-period-ahead) using the Triple index using Triple Exponential Smoothing show relatively Exponential Smoothing: Brown’s One-Parameter small error value with accuracy of 85.38%. This research Quadratic method [6][7]. contributes to provincial governments in distributing aids We predict the 2015 poverty rate based on the to poor population and eventually in reducing poverty rate. historical data of 2006-2014 for all regencies and cities Index Terms—Poverty Prediction; Triple Exponential in the Central Java Province. The initialization on Smoothing; Spatial Model; Accuracy. exponential quadratic smoothing of brown can be very simple, if S′1 = S′′1= S′′′1. Prediction starts from period 2. It then can be said that in period 2 the value of S′2 , I. INTRODUCTION S′′2 dan S′′′2 can be determined using the first ′ smoothing process of the equation 푆푡 = 훼푋푡 + (1 − Poverty is a fundamental problem of developing ′ 훼)푆푡−1 and followed by the second and third smoothing countries around the world. Accelerating the poverty ′′ ′ ′′ using the equation 푆 푡 = 훼푆푡 + (1 − 훼)푆 푡−1 and alleviation requires concerted efforts that consists of ′′′ ′′ ′′′ target setting, program design and coordination, equation 푆 푡 = 훼푆푡 + (1훼)푆 푡−1 [11]. In order to generate the smoothing value of a certain value, we use monitoring and evaluation, and budget effectiveness of ′ ′′ ′′′ these programs [1][2]. One of the main causes of high the equation 푎푡 = 3푆푡 − 3푆푡 + 푆 푡 and 푐푡 = 훼2 poverty rate is delayed aid distribution to poor areas that (푆′ − 2푆′′ + 푆′′′). The results of smoothing (1−훼)2 푡 푡 푡 eventualy is driven by less accurate prediction of poverty process from the equations are then used to generate index and the absence of spatial model of poverty index predicted value by using the equation 퐹푡+푚 = 푎푡 + prediction as the basis of aid distribution by related local 1 푏 푚 + 푐 푚2. government units or Satuan Kerja Perangkat Daerah 푡 2 푡 [3][[4][5]. This paper use UML to develop spatial model of poverty index spatial using triple exponential smoothing : brown’s one-parameter quadratic. The model produced can determine regional poverty level in the Central Javaprovince. This research facilitates poor areas prediction and mapping that will be visualized into Central Java Province thematic map so that stakeholders are better able to monitor poor areas and implement poverty alleviation program better and more effectively.

Figure 2: Use case diagram Figure 2 shows functional design for users. The design has a function to further see poverty index data of each region (regency or city) in the Central Java Province in the form of Central Java Province thematic map. Besides the thematic map of the Central Java Province, we also present our data in the form of table and graphic. Graphic facilitates users in comparing data between regencies/ cities. Figure 1: The Proposed Model The next functions are extend. Users can download data in the table form with extension of .xls. However, if We obtain our research data from Central Statistical users only want to look at available data, they do not have Bureau of Central Java Province [2]. More specifically, to download the data. Here, admin’s role is processing we use the following data: macro data that consist of predicted and actual data such as adding new data each Head Count Index – P0 is the percentage of population year, converting data if there are mistakes in data living below poverty line, Proverty Gap Index – P1 inputting, and deleting unused data. User can also see measures the average expenditure gap of each poor regional mapping according to predicted index value, and resident toward poverty line. Higher index value implies prediction results one year ahead. wider expenditure gap of poor population relative to poverty line and index. The third data is the Proverty Severity Index-P2 that indicates expenditure distribution among poor population. Higher index value implies higher expenditure gap between poor population [2].. Figure 1 shows the first stage of data preprocessing, our experiment uses poverty index data of 35 regencies/ cities in Central Java Province. We use Index P0 or poverty percentage, P1 or poverty depth index, and P2 or poverty severity index in 2014. The next process is data cleaning by doing replace missing value to clear the poverty data of noise and distortion values [8]. The second stage is poverty index data prediction using triple exponential smoothing method. The third phase of testing the model, tests using the test size ME, RMSE, MPE, MAPE, MASE, and Euclidean Error to search results prediction error value [8]. Our research object is 35 regencies/ cities in the Central Java Province. We use three poverty indicators as suggested by BPS: Percentage of Poor Population (P0), poverty depth index (P1), and poverty severity index (P2). Our third stage involves model design and user interface design using Unified Modeling Language (UML). Figure 2 displays use case diagram.

poverty percentage. Yellow for regions with medium poverty percentage, and green for regions with low poverty percentage [9]. In classifying the regions based on the poverty percentage, we refer to the data from Subdistrict Social Welfare Agency - Badan Tenaga Kesejahteraan Sosial Kecamatan [10].

III. EXPERIMENTAL RESULT

Based on Table 1, error value of prediction results with the Triple Exponential Smoothing method use error tests ME, RMSE, MAE, MPE, MAPE, and EE. The average error is relatively small (approaching 0), we perform accuracy test using constan value α=0.2 and our results indicate a good fit. The results of average of error value prediction test (ME) of 0.183 shows that the TES Brown’s One-Parameter Quadratic method can be used to predict povery index data. The average of prediction error tested with the Euclidean Error method is 14.623, implying that prediction accuracy is 85.38. Table 1 Tests of average error of prediction results of P0, P1 dan P2 index in Central Java Province 2015 Poverty ME RMSE MAE MPE EE Index P0 -0.52 3.47 2.93 -9.73 9.2 P1 0.01 0.44 0.38 -7.53 8.80 P2 0.02 0.18 0.16 -22.9 25.6 3 81 Figure 3: Admin’s Activity Diagram Figure 4 is the spatial model of actual data of poverty depth index 2014. There are 16 regencies/ cities with low Activity Diagram at Figure 3 displays user admin’s poverty percentage, namely: Jepara, Pati, Kudus, Tegal, activities operating model. The diagram indicates that Wonogiri, Sukoharjo, Boyolali, Batang, Temanggung, admin will process all data in model. Initially, admin has Semarang, Magelang, Surakarta, Salatiga, Semarang, to fill in the form of Login. If she mistypes username and Tegal, and Pekalongan. There are 9 regencies/ cities with password, she will be redirected to form Login page. moderate poverty percentage, namely Pekalongan, Blora, However, if she types username and password correctly, Sragen, Purworejo, Demak, Karanganyar, Kendal, she will enter the main page. The main page of user Pekalongan, dan Klaten. Lastly, there are 10 regencies/ admin exhibit several functions, such as displaying data, cities with high poverty percentage, namely Rembang, adding actual data and prediction results, data Grobogan, Wonosobo, Kebumen, Cilacap, Banyumas, conversion, and data deletion. Admin can opt available Purbalingga, Pemalang, Brebes, dan Banjarnegara. functions. Converted data will be processed further for being updated and updated data will be immediately displayed in the form of thematic map. The fourth stage involves testing, testing process, and analysis of prediction results of poverty index with the method Triple Exponential Smoothing: Brown’s One- Parameter Quadratic that looks at error terms of prediction results. We use the test size ME, RMSE, MPE, MAPE, MASE, and Euclidean Error to search prediction results error value. The smaller the error value the more accurate the prediction results [6]. We then classify regions into three groups with different colors to represent the level of poverty percentage. Red color represents regions with high

IV. CONCLUSION AND SUGGESTION

The spatial model of predicted data of poverty index using the method of Triple Exponential Smoothing: Brown’s One-Parameter Quadratic can produce prediction results of poor regions one year ahead. Governments can immediately distribute aids to potentially poor regions so that poverty rate decreases. It is expected that further studies could increase the prediction accuracy by using ARIMA or kriggin spatial prediction methods and increase object details in the spatial models from village level to sub-village level (dusun).

ACKNOWLEDGMENTS

Figure 4: Spatial model of poverty depth index data 2014 This work was supported in part by RISTEKDIKTI Indonesia.

REFERENCES

[1] Republik Indonesia. Peraturan Presiden No.166 Tahun 2014 tentang Percepatan Penanggulangan Kemiskinan, Sekretariat Kabinet RI, Jakarta : Indonesia, 2014. [2] Badan Pusat Statistika (BPS). Data dan Informasi Kemiskinan Jawa Tengah 2010 – 2014, ISSN: 2407-3369, No. Publikasi: 33520.1604, Semarang : Indonesia, 2014. [3] SMERU Research Institute. Mengintegrasikan Aspek Kemiskinan ke dalam Perencanaan Spasial Perkotaan : Solusi Untuk Mengatasi Kemiskinan Perkotaan, Catatan Kebijakan, No. 01/2012, Jakarta : Indonesia, 2012. [4] R. Sri, et al. Perancangan Sistem Identifikasi dan Pemetaan Potensi Kemiskinan untuk Optimalisasi Program Kemiskinan, Jurnal Sistem Informasi (JSI), Vol. 6 No 2, 2014 731-743. [5] B. Alessandro C. et al. Model Prediksi Variabel Makro untuk Menentukan Daerah Menggunakan Kombinasi Metode Double Exponential Smoothing dan Fuzzy MCDM (Studi Kasus: Figure 5: Spatial model of predicted poverty index data 2015 Provinsi Jawa Tengah), Laporan Penelitian Master of computer science, UKSW-Salatiga, Jawa Tengah, 2014. Figure 5 is the spatial model that displays predicted [6] Makridakis, S., Victor, E., & Steven, C. Forecasting Methods poverty index one year ahead (2015) and visualized in the and Applications, Binarupa Aksara Publisher, Jawa Barat, 2002. [7] Ngopya, F. The Use Time Series in Crop Forecasting, Regional form of thematic map of the Central Java Province. Early Warning System for Food Security, Food, Agriculture and Spatial model at Figure 5 shows that most regions (28 Natural Resources (FANR) Directorate, Botswana, 2009. regencies/ cities) in the Central Java Province exhibit [8] [Hartomo, K. et. al. ESSPI : Exponential Smoothing Seasonal high poverty percentage. There are only two regions with Planting Index, A New Algorithm For Prediction Rainfall, International Journal of Computer Science and Information moderate poverty category and 5 regencies/ cities with Security, Vol. 14, No. 6, 2016. low poverty category. This prediction can be executed [9] Bernhardsen, T. Geographic Information Systems: An not only in regencies/ cities within the Central Java Introduction, 3rd Edition, John Wiley & Sons Ltd, Canada, Province, but also all regencies/ cities throughout (2002). [10] Gunarto, W. Taslim, Perlunya Akurasi Data PPLS 2011 dan Indonesia so that each regency/ city can be classified Peran TKSK Dalam Validasi, Konsultan TKPK Provinsi Jawa based on its predicted value of poverty index. Tengah, 2015.

Web Based Service Modeling for Land Transaction Using GPS and Google Maps

Eko Sediyono 1, Vikky Aprelia Windarni 2, Adi Setiawan 3 1,2Master of Information System, Universitas Kristen Satya Wacana, Salatiga, Indonesia 3Dept. of Mathematics, Faculty of Science and Mathematics, Universitas Kristen Satya Wacana [email protected]

Abstract—Google maps Application Program Interface Originally, before finding the proper location, (API) provide a lot of facilities to manipulate maps and prospective buyers should seek into newspaper ads or added content into the map through various services that seek information from friends or brokers, and then look makes it possible to make a mapping applications. Based the location physically. This way takes time and effort, on the easiness of this tools this paper discuss the modeling moreover the location sought is far from buyer's place. of web based land trading using Google maps and GPS. The aim of this research is to develop web based services This study aims to develop the application of land application to ease the public community in land trading. information system that can be used to make it easy for After going through the development and assorted user in viewing sites which will be sold or purchased. experiments, it can be manifested easy application that The system can provides the land certificates links between public community, especially the people who information, and authenticity of the certificates. are doing purchase of the land, with the parties upon the land authority (National Land Agency (BPN), Official land II. RELATED RESEARCH certificate makers (PPAT), and the head of District office). Developed application provide certificates in pdf format, Singh and Aggarwal [1], succeed to develop the location that mapped above Google maps, information possession and the land status as wrote in the certificates application on e-Land record information system that be implemented using Google Map and integrated with Index Terms— Modeling, Land trading, Google Map, Mobile Commerce. This application called Google Maps API. mobileLoanapp. This application can be used by citizen to do land trading especially on banking loan procedure. I. INTRODUCTION It can also display land parcels that is trading of. All loan transaction done by using M-commerce. This Land and housing is a primary needs for most application connect bank server from the client’s saving Indonesian family. There are many steps to follow, account and land server (official server of revenue while we buy land and housing. The procedure in land department to provide land records). The development and housing transaction is rather complicated, so it takes of mobileLoanapp give benefit to potential customers of patience. land and housing. This application provide According to Indonesia agrarian regulation, land (and authentication to customer (creditors) and the relevant housing) trading is a process which can be evidence of authentic land documents. The adoption of transition rights from seller to buyer. The based mobileLoanApp give the positive impact to the public principles are clear and cash. It means that the welfare generally. transaction must be conducted under the official public Isnandar [4], studied about accuracy of the utilization authorities and the payment is cash. The consequences of quickbird image using Google earth to do land of this regulation, if the payment is not fully paid, then mapping. The result of this research, the image obtained the transaction cannot be done. In this case the official using the screen, premium and mosaic methods in the public authority is land certificate maker officers relative flat area having better accuracy compare to the (PPAT). They are appointed by the head of National hilly area. More specific, premium methods give more Land Agency of Republic of Indonesia (BPN). accurate image than both screen and mosaic methods. Before doing transaction, the seller and buyer should Rifialy and Sediyono [5], do research related to the ensure that the land was not under dispute or under bank use of cloud computing and Google maps to provide collateral. If the land is in one of those problems then information mapping of land conversion in South-east PPAT be able to prevent the proposed purchase Minahasa, North Celebes, Indonesia. The result of this agreement letter. research is an application used to monitor the land use

and potential land conversion. This application using sellers on going to trade the land and housing. It prepare Google map and Google satellite to provide the information about location, and information in the information about land, the conversion and factors land certificate digitally. The proposed design system is influencing food production. It be described using developed using UML. The UML context diagram and digital map and cloud computing. sequence diagram can be seen in figure 1, 2 and 3 Sendow and Longdong [6], conducted a study of respectively. Manado (north Celebes, Indonesia) mapping. This research discusses about the optimization of mapping information service in this city. This application due to give informative and responsible to give accurate information. To give the accurate data they use Geographic Positioning System (GPS) to navigate and survey the points location on the certain places. Unlike papers previously discussed, this paper discuss the design of digital land mapping based on land certificate in Salatiga (Central Java, Indonesia). This application useful for the potential customers of land and housing to seek the proper location. The geographical position shown using GPS navigation. The Figure 1. Context Diagram of the proposed land information accuracy of the point location can be confirmed to the system location survey and the official information in the land certificate There are two entities in the figure 1, they are : 1. National Land Agency (BPN) - the input to this III. EXISTING SYSTEM entity are land certificate ID number, and land map based on certificate. While the output to the system Land certificate trading is a transfer process of the are coordinate position of the land, land certificates. It is existing a long time ago in Indonesia. ownership, and certificate ID. This trading based on common law, and must be 2. Users (Citizens) - input to this entity are land meeting requirements as: clear, cash and real. Clear certificate ID number. means conducted in the presence of officials of the public authorities. Cash means paid in cash, and real means trading be done in real. So, while prices have not BPN Login BPN Database Home Land Ownership Coordinate Land Map Certificate Setting User Exit 1: Input username and Password agreed yet, the trading could not be done as referred to. 2: LoginValidation 3: Return Validation The transaction need the accurate data during the 4: Show The Home Page process, they are : 5: Show The Home Page 6: Select Land Ownership for Input of Land Ownership 1. Seller data : Identity card, family card, the original 7: Show Land Ownership 8: Select Coordinates Land for Input Coordinate Land

land certificate that will be traded, proof of land 9: Show Coordinate Land and building tax payment (PBB) at least last 5 10: Select Map Certificate for See Certificate (Digital Mapping) 11: Show Map of Land Certificates years, and tax ID card (NPWP); 12: Setting User 13: Show Setting User 2. Buyer data : Identity card, family card, and NPWP. 14: Click Logout From Application 3. Certificate of the purchase made in office of PPAT 15: Logout From Application 4. The process of land certificate transform in the Land Office (BPN). This stage produce trading certificate. In the next stage PPAT submit the certificate of the purchase to the Figure 2. Sequence Diagram of BPN

Land Office to do process of changing the owner name Figure 2 shows the actor, it is BPN, and eight objects. of the land certificates done by BPN. In the process of changing the certificate owner name, They are login, BPN database, home, land ownership, the buyer's name as a new right holder will written on a land coordinate, Map certificate, setting user, exit. This page and column that is in the new land certificate. module accessed by BPN to maintain the data.

IV. PROPOSED SYSTEM -- DESIGN AND ARCHITECTURE

The proposed system is not to computerized the existing system but rather to prepare customers and

can be seen clearly on the map. The determination of the point coordinates be adjusted to the land boundaries :User :Login : UserDatabase : Home : View Certificates : CertificatesDatabase : Map The Certificates : Exit stated on the owner's land certificate and the real 1: Input Username and Password 2: Checks validation location. 3: Return validation Information that accompanies the coordinates location 4: Show to the home page 5: Show home page was certificates ID number (No sertifikat), owner's 6: Select see certificates name (Nama pemilik), location (Lokasi) consist of 7: Show certificates

8: Show certificates village (desa), district (kecamatan), regency 9: Insert certificates number (kabupaten), and province (provinsi), note (Catatan) tell 10: Check certificates number

11: Return data about the ownership of the land history, land area (Luas 12: Show data tanah), and coordinate location (Koordinat). The source 13: Show map of land certificates

14: Show map of land certificates of this information come from the original certificate. 15: Display certificates The official and original certificates scanned and save to 16: Show certificates data

17: Show certificates data pdf files. The display of the screen that can be used by

18: Click exit from application users (customer and seller), can be seen in figure 5. 19: Exit from application

Figure 3. Users Sequence Diagram

Figure 3 shows one actor, it is users, and seven objects. They are login, User database, home, view certificate, certificate database, map the certificate, and exit. Principally this module can be used by public to trade the land.

Figure. 5. Display of the land certificate and location on the Google map. V. IMPLEMENTATION VI. ANALYSIS AND DISCUSION The design illustrated in figure 1 implemented by dividing it into 2 module: module 1 for BPN to maintain This application can be used to show the land location the information and module 2 for user (sellers and that is traded. The advantage of using Google map is the customers) to use it for land trading. The BPN do the situation of the surrounding environment can be seen digitalization of location as stated in the certificate. One from this map, including contour and slope of the land. example of the digitalization can be seen in figure 4. Thus buyers do not need to visit directly to the location. The additional information about the land ownership can be seen by clicking one of the location corners. By clicking this place users will know the information about certificate ID number, land ownership, the location, historical notes of ownership changes, total area and coordinate location. One example of these information can be seen in figure 6.

Figure 4. Digital mapping accordance with the information in the land certificate.

Figure 4 show the result of the digital mapping using Garmin GPS Oregon 650. The coordinate point in each corner plotted to the Google map, so that the location

Based on the research and development of web based application in modeling the land transaction can be concluded that, this application can be use to maintain clearness of land transaction that is one of land trading principles (clear, cash and real). Unfortunately this application is not used officially by BPN. It is because the policy and procedures to be the official software is not simple and requires the act of underlying. Technically this application can be used easily by public community especially in land and house trading. The challenge of the future research is that this application will be integrated to citizenship data and bank customers data.

ACKNOWLEDGMENT

This work was supported fully by Directorate Research and Community Service of Department of Research, Technology and Higher Education, Republic of Indonesia, fiscal year 2017.

REFERENCES

[1] Singh, Kanwalvir and Himanshu Aggarwal, 2013, Design of e- Land Record Information System with Google Map Using Mobile Commerce, Journal of Software Engineering and Applications, 2013, 6, 221-228 [2] Law No. 3 of 1997 on the Implementation of Government Number 24 Year 1997 on Land Registration. http://www.bpn.go.id/DesktopModules/EasyDNNNews/ DocumentDownload.ashx?portalid=0&moduleid=1671&articlei Figure 6. Display of additional information about the certificate d=668&documentid=701, accessed on may march 28, 2016. [3] Muis, Saludin, Global Positioning System (GPS), Yogyakarta : The corner coordinates of the locations shown by Graha Ilmu., 2012. figure 6 can also be used to measure broad estimates of [4] Isnandar, Andang, "Study the use of accuracy image quickbird in google earth to land parcels mapping" Bandung Institute of land area appointed. Windarni, et.al. [18] discuss some Technology. 2008. methods to calculate the land area based on the [5] Rifialy. Leonardo and Sediyono, Eko; Setiawan, Adi, " The Use coordinates of the GPS. By using Heron's formula Of Cloud Computing In Google Maps For Mapping Information obtained calculation of land area by the level of Over The Function Of Land In Southeast Minahasa", National Seminar on Information Technology, 2013, pp.52-58. accuracy of the average 80 %. The biggest error come [6] Sendow, T.K and Longdong, Jefferson. "The Study Mapping ( from the GPS precision. According to the specification city case study Manado )". Scientific Journal Media Engineering the tool, it has tolerance on around radius 10 meters. Vol.2, Numb.1, pp.35-46, March. 2012. The accuracy of using Google map and GPS is also [7] Sugiyono, Quantitative Research Method , Qualitative and R & D. Bandung: Alfabeta., 2010. discussed by Windarni et.al. [19]. The result of this [8] National Land Agency. Http://peta.bpn.go.id/, accessed on research become the basis for developing a model of march 15th, 2016. web-based land services, that is discussed in this paper. [9] Amelia, N.R and Akhbar; Arianingsih, Ida, 2015, "Pembuatan These services can be used for the purpose of Peta Penutup Lahan Menggunakan Foto Udara yang Dibuat dengan Paramotor di Taman Nasional Lore Lindu (TNLL)" (The communities in finding and offers land and housing. Development Of Land Cover Maps Using Aerial Photo of More advance application proposed by Singh et.al. Loren Lindu National Park /NPLL)", Warta Rimba Vol.3, [1]. Their paper discusses application that connect bank Numb.3, pp.65-72, December. 2015. server as a loan provider to the registered customers, [10] Harsono. N, Subhan.A , Sukaridhoto, S. Sudarso, A, 2006, "Teknik Pemetaan Wilayah Secara Cepat dan AKurat land database server, and the citizens data from the local Menggunakan GPS yang Dikoordinasikan Melalui Jaringan 3G Government. If the data of Indonesian citizen can be atau yang Setara" (Fast and Accurate Engineering of Aerial integrated as mentions in the paper, so are unlikely Mapping Using GPS Coordinated Through 3G Network and fictitious transactions happened. Equivalent", Proceedings of the National Conference on Information & Communication Technology for Indonesia, 3-4 May 2006, Bandung VII. CONCLUSIONS

[11] Massart Thierry, Meuter Cedric, V B Laurent. On the [16] H Lin, JG Sun, YM Zhang. Theorem proving based on the complexity of partial order trace model checking, Inform. extension rule, Journal of Automated Reasoning, 31(2003) 11- Process. Lett. 106(2008) 120-126 21. [12] M. Davis, H. Putnam. A computing procedure for quantification [17] K Xu, F Boussemart, F Hemery, C Lecoutre. Random constraint theory. Journal of ACM, 7(3)(1960) 201-215. satisfaction: easy generation of hard (satisfiable) instances , [13] Dechter R, Rish I. Directional resolution: the davis-putnam Artificial Intelligence, 171(2007)514-534. procedure. Proceeding of 4th International Conference on [18] Windarni V. A., Eko Sediyono, Adi Setiawan, 2016, Analysis of Principles of KR&R, Bonn, Germany: Morgan Kaufmann, Land Area Calculation Using GPS Technology, Jurnal Ilmiah (1994) 134-145. Kursor, Menuju Solusi Teknologi Informasi, Vol 8, No. 4, 2016 [14] M. Davis, G. Logemann, D. Loveland, A machine program for [19] V. A. Windarni, E. Sediyono, and A. Setiawan, 2016, "Using theorem proving. Communications of the ACM, 5(1962) 394- GPS and Google maps for mapping digital land certificates," 397. International Conference on Informatics and Computing (ICIC), [15] R. E. Bryant. Graph-based algorithms for Boolean function 2016. manipulation , IEEE Transactions on Computers, 35(1986)677- 691.

Improving Jaccard Index for Similarity-based Collaborative Filtering Systems

Soojung Lee Gyeongin National Univ. of Education, 155 Sammak-ro, Anyang, Kyunggi-do, Republic of Korea 13910 [email protected]

Abstract—Collaborative filtering has been successfully between two users are available. This problem often exploited in many commercial recommender systems, results in unreliable similarity calculated by the measure. where recommendation of items is basically made by This paper addresses this drawback of CF and suggests accumulating ratings of similar users. It is a fundamental an improved index of Jaccard. Jaccard index reflects the problem in these systems that rating data sparsity often number of items co-rated by two users and has been causes similar users produced by the system unreliable. Jaccard index reflects the number of co-rated items and useful in similarity computation for CF systems [3-6] to has often been used with the traditional similarity compensate for data sparsity. Through extensive measures to handle this problem. This paper proposes a experiments, we show that our suggested index exhibits novel improvement of Jaccard index that also takes the significant improvement in prediction accuracy of rating values of co-rated items into account by computing ratings, even better than a most well-known similarity the indexes separately according to the values and merging measure of Pearson correlation under certain them together. Performance of the proposed index is environment. investigated in depth, to find that it significantly The remainder of this paper is organized as follows: outperforms Jaccard index in terms of prediction accuracy. In the next section, we discuss how previous works Furthermore, its performance turns out to be comparable to Pearson correlation in a dense dataset but is superior to handle data sparsity problem and some preliminary in a relatively sparse dataset. experiments for introducing the proposed index. Section 3 presents the proposed index, followed by experimental Index Terms—Collaborative filtering; Jaccard index; results in Section 4. Section 5 concludes this paper. Recommender system; Similarity measure II. PRELIMINARIES I. INTRODUCTION A. Data Sparsity Recommender systems have been a great aid in Similarity-based collaborative recommender systems searching products of user preference in many compute similarity between two users from the ratings commercial systems. The most popular implementation of items that both users have assigned. Therefore, when technique for these systems is collaborative filtering the number of co-rated items is low, the computed (CF), where other users showing preferences similar to similarity becomes far unreliable. Pearson correlation, a the current user’s are consulted to produce representative CF algorithm, also suffers from this, recommended items [1,2]. Similarity is computed based although it is known to perform efficient for a large on the history of user ratings given to the items. dataset [2,7]. There are two main types of CF systems studied in Several approaches have been developed to handle literature, user-based and item-based [2]. The former the sparsity problem. Dimensionality reduction first seeks for similar users and then items given with techniques have been proposed to maintain only high ratings by these users are recommended. The item- significant ratings information and reduce the size of the based CF looks for similar items, instead of similar user-item matrix [1,2]. Various reduction techniques are users, to the items that the current user has preferred. based on matrix factorization [8,9], while many others This paper focuses on the user-based CF. are using a technique named singular value From the above discussion, it is inferred that decomposition [10]. However, these techniques may similarity computation critically affects the performance require high computational cost and some useful of CF. Various similarity measures have been developed information may be discarded by the reduction process in literature [1]. However, most of the measures suffer [2]. As another simple alternatives, default voting refers from data sparsity problem, which refers to the situation to a method of giving some defaults, e.g., the average of where only a few ratings associated with common items the clique, to unrated items [2,11]. Imputation-boosted

algorithms address the sparsity problem and utilize several techniques such as machine learning, Bayesian 10 multiple imputation, and linear regression [12]. 8 The above works make changes through some 6 4 techniques to the user-item matrix or give a default min.rating max.rating value to unrated items. Instead, the original matrix itself 2

89 93 is used for collaborative filtering in many other studies, -2 97 user id with the compensation for data sparsity being made by -4 reflecting the size of intersection item sets onto -6 similarity computation. This strategy seems far simple -8 -10 but often leads to great improvement on the quality of recommender systems. Tanimoto coefficient refers to (a) the intersection ratio which is defined as the ratio of the 20 number of items co-rated by two users out of the total 18 16

number of items rated by them [7]. Instead of 14 rating rating range computing the ratio, Herlocker et al. reflect the number 12 10 of common items itself on the similarity [13]. Here, the 8 similarity is weighted by n/50 where n is the number of 6 4 common items, resulting in lower similarity between 2 two users having fewer items in common. 0 30 40 50 60 70 80 Similar to Tanimoto coefficient, Jaccard index number of ratings measures the proportion of the number of co-rated items (b) [14]. As the index does not take the ratings into account but only considers the relative number of common Figure 1: Distribution of ratings on Jester dataset: (a) minimum and items, it is usually incorporated into existing similarity maximum ratings of the users (b) the range of ratings for varying measures to compute similarity [3-6,15] with the total number of ratings of the users. purpose of overcoming the defects of previous similarity measures caused by data sparsity. Such incorporation is usually made in the form of III. IMPROVEMENT OF JACCARD INDEX multiplication, where Bobadilla et al. combine the index with the mean squared differences [3], Saranya et al. A. Motivation with Pearson correlation [5], and Sun et al. with UOD The study presented by Bobadilla et al. examines the (Uniform Operator Distance) [6]. As a result, the distribution of the ratings of MovieLens and NetFlix resulting combinations are reported to bring datasets3. These datasets allow the integer range of performance improvement in various aspects. Hence, [1..5] for ratings. It is reported that users tend to give this paper focuses on analyzing Jaccard index to further ratings higher than the median and avoid the extremes. enhance its performance with regard to similarity From this result, it may be convincing to state that measure. two users giving the same highest/lowest rating to a common item are more similar than those giving the same median rating to the item. This observation B. Preliminary Experiments motivated our research to improve Jaccard index. That With the aim at improving Jaccard index, we is, it seems worthwhile to check two ratings of a investigated the ratings of the users to examine their common item whether they are both normal or rating behavior. Fig. 1 shows the rating distribution with extremes. Hence, our basic idea is to divide the whole 100 users chosen arbitrarily from the Jester dataset. It is rating range of the system into a pre-determined number seen that not a few users tend to give far lower or higher of subintervals and to apply Jaccard index to each ratings than the maximum(10) or minimum(-10) subinterval separately. Also, as the frequency of ratings allowed in the system. To see if this happens due to yet differs by their values, i.e., extreme values are more insufficient number of ratings given by the users, we rarely given, we assign weights to subintervals checked the range of ratings of users with varying differently. That is, we associate a higher weight to the number of ratings as shown in Fig. 1(b). There seems no Jaccard index corresponding to the extreme range of correlation between the two, so it is not assured that the subintervals. Furthermore, as seen from the results user ratings span the whole rating range of the system if shown in the previous section, users' cognitive ranges of the user has assigned a sufficient number of ratings. ratings differ with virtually no correlation with the This observation is reflected on the proposed index, number of ratings given by them, thus we take the which is discussed in the next section. boundaries of the subintervals differently for each user.

JPI, and JPI×COR. The ratio of training and testing data is set to 80:20 for each experiment. Performance is B. Proposed Index evaluated based on the prediction quality, where MAE In order to measure how many items are commonly (Mean Absolute Error), i.e., the mean difference rated with normal or extreme values by two users, the between the predicted rating of an unrated item and its rating range allowed in the system is divided into n corresponding real rating, is measured. The predicted subintervals, for each of which Jaccard index is rating is computed by accumulating ratings of users computed separately. similar to the current user, while weights are imposed Specifically, let 푅 = [퐿, 퐻] represent the rating range according to the degree of similarity. These nearest in the system. Then it consists of the subintervals neighbors of the user is denoted by topNN in the next defined as follows. section. 푛 [ ] 푅 =∪푘=1 퐵푘−1, 퐵푘 , where 퐵0 = 퐿 ≤ ⋯ ≤ 퐵푘−1 ≤ 퐵푘 ≤ ⋯ ≤ 퐵푛 = 퐻. Let Table 1 ru,i denote a rating given by user u to item i. Also let Iu Characteristics of the datasets represent the set of items rated by user u. Then MovieLens 1M Jester 푛 푛 Number of ratings ≥20 movies ≥36 퐼푢 =∪푘=1 퐼푘,푢 =∪푘=1 {푖 ∈ 퐼푢| 푟푢,푖 ∈ [퐵푘−1, 퐵푘]} We define Jaccard index between users u and v for the (per user) jokes kth subinterval as follows. Matrix size 1000ⅹ3952 998ⅹ (usersⅹratings) |퐼 ∩ 퐼 | 100 푘,푢 푘,푣 Rating scale 1~5 (integer) -10 ~ 퐽푎푐푐푎푟푑푘(푢, 푣) = |퐼푘,푢 ∪ 퐼푘,푣| +10 (real) Jaccard index for the whole range, named JFI Sparsity level 0.9607 0.2936 (Jaccard index for Fixed Intervals), is calculated as an arithmetic average of the Jaccard indexes for the B. Performance Results subintervals. 1 We first examined performance of JPI with varying 퐽퐹퐼(푢, 푣) = ∑푛 퐽푎푐푐푎푟푑 (푢, 푣), 푚 푘=1 푘 number of subintervals and with different associated where 푚 = |{푘 | 퐽푎푐푐푎푟푑푘(푢, 푣) ≠ 0}|. weights. Fig. 2(a) shows the subinterval bounds yielding As discussed before, we take a cognitively different best MAE results for three different numbers of range of ratings of each user into account. Thus, the subintervals with Jester dataset. JPI-50% means that two subintervals are defined for each user u as follows. subintervals are used where the midpoint between the 푛 푅 =∪푘=1 [퐵푘−1,푢, 퐵푘,푢], where 퐵0,푢 = 퐿, 퐵푛,푢 minimum and maximum ratings of each user is selected = 퐻 for all 푢. as a bound. It is observed that Jaccard performs With these new subintervals, we compute Jaccardk(u, significantly worst. Among JPI results, JPI-(30,70)% v) and the final Jaccard index for the whole range, outperforms all the others. Although Jester has a very named JPI (Jaccard index for Personalized Intervals), low sparsity level and allows the relatively larger range as below. of ratings, dividing the rating range of the user into 푛 more than three does not contribute to improving 1 퐽푃퐼(푢, 푣) = ∑ 훼 퐽푎푐푐푎푟푑 (푢, 푣) prediction accuracy, as seen from the worse 푚 푘 푘 푘=1 performance of JPI-(20,50,80)% than that of JPI- where 0<훼푘 <1, the weight of Jaccard index at each (30,70)% . subinterval k, should be determined experimentally, Fig. 2(b) depicts MAE performance of JPI with although 훼푘 for k's near to zero or n is presumed larger different weights, 훼푘′ s. For these results, the best for better performance. performing (30,70)% bounds as shown in Fig. 2(a) are experimented with, thus 훼1, 훼2, and 훼3 are used. JPI- IV. PERFORMANCE EXPERIMENTS w1.5 represents 훼1=훼3=1.5 and 훼2=1.0. As seen in the figure, the better results tend to be achieved with the A. Experimental Background higher weights associated with 훼1 and 훼3. This implies Our experiments are conducted with two datasets that those users giving approximately the same extreme popularly used in the related studies. They have very ratings to a common item should be regarded as more different characteristics, as presented in Table 1. The similar with each other. sparsity level is defined by 1-(total number of As the JPI parameters yielding the best MAE have ratings/matrix size). We chose the first 1000 or 998 been obtained, final experiments comparing all the users each from the datasets due to the limited capacity similarity measures of our concern are conducted with of the PC used for experiments. these parameters. Fig. 2(c) shows the result. It is The similarity measures of our experiments are surprising that JFI-(30,70)% or JPI-w3.5 yield MAE Jaccard index (Jaccard), Pearson correlation (COR), JFI, very competitive with COR which is known efficient for

a large data set like Jester2,7. Moreover, JPI-w3.5 they both significantly outperform COR. This is combined with COR turns out consistently superior surprisingly notable, since COR takes the values of user regardless of the number of topNNs. Specifically, it is ratings for computing similarity into account whereas approximately 0.025~0.05 better than COR for large JPI and JFI consider only the number of common topNNs. ratings in each interval. As with Jester dataset, JPI is combined with COR to

3.44 see if more improvement can be achieved. In the figure, it is rather unexpected that the combined metric 3.4 performs worse as more neighbors are consulted, which Jaccard is quite different behavior from the traditional similarity 3.36 JPI-(30,70)% measures. In fact, Fig. 3(a) gives slight indication of JPI-50% such behavior, but augmented when combined with 3.32 JPI-(20,50,80)% COR. The reason for this result is to be thoroughly

3.28 investigated through more extensive experiments. Hence, the incorporation as suggested by several 3.24 previous studies [3,5,6] should be contemplated in the 5 10 15 20 25 30 35 40 45 50 55 60 topNN aspects of the related datasets, the types of the combined metrics, the number of nearest neighbors, etc. (a) 3.27 JPI-w1.0 JPI-w1.5 JPI-w2.0 JPI-w2.5 0.72 JPI-w3.0 JPI-w3.5 3.26 0.718

Jaccard 0.716 3.25 JPI-(30,70)% JPI-70% 0.714

3.24 5 10 15 20 25 30 35 40 45 50 55 60 topNN 0.712 5 10 15 20 25 30 35 40 45 50 55 60 (b) topNN

COR JFI-(30,70)% (a) 3.29 JPI-w3.5 JPI-w3.5*COR

3.27 COR 0.737 JFI-(30,70)% 3.25 JPI-w2.0 0.732 JPI-w2.0*COR

3.23 0.727

0.722 3.21 5 10 15 20 25 30 35 40 45 50 55 60 topNN 0.717

Results of experiments with MovieLens dataset are shown in Fig. 3. Compared with Jaccard, JPI performs Figure 3: MAE results with MovieLens dataset significantly better, where MAEs for two different numbers of subintervals seem almost similar. Hence, even with a dataset with a small rating range, the proposed strategy proved effective. This outcome is also V. CONCLUSIONS the case with COR as depicted in Fig. 3(b), where JPI- This paper proposed an improvement of Jaccard index w2.0 results used the bounds (30,70)%. While JFI and which considers not only the number of co-rated items JPI perform almost alike although JPI is slightly better, but also their rating values. This strategy, when used as

a similarity metric for collaborative filtering, is proved Discovery, pp. 141-151, 2001. significantly efficient in terms of predication accuracy [12] X. Su, T. M. Khoshgoftaar, and R. Greiner, “A mixture imputation-boosted collaborative Filter,” Proc. the 21th through extensive experiments, compared to Jaccard International Florida Artificial Intelligence Research Society index no matter the sparseness of the datasets. It is Conference, pp. 312-317, 2008. further found that the proposed index outperforms the [13] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, well-known efficient similarity measure in collaborative “An algorithmic framework for performing collaborative filtering,” filtering, Pearson correlation, in MovieLens dataset. In a Proc. the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237, denser dataset such as Jester, the proposed index 1999. performs comparably to Pearson correlation, but a [14] G. Koutrica, B. Bercovitz, and H. Garcia, “FlexRecs: measure combining the two performs much better. Expressing and combining flexible recommendations,” Proc. the These findings are notable, since the index does not take ACM SIGMOD International Conference on Management of Data, pp. 745–758, 2009. the actual ratings into account for similarity [15] J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “A computation. It is also found that the degree of collaborative filtering approach to mitigate the new user cold start performance enhancement accomplished by problem,” Knowledge-Based Systems, vol. 26, pp. 225-238, 2012. incorporation of Jaccard index into a traditional similarity measure may dependent on several factors such as the characteristics of the dataset, the number of nearest neighbors, and the type of the combined similarity metric, leaving much thorough experiments and analysis to be made in the future research.

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[1] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutierrez, “Recommender systems survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013. [2] X. Su and T. M. Khoshgoftaar, “A survey of collaborative filtering techniques,” Advances in Artificial Intelligence, vol. 2009, 2009. [3] J. Bobadilla, F. Serradilla, and J. Bernal, “A new collaborative filtering metric that improves the behavior of recommender systems,” Knowledge-Based Systems, vol. 23, no. 6, pp. 520-528, 2010. [4] H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, “A new user similarity model to improve the accuracy of collaborative filtering,” Knowledge-Based Systems, vol. 56, pp. 156-166, 2014. [5] K. G. Saranya, G. S. Sadasivam, and M. Chandralekha, “Performance comparison of different similarity measures for collaborative filtering technique,” Indian Journal of Science and Technology, vol. 9, no. 29, 2016. [6] H.-F. Sun, et al., “JacUOD: A new similarity measurement for collaborative filtering,” Journal of Computer Science and Technology, vol. 27, no. 6, pp. 1252-1260, 2012. [7] H. -J. Kwon, T. -H. Lee, J. -H. Kim, and K. -S. Hong, “Improving prediction accuracy using entropy weighting in collaborative filtering,” Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, pp. 40-45, 2009. [8] Y. Koren, R. Bell, and CH. Volinsky, “Matrix factorization techniques for recommender systems,” IEEE Computer, vol. 42, no. 8, pp. 42-49, 2009. [9] X. Luo, Y. Xia, and Q. Zhu, “Applying the learning rate adaptation to the matrix factorization based collaborative filtering,” Knowledge-Based Systems, vol. 37, pp. 154-164, 2013. [10] F. Cacheda, V. Carneiro, D. Fernandez, and V. Formoso, “Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems,” ACM Transactions on the Web, vol. 5, no. 1, Article 2, 2011. [11] S. H. S. Chee, J. Han, and K. Wang, “RecTree: An efficient collaborative filtering method,” Proc. the 3rd International Conference on DataWarehousing and Knowledge

Model and Scenario Development to Increase Soybean Fulfillment Ratio and Price Projection

E. Suryani1, R. A. Hendrawan1, I. Muhandhis2, L.P Dewi3, Epi Taufik4 1Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo - Surabaya 60111, Indonesia 2Informatics Engineering, Wijaya Putra University, Surabaya, Indonesia 3Informatics Engineering, Petra Christian University, Surabaya, Indonesia 4Production Science and Technological Farm, Institut Pertanian Bogor, Bogor, Indonesia [email protected]

Abstract— The limited of domestic soybean production [2]. due to supply shortages leads to low fulfillment ratio and Agriculture in Indonesia still faced formidable high soybean prices. Indonesia soybean industry requires challenges [3], among others such as: (1) the impact of 2.4 million tons of soybeans per year, however, domestic climate change on the agricultural sector, such as animal production was only around 900,000 tons. In 2015 the pests and diseases, declining productivity and declining demand reached around 2.5 million tons, while production was only 0.99 million tons or about 40% of the national productivity of the crop, (2) the increase in food prices soybean demand or production deficit of around 1.51 that correlate to the level of inflation and poverty levels, million tons. Therefore, we analyzed soybean fulfillment (3) the availability of the production of soy, sugar and ratio and price to improve the fulfillment ratio and to meat are limited domestic and international, on the other project the price after productivity improvement. As a hand the need for a third of domestic consumption of method we used to analyze and to model soybean fulfillment these commodities increases, (4) the increase in imports ratio and price, we utilized system dynamics simulation of food and feed will reduce foreign exchange, (5) the model based on consideration that system dynamics can limited funding that is easily accessible agricultural accommodate nonlinear relationships among variables that farmers / ranchers, (6) the limited land and water have significant contribution to soybean fulfillment ratio and price projection. Research results show that soybean infrastructure, (7) agricultural extension system has not demand depends on food industry demand, non-food been effective. industry demand, soybean use for feed, as well as soybean The soybean production deficit has been met by use for farm seed. Food industry demand depends on people imported soybean. It is therefore, local soybean population, and demand from some commodities such as contribution decreases and its role were replaced by tahu, tempe, tauco, oncom, ketchup, fresh soybean. Land imported soybean. This matter has led to dependence on productivity depends on seed productivity, the impact of imported soybeans. In addition, conditions of imported lost seed, and climate change. Soybean fulfillment ratio will soybean prices are often cheaper even made farmers are equal to one (100%) in the year 2025, which means that not motivated to grow local soybean, which caused local under these conditions, we can fulfill the national soybean demand. The projection of local soybean price would be soybean supply decreased [4]. Regarding the price issue, around Rp19,300 per kg in 2035. the other problem that has been faced is the low productivity of local soybean compared with imported Index Terms— Fulfillment ratio; Production; Soybean soybeans. The low productivity of local soybean is demand; System dynamics. caused by technical factors that occur on farm level, such as less optimal use of production inputs or during post- I. INTRODUCTION harvest handling. It is therefore, local soybean has not been able to compete with the imported soybean, both in Currently, Indonesia soybean industry requires 2.4 terms of price and productivity of products. million tons of soybeans per year, however, domestic production is only around 900,000 tons [1]. This II. LITERATURE REVIEW condition continued until in 2015, the demand reached around 2.5 million tons, while production was only 0.99 million tons or about 40% of the national soybean A. Soybean Productivity demand or production deficit of around 1.51 million tons One of the main targets in the Strategic Plan of the

Ministry of Agriculture 2010-2014 is the achievement of utilize this process development for policy design to be self-sufficiency in soybean production of 2.7 million tons considered [12]. Sterman also agrees with Ford that in 2014 [3]. Based on soybean production data in 2011, defining the problem is the most important step in the achievement of new production reached 870 thousand process [13]. System dynamics can also be used to tons, or 32.2 percent of the target of production in 2014. promote structural insights about the relationships among With the trend of soybean harvested area and production components in the system [14]. of locally decreasing, the dependence on soybean imports, mainly from the United States as the world's largest soybean exporter will be higher. During the III. MODEL DEVELOPMENT period 1969-1985, the increase in production soybean reached 4.75%, while surge in demand reached 5.74% / To determine soybean fulfillment ratio and price, we year. Later in the period 1983-1989, although production have developed some submodels such as national increased to 6.44% / year, soybean imports also increased population, demand, land productivity, soybean 9.20% / year [5]. Regarding productivity, currently the production, fulfillment ratio, and price. national soybean production is still about 1.5 tons / ha. If we want self-sufficiency achieved, productivity should A. National Population Submodel be increased at least 1.7 tons / ha [6]. The national target Figure 1 represents the submodel of national of soybean production was 1.27 million tons in 2015, it population. will be raised to 2.63 million tons in 2016 and in 2017 it is targeted at 3 million tons [7]. Production of soybeans at the farm level can actually still be improved through technological innovation, productivity improvement strategies and planting areas [8]. Soybean agroindustry can be developed if supported by farmers and farmer groups that conduct partnership with food and nonfood industry. The development of soybean-based industries should be supported by the government by facilitating Figure 1: National population submodel permits and infrastructure improvements [9]. Three causes of soybean production are difficult to From Figure 1, national population rise: 1)the possibility of La Nina will attack Indonesia in depends on birth rate and death rate, as the near future, 2), the area of soybean is also not seen in Eq. (1)-(3). increased, 3)soybean prices are still less attractive to farmers [10]. Population= INTEG (Birth-Death, The fulfillment of soy consumption is highly 2.1197e+008) (1) dependent on imports because domestic soybean prices Birth= Birth Rate will be influenced by fluctuations in the price of soybeans Average*(Population/1000) on the international market. The soybean commodity (2) need is increasing from year to year because it has several Death= (Population/1000)*Death Rate (1) functions, as a staple food, animal feed, as well as large Average (3) scale industrial raw materials to small or household [11]. A half of the needs of the world's soy is produced by the Figure 2 represents national population US, so the end of the world soybean stocks become very starting from 2000 to 2016. As we can see limited, and has led to rising soybean prices in the world from Figure 2, the average growth rate of market [4]. population was around 1.4% per year.

B. System Dynamics In this research, we utilized system dynamics simulation model as a tool for the model development. System dynamics enable us to develop scenarios based on different assumptions. System Dynamics has an advantage in its learning capability in the usage of assisting model construction, so that we can make a created model adaptive to its surround outside environment. By assigning an expected output pattern as a new set of training data to a given model, we can obtain Figure 2: Indonesia population 2000-2016 a new structure after the training process. We might

B. Demand Submodel Figure 3 represents submodel of national soybean demand.

Figure 4: Land productivity submodel

As we can see from Figure 4, Java land productivity depends on seed type, Figure 3: National soybean demand submodel temperature, rainfall, and lost seed rate as seen in Eq. (6).

From Figure 3, the demand depends on food Java Land Productivity= industry demand, nonfood industry demand, ACTIVE INITIAL (Java land productivity soybean use for feed, as well as soybean use increase- for farm seed. Food industry demand depends Java land productivity decrease, 1.2) on population, and the demand from some (6) commodities such as tahu, tempe, tauco, oncom, ketchup, fresh soybean as seen in Eq. Figure 5 represents Java land productivity (4)-(5). starting from 2000 – 2016. As we can see from Figure 5, Java land productivity was between Soybean Demand = 1.29 and 1.54 Tons/Ha. NonFood Industry Demand + Food Industry Demand + Feed Demand+Seed Basic Material Demand (4)

Food Industry Demand= (FreshSoybean Consumtion Rate Avg + Kecap Consumtion Rate Avg + Oncom Consumtion Rate Avg + Tahu Consumtion Rate Avg + Tauco Consumtion Rate Avg + Tempe Consumtion Rate Avg) * Population * Conversion Factor Figure 5: Land Productivity (5) D. Soybean Productivity Submodel Simulation result shows that national In general soybean land can be classified demand in 2000-2016 has been fluctuated into two types, those are wet land and dry land. between 2.07 million tons and 2.5 million tons. Crop Area depends on Wet Land and Dry Land Area. Crop Area will influence Harvest Crop. C. Land Productivity Submodel It depends on Pest Attack Rate and Crop Area. Figure 4 demonstrates submodel of Java Soybean Production depends on Land land productivity. Productivity and Harvest Crops as seen in Figure 6.

should be fulfilled from import).

F. Price Submodel This submodel was developed to analyze the soybean price base on the existing condition. Simulation result shows that national soybean price, in 2000-2016 was between Rp2,500 and Rp8,200.

IV. MODEL VALIDATION

Model validation is required to check the model accuracy. A model will be valid if the error rate is less than 5% and error variance is less than 30% [15]. We validate the population, demand, and production submodels. Error rate and error variance are defined in Figure 6: Soybean production submodel Eq. (9)-(10).

All farm mechanism can be described in Eq. S − A ErrrorRate=   (7)-(8). (9) A

Farm Production = Ss − Sa ErrorVariance = Harvest Crop*Land Productivity Sa (7) (10) Harvest Crop = Crop Area-(Pest attack rate* Crop Area) Where: (8) 푆̅= the average rate of simulation 퐴̅the average rate of data Figure 7 demonstrates simulation running Ss= the standard deviation of simulation for national farm production. As we can see Sa= the standard deviation of data from Figure 7, farm production was around 1.1 million tons in 2016. Error rate of some soybean variables are shown as follows: Error rate “Population” = [228183199-230854302] = 0.01 230854302 Error rate “Soybean Demand” = [2165417-2268322] = 0.04 2268322

Error rate “Java Soybean Production” = [527352-545385] = 0.03 545385 Error variance of some soybean variables are depicted

as follows: Figure 7: National soybean production Error variance “Population” = [14259931-14608941] = 0.02 E. Fulfillment Ratio Submodel 14608941 This submodel is developed to check the Error variance “Soybean Demand” = fulfillment ratio of national soybean [195006-202661] = 0.04 production and domestic soybean demand. The 202661 fulfillment ratio of national soybean was Error variance “Java Soybean Production” = around 0.4 in 2016. This condition means that [54903-63265] = 0.13 we need to import soybean to fulfill national 63265 demand (around 60% of national demand

From the above calculation, we can see that all the required to have system understanding of the system error rate are less than 5% and error variance are less than being modeled. Scenarios are developed to improve the 30%, which means that our models are valid. system performance which can be done through parameter and structure scenarios. Soybean demand depends on food industry demand, V. SCENARIO DEVELOPMENT non-food industry demand, soybean use for feed, as well as soybean use for farm seed. Food industry demand Scenarios are developed to improve the system depends on population and demand from some performance. There are two kinds of scenarios, those are commodities such as tahu, tempe, tauco, oncom, ketchup. (1) Parameter Scenario; (2) Structure Scenario. In this Land productivity depends on seed type, temperature, research we have developed structure and parameter rainfall, and lost seed rate. Soybean production can be scenarios to create a significant impact to the model. improved by utilizing a high quality of seeds, water Some scenarios are developed to improve national irrigation, balanced fertilization, as well as pest control. soybean production, to increase the fulfillment ratio of Fulfillment ratio can be increased through land soybean, as well as to project the price after land intensification and land expansion. National fulfillment productivity improvement. ratio would be equal to 1 (100%), starting from 2025, after land intensification and expansion. Local soybean price depends on the volume of imported soybean, A. Land Productivity Improvement fulfillment ratio of soybean, impact of supply and This scenario is developed to improve land demand, as well as the impact of inflation. productivity in Java through the use of productivity seed, water sufficiency, balance fertilization, and pest control. ACKNOWLEDGMENT The average of land productivity after the improvement would be around 2.2 tons/ha. This research is supported by ITS-Research Center; Ministry of Research, Technology, and Higher B. Soybean Production after Land Intensification education; as well as Australia Indonesia Centre. and Expansion This scenario is developed to analyze soybean REFERENCES production after land expansion and intensification (land productivity improvement). Land expansion can be [1] Elshinta, “Five Issues of Low Soybean Productivity”, article, 2015. conducted in several areas in Java, totally of around [2] Data Centers And Agricultural Information Systems, “Outlook: 290,103 ha which consist of 108,000 ha from Perhutani Agricultural Commodities Of Soybean Crops”, Agricultural I, 111,000 ha from Perhutani II, and 71103 ha from Bulletin, 2015. Perhutani III. National soybean production projection [3] Indonesia Ministry of Agriculture, “Annual Performance Plan of Ministry of Agriculture 2014”, 2014. after land expansion and intensification in 2035 would be [4] Indonesia Ministry of Agriculture, “Indicator of Agriculture around 4.27 million tons. Development”, 2012. [5] Central Bureau of Statistics, “Production of Paddy and Secondary C. Soybean Fulfillment Ratio Improvement Crops”, 1990. [6] Indonesia Ministry of Agriculture, “Soybean Self-Reliance This scenario is developed to check the fulfillment ratio 2017”, Sinar Tani Bulletin, 2015. after land expansion and intensification. Soybean [7] A. Hendriadi, “National Target for 2016 Soybean Production fulfillment ratio will equal to one (100%) in 2025, which Increases”, News of Agricultural Research and Development, means that under these conditions, we can fulfill the 2016. [8] D. Harnowo, ““Produksi Kedelai Nasional Masih Rendah national soybean demand. (National Soybean Production Still Low) “, in Seminar Nasional Agribisnis Kedelai: Antara Swasembada dan Kesejahteraan D. Soybean Price Projection Petani, Yogjakarta, 2015. This scenario is developed to analyze the proper price [9] M. Astuti, “National Soybean Production Still Low “, in Seminar Nasional Agribisnis Kedelai: Antara Swasembada dan of local soybean by considering the impact of imported Kesejahteraan Petani, Yogjakarta, 2015. soybean, fulfillment ratio of soybean, impact of supply [10] National Soy Board, “Soybean Production This Year Is Predicted and demand, as well as the impact of inflation. The to Increase Hard”, news of detikFinance, 2016. projected local soybean price would be around Rp19,300 [11] Satria, “National Soybean Production Still Low”, news in Universitas Gajah Mada, 2015. per kg in the year 2035. [12] Y.T. Chen and B. Jeng, “Yet another Representation for System Dynamics Models, and Its Advantages”, in 20th International Conference of the System Dynamics Society, Palermo, Italy, July VI. CONCLUSION 28 – August 1, 2002. [13] J. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, Boston: McGraw-Hill, 2000. To develop system dynamics simulation model, it is

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Automation System to Control the Soil and Water Management for Rice

Kunyanuth Kularbphettong1, Wannaporn Phoso1, and Nitiphon Numphan1 1Suan Sunandha Rajabhat University, Bangkok 13000, ThailandAffiliation [email protected]

Abstract—The Internet of Things becomes increasingly ponded water depth of about 5–10 cm. Rice can be prevalent and it is the inter-networking of physical devices grown in from sandy soil to clay but rice is growing connected devices together. Soil and water management better in clay because it can keep water longer. The pH play as an important role in Agricultural smart farming. is, in the range of 5.5-6.5, better for rice cultivation. In this paper, the prototype of automation system for Also, the Internet of Things (IoT) is an internetworking measuring and monitoring soil and water was developed to control the pH of water and the level of water in the rice system linked with computing devices, mechanical and field by using Internet of Things (IoT), and Mobile digital machines, objects, animals or people that are computing technologies. The results found that the system provided with unique identifiers that enable these can solve the flood problem in the rice field during rainy objects to collect and exchange data [2]. The smart IOT season and control the pH of water and soil for growing system was developed on the measurement of physical rice and suggest the farmers how to prepare the field to parameters such as soil moisture content, nutrient cultivate rice. content, and pH of the soil that plays a vital role in farming activities [3]. Index Terms—Automation system; Internet of Things Therefore, this research aims to develop IoT solutions (IoT); soil and water; mobile application for automation system to control the soil and water management for rice fields. The remainder of this paper I. INTRODUCTION is organized as follows. Section 2 presents the system overview of this project. Section 3 we describe the Rice represents a very important crop for the Thai experimental setup based on the purposed model and economy and it is one of the main foods and source of section 4 shows the results of this research. Finally, the nutrition of Thailand. Thailand is predominantly an conclusion and future research are presented in section agricultural country and agricultural products in 5. Thailand have not only been produced for their own consumption but also being a major source of income II. SYSTEM OVERVIEW from exporting. However, cultures and technologies To implement the project, questionnaires and user’s from many countries have come and changed the rice requirements were applied in the design and trade targets and now most of farmers use more modern development of this mobile application for automation technology. system to control the soil and water management for In the 21st century, Agricultural Version 4.0 rice fields. The information was used as a source of becomes the agricultural era of paradigm shift to be information for management mobile application and revolutionary farmers who should have knowledge, database management and internet of things were technology, processing and marketing strategy together. applied to make the system from physical With recent advances in Information Technology of instrumentation of devices to the cloud technology used smart mobile devices, it is possible to take advantage of to collect data [4]. RAD (Rapid Application these devices to design an application to educate and Development) was used to implement the mobile provide farming or cultivation with high precision with learning application [5], and user’s requirements were friendly environment to increase agricultural production analyzed for design processes. and protect the environment. The system can be divided to be 4 parts as following: According to Bouman et al [1], rice is grown in a user profile part, a learning and recommend part, a pH bounded fields that are kept flooded from crop measurement part, and a water management part. Every establishment to close to harvest by maintaining a subsystem is integrated with different sensors and

devices and wireless communication modules are used to interconnect between server and computing devices. Users can control the board through the mobile application and database is an interactive function used to manage microcontroller and figure 1 was presented the system overview. In a user profile part, the system provides basic information for farmers and learning and a recommend part suggests the information and knowledge about rice crop. An automatic rice water quality monitoring system has been designed to be inspection sub for pH sensor Figure 2: The output of pH electrode [6] and temperature sensor to remind farmers through mobile application. The water flow sensor is a sensor for measuring the flow of water. Moreover, the experimental performance of automation control soil and water management system was shown in figure 3.

Figure 1: System overview

Also, a water management part controls and measures the level of water in rice field and it will alert farmers when the level of water is exceeded the indicated level.

III. EXPERIMENTAL SETUP

This section presented the related equipment and experimental procedures used to collect the data and control the system presented within this paper. The sensors measure the moisture of soil were collected by plugging the sensors with board and Table 1 was described the percentage value.

Table 1 The measurement levels of the soil’s moisture

condition indicated value Dry soil 0 32 –% Humid soil 33 – 74 % In water (soil soggy) 75 – 100 %

pH sensor module measures the pH range from 0 to 14 and operates in the temperature range of 0 ◦ −80◦ C. Figure 3: The output of pH electrode [6] and plugs via BNC connection and fig 2 was shown the pH value. Also, the system still records the values obtained from the sensors according to user setting and the

application retrieves and displays information from the 1.the speed of program as a whole 4.50 0.71 database through web application. 2. the speed of search data 4.40 0.70

IV. EXPERIMENT RESULTS 3. the speed of data presentation 4.60 0.70 4. the speed of showing the link 4.30 0.67 To test and evaluation the prototype mobile 5. the speed of edit data 4.60 0.52 application system, Black box Testing and Questionnaires with experts were applied. Black box The security and verify data of the system testing approach is tested based on the performances of 1. the security and verify data of set the 4.50 0.97 the system and collected errors of the system. User’s permissions of using satisfaction was evaluated by questionnaires. To 2. the security and verify data of determining a 4.50 0.85 evaluate the quality assessment system, Mean (x) and standard deviation (SD) were used to assess the qualities user account 4.60 0.70 of the project. 3. the security and verify data of verify the accuracy of input data Table 2 The results of the system The table 2 displays that assessment of the performance of the prototype system to meet the needs Experts of the experts respectively in average of 4.52 and 풙̅ SD standard deviation of 0.59.

The ability of the system Table 3 The results of the black box testing 1. the ability of the system to provide 4.50 0.85 information Experts Users 2. the ability of the system to link menu 4.60 0.70 1.Function Requirement Test 4.52 4.83 3. the ability of the system to search 4.70 0.67

4. the ability of the system’s response time 3.80 0.42 2. Functional Test 4.5 4.57

5. the ability of the system to work automatically 4.70 0.48 3. Usability Test 4.7 4.93 6. the ability of the system to manage the 4.80 0.42 4. Performance Test 4.48 4.83 database The accuracy of the system 5. Security Test 4.47 4.73 1.the accuracy of the system to display 4.57 0.79 information 4.57 0.53 2. the accuracy of the system to information Black Box testing was tested the error of the project retrieval as following: functional requirement test, function test, 3. the accuracy of the system to update 4.43 0.53 usability test, performance test and security test. 4. the accuracy of the system in storage 4.86 0.38 Functional Requirement test was evaluated the ability of the system to serve the needs of the users and 4.00 0.52 5. the accuracy of the system to report Functional test was used to evaluate the accuracy of the 6. the accuracy of the system in the overall 4.57 0.60 system. system functions The suitability of the system Usability test was tested the suitability of the system. 1.the suitability of the functions with ease of 4.60 0.7 Performance test was assessed the processing speed of system usage the system. Finally, Security test was used to evaluate 2. the suitability of text display clarity 4.7 0.67 the security of the system [7] as shown in figure 4. 3. the suitability of using colour 4.7 0.67

4. the suitability of data presentation 4.5 0.53

5. the suitability of user interface 4.4 0.52

The speed of the system

[3] Chetan Dwarkani M, Ganesh Ram R ; Jagannathan S ; R. PriyatharshiniSmart farming system using sensors for agricultural task automation 2015 IEEE Technological Innovation in ICT for Agriculture and Rural Development (TIAR) 10-12 July 2015. [4] Höller, J.; Tsiatsis, V.; Mulligan, C.; Karnouskos, S.; Avesand, S.; Boyle, D. (2014). From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Elsevier. ISBN 978-0-12-407684-6. [5] Javatechig, “Rapid Application Development Model”, Information, http://javatechig.com/se-concepts/rapid- 7application-development-model [6] PH meter(SKU: SEN0161), “pH Electrode Characteristics”, https://www.dfrobot.com/wiki/index.php/PH_meter. [7] Laurie Williams. “Testing Overview and Black-Box Testing Techniques”. , 2006.

Figure 4: the results between experts and users

The results show that the ability of the system by 5 experts and 30 users is well in all aspects and it can be concluded that the prototype of automation system to control the soil and water management for rice field application is good.

V. CONCLUSION

With the emergence of an information-driven society in agriculture, the developed prototype system could monitor and control the pH of water and the level of water in the rice field by using Internet of Things (IoT), and Mobile application. The results found that data file indicated the instantaneous status of the level of water and the system can solve the flood problem in the rice field during rainy season and control the pH of water and soil for growing rice and suggest the farmers how to prepare the field to cultivate rice through mobile devices. However, in term of the future experiments, we are looking forward to advanced technologies to support in precision agriculture. References

ACKNOWLEDGMENT

The authors gratefully acknowledge the financial subsidy provided by Suan Sunandha Rajabhat University.

REFERENCES

[1] Bouman, B. A. M., Humphreys, E., Tuong, T. P., & Barker, R. (2007). Rice and water. In Advances in Agronomy (Vol. 92, pp. 187–237). Elsevier. http://linkinghub.elsevier.com/ retrieve/pii/S0065211304920044. [2] Definition Internet of Things (IoT), “IoT Agenda” http://internetofthingsagenda.techtarget.com/definition/Internet- of-Things-IoT

An Application of Generalized Inverses of Matrices on Hill Cipher Cryptography over

Field 97

Mukhammad Solikhin1, 2, Rismal1, 3 1Faculty of Informatics and Electrical Engineering, Institut Teknologi Del, Toba Samosir 22381, Indonesia. 2Department of Informatics Engineering. 3Department of Information System. [email protected]

Abstract—Cryptography is a method for securing the information by using a particular algorithm. message using a specific mathematical algorithms. In Cryptographic system generally consists of two cryptography there are two main concepts that encryption processes, namely the process of encryption and and decryption. Encryption is the process of changing decryption. Encryption is a process of changing the information or data will be sent into a form which is hardly understandable information to not be able to understand. recognizable as the information initially by using a specific algorithm. Decryption is the reverse of encryption that is While decryption is the reverse process, that is to change changing back into a disguised form of preliminary the information that is not understandable information to information by the same algorithm, the initial information be able to understand. In a cryptographic system, or data to understand the meaning commonly referred to as understandable information is called plaintext while plaintext (clear text), while after the encrypted form of unintelligible information is called ciphertext. plaintext to ciphertext is called (fuzzy text). One algorithm One of the algorithms in cryptographic systems is the used is the Hill algorithm or commonly known as the Hill Hill Cipher introduced by Lester S. Hill [1], [2]. The Cipher is an algorithm that uses a square matrix for basic idea is to make a 1-1 correspondence between the encryption and decryption, this time the author extended letters and number with field so that we have a the Hill cipher cryptographic techniques to not only use 26 square matrix as a key but also the matrix is not square- can sequence of number corresponding to the letters of the be applied to the symmetric-key cryptography techniques alphabet. Table 1 is one example of the one-to-one with the help of pseudoinvers theorem, namely theorem correspondence of the alphabet into the field 26. which ensures that every not-square matrix over a field have an inverse matrix is called matrix pseudoinvers. The Table 1 author also uses a theorem that facilitates the search One-to-one Correspondence of The Alphabet Into The Field 26 pseudoinvers matrix, for some of them is if the matrix is invertible or has full column rank or it could be if it has full A B C D E F G H I row rank, next based on that theorems on the terms, the 14 11 2 21 18 7 6 22 17 authors also make an implementation of the extended J K L M N O P Q R cryptographic hill cipher program the aim that by the 3 10 23 13 0 16 25 9 4 theorems turns can be made a machine that can be used to S T U V W X Y Z exchange secret messages, but it was found that the 20 19 5 8 24 15 1 12 extended hill cryptographic cipher generates ciphertext that is longer than the plaintext so very more profitable because the message is becoming increasingly difficult to decode.

Index Terms—Ciphertext, Cryptography, Decryption, Encryption, Generalized Inverse, Hill-Cipher, Moore- Penrose Inverse, Plaintext.

I. INTRODUCTION

Cryptography is a technique or effort to store information from parties that are not feasible to obtain the

In the encryption process, a plaintext 푝 is converted II. HILL-CIPHER CRYPTOGRAPHY into a sequence of numbers, based on Table 1, then arranged into matrix form 푃 and multiplied by an A plaintext 푝 of length 푛 will be encrypted by firstly invertible matrix 퐾 so that it produces a ciphertext of changing the plaintext using a 1-1 correspondence, to matrix 퐶 whereas for decrypting the message, the obtain a sequence of numbers. Then the sequence of ciphertext of matrix 퐶 is multiplied by the inverse of numbers is converted into square matrix form and matrix 퐾 in order to recover the plaintext of matrix 푃. A obtained plaintext matrix 푃 with integer member entries −1 pair of invertible matrices 퐾 and 퐾 selected in the of field 26. The encryption process is continued by decryption-encryption process are commonly referred to multiplying the plaintext matrix 푃 by an invertible matrix key matrices. Figure 1 below is a decryption-encryption 퐾 whose size corresponds to the plaintext matrix 푃 so as to produce a new matrix called ciphertext of matrix 퐶. Plaintext Then the ciphertext of matrix 퐶 is converted back into a sequence of numbers and corresponds with the corresponding alphabet to obtain ciphertext 푐. It will then be sent to the recipient as an encrypted message. Encryption Key As for the decryption process, ciphertext 푐 is corresponded back to the numbers and arranged into square matrix form, so that the matrix ciphertext 퐶 is Ciphertext obtained, then the matrix ciphertext 퐶 is multiplied by the inverse of the key matrix 퐾, i.e. 퐾− 1 and obtained the plaintext of matrix 푃. The next step is to alters the plaintext of matrix 푃 into the sequence of numbers and Decryption Key corresponds with the corresponding alphabet to recover the original plaintext 푝.

Table 2 Plaintext Another One-to-one Correspondence of The Alphabet Into The Field . process using cryptographic Hill-ciphers. 26 Figure 1: General Cryptography System Scheme A B C D E F G H I 0 1 2 3 4 5 6 7 8 The Hill-cipher cryptographic in Figure 1 above has J K L M N O P Q R some disadvantages such that the key matrix used must 9 10 11 12 13 14 15 16 17 be invertible, which implies that the key matrix must be S T U V W X Y Z square, it becomes a barrier in the selection of the matrix 18 19 20 21 22 23 24 25 as the key matrix. The second deficiency is that in the process of changing the plaintext or ciphertext from the For example, using Table 2, a plaintext “SECRET” sequence of numbers into square matrices, it is not corresponding to the sequence of numbers always possible to obtain an exact square matrix, for “18,4,2,17,4,19” is encrypted using key 5 −1 example the plaintext of “talking” by using 퐾 = [ ] (1) correspondence in Table 1 turns into a sequence of 9 −2 numbers “19,14,23,10,17,0,6” which can not be directly will produce ciphertext converted into square matrix form, it causes the 퐶 = (푃. 퐾) 푚표푑 26 (2) 18 17 encryption-decryption process can not be done directly 5 −1 = [ 4 4 ] [ ] 푚표푑 26 (3) on the plaintext. 9 −2 2 19 In this study we discussed the concept of an extended 243 −52 matrix inverse that will provide a solution to the = [ 56 −12] 푚표푑 26 (4) problems or weaknesses that exist in the Hill-cipher 181 −40 cryptographic system over field 26. 9 0 This paper is organized as follows. In section 2, Hill- = [ 4 14] (5) cipher cryptographic system. In section 3 generalized 25 12 inverses of matrices is presented. An application of which corresponds to the word “JEZAOM”. Generalized Inverses of Matrices are also presented in To decrypt the ciphertext, it uses the inverse of the key section 4. Finally, our work of this paper is summarized matrix 퐾, with 2 −1 in the last section. 퐾−1 = [ ] (6) 9 −5 and obtained

−1 푃 = (퐶. 퐾 ) 푚표푑 26 (7) PROPOSITON 1[4]. For each 퐴푚×푛 there are unitary 9 0 푇 2 −1 matrix 푈푚×푚 and 푉푛×푛 such that Σ = 푈 퐴푉. = [ 4 14] [ ] 푚표푑 26 (8) 9 −5 THEOREM 2[4].The Moore-Penrose Inverse of 퐴 ∈ 25 12 퐻푚×푛, denoted by 퐴픣 is the unique matrix 푋 ∈ 퐻푛×푚 18 −9 satisfying the following equations, = [134 −74] 푚표푑 26 (9) 퐴푋퐴 = 퐴 (20) 158 −85 18 17 푋퐴푋 = 푋 (21) = [ 4 14] (10) (퐴푋)∗ = 퐴푋 (22) 2 19 (푋퐴)∗ = 푋퐴 (23) that corresponds to the plaintext “SECRET”. With 퐴∗ is the conjugate transpose of 퐴. The decryption-encryption process mathematically is PROPOSITON 2[5]. If 퐴 is invertible, then 퐴픣 = 퐴−1. described as follows, given 풦, a set of invertible PROPOSITON 3[5]. If 퐴 has the right inverse 푅, matrices, 풫 is the plaintext set and 풞 is the set of 픣 (퐴푅 = 퐼푚), then 퐴 = 푅. ciphertext over field n. Hill-cipher takes 퐾푚×푚 ∈ 풦 PROPOSITON 4[5]. If 퐴 has the left inverse 퐿, (퐿퐴 = over field n, 퐼 ), then 퐴픣 = 퐿. 푘 푘 … 푘 푛 11 12 14 PROPOSITON 5[5]. If 퐴 has a full column rank, then 푘 푘 … 푘 퐾 = [ 21 22 24 ] (11) 퐴픣 = (퐴∗퐴)−1퐴∗. ⋮ ⋮ ⋱ ⋮ 픣 푘 푘 … 푘 PROPOSITON 6[5]. If 퐴 has a full row rank, then 퐴 = 푚1 푚2 푚푚 ∗ ∗ −1 as a key. A plaintext 푥 with length 푚 is, 퐴 (퐴퐴 ) . 픣 픣 ∗ 픣 픣 ∗ 푥 = [푥1 푥2 … 푥푚] ∈ 풫 (12) PROPOSITON 7[5]. (퐴 ) = 퐴 and (퐴 ) = (퐴 ) . will be encrypted into ciphertext THEOREM 3. Given any matrix 퐴푚×푛 with rank 푟 푦 = [푦1 푦2 … 푦푚] ∈ 풞 (13) over a field, there is a unique Moore-Penrose Inverse of by using the key matrix 퐾 through the equation 퐴, 퐴픣, 푛 × 푚 size. 푦 = 퐾푥 푚표푑 푛 (14) Proof 푦 1 For uniquiness, let 푋 and 푌 are Moore-Penrose inverse 푦 2 of 퐴 then 푋 and 푌 satisfy THEOREM 2 so that [ ⋮ ] 퐴푌, 퐴푋, 푋퐴 and 푌퐴 are hermit matrices, consequently 푦 푚 (15) 퐴푌 = (퐴푌)∗ 푌퐴 = (푌퐴)∗ (24) 푘11 푘12 … 푘14 푥1 ∗ ∗ 퐴푌 = ((퐴푋퐴)푌) 푌퐴 = (푌(퐴푋퐴)) (25) 푘 푘 … 푘 푥2 = [ 21 22 24 ] [ ] 푚표푑 푛 ∗ ∗ ⋮ ⋮ ⋱ ⋮ ⋮ 퐴푌 = ((퐴푋)(퐴푌)) 푌퐴 = ((푌퐴)(푋퐴)) (26) ∗ ∗ ∗ ∗ 푘푚1 푘푚2 … 푘푚푚 푥푚 퐴푌 = (퐴푌) (퐴푋) 푌퐴 = (푋퐴) (푌퐴) (27) While for the decryption process of ciphertext 푦 to 퐴푌 = (퐴푌)(퐴푋) 푌퐴 = (푋퐴)(푌퐴) (28) plaintext 푥 is by multiplying the inverse of 퐾 퐴푌 = (퐴푌퐴)푋 푌퐴 = 푋(퐴푌퐴) (29) 푦 = (퐾푥) 푚표푑 푛 (16) 퐴푌 = 퐴푋 푌퐴 = 푋퐴 (30) 퐾−1(푦) 푚표푑 푛 = 퐾−1(퐾푥) 푚표푑 푛 (17) 푌(퐴푌) = 푌(퐴푋) (푌퐴)푋 = (푋퐴)푋 (31) (퐾−1푦) 푚표푑 푛 = (퐾−1퐾)푥 푚표푑 푛 (18) 푌퐴푌 = 푌퐴푋 푌퐴푋 = 푋퐴푋 (32) (퐾−1푦) 푚표푑 푛 = 푥 (19) 푌 = 푌퐴푋 푌퐴푋 = 푋 (33) In the next sub-section, it will be discussed about the So 푌 = 푋. generalized inverses of matrices, which will be For existence, based on THEOREM 1, for each matrix implemented on Hill-cipher cryptography. form 퐴푚×푛 there exists unitary matrix 푈푚×푚 and 푉푛×푛 푇 and singular matrix 훴푚 × 푛 so that 퐴 = 푈Σ푉 , which III. GENERALIZED INVERSES OF MATRICES 휎1 0 0 ⋯ 0 0 퐷 0 0 ⋱ ⋯ 0 DEFINITION 1[3]. Singular value of 퐴 are square Σ = [ ] = 0 0 휎푟 ⋯ 0 (34) 0 0 roots of positive eigen value from matrix 퐴푇퐴, denoted ⋮ ⋮ ⋮ ⋱ ⋮ [ ] by 휎1, 휎2, … , 휎푛 such that 휎1 ≥ 휎2 ≥ ⋯ ≥ 휎푛 > 0 and 0 0 0 ⋯ 0 푚×푛 and 휎 is the singular value 푖-th from matrix 퐴. Defined 휎푖 = √휆푖 for 1 ≤ 푖 ≤ 푛. 푖 THEOREM 1[3]. Let 퐴 be an 푚 × 푛 matrix with rank matrix −1 퐷 0 휎1 0 0 ⋯ 0 푟. Then there is 훴푚 × 푛 matrix with Σ = [ ] and the 픣 0 ⋱ 0 ⋯ 0 0 0 픣 퐷 0 diagonal entries matrix 퐷 is 푟 singular value of 퐴, and Σ = [ ] = 0 0 휎−1 ⋯ 0 (35) 0 0 푟 ⋮ ⋮ there is orthogonal matrix 푈푚×푚 and 푉푛×푛 such that 퐴 = ⋮ ⋱ ⋮ [ ] 푈Σ푉푇. 0 0 0 ⋯ 0 푛×푚

Construct matrix form 퐴픣 by cryptographic. 퐴 = 푈훴푉∗ (36) 퐴픣 = (푈훴푉∗)픣 (37) IV. EXPERIMENTAL RESULT UNITS 퐴픣 = (푉∗)픣(푈훴)픣 (38) 퐴픣 = (푉∗)−1훴픣푈픣 (39) Table 3 is one of example a correspondence of 1-1 to 퐴픣 = 푉훴픣푈−1 (40) the integer number modulo 97, 97 is chosen because 97 is the prime number, it is consequent by 퐴픣 = 푉훴픣푈∗ (41) [6],[7],[8],[9],[10] any nonzero elements having the Will show 퐴 and 퐴픣 satisfy THEOREM 2 픣 ∗ 픣 ∗ ∗ inverse. Thus, it can guarantee any key matrix having a 퐴퐴 퐴 = (푈훴푉 )(푉훴 푈 )(푈훴푉 ) (42) nonzero determinant selected always has inverse in field 퐴퐴픣퐴 = 푈훴(푉∗푉)훴픣(푈∗푈)훴푉∗ (43) 97. Suppose a 1-1 correspondence table from alphabet 픣 픣 ∗ 퐴퐴 퐴 = 푈훴훴 훴푉 (44) to integer modulo 97 is given in Table 3 below. 퐴퐴픣퐴 = 푈훴푉∗ (45) 퐴퐴픣퐴 = 퐴 (46) Table 3 It is show satisfy Equation (20), next One-to-one Correspondence of The Alphabet Into The Field 97. 퐴픣퐴퐴픣 = (푉훴픣푈∗)(푈훴푉∗)(푉훴픣푈∗) (47) 퐴픣퐴퐴픣 = 푉훴픣(푈∗푈)훴(푉∗푉)훴픣푈∗ (48) A B C D E F G H I 픣 픣 픣 픣 ∗ 0 1 2 3 4 5 6 7 8 퐴 퐴퐴 = 푉훴 훴훴 푈 (49) J K L M N O P Q R 픣 픣 픣 ∗ 퐴 퐴퐴 = 푉훴 푈 (50) 9 10 11 12 13 14 15 16 17 퐴픣퐴퐴픣 = 퐴픣 (51) S T U V W X Y Z a It is show satisfy Equation (21), next 18 19 20 21 22 23 24 25 26 ∗ b c d e f g h i j 픣 ∗ ∗ 픣 ∗ (퐴퐴 ) = ((푈훴푉 )(푉훴 푈 )) (52) 27 28 29 30 31 32 33 34 35 ∗ ∗ (퐴퐴픣) = (푉훴픣푈∗) (푈훴푉∗)∗ (53) k l m n o p q r s 36 37 38 39 40 41 42 43 44 픣 ∗ ∗ ∗ 픣 ∗ ∗ ∗ ∗ (퐴퐴 ) = [(푈 ) (푉훴 ) ][(푉 ) (푈훴) ] (54) t u v w x y z { | ∗ ∗ (퐴퐴픣) = [푈(훴픣) 푉∗][푉훴∗푈∗] (55) 45 46 47 48 49 50 51 52 53 } ~ ! “ # $ % & 픣 ∗ 픣 ∗ ∗ ∗ ∗ (퐴퐴 ) = 푈(훴 ) (푉 푉)훴 푈 (56) 54 55 56 57 58 59 60 61 62 ∗ ∗ (퐴퐴픣) = 푈(훴픣) 훴∗푈∗ (57) ‘ ( ) * + , - . / 픣 ∗ 픣 ∗ ∗ 63 64 65 66 67 68 69 70 71 (퐴퐴 ) = 푈(훴훴 ) 푈 (58) 0 1 2 3 4 5 6 7 8 ∗ (퐴퐴픣) = 푈(훴훴픣)푈∗ (59) 72 73 74 75 76 77 78 79 80 ∗ 9 : ; < = > ? @ [ (퐴퐴픣) = 푈훴(푉∗푉)훴픣푈∗ (60) ∗ 81 82 83 84 85 86 87 88 89 (퐴퐴픣) = (푈훴푉∗)(푉훴픣푈∗) (61) \ ] ^ _ ¼ ½ ¾ ∗ (퐴퐴픣) = 퐴퐴픣 (62) 90 91 92 93 94 95 96 It is show satisfy Equation (22), next ∗ Suppose the plaintext “T@Lk!n6” will be encrypted 픣 ∗ 픣 ∗ ∗ (퐴 퐴) = ((푉Σ 푈 )(푈Σ푉 )) (63) using “$3cRet.” as a key, based on Table 3 in succession ∗ ∗ (퐴픣퐴) = (푈Σ푉∗)∗(푉Σ픣푈∗) (64) corresponding to the “19,88,11,36,57,39,6” and ∗ ∗ “60,75,28,17,30,45,70”. It has a plaintext matrix and (퐴픣퐴) = [(푉∗)∗(푈Σ)∗] [(푈∗)∗(푉Σ픣) ] (65) ∗ ∗ consecutive keys as (퐴픣퐴) = [푉Σ∗푈∗] [푈(Σ픣) 푉∗] (66) 19 ∗ ∗ (퐴픣퐴) = 푉Σ∗(푈∗푈)(Σ픣) 푉∗ (67) 88 ∗ ∗ (퐴픣퐴) = 푉Σ∗(Σ픣) 푉∗ (68) 11 푃1 = 36 , 퐾1 픣 ∗ 픣 ∗ ∗ (74) (퐴 퐴) = 푉(Σ Σ) 푉 (69) 57 ∗ (퐴픣퐴) = 푉(Σ픣Σ)푉∗ (70) 39 ∗ [ ] (퐴픣퐴) = 푉Σ픣(푈∗푈)Σ푉∗ (71) 6 ∗ = [60 75 28 17 30 45 70] (퐴픣퐴) = (푉Σ픣푈∗)(푈Σ푉∗) (72) Ciphertext 퐶1 obtained with 픣 ∗ 픣 (퐴 퐴) = 퐴 퐴 (73) 퐶1 = (퐾1푃1) 푚표푑 97 (75) It is show satisfy Equation (23). Finally based on Equations (46), (51), (62), and (73) show that 퐴픣 is generalized inverse of 퐴. The following subheading is the application of the generalized inverses of matrices into Hill-cipher

The extended matrix inverse concept can be applied to 19 extend the cryptographic system of Hill-cipher in integer 88 modulo 26 to integer modulo 97. Besides with the

11 (76) concept, the selection of the key matrix for the = [60 75 28 17 30 45 70] 36 푚표푑 97 decryption-encryption process becomes more varied, 57 because it does not require the use of a square matrix as 39 the key matrix. A non-square matrix providing the certain [ 6 ] properties can also be used as a key matrix for the [ ] = 12545 푚표푑 97 (77) encryption-decryption process. The application of the = 32 (78) extended matrix inverse concept to cryptographic Hill- Based on Table 3 obtained ciphertext “g”. cipher can also simultaneously encrypt plaintext into If the plaintext of matrix 푃 and the key matrix 퐾 are ciphertext or vice versa without having to convert the changed, then different ciphertext will be obtained. For plaintext into square matrix form. Another advantage is example the plaintext and matrix keys above are changed by using the inverse of non-square matrix, and changing into form the plaintext matrix and the key matrix to be non-square, 푃2 = [19 88 11 36 57 39 6], 퐾2 it creates ciphertext produced more difficult to solve 60 because the length of the resulting ciphertext will be very 75 different from the original plaintext length, thus further 28 (79) strengthening the cryptographic system from hacking. = 17

30 45 ACKNOWLEDGMENT [70] Sponsor and financial this work was supported by Then, it is obtained the matrix ciphertext 퐶2 as follows LPPM Institut Teknologi Del. 퐶2 = (퐾2푃2) 푚표푑 97 (80)

60 REFERENCES 75 [1] Hill L.S. Cryptography in an Algebraic Alphabet. American 28 (81) = [ 36 ] 푚표푑 97 Mathematical Monthly 1929; 36: 306-312. 17 19 88 11 57 39 6 [2] Hill L.S. Concerning Certain Linear Transformation Apparatus of 30 cryptography. American Mathematical Monthly 1931; 38: 135- 45 154. [70] [3] Lay D.C. Linear Algebra and Its Applications. Addison-Wesley 73 42 78 26 25 12 69 2012; 4ed. 67 4 49 81 7 15 62 [4] R. Penrose , A generalized inverse for matrices, Proc. Camb. Philos. Soc. 1955; 51: 406–413. 47 39 17 38 44 25 71 [5] Goldberg, J.L. 1991, “Matrix Theory with Applications”, Mc = 32 41 90 30 96 81 5 (82) Graw-Hill, Inc., USA. 85 21 39 13 61 6 83 [6] Menezes,A.J., Oorcshot, P.C dan Vanstone, S.A., 1997, 79 80 10 68 43 9 76 “Handbook of Applied Cryptography”, CRC Press, Inc. USA. [69 49 91 95 13 14 32] [7] Nicholson, W.K., 2001, “Elementary Linear Agebra: First By using Table 3, it is obtained ciphertext “1+vg=7- Edition”, International Edition, Mc Graw-Hill, Inc., Singapore. [8] Scheick, J.T., 1997, “Linear Agebra with Applications, qEnpV8x6xR\nK]a9meN,½ZHs¾%rNMPZ9GJO- International edition”, Mc Graw-Hill, Inc., Singapore. &/F;4g”. [9] Stinson, D.R., 1995, “Cryptography Theory and Practice”, CRC Press, Inc., Boca Raton, Florida. [10] Gallian, J. A., 2010, “Contemporary Abstract Algebra: 7th Ed”, V. CONCLUSION Brooks/Cole.,USA

Tunnel Excavation in Gravel Formation Using Distinct Element Method

Sung-Chi Hsu1, Ching-Te Lin2 and Ming-Der Yang3 1,2Department of Construction Engineering, Chaoyang University of Technology, Taichung, Taiwan. 3Department of Civil Engineering, National Chung-Hsing University, Taichung, Taiwan. [email protected]

Abstract—Underground pipeline and tunnel have been improvement, debris flow, landslide and underground constructed using pipe-jacking method and open-face or excavation6-11. close-face shield machine in gravel formation in Taiwan. Some problems had been encountered during tunneling construction using open-face shield machine. In order to II. GRAVEL FORMATION study the failure mechanisms and failure zones of open face underground pipe excavation, different diameters, Gravel formation is often encountered in central overburdens, and rotation characteristics of the gravel are Taiwan during deep excavation and tunneling considered for numerical simulation. Distinct element construction. Gravel usually locates roughly three 2D method based numerical software, PFC , is used to model meters below the surface. The average grain size or D the tunnel excavations. The displacements in front of the 50 of gravel formation is about 8 to 20 cm. The tunnel due to slipping and rolling of the particles are observed and compared at certain time step. The results percentages of boulder (or gravel), sand, and fine obtained from the analyses show that the failure of content are approximately 75 to 86%, 10 to 26%, and 1 excavation face starts near the crown (roof) and extends to 15%, respectively12,13. Accordingly, the shear forward and upward. The extended failure zones above strength of the gravel formation is controlled by the and in front of the tunnel can be identified from the coarse content. The measured peak and residual friction numerical experiments. The forepoling method should be angles were around 54.3° and 44.9°, and apparent required before the excavation. cohesions were 1.5 t/m2 and 0, respectively, based on the large direct shear tests conducted by Chu et al.14. Index Terms—Pipe Jacking; Excavation; Distinct The properties of gravel layer are mainly determined or Element Method; Stability Analyses. dominated by interlocking, slipping among gravels, I. INTRODUCTION rolling between particles and the sizes of diameter. Gravels should be regarded as a discontinuation for In order to elevate the living standard and to fulfill the simulation and analysis. The percentage of fines content nation policy and global trends, lifelines, such as (such as clay and silt) and sand is low, and high electricity, telecommunication, cable, water supply, and percentage of coarse-grained soil existing in the gravel sewerage, and critical infrastructures are suggested to be formation in this area. Thus, the distinct element method constructed underground. However, the cut and cover (DEM) is selected to model the stability of underground method is not recommended to be used for building openings after excavation by pipe jacking method, and underground lifelines, considering the great impacts on to reveal the failure processes and extent triggered by social and transportation. Thus, pipe-jacking or shield the openings. tunneling methods are proposed and designated to use in many locations in Taiwan. This study highlights on the III. PIPELINE IN TAICHUNG simulation of construction of sewerage tunnel in gravel formation in Taichung, Taiwan. The waste water produced by the companies inside the There are a lot of researches using model tests and Central Taiwan Science Park (CTSP) will be treated and numerical approaches to model tunnels and opening by transported through the outflow pipeline. The pipeline pipe-jacking or the other tunneling methods to starts from the Science Park to Ta-Tu River through western and southern parts of Taichung City. The understand the behavior, stresses, movement, support elevation above sea level of the pipe is beginning from and stability around the openings1-5. The distinct 117.8m high down to 2.18m. The slope of the pipeline element method had been used to model laboratory is planned at 0.1 to 0.2 percent. The overburden depth testing, rock slope, anchor, tunnel, ground above the pipeline ranges from 2.45 to 20 meters.

Three different methods, cut and cover, pipe-jacking, zero and 31 degrees, respectively. and shield tunneling methods, are suggested and used for underground excavation. Based on the previous and Table 1 2D accumulated construction experiences using the pipe- Material parameters used in the numerical simulation using PFC jacking method, especially in populated areas, the pipe Kn 1020 N/m jacking method was selected to replace traditional cut- Wall 20 and-cover method as a result of the improvement of Ks 10 N/m machines and construction techniques. For the pipe- Density 26 kN/m3 jacking method, the size of a pipe is usually not Friction Angle (φ) 31° restricted, and the environmental constraint and the Ball Kn 1x108 N/m required working space is also much smaller compared Ks 1x108 N/m with open-cut excavation. Thus, pipe-jacking method Spin Free to spin has become the very common method used to build the underground sewage pipe in urban area in gravel 1 Figure 1 shows the results of in situ grain size formation in central Taiwan . The total length of the distribution. The sizes of the generated particles are tunnel constructed using pipe-jacking method is around based on this distribution13. However, small particles are 5 kilometers, which is about 29% of the total pipeline excluded from the analyses since the number of balls length. However, cave-in failures could be encountered will be too many to model. Hence, the grain sizes of occasionally during pipe-jacking excavation. particles below 20%, which is 3 mm, are not considered for the DEM modeling to reduce the computation time IV. DISCRETE ELEMENT METHOD (DEM) and accelerate the numerical simulation.

The discrete element method (DEM), also named as the V. SETUP OF NUMERICAL SIMULATION MODEL distinct element method, is a numerical technique for computing the separation, relative large movement and There are four major steps used to simulate an opening interaction between granular particles or moving in gravel formation subjected to cave in at different objects. The scheme was proposed and developed by overburdens. First, create a box (walls to represent Cundall15-17 in 1971 to apply on numerical analyses of 6,7 boundaries) to allow the balls, representing soil rock engineering . The method can be used to evaluate particles, to be generated within the space. The size of the displacements, rotation, stresses, and separation the box is 15m wide and 150m high. Second, generate between particles or objects. It permits large the balls, based on the grain-size distribution and displacements and rotations of discrete bodies including randomly assign the ball within the box, and allow the complete detachment and also distinguishes new balls to settle down and deposit. Third, determine the contacts automatically as the simulation progresses. The height of the desired overburden and diameter of the commercial program, two-dimensional Particle Flow opening, then delete the balls inside the opening and Code, i.e. PFC2D, established by Itasca Consulting 18 generate walls around the opening. Finally, analyze and Group is used here in this paper to simulate the observer the stability or movement of the tunnel face. mechanical properties and frictional behavior of gravel formation in Taichung, Taiwan. The material parameters for the gravel formation in 100 Taichung City are obtained from Chu et al.12 and other related studies and listed in Table 1. The total unit 80 weight, specific gravity, and water content are estimated 60 to be around 2.0 to 2.2 t/m3, 2.5 to 2.66, and 5%, respectively. Thus, the normal stiffness (kn), shear 40 stiffness (ks), density, and friction angle between

Pass Percentage (%) Percentage Pass 20 gravels (ball), may consider as microscopic friction angle, and boundary (wall) are also listed in Table 1. 0 The average porosity of the gravel formation is about 1000 100 10 1 0.1 0.22. These microscopic properties between particles Grain Diameter (mm) were attained based on the study by Hsu et al.11. The internal frictional parameters between gravel particles Figure 1: The obtained in situ grain-size distribution curve of gravel are acquired from a series of numerical experiments formation in Taichung, Taiwan 2D using PFC . The results of simulated direct shear tests will yield similar shear strength as tested in situ if the cohesion and internal friction angle are assumed to be

The properties of the balls and walls are assigned after generation of the balls and walls. The input parameters for the numerical analyses are tabulated in Table 1. The tunnel inside diameter (D) is 1.8m for modeling. The depth from ground surface to the top of the tunnel is defined as overburden (Z). The overburden Z is chosen as 3D for the numerical model tests. The generated balls, walls, and the opening for D=1.8m and Z/D=3 are shown in Figure 2. There are 43,475 balls used in this model-test simulation. The length of tunnel opening is 5.5m and the length from the tunneling facing to the left boundary is 9.5m.

Z Figure3: Schematic drawing of the lateral extent (L) in front of the pipe and influence range (I) due to movement of balls and induced cave-in failure. 5.5m D 1.5m 15m B. Impact Analyses of Opening on Face Stability The influence range developed at the ground surface Figure 2: The generated balls (particles), walls (boundaries), and the will cause differential settlement on the buildings and opening to simulate pipe-jacking tunneling in gravel formation (D=1.8m; Z/D=3; Balls=43475). infrastructures within it. The lateral extent ahead of the top opening can be used to determine the unstable VI. RESULTS OF NUMERICAL ANALYSIS length and zone to install the required support, such as forepoling. The underground opening is unstable if the A. Influence Range on Ground Surface particles are allowed to spin or to rotate freely, and the opening face is easy to collapse because of excavation, In order to study and understand the scale and impact as shown in Figure 4. The generated balls are circular of cave-in collapsing after tunnel opening, the extent shape, thus, sliding and rolling are more permissible and range of particle movement are examined. The between the particles if the balls can spin freely. Surface lateral extent (L) represents the horizontal cave-in range subsidence could develop if the overburden is shallow. advanced in front of the top opening. The influence range (I) is the size of subsidence at the ground surface due to cave-in sliding developed upward. A schematic drawing of the lateral extent and influence range due to cave-in displacement after tunnel opening is shown in Figure 3. The major cave-in failure usually initiates from the top heading close to the crown (roof) of an opening, and leads to subsequent sliding and rolling failure. The cave-in zone may extend forward and then upward progressively. However, the cave-in sliding may not outspread all the way to ground surface if Z/D becomes larger. The arching effect between gravel particles may Figure 4: Cave-in collapsing after tunnel opening if the overburden is take place before the moving region extend to the not very deep (D=1.8m; Z/D=3) ground surface.

VII. CONCLUSIONS [4] Luis María Díaz-Díaz, Joshua Omer, Daniel Arias and Luis Pando, “Finite elements analysis of an underground collector installed by pipe-jacking method,” EGU General Assembly, This research uses distinct element method based 2016, Vienna Austria, 12213. numerical program PFC2D to simulate an underground [5] X. Ji, W. Zhao, and P. Jia, “Pipe Jacking in Sandy Soil Under a opening in gravel formation, and to study the failure River in Shenyang, China,” Indian Geotechnical Journal, 2016, mechanism related to particle properties. A series of doi:10.1007/s40098-016-0195-5 [6] J. M. Ting, B. T. Corkum, C. R. Kauffman, and C. Greco, numerical model tests are performed in this paper and “Discrete Numerical Model for Soil Mechanics,” Journal of some conclusions can be obtained as follows. Geotechnical Engineering, ASCE, 115, 1989, pp. 379−398. 1. The major cave-in failure initiates from the top [7] L. Rothenburg and R. J. Bathurst, “Micromechanical features of heading close to the crown of an opening. granular assemblies with planer elliptical particles,” Geotechnique, 42, 1, 1992, pp. 79-65. 2. The lateral extent ahead of the top opening can be [8] Sung-Chi Hsu. Stability and Failure Mechanisms of Slopes in used to select the minimum length for forepoling Weak Rock Masses, Ph.D. Dissertation, The University of Texas method. at Austin, 1996. 3. The cave-in sliding may not outspread all the way to [9] T. T. Ng and R. Dobry, “Numerical Simulation of Monotonic and Cyclic Loading of Granular Soil,” Journal of Geotechnical ground surface if Z/D becomes larger. Engineering, ASCE, 2, 1994, pp.388-403. 4. The range of cave-in is reduced a lot as certain [10] P. A. Thomas and J. D. Bray, “Capturing Nonspherical Shape of bigger particles are not allowed to spin. Granular Media with Disk Clusters,” Journal of Geotechnical 5. Interlocking effects between particles becomes more and Geoenvironmental Engineering, ASCE, 125, 1999, pp.169- 178. prominent if more particles cannot spin freely. [11] Sung-Chi Hsu, Bo Jing Lai, Wei Hsu, and Jiunnren Lai, “Modeling of Gravel Properties and Anchors Using Discrete ACKNOWLEDGMENT Element Method,” Advanced Materials Research, 189-193, 2011, pp.1726-1731. doi: 10.4028/www.scientific.net/AMR.189-193.1726. The authors wish to thank the Ministry of Science and [12] B. L. Chu, G. M. Pan and G. S. Chang, “In situ geotechnical Technology (MOST) for providing the financial support properties of gravel formation in western Taiwan,” Sino Tech to this research (Grant No. NSC98-2221-E-324-029). 55, 1996 pp.47-55. (in Chinese) [13] Sung-Chi Hsu and Chin-Ming Chang, “Pullout performance of vertical anchors in gravel formation,” Engineering Geology, 90, REFERENCES 1-2, 2007, pp.17-29. [14] P.A. Cundall, “A Computer Model for Simulating Progressive [1] Bin-Lin Chu, Han-Ho Sun, and Kin-Long Hsu, “Case studies of Large Scale Movements in Blocky Rock Systems,” Proceedings pipe jacking technique in gravel formation. Sino-Geotechnics,” of the Symposium of the International Society of Rock 106, 2003, pp. 15-24. (in Chinese) Mechanics, 1, 1971, Paper No.II-8. [2] Song Zhou, Yingyi Wang and Xingchun.Hunag, “Experimental [15] P. A. Cundall and O. D. L. Strack, “A Discrete Numerical Model study on the effect of injecting slurry inside a jacking pipe for Granular Assemblies,” Géotechnique, 29, 1979, pp.47-65. tunnel in silt stratum,” Tunneling in Underground Space [16] P.A. Cundall and R.D. Hart, “Numerical Modeling of Technology, 24, 4, 2009, pp. 466–471. doi: Discontinua,” Keynote Address, Proceeding of the 1st U.S. 10.1016/j.tust.2008.11.003. Conference on Discrete Element Methods, 1989, pp.1-17. [3] M. Sugimoto and A. Asanprakit, “Stack pipe model for pipe [17] Itasca Consulting Group, Inc. PFC2D (Particle Flow Code in 2 jacking method,” Journal Construction Engineering Dimensions), Minneapolis: ICG, 2004. Management, 136, 2010, pp. 683–692. doi: 10.1061/(ASCE)CO.1943-7862.0000172

Program-Based Robotics Using Prolog

Tarik A. Rashid1,2, Imad H. Hassan2, Nawzad Al-Salihi1, Akreen M. Saleh2, Ahmed S Qosaeri1 1Department of Computer Science and Engineering, University of Kurdistan Hewlêr, Erbil, Kurdistan, Iraq 2Salahaddin University-Erbil, Software Engineering Department, Hewlêr, Kurdistan, Iraq [email protected]

Abstract—Robotics and logic are measured as presented in the second section. The third section indispensable fundamentals of artificial intelligence describes the prolog programing language. The fourth course. In this work, an applied recognition of a modest section describes the objectives of the paper work. The declarative model to govern a moveable two-leg robot is fifth section demonstrates the methodology, and finally, constructed via using a programming logic language the main points are concluded in the last section. (prolog). Prolog is a declarative programing language. It is mainly depending on predicates, clauses, and it is a goal driven programing language. The task is formalized, II. RELATED WORKS domain dependent, and written in prolog and the Prolog produces an answer to the goal from the user. The In this section, some of the important previous works structure and representation of a two-leg robot tasks in that are related to robotics and logic using prolog or practical settings is presented and how this can be other logical programming languages are introduced in expressed in prolog programming language. The software detail. program is aimed at teaching learners of artificial David Poole in 1995 in [5], presented the logic intelligence the knowledge in perceptive robotic programs as specifications for controlling robots. These

Index Terms—Prolog Programming Language; specifications determine what agents must perform Robotics; Robot Control Methods; Two-legs Robot based on their senses and history of inputs and actions. Programming. He stated that these specifications must be assessed as a prolog program, and the fact can be used and evaluated I. INTRODUCTION in time so that to obtain a more resourceful agent. Their research produced an explanation about nonholonomic Logic and robotics are considered essential elements maze travelling robot. The same language is used for of artificial intelligence course. Artificial intelligence is modelling the environment and agent [5]. Pozos in 2007 regarded a thought-provoking subject from academic in [6], introduced a model to help researchers and and theoretical viewpoints. The relationship between teachers in cognitive robotics by manipulating mobile logic, and robotics has been there since STRIPS and robot behavior. Their design was relied on condition Shakey1. This relationship recorded as uncontrolled, calculus. The start state is detailed, and the goal was and reproduced by means of using logic in prominent given. They used prolog to generate responses to the level robotics [2, 3]. Moreover, the logic programming given goals. They used Visual Basic for the in the reason of logic, and control was demonstrated in implementation of the interface [6]. [4]. In [5], the system used circumstances which have Royakkers in 2012 in [7], examined the robotics’ been implemented in Prolog. Their system takes those social importance for conceivable future in two circumstances and conveyed the activities required for continents (Europe and the United Stated of America) completing goals. Yet, a real-world side or a practical through re-viewing the developments of robotics in aspect of this area is not generally introduced. This various fields such as: the homebased, health care, road research work attempts to demonstrate an easy approach traffic, the police force, and the military. He stated that of conveying both concept and groundwork together. societies agree to make use of robots for conducting This paper presents a program in prolog which can dull, risky, and murky manufacturing tasks. In his control a two-leg robot by challenging instructions of a investigation, he stated that at the current time the related task given. To do so, this paper studies the robots are conducting tasks outside factories. The information of programing in logic for controlling the researcher provided literature work about previous robot. research works related to the societal problems raised by The key motive behind this research work is to the new robotics such as which robot technologies are provide the knowledge in perceptive robotics to learners imminent; what they can do; and the ethical issues that of artificial intelligence. The research work in this paper are concerned [7]. Pineda at el., in 2013 in [8], is planned as follows: details of literature works are introduced Silo as a declarative situation-oriented

logical language. SitLog is used to program situated V. METHODOLOGY service robot problems. In their research work, they explained the building, and depiction of service robot responsibilities in real-world surroundings, and how The method, and design of any robots is generally these can be described in SitLog. Their formalism was consisted of three fragments: the mechanical structure self-directed in terms of domain, and tasks utilized in of a two-leg robot, the electronic circuit, and the various locations. The language shared formalism of the programming design. In this research paper, the design recursive transition network, stretched with operation to is only intensified on the programming of the robot. In prompt active, and contextualized task structures, along the programming part, prolog language is used. The with a functional language to direct information of design of a software program allows the movements of content, and control. Moreover, the Prolog provided the a two-leg robot. An interface for controlling the robot is SitLog interpreter, and its programs followed provided. The implementation of this work is thoroughly the symbolization of the Prolog. This developed in prolog language, and it is divided into the permits SitLog to use through clarifications of complex following sections: applications in both compact, and modular arrangements and to have declarative description [8]. A. Planning Design In this research work, the two-leg structure. and There are a two-leg robot, and three other objects in representation are practically introduced, furthermore, this case: sphere, cube, and cylinder. The leg robot can the implementation of a pseudocode, and flowchart of kick or stays away from the coming object, if the object the two-leg robot is written in the Prolog. is a sphere, and the leg is free, then it kicks the object, otherwise, it stays away from object; the user of the III. PROGRAMMING IN PROLOG program is able to govern the legs of robot via using In the context of artificial intelligence, programming some instructions displayed on a screen. Likewise, the in logic or prolog is one of the most broadly used program would end if the user chooses the exit programming languages in the academic and research command. There is a responsive user interface that areas of artificial intelligence [9]. Proglog does not demonstrates relevant, and suitable instructions to the belong to imperative languages such as C, C++, PHP, user. The most key instructions are: Java, etc. and it is not an object-oriented programming a) Receive object language. Prolog is a declarative programming b) Kick object language. In other words, when employing a solution to c) Free legs a certain problem, it is required to determine what d) Display (display the status of robot and predicates, clauses (rules & facts) and the goal are. Then objects) after, the Prolog interpreter generates the solution for e) Exit the given problem as an alternative for postulating how a certain goal is completed in a certain situation [10-12]. The user has been restricted with some rules, these The prolog is so suitable for solving most problems that rules are clearly detailed below: are related to artificial intelligence, natural language 1) The robot is attached with X and Y arguments, processing, and databases, etc. however, it was reported which indicate the position of the robot. that it isn’t suitable for other problems such as graphics 2) Only one leg of robot can receive the object at or numerical algorithms [9]. It is recommended to read a time. materials to learn how to use Prolog as a programming 3) Before receiving the object, the legs should be language for tackling problems in computer science and free. artificial intelligence subjects [9-14]. The reader can 4) If the received object is Sphere. also learn how the Prolog interpreter works. a) If the Sphere has speed greater than 40 mph, then the robot kicks the Sphere directly and frees the IV. OBJECTIVES legs. b) Otherwise, the robot places/sets the Sphere up, The main objective of this paper is to design and then either, assemble a two-leg of robot. Thus, there is a robot with i. Kicks (passes) the Sphere slowly, if the robot a two-leg, and random objects around it. In this case, far from the goal. the objects are Sphere (such as football), Cube, and ii. Or returns 3 steps, then, kicks the Sphere Cylinder. The legs can receive these objects. Perceiving strongly, if the robot is close to the goal. the object shapes determines the actions of the legs. If 5) If the received object is non-Sphere, then, the the object is Sphere, and the legs are free, then the robot robot stays away from the object by changing the robot kicks the object otherwise stays away from the object, position (changing X and Y) then frees the legs. and frees the legs. 6) The object should be free to receive it.

7) Only non-free legs can be free. the two-leg robot, then, the flowchart was applied. The 8) Non-sphere objects cannot be kicked. coding is conducted in prolog. The full programming The user is asked by the program about the object code for a two- leg robot system is demonstrated below name to obtain, when the object obtained is sphere, (See Appendix B). then, the user is asked by the program to provide the sphere speed, or else, it continues; on the other hand, the VI. CONCLUSION program examines if the legs are free before that, if they are free, then the legs continue; or else, it notifies the user that the legs are not free, and goes back to the user The two-leg robot is presented via a programming menu. Then it must check if the object is free, this logic language in this paper. The software program is means that is not congested or acknowledged now. aimed at teaching learners the knowledge in perceptive If the user selects the kick object command, then, the robotic in artificial intelligence. A design of the two-leg program would check if the legs are free or not. If the robot is suggested in which all functionalities to the legs are free, then it continues, otherwise, it goes back detailed task are defined, also feasible explanations and to the menu; then, the program must check the current steady strategies to solve the problems are guided received object in case of a sphere object, in addition, through a flowchart. Then finally, the plan of flowchart the program can check the speed of the sphere, if it is is applied, and coded in prolog. more than 40 mph, then, it shows a message “the robot kicked the sphere directly”, then the program frees the legs, and the program returns back to the menu, REFERENCES otherwise, if the speed of the sphere is less than 40 mph, then the robot sets the sphere up, then, the program [1] R. E. Fikes, N. J. Nilsson, “STRIPS: A new approach to the checks if the robot is far from the goal by comparing the application of theorem proving to problem solving,” Artificial Intelligence, vol. 2, no. 3-4, pp. 189–208, 1971. robot coordinates with stadium boundary, then the [2] Y. Lesp´erance, H. Levesque, F. Lin, D. Marcu, R. Reiter, R. B. program shows a message “The Sphere is passed Scherl, “A logical approach to high-level robot programming – a slowly, and Legs are free now”, it will free the legs and progress report," In B. Kuipers, editor, Control of the Physical it returns back to the menu, otherwise, if the robot is World by Intelligent Systems, Papers from the 1994 AAAI Fall Symposium, 79–85, New Orleans, November 1994. near the goal, then the program will show a message [3] P. E. Caines, S. Wang, “COCOLOG: A conditional observer and “Robot is near the Goal, Return back 3 steps, then, it controller logic for finite machines,” SIAM Journal of Control, shoots the Sphere and Legs are free now”, the robot November 1995. returns back three steps by changing the robot [4] R. Kowalski, “Algorithm = logic + control,” Communications of the ACM, vol. 22, pp 424–431, 1979. coordinates, then it kicks the sphere strongly, the [5] D. Poole, “Logic Programming for Robot Control,” Proc. 14th program frees the legs and it returns back to the menu. International Joint Conference on AI (IJCAI-95), Montreal, In case if the object is not a sphere, the program shows a August,1995. message “Far away from the object” and the program [6] P. Pozos, E. Yescas, J. Vasquez, “Planning using situation calculus, Prolog and a mobile robot,” In: Latin-American changes the robot’s coordinate and frees the legs, then it Workshop on Non-Monotonic Reasoning, Proc. of the LANMR07 returns to the menu. If the free command is entered by Workshop, vol. 286 of CEUR Workshop Proceedings, 2007. the user, then, the program would examine whether the [7] L. Royakkers, R. V. Est, “A Literature Review on New leg is even now free or full; an error note of “The leg Robotics: Automation from Love to War,” Int J of Soc Robotics, DOI 10.1007/s12369-015-0295-x, vol. 7, pp. 549–570, 2015. already is free!” is displayed when the leg is free, then [8] L. A. Pineda, L. Salinas, I. V. Meza1, C. Rascon, G. Fuentes, after, it goes back to the user menu. If the leg is full, “StiLog: A Programing language for Service Robot Task,” then it frees the leg by placing the object that is already International Journal of Advanced Robotic System, DOI: received, and then it returns to the menu. 10.5772/56906, vol. 10, no. 358, 2013. [9] U. Endriss, “Lecture Notes an Introduction to Prolog If the Display command is entered by the user, then, Programming, Institute for Logic, Language and Computation,” the status of the robot is displayed by the program; the Version: 1 November 2016. program shows which object is obtained with the legs [10] I. Bratko, Prolog Programming for Artificial Intelligence, 4th status when they are free or not, the program also shows edition, Addison Wesley Publishers, 2012. [11] F. W. Clocksin, C. S. Mellish, Programming in Prolog, 5th where the robot is in the stadium. In an ideal case, all edition, Springer Ver-lag, 2003. the objects status statement is “free” and leg status [12] L. Sterling and E. Shapiro, “The Art of Prolog,” 2nd edition, statement is “0”. The flowchart is designed for the two- MIT Press, 1994. log robot with all possibilities and functionalities [13] P. Jackson, Introduction to Artificial Intelligence, Second, Enlarged Edition, Dover Publications, 2013. indicated (See Appendix A, Figure 1). [14] D. L. Poole, A. K. Mackworth, Artificial Intelligence, Cambridge: Cambridge Univ Press, 2017. B. Coding and Implementation After settling the planning design and the flowchart of

ACKNOWLEDGMENT

The authors would like to thank the Department of Computer Science and Engineering, University of Kurdistan Hewlêr, Erbil, Kurdistan, Iraq.

APPENDIX A

The full flowchart for the programming code for a two- leg robot system

Figure 1: Flowchart of a Two-Leg Robot.

APPENDIX B Legs = 1, The full programming code for a two- leg robot system write("\nPlease free the legs before getting new Object.\n"), robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 0).

% Option#2: Shooting Object %PROLOG CODE robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 2):- domains % IF ( object is sphere AND legs are free AND speed >= 40 ) THEN (directly s=string. shoot the sphere) i=integer. Legs =1, predicates Shape = "sphere", nondeterm robot( Speed >= 40, s, % current object shape. {sphere,cube,cylnder} write("\nThe Shpere is shooted directly and Legs are free now\n"), i, % speed robot("free", 0, 0, X, Y, X_min, Y_min, X_max, Y_max, 0); i, % leg status {0:free,1:reserved} % IF ( object is pshere AND leg is free AND speed < 40 AND far from Goal ) i, % x position THEN (pass the sphere slowly) i, % y position Legs =1, i, % Xmin Shape = "sphere", i, % Ymin Speed < 40, i, % Xmax X_max/2 < X, i, % Ymax write("\nThe Shpere is passed slowly and Legs are free now\n"), i % menu option robot("free", 0, 0, X, Y, X_min, Y_min, X_max, Y_max, 0); ). % IF ( object is pshere AND leg is free AND speed < 40 AND far new the Goal nondeterm objcheck(s,i). % cheks only for invalid object inputs ) THEN (return back 3 steps and shoot the sphere) Legs =1, clauses Shape = "sphere", objcheck(Obj,Status):- Speed < 40, Obj="cube",Status=1 ; X_max/2 > X, Obj="sphere",Status=1 ; write("\nRobot is near the Goal, Return back 3 steps then, shoots the Sphere Obj="cylinder",Status=1 ; and Legs are free now\n"), Status=0. New_X = X - 3, New_Y = Y, robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 0):- robot("free", 0, 0, New_X, New_Y, X_min, Y_min, X_max, Y_max, 0); nl,write( "Options: \n", % IF ( object is not pshere AND Legs are free ) THEN (Far away from the "======\n", object) "Press 1 to Receive Object\n", Legs =1, "Press 2 to Kick Object\n", Shape <> "sphere", "Press 3 to Free Legs\n", write("\nFar away from the object\n"), "Press 4 to Display Robot Status\n", New_X = X + 2, "Press 5 to Exit\n", New_Y = Y + 6, "Choose Option[1-5]: " ), robot("free", 0, 0, New_X, New_Y, X_min, Y_min, X_max, Y_max, 0). readint(N), write("======\n"), % Option#3: Free Legs robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, N). robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 3):- Legs =1, % Option#1: Recieving Object write("\nThe Legs are now free\n"), robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 1):- robot("free", 0, 0, X, Y, X_min, Y_min, X_max, Y_max, 0); Legs = 0, Legs =0, write("Shape[shpere,cube,cylinder]: "), write("\nThe Legs are already free\n"), readln(NewShape), robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 0). objcheck(NewShape,Status), NewShape = "sphere", % Option#4: Display Status Status = 1, robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 4):- write("Speed[0-100]: "), write( readint(NewSpeed), "======\n", robot(NewShape, NewSpeed, 1, X, Y, X_min, Y_min, X_max, Y_max, 4); "Robot Status:\n", Legs = 0, "======\n", write("Shape[shpere,cube,cylinder]: "), "Object:\t", Shape,"\n", readln(NewShape), "------\n", objcheck(NewShape,Status), "Speed:\t", Speed,"\n", NewShape <> "sphere", "------\n", Status = 1, "Legs: \t", Legs,"\n", robot(NewShape, Speed, 1, X, Y, X_min, Y_min, X_max, Y_max, 4); "------\n", % IF ( object is not [sphere|cube|cylinder] ) THEN (Invalid Object!) "X: \t", X,"\n", Legs = 0, "------\n", write("Shape[sphere,cube,cylinder]: "),readln(NewShape), "Y: \t", Y,"\n", objcheck(NewShape,Status), "------\n"),nl, Status = 0, robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 0). write("\nInvalid Object! Please enter one of the follwoing object:\n[sphere, cube, cylinder]\n"), % checks for invid menu options robot("free", 0, Legs, X, Y, X_min, Y_min, X_max, Y_max, 1); 84

robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max,N):- N > 5, write("\n## Invalid Option Number please choose[1-5] ##\n"), robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 0); N < 0, write("\n## Invalid Option Number please choose[1-5] ##\n"), robot(Shape, Speed, Legs, X, Y, X_min, Y_min, X_max, Y_max, 0). robot(_,_,_,_,_,_,_,_,_,5):- nl, write("GOOD BYE"), nl.

Goal robot("free", 0, 0, 30, 40, 0, 0, 100, 64, 0).

Optimum Spacing Distance for Improving Frequency Spectrum of Piezoelectric Energy Harvester with Magnets

Li Wah Thong1,2, Yu Jing Bong2, Swee Leong Kok2 1Facutly of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia 2Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia [email protected]

Abstract—Bistable configurations for piezoelectric vibration-to-electrical energy conversion mechanism in energy harvester has been studied widely with aims to the literature are modeled using either electromagnetic, induce broader spectrum of frequency for harvesting electrostatic, magnetostrictive, or piezoelectric energy ambient vibration energy. The exploitation of magnetic mechanism [1-4]. force and its subsequent nonlinear characteristics has A conventional piezoelectric energy harvester (PEH) received much attention by researchers to design the piezoelectric energy harvester and creates the possibility of is usually designed using a single piezoelectric introducing nonlinearity that leads to widening of the cantilever with or without load as a linear resonator, and operating frequency bandwidth. As the in-depth analysis typically suffers from a narrow operating frequency of nonlinear vibration energy harvesters is categorically bandwidth [5]. The simplicity in incorporating complex, it is therefore challenging to identify the best piezoelectric harvesters has encourages in-depth spacing displacement between two magnetic forces for research to increase its performance and extending its harvesting vibrational energy in a broader spectrum of operational frequency spectrum. Since piezoelectric frequency. In this paper, the aim is to investigate the energy harvester is a resonant-based model, one of the fluctuations of resonant frequency and the energy requirements to obtain maximize electrical power output harvester voltage output as the spacing displacement varies accordingly. Experimental results show that the is to ensure that the excitation frequency of the ambient change of spacing displacement will unswervingly affect surroundings is matched to the resonant frequency of the frequency response curves of the energy harvester and the harvester. A minor frequency disparity or deviations thus its performance throughout the vibration energy from the harvester resonant frequency will significantly harvesting process. Thus, by choosing the optimal spacing reduce the harvested power as the imparted stress-strain displacement between the magnetic force, a suitable effect in the piezoelectric mechanism has been affected piezoelectric energy harvester can be designed to achieve accordingly. Unfortunately, in real world circumstances, optimum performance in a broader operational frequency the frequency spectrum of the vibrational energy in spectrum. ambient environment changes dynamically and can be

Index Terms— Energy harvesting; Nonlinear dynamics; unpredictable over time. In order to mitigate this issue, Piezoelectric; Vibration. advances in design and development of the piezoelectric energy harvester has been expanded in several aspects I. INTRODUCTION such as altering design configurations [6, 7], manipulating mechanical nonlinearities [8, 9], and The application of piezoelectric cantilever effect in improving electronic circuitry [10, 11] with intentions to transforming ambient mechanical vibration energy to increases the spectrum of operational frequency and usable electrical energy has established emergent thus its power output. These innovations has seen viable attention over the years. The vibrational energy success in its intended purpose to provide better harvesting has been applied widely to supply electrical solutions for vibrational energy harvesters. energy to power up low-power electronics, In view of the design aspects discussed in the microsystems, and wireless sensors. It has also been literatures, most of the methodology focuses on either regarded as an alternative source to chemical batteries tuning the resonant frequency or widening the that are relatively small and have restricted life duration. operational frequency spectrum of the energy harvester Piezoelectric energy harvesters are usually integrated in [12, 13]. For techniques involving active resonant areas where the usage of batteries are unfeasible and frequency tuning, the resultant development of the inappropriate. The most commonly deliberated energy harvester may not be practicable as the tuning of

the actuators actually consume more power than the The bistable piezoelectric energy harvester is device can harvest. In passive resonant tuning, the designed by introducing two magnets into a linear increase usage of sensors and actuators indirectly create piezoelectric cantilever beam setup. It is comprises of an upsurge in cost and intricacy of the system. an unimorph piezoelectric cantilever beam which is The research studies for increasing the bandwidth of firmly clamped at one end and held to reduce the effect the operating frequency has develop positive of gravity. A magnetic mass weighing 0.75 gram is prospective in recent years in terms of extending range attached at the piezoelectric cantilever free end to act as of frequencies to be processed. This innovative strategy a mass as well as to provide the restoring magnetic force allows the energy harvester to response to several for the energy harvester. Another similar magnetic mass vibration frequency excitations at the same time. There is fixed in a static position and is oriented in opposite is no tuning mechanism involved, however, there may polarity with the magnetic mass on the cantilever end. be a decrease of maximum power harvested at the This static magnet will be adjusted accordingly during instance. The strategies involved in widening the the experiment in terms of displacement displacement, operational frequency spectrum includes designing an D in x-axis, as shown in Figure 1. The piezoelectric array of piezoelectric cantilevers as generators [14], energy harvester under analysis is subjected to a introducing mechanical stopper to limit the harvester transverse harmonic displacement by the seismic amplitude [15], and accustoming nonlinearities into the vibration shaker. harvester system through bistable configurations [16, 17]. Many researchers have enthusiastically expanded the bistable configurations which comprises of two stable equilibria energy states created by the exploitations of magnetic field force [18-20]. Many researchers applied the extension of magnetic restoring force rule to the design the piezoelectric energy harvester and creates the possibility of introducing nonlinearity which leads to widening of the operating frequency bandwidth. With these motivations, the Figure 1: A schematic of piezoelectric energy harvester empirical study in this research paper thus focuses on This design is applied to regulate the stiffness of the widening the operational frequency spectrum through piezoelectric cantilever by varying the distance between exploitations of magnetic field. the magnetic mass. The magnets are placed in repulsive In this study, the noteworthy impact in terms of configurations where a decrease in the displacement resonant frequency and the voltage output of the between both magnets will cause a change in the static piezoelectric energy harvester as the spacing position of the piezoelectric cantilever. The change of displacement between two repulsive magnets varies static position is dependent on the amount of hardening horizontally will be investigated. The proposed model is effect as the spacing displacement between the two designed by incorporating magnets as mass on the repulsive magnets decreases. The apparatus for the single unimorph piezoelectric cantilever beam and experimental setup is as depicted in Figure 2. inducing the effects of magnetic field force to control the stress-strain effect in the cantilever beam. This article presents an empirical study on the usage of Magnetic mass magnetic force and its spacing displacement variations to increase the spectrum of frequency for broadband bistable energy harvesting purposes. This paper is organized as follows. In section 2, the Static magnet related methodology and conceptual of the magnetic force effect on the piezoelectric cantilever is described. Piezoelectric cantilever The influence of the horizontal spacing displacement between two magnets on its resonant frequency is also presented in section 3. In section 4, the analysis on the Seismic impact of spacing displacement between two magnets shaker on the performance of the harvester is investigated. Finally, the research study of this paper is summarized and concluded in the last section. Figure 2: Layout of experiment apparatus of the piezoelectric energy harvester

II. METHODOLOGY AND DESIGN ANALYSIS

I II III IV Displacement in x-axis

Figure 4: Deviations of the resonant frequency of the energy harvester as a function of spacing displacement, D for static magnetic mass under stationary condition Figure 3: Top view of experiment apparatus of the piezoelectric energy harvester with 5mm displacement in x-axis Based on the analysis, it is observed that for spacing

The empirical study of the bistable energy harvester is displacement above 8 mm as in region IV, the resonant divided into two categories. The first part of the frequency of the energy harvester tend to stabilize, experiment involves studying the effect of the spacing signifying the vanishing effect of the magnetic force. displacement, D between two repulsive magnets on the As the spacing displacement between the magnets resonant frequency of the energy harvester. In the decreases, the resonant frequency of the energy second part of the experiment, the performance of harvester decreases accordingly, as shown in region III. energy harvester in terms of voltage output will be It is also observed that for displacement ranges between further analyzed based on the variety of the spacing 3 mm to 4mm (region II), there is a sharp decrease of displacement between the two magnets. resonant frequency until its minimum frequency of 114 Furthermore, both of these experiments were done Hz. It is then followed by a sharp increase of resonant under two different circumstances scenarios. One of the frequency (region I) as we decrease the spacing scenario involves pairing of the static magnetic mass displacement, placing the magnets into stronger under stationary condition while another scenario repulsive mode. As the strength of the repulsive magnet involves pairing of the static magnetic mass under force increases, the static position and bending effect of similar vibration source, as depicted in Figure 2. The the piezoelectric cantilever beam tend to change frequency response curves for both scenarios will be respectively and thus, consequently causing a drastic investigated respectively. Figure 3 illustrates the top change of resonant frequency response. This resultant view of the apparatus setup with horizontal spacing analysis also shows that the piezoelectric energy displacement of 5mm between the two magnets. harvester may be unfeasible and exhibits unstable resonant frequency if the repulsive magnetic force III. ANALYSIS ON THE EFFECT OF SPACING DISTANCE between the magnets is too strong due to excessively ON THE RESONANT FREQUENCY OF THE PIEZOELECTRIC close distances. CANTILEVER In the second scenario, the fixed magnet is placed An analysis on the effect of applying magnetic forces under the same vibration source as the piezoelectric into adjusting the resonance frequency of the energy cantilever beam to observe the effect on its resonant harvester has been investigated as follows. In the first frequency. Figure 5 shows the variations of the resonant scenario, the fixed magnet is placed under stationary frequency when the spacing distance between two condition to observe the effect on the resonant magnets are altered accordingly under the common frequency of the energy harvester. The displacement vibration source. It is observed that a similar pattern of between the two repulsive magnets are adjusted along resonant frequency is obtained in comparison with the the x-axis, ensuring there is no changes in the y-axis and first scenario. z-axis of the cantilever beam. The plotted curves in Figure 4 shows the deviations of the resonant frequency as a function of spacing displacements between two repulsive magnets for static magnetic mass under stationary condition.

affects the level of the harvested voltage. It is also noted that by varying the spacing displacement, the energy harvester has the ability to harvest energy in a broader spectrum of frequency. Note that for spacing distances of 8 mm to 14 mm, the frequency response curves shares the same interception point at frequency 146 Hz which yields the Vrms of 4V. Among the four frequency response curves mentioned above, the best spacing displacement that is able to harvest optimized energy in the broadest frequency spectrum is 8 mm. I II III However, if the spacing displacement is decrease further, the broadband spectrum of frequency will be

Figure 5: Deviations of the resonant frequency of the energy harvester shifted accordingly. For spacing distances of 5 mm and as a function of spacing displacement, D for static magnetic mass 6 mm, the frequency response curves share the same under the same vibrating source intersection of 141 Hz for Vrms of 4V. In this case, the spacing displacement of 5 mm yields a broader However, it is observed that the change of resonant spectrum of frequency for energy harvesting at Vrms of frequency may not be as abrupt as the static magnetic 4V. It is observed that for different segment of mass under stationary condition. Based on the analysis, frequency spectrum, the range of broadband frequency the resonant frequency of the energy harvester stabilizes spectrum increases as the spacing displacement between as the displacement between both magnets increases the repulsive magnet decreases. This results implies that above 6mm, as shown in region III. As we place the broadband energy harvester can be achieved by magnets to be very close to each other, the resonant choosing the best spacing displacement between the frequency of the energy harvester decreases gradually repulsive magnets within the excitation frequency for distances between 2 mm to 3 mm (region I). It is domain. also observed that for displacement ranges between 4 mm to 6mm (region II), the resonant frequency slowly increases to its steady-state frequency from its minimum frequency of 123 Hz. As the consequence of this analysis, we observe that the repulsive effect between the magnets are stable only at distances above 6 mm in the energy harvester. For smaller values of the spacing displacement, the hardening effect of the both magnets increases relatively causing the instability in the resonant frequency of the energy harvester. This results is very valuable to tune the resonant frequency of the energy harvester for matching of accessible excitation frequency in the surroundings. Figure 6: Frequency response curves of the open circuit voltage of the energy harvester for different values of spacing displacement, D IV. ANALYSIS ON THE EFFECT OF SPACING DISTANCE for static magnetic mass under the stationary condition. ON PERFORMANCE OF THE ENERGY HARVESTER

In this section, the analysis on the performance of the energy harvester as the displacement between the magnets varies is studied. The frequency response curves of the open circuit root mean square (rms) voltage for different ranges of spacing distances between the two repulsive magnets are plotted in Figure 6 and Figure 7 respectively. In Figure 5, the static magnet is placed under stationary condition while for Figure 7, the static magnet will be vibrating under the same vibration source as the piezoelectric cantilever beam. The plot in Figure 6 shows that the spacing displacement between the two magnets significantly Figure 7: Frequency response curves of the open circuit voltage of the

energy harvester for different values of spacing displacement, D for frequency in the surroundings. static magnetic mass under the same vibrating source. Reviewing the plots in Figure 7, it is observed that the ACKNOWLEDGMENT results shares almost the similar sentiments as the first scenario even though the static magnets are placed The authors would like to acknowledge the Faculty of under the same vibration source. However, the plotted Engineering and Technology, Multimedia University graphs are more spread out indicating that slight and Faculty of Electronic and Computer Engineering, changes of spacing displacement between the magnets Universiti Teknikal Malaysia Melaka for the support will change the resonant frequency and bandwidth of given in conducting this research. This research was the energy harvester. It is noted that the spacing also supported by the Fundamental Research Grant displacement of 6 mm yields the highest level of Scheme (WBS. 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Optimum Condition for Molasses Wastewater Decolorization by Yeast

Jaruwan Chutrtong1, Waradoon Chutrtong2 1Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok 10300, Thailand 2Faculty of Science, Srinakarinwirot University, Bangkok 10110, Thailand [email protected]

Abstract— Molasses wastewater is waste from alcohol difficult because melanoidin is hard-biodegradable beverage industry which is huge industry. Its properties substance. are high pollution. If released to environment without Microorganisms that have been studied include both treatment, it can cause a lot of environmental problems. bacterial fungi and yeast. Miranda et al. (1996) studied So, there are varieties methods for molasses wastewater the color removal of molasses wastewater using treatment which highly effective in reducing the COD and BOD levels of the residue to level that does not pose Aspergillus niger. It was found that 69 % of the residue problem to the environment. However, molasses color was removed and 75 % COD was reduced. Benito wastewater contain difficult to remove melanoidin et al. (1997) studied the color removal of molasses substance which causes dark colors. So the treated water is wastewater by using Trametes versicolor. It was found still dark. If discharged treated water to natural water that 82% of the residue color was removed and 77 % sources, the water will be darker. This research is to study COD was reduced. Jiminez et al. (2003) studied the the optimum conditions for microorganism usage to method of wastewater treatment by using aerobic and remove wastewater by biological methods. Results of yeast anaerobic system that uses 4 strains of fungus. selection from natural source show less effective in color Sirianuntapiboon et al. (2004) selected 205 strains of reduction than the 3 yeasts from laboratory, Citeromyces siamensis, Issatchenkia orientalis and Saccharomyces yeast from Thailand fruit samples and found some strain cerevisiae. Conduct the experiment to find the conditions had the highest color removal capacity of 68.91 % at that will promote molasses wastewater color reduction of 30oC. Sirianuntapiboon et al. (2004) studied 170 species those three yeast species. It is found that the addition of 1 of acetogenic bacteria and found that species which able percent glucose resulted in the best color loss by all three to remove maximum color of 76.4 ± 3.2 % at 30oC. yeasts compared to the addition of sucrose and lactose. It is Raghukuma et al. (2004) studied the melanoidin also found that the addition of 1 percent peptone gave the removal by whitewash fungi, Flavadon flavus. By 10 % best color removal efficiency by Citeromyces siamensis and cell immobilization with polyurethane foam, Flavadon Issatchenkia orientalis compared to the addition of peptone flavus was able to remove 60 - 70 % of color. and urea. While Saccharomyces cerevisiae is effective in reducing the color of molasses wastewater when adding This study was conducted determining the suitable yeast extract 1 percent. conditions of a medium in testing the efficiency of Saccharomyces cerevisiae, another familiar yeast Index Terms— Decolorization, Wastewater, Yeast. species, for molasses wastewater decolorization compare with Citeromyces siamensis, Issatchenkia I. INTRODUCTION orientalis. This is a find more effective treatment of using microorganisms to remove water pollution. Molasses is one of the raw materials in alcohol production. Waste water from the production of alcohol II. METHOD using molasses has many substance which cause Study on the optimum conditions for the reduction of pollution to environment and one is its dark brown to Molasses wastewater by comparing the appropriate black color. The dark color of wastewater resulted from carbon sources and nitrogen sources. melanoidin which formed by the combination of sugar and amino acids at high temperatures through a A. Appropriate carbon sources browning reaction or a mallaird reaction. It can make Inoculate yeast to. MYGP Broth which add feeds more dirty and darker to water source. This problem is synthetic molasses wastewater 30 ml. incubate at 30 °C solved in a variety of ways include the use of for 24 hours. Then measured the absorbance with a microorganisms to remove the color but therapy is spectrophotometer at wavelength 660 nm. The desired

value is 0.5.value. Add this yeast solution in volume 5% to 30 ml of synthetic molasses wastewater which has 1%, 2% and 3% of sucrose, lactose and glucose. Incubate at room temperature for 1-7 day in 200 rpm shaker. At the end of each period, pick sample. Analyzed by centrifugation at 5,000 rpm for 20 min. get supernatant. Diluted 1 mL of supernatant with 0.1 M acetate buffer.

Then the absorbance was measured by spectrophotometer at 475 nm. Figure 1. Color of wastewater when cultivated with B. Appropriate nitrogen sources Citeromyces siamensis by adding Analyze the same as in A. but vary nitrogen sources 1% glucose, sucrose and lactose, respectively. instead carbon source instead of a carbon source by adding 1%, 2% and 3% of Urea, Peptone, และ Yeast Table 2. Extract. Also measure the absorbance at 475 nm. at the results of carbon sources on the reduction of the wastewater color end of each period. matter by Issatchenkia rientalis

OD at 475nm of distillery wastewater lengths at Carbon room temperature during cultivated period( days) III. EXPERIMENTAL RESULT source 0 1 2 3 4 5 6 7

Glucose A. Appropriate carbon sources 1% 4.4 4.2 4.0 3.5 2.9 2.1 1.8 1.75 Table 1, 2. and 3. show results of carbon sources on 6 2 1 7 7 5 1 the reduction of the wastewater color matter by 2% 4.6 4.5 4.3 3.9 3.1 2.3 2.1 2.03 Citeromyces siamensis, Issatchenkia orientalis and 5 8 5 2 8 5 4 Saccharomyces cerevisiae 3% 4.8 4.6 4.2 3.7 3.4 2.7 2.3 2.36 1 6 3 3 8 8 6 Table 1. Sucrose results of carbon sources on the reduction of the wastewater color 1% 4.5 4.3 4.0 3.6 3.1 2.4 2.1 1.93 matter 1 7 5 7 2 4 6 by Citeromyces siamensis 2% 4.7 4.5 4.3 3.8 3.2 2.9 2.5 2.36

OD at 475nm of distillery wastewater lengths at 8 3 1 4 3 7 1 Carbon room temperature during cultivated period( days) 3% 4.9 4.7 4.3 4.0 3.7 3.1 2.7 2.57 source 8 7 4 2 7 4 5 0 1 2 3 4 5 6 7 Lactose Glucose 1% 4.5 4.3 4.1 3.7 3.2 2.8 2.6 2.22 1% 4.4 4.2 3.9 3.3 2.9 2.4 2.18 1.93 3 5 2 8 4 6 6 2 2 6 5 6 3 2% 4.6 4.4 4.2 3.8 3.5 2.7 2.5 2.38 2% 4.6 4.4 4.2 3.9 3.5 2.5 2.23 2.04 6 3 2 4 7 7 2 3 3 5 4 5 2 3% 4.7 4.5 4.3 3.9 3.5 3.0 2.7 2.56 3% 4.4 4.6 4.3 3.9 3.4 2.8 2.52 2.34 4 6 6 2 7 4 7 8 2 4 6 6 2 Sucrose 1% 4.4 4.3 4.0 3.2 2.8 2.3 2.16 2.08 6 3 6 2 3 4 2% 4.6 4.5 4.2 3.6 3.1 2.6 2.51 2.35 3 3 3 3 5 9 3% 4.8 4.6 4.0 3.7 2.9 2.7 2.66 2.53 4 7 8 7 7 4 Lactose 1% 4.4 4.3 4.1 3.6 3.0 2.6 2.41 2.24 5 4 4 3 3 3 2% 4.6 4.4 4.0 3.7 3.1 2.7 2.58 2.44 4 0 4 3 6 2 Figure 2. Color of wastewater when cultivated with 3% 4.7 4.5 4.2 3.6 3.1 2.9 2.62 2.58 Issatchenkia orientalis by adding 9 9 3 2 5 7 1 % glucose, sucrose and lactose, respectively

Table 3. Table 4. results of carbon sources on the reduction of the wastewater color results of nitrogen sources on the reduction of the wastewater color matter by Saccharomyces erevisiae matter by Citeromyces siamensis

OD at 475nm of distillery wastewater lengths at room OD at 475nm of distillery wastewater lengths at Carbon temperature during cultivated period( days) Carbon room temperature during cultivated period( days) source 0 1 2 3 4 5 6 7 source 0 1 2 3 4 5 6 7 Glucos Yeast e 4.4 4.2 3.9 3.5 2.8 2.5 2.3 2.12 extract 1% 4 3 5 2 4 1 6 1% 4.4 4.5 4.1 3.5 2.9 2.6 2.34 2.14 2% 4.6 4.4 4.2 3.8 3.4 2.5 2.3 2.29 6 6 3 6 4 5 1 8 6 2 1 3 7 2% 4.5 4.3 4.1 3.6 3.2 2.8 2.51 2.35 3% 4.7 4.5 4.3 4.0 3.5 2.9 2.6 2.43 5 5 2 4 2 2 9 2 5 4 5 3 8 3% 4.6 4.4 4.0 3.6 2.9 2.5 2.31 2.49 Sucrose 9 3 3 5 3 3 1% 4.4 4.2 4.0 3.6 3.1 2.5 2.3 2.12 Peptone 2 3 3 3 3 2 2 1% 4.6 4.5 3.9 3.4 2.6 2.3 2.16 2.01 2% 4.5 4.3 4.2 3.4 3.0 2.7 2.5 2.35 2 1 2 1 3 6 6 6 2 6 3 2 5 2% 4.4 4.2 4.0 3.5 2.9 2.7 2.58 2.36 3% 4.7 4.5 4.3 4.0 3.6 2.7 2.4 2.63 1 5 2 2 3 7 3 6 4 6 4 2 2 3% 4.7 4.5 4.0 3.7 3.1 2.9 2.74 2.56 Lactose 8 3 4 2 6 5 1% 4.4 4.2 4.0 3.5 3.1 2.7 2.5 2.36 Urea 4 5 4 2 6 8 6 1% 4.4 4.3 4.0 3.6 3.0 2.7 2.42 2.26 2% 4.6 4.4 4.1 3.7 3.5 3.0 2.7 2.57 7 4 7 4 3 1 2 3 6 4 4 2 2 2% 4.4 4.3 4.1 3.7 32 2.9 2.67 2.43 3% 4.7 4.5 4.3 4.0 3.6 3.1 2.8 2.68 8 2 4 6 4 6 4 8 7 3 6 3 6 3% 4.7 4.3 4.3 3.8 3.1 2.9 2.83 2.64 1 7 3 3 4 1

Figure 3. Color of wastewater when cultivated with Figure 4. Color of wastewater when cultivated with Saccharomyces cerevisiae by adding Citeromyces siamensis by adding 1 % glucose, sucrose and lactose, respectively 1 % yeast extract, peptone and Urea respectively.

B. Appropriate nitrogen sources Table 4., 5. and 6. show results of nitrogen sources on the reduction of the wastewater color matter by Citeromyces siamensis, Issatchenkia orientalis and

Saccharomyces cerevisiae.

Table 5. results of nitrogen sources on the reduction of the wastewater color Table 6. matter by Issatchenkia orientalis results of nitrogen sources on the reduction of the wastewater color matter by Saccharomyces cerevisiae OD at 475nm of distillery wastewater lengths at room Carbo temperature during cultivated period( days) OD at 475nm of distillery wastewater lengths at n 0 1 2 3 4 5 6 7 Carbon room temperature during cultivated period( days) source source 0 1 2 3 4 5 6 7 Yeast Yeast extract extract 4.4 4.2 4.0 4.3 3.5 2.7 2.42 2.22 1% 4.4 4.2 4.0 3.7 3.4 2.7 2.2 2.14 1% 5 3 5 7 7 2 3 6 4 3 4 5 8 2% 4.4 4.2 4.0 3.6 3.2 2.9 2.63 2.44 2% 4.6 4.5 4.4 3.9 3.6 2.9 2.5 2.44 4 4 4 6 5 1 7 2 1 5 6 3 8 3% 4.6 4.4 4.1 3.8 3.4 3.0 2.77 2.52 3% 4.7 4.5 4.3 3.9 3.5 2.4 2.7 2.63 4 8 5 5 5 5 4 6 2 5 2 5 7 Peptone Pepton 1% 4.4 4.2 3.9 3.6 3.1 2.9 2.66 2.42 e 1% 4.4 4.2 4.0 3.7 3.3 2.3 2.1 1.93 7 3 3 6 3 6 7 4 7 8 4 6 6 2% 4.5 4.3 4.1 3.7 3.0 2.7 2.46 2.34 2% 4.6 4.4 4.2 3.6 3.0 2.6 2.3 2.26 2 2 2 3 3 6 5 3 4 3 2 6 1 3% 4.7 4.6 4.3 4.0 3.5 2.9 2.74 2.58 3% 4.7 4.6 4.4 4.0 3.6 2.9 2.6 2.43 9 2 4 2 6 6 9 1 3 6 5 5 2 Urea Urea 1% 4.4 4.2 3.9 3.4 3.1 2.8 2.67 2.49 1% 4.5 4.3 4.1 3.7 3.3 2.5 2.2 2.16 3 4 3 5 3 4 2 4 1 6 3 4 6 2% 4.5 4.3 4.0 3.7 3.4 3.0 2.73 2.57 2% 4.7 4.5 4.3 4.0 3.5 2.8 2.5 2.37 8 6 6 5 3 2 3 4 7 8 5 2 7 3% 4.7 4.5 4.3 4.9 3.1 2.8 2.54 2.74 3% 4.9 4.8 4.6 4.2 3.7 3.0 2.6 2.58 1 6 4 7 4 3 1 5 4 3 2 3 3

Figure 5. Color of wastewater when cultivated with Figure 6. Color of wastewater when cultivated with Issatchenkia orientalis by adding Saccharomyces cerevisiae by adding 1 % yeast extract,peptone and Urea respectively. 1% yeast extract, peptone and Urea respectively.

IV. CONCLUSIONS

Based on experimental results, it was shown that the most effective carbon source for the reduction of

molasses wastewater color was glucose 1%. The best source of nitrogen which effective in reducing the color of molasses wastewater for Citeromyces siamensis and Issatchenkia orientalis was 1 peptone while the best

source of nitrogen which effective in reducing the color of molasses wastewater for Saccharomyces cerevisiae 95

was 1% yeast extract. In comparison, Issatchenkia orientalis had the highest color reduction when 1 % of glucose and peptone were added. Saccharomyces cerevisiae was the least effective.

ACKNOWLEDGMENT

This work was supported by Suan Sunandha Rajabhat

University. I thank my colleagues who provided insight and expertise that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper.

REFERENCES

[1] APHA, AWWA and WPCF.2005.Standard methods for examination of water and wastewater (10thed.). American Public Health Association, New York. 489-495. [2] Barnett, J.A., Payne, R.W. and Yarrow, D. 2002. Yeasts: Characterisitcs and Identification. p. 350, 400, 450, 642, 678, 724, 771, 786 and 333. [3] Benito, G.G., M.P. Miranda and D. Rodriguez de los Santos. 1997. Decolorization of wastewater from an alcoholic fermentation process with trametes versicolor. Bioresource Technology. 61:33-37. [4] Jimenez, A.M., R. Borja and A. Martin. 2003. Aerobic- anaerobic biodegradation of beet molasses alcoholic fermentation wastewater. Process Biochemistry. 38:1275-1284. [5] Miranda, M.P., G.G. Benito, N.S. Cristobal and C.H. Nieto. 1996. Color elimination from Molasses wastewater by Aspergillus niger. Bioresource Technology. 57:229-235. [6] Raghukumar, C., C. Mohandass, S. Kamat and M.S. Shailaja. 2004. Simultaneous detoxification and decolorization of molasses spent wash by the immobilized white-rot fungus Flavodon flavus isolated from a marine habitat. Enzyme and Microbial Technology. 35:197-202. [7] Raghukumar, C., C. Mohandass, S. Kamat and M.S. Shailaja. 2004. Simultaneous detoxification and decolorization of molasses spent wash by the immobilized white-rot fungus Flavodon flavus isolated from a marine habitat. Enzyme and Microbial Technology. 35:197-202. [8] Nakajima-Kambe, T., M. Shimomura, N. Nomura, T. Chanpornpong and T. Nakahara. 1999. Decolorization of molasses wastewater by Bacillus sp. under thermophilic and anaerobic conditions. Journal of Bioscience and Bioengineering. 87:119-121. [9] Sirianuntapiboon, S., P. Sihanonth and S. Hayashida. 1990. Absorption of melanoidin to Rhizoctonia sp. D-90 mycelia. Microbial Utilization of Renewable Resource. 7:321-329. [10] Sirianuntapiboon, S., P. Phothilangka and S. Ohmomo. 2004. Decolorization of molasses wastewater by a strain No.BP103 of acetogenic bacteria. Bioresource Technology.92:31-39. [11] Sirianuntapiboon, S., P. Zohsalam and S. Ohmomo. 2004. Decolorization of molasses wastewater by Citeromyces sp. WR- 43-6. Process Biochemistry. 39:917-924.

Evaluation of Techniques for Identifying Multipath Propagation Clusters in Wireless Systems by Bayesian Information Criterion Model-Based Clustering

Lawrence Materum and Daniel Dominic Abinoja Electronics and Communications Engineering Department De La Salle University, Manila, Philippines [email protected]

Abstract—This work focuses on evaluating clustering reliability of the techniques have not been well verified. techniques for identifying the grouping of multipaths in Attempt in [14–16] included scatterer verification, but wireless channels. A Bayesian model-based clustering again multi-comparison with other methods have not approach is employed for removing user bias in estimating been done. Moreover, the parameters used in clustering each parameter value utilized by each multipath clustering approaches are pre-selected by the user, thus influencing technique. Agglomerative hierarchical clustering, quantum clustering, and K-means applied to 5.3 GHz the range of possible values. Multipath clustering indoor and 285 MHz semi-urban propagation channel approaches must therefore be evaluated to know their environments performed adequately in multipath cluster effectiveness for wireless channel characterization. The membership and accuracy variation, but the results also main objective of this research is to determine the best indicate that better multipath clustering techniques should multipath clustering technique. In this work, the authors be developed. evaluated key clustering techniques using a Bayesian information criterion model-based approach Index Terms—Bayesian Information Criterion; Channel to remove the user bias in the estimation of parameters Models; Clustering Methods; Multipath Channels; used for clustering. The evaluation of multipath Radiowave Propagation. clustering techniques with the Bayesian approach is the I. INTRODUCTION novel contribution of the authors. The outcomes indicate that agglomerative hierarchical clustering, quantum As wireless communication systems continue to clustering, and K-means have better performance than improve, there is an ever increasing demand for the rest of the considered clustering techniques in terms performance and efficiency. Among the design of accuracy and robustness applied to the indoor methodologies, the simulation at the physical layer with propagation environments at 5.3 GHz bands and semi- an emphasis on the propagation modeling for wireless urban channel environments considered at 285 MHz. channels adaptable for current and future Nevertheless, the accuracy outcomes point that better communication systems is a powerful tool for assessing multipath clustering techniques should be developed the performance of wireless systems bypassing the need and assessed together with existing methods. The rest of to build them prior to real-world testing. Accurate the paper is organized then as follows. Section II descriptions of the propagation channel are important in discusses the propagation data that was used, the the development of wireless channel models, and clustering techniques selected, the similarity measures, multipath clusters play a role in achieving their and how the evaluation was done. Section III provides accuracy [1], [2]. A multipath cluster is a group of the evidence of addressing the lack of assessment of propagation paths that serves as a channel for evaluation of clustering techniques, and show which communications between transceivers. Majority of the techniques perform well under certain environments. literature have used numerical clustering techniques Finally, Section IV highlights the gist of the results and with internal clustering validity indices [3–13] but the inferences drawn.

arrival (AOA) , AOA , co-elevation AOA , AOA , azimuth

angle of departure (AOD) , AOD , and co-elevation AOD

, AOD . Each x produced by C2CM is part of a multipath cluster C k . A multipath cluster is defined as a group of with similar , , , , and

, such that Ck C , where K = C is the number of multipath clusters. The multipath cluster set C

generated by C2CM is referred to here as Cref . Both indoor (5.3 GHz ± 10 MHz) and semi-urban (285 MHz ± 10 MHz) environment channels were generated from

Figure 1: Illustration of clustering of multipaths. Each line from the C2CM. Figure 1 depicts a typical clustering of base station to mobile station (or vice versa) is a multipath. The multipaths in a wireless communications scenario grouping (indicated by the same color for ease) is referred to as a wherein the BS is sending data to the MS. In order for multipath cluster, i.e. they can have similar characteristics such as the clustering techniques avoid the angular ambiguity in delay, direction of departure, direction of arrival, and Doppler shift. the circular data, i.e. the  dimension in X re f , each spherical coordinate in x is transformed into its Table 1 Parameter Values for the Clustering Techniques rectangular coordinate, and this results to an with dimension D = 7 . Afterwards, X re f is transformed by Clustering Parameter Minimum Maximum statistical whitening as a standard preclustering technique normalization AHC K K ref − 4 K ref − 4 K-means K ref − 4 FCM fuzzification 1.4 2.6 −1/ 2 T X = G E X ref (3) CNN Gaussian RMS QC width 0.2 0.8 soft margin SVC 9 11 factor

II. PROPAGATION CHANNEL, CLUSTERING TECHNIQUES, BICMC, AND SIMILARITY MEASURES

A. Propagation Channel Data In order to have a basis for the number of multipath clusters, and the membership of multipaths in their clusters, the COST 2100 channel model (C2CM) [17], [18] was used as a reference. C2CM is a geometry- based stochastic channel model that reproduces the properties of single- or multi-link multiple-input multiple-output (MIMO) channels over temporal, spectral, and spatial domains for multiple base stations (BSs) and multiple mobile stations (MSs). It produces propagation channels with multipath clusters within the visibility regions of the BS-MS links. From C2CM, a time snapshot of a channel scenario is represented by an L D double-directional propagation channel matrix

T X ref = x1, x2 ,  x ,  xL−1, xL  (1) where  T is the transpose operator, and the  -th multipath vector

x = [  ,AOA ,AOA ,AOD ,AOD] (2) Figure 2: Flow of the evaluation the clustering techniques by BICMC. describes the -th multipath delay   , azimuth angle of

where G and E correspond to the diagonal matrix of eigenvalues and orthogonal matrix of column D. Similarity Measures T eigenvectors of the covariance matrix EX ref X ref , In order to determine the degree of resemblance respectively. between the computed clusters Cc a l c and reference clusters Cre f the Jaccard indexJa c [31], efficiency

B. Clustering Techniques measure E ff , and purity measure E ff [32] were In order to represent clustering techniques, calculated and assessed. These similarity measures are agglomerative hierarchical clustering (AHC) [19], defined as follows competitive neural networks-based clustering (CNN) [20], fuzzy C-means (FCM) [21], K-means [22], C C quantum clustering (QC) [23], and support vector ref calc M11 Jac (Cref ,Ccalc ) = = [0,1] (5) clustering (SVC) [24] were evaluated for multipath Cref Ccalc M11 + M10 + M 01 clustering. These techniques were chosen to serve as C C M  (C ,C ) = ref calc = 11 [0,1] main methods following the taxonomies of [25], [26]. Eff ref calc M + M (6) Cref Ccalc 11 10 The reader is referred to the respective reference(s) for C C M each clustering technique for further details.  (C ,C ) = ref calc = 11 [0,1] Pur ref calc M + M (7) Cref Ccalc 11 01 C. Bayesian Information Criterion Model-Based Clustering (BICMC)

The parameters used by AHC, CNN, FCM, K-means, where  refers to cardinality, M11 is the total number of QC, and SVC are usually supplied by the user and then multipath clusters in Cre f that are the same as in , their range of parameter values is processed iteratively M10 is the total number of multipath clusters in that until a stopping criterion is met. For example in K- are not in , and M 01 is the total number of means, the number of clusters K is iterated from K = 2 multipath clusters in Cc a l c that are not in Cre f . to Kmax , which is set by the user. A problem here is the These similarity measures were used in evaluating the myriad of internal clustering validity indices (ICVIs) final results of clustering without and with the use of and approaches for determining which is the correct BICMC. For the results with the use of BICMC, one. Most of these ICVIs do not take into consideration Figure 2 summarizes how it was implemented. Based on ˆ underlying statistical models of X , which according the BIC scores sBIC () of the acquired parameters for to [27], [28] have a mixture model of probability each clustering technique, the similarity measures distributions. In order to take account of the mixture between the reference multipath clusters and the models of a Bayesian information criterion model- computed multipath clusters were obtained. based clustering (BICMC) approach [27–30] is used for the determining the final parameter(s) for each III. PERFORMANCE OF CLUSTERING TECHNIQUES clustering technique. In the evaluation of the clustering techniques, the BIC score of the parameter estimate ˆ of the clustering technique is computed as ˆ ˆ sBIC ()= 2ln (X | , Σ )− p ln (L) (4) where the negative of the standard formulation is taken ˆ so that higher sBIC() would point to parameter estimate ˆ based on the preferred covariance matrix model Σ of from spherical, diagonal, and general covariance families constrained by the number of free parameters p = −(DK 2 + D2 K). Expectation-maximization (EM) was used in computing for the log-likelihood ln (X | ˆ , Σ ) . Table 1 lists the parameter values used by the clustering techniques.

(a) AHC (d) CNN

Figure 5: Performance in indoor environment channel scenarios.

(b) K-means (e) QC

Figure 6: Performance in semi-urban environment scenarios.

Table 2 (e) SVC (c) FCM Summary of Clustering Techniques Performance

Figure 3: Histogram of the difference between C ref and Rank Robu Time E{Jac} E{Jac} C calc . The number of multipath clusters was obtained with in st- complexity ˆ with sBIC () maximizingJac . terms ness BICM of with in Tech- C out term nique E{Jac} BIC s of with MC  BICM Jac C 0.481 0.45 0.13 AHC 1 O(L2 ) 0 34 64 [26] 0.465 0.45 0.17 O(LD) QC 2 9 67 52 [23] K- 0.449 0.41 0.13 O(LKD) 3 means 0 56 36 [26] 0.405 0.23 0.25  O(L) FCM 4 0 98 49 Figure 4: Consolidated performances of clustering techniques [26] 0.310 0.30 0.25 O(LS ) with and without BICMC across all propagation environment CNN 5 scenarios. 9 49 92 [33] 0.280 0.28 0.16 O(L2D) SVC 6 6 30 89 [24]

In this section, the performance indicators of the clustering techniques were assessed based on the number of multipath clusters, the membership of multipath clusters, and robustness in terms of the variation of the similarity measure. Each multipath clustering technique is compared with respect to these aspects including references on their time complexity.

A. Number of Clusters The histograms of the difference between the

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reference and calculated number of multipath clusters the goal of this work is not to prove BICMC, but to are shown in Figures 3(a) to 3(f), corresponding to each assess the performance of the clustering techniques. clustering technique wherein a difference of zero means BICMC was used to avoid the influence of the user in the reference and calculated number of clusters brought choosing the parameters used by the clustering upon by sBIC () are the same while the obtained Jac is techniques and avert user-bias in selecting parameter maximized. That is to say the correct number of values for the clustering techniques. Nonetheless, the multipath clusters at zero difference where the multipath results without the use BICMC were closed to the one cluster membership is highly accurate based on . As with BICMC since the chosen values for the iteration of could be seen in Figure 3, sBIC () indicates that it was the clustering technique parameters were the same ones able to point to the correct number of multipath clusters used for BICMC. In actual propagation data in which though at mid-values of the relative frequency across the double-directional estimates are available for the clustering techniques. clustering, the use has no idea with the limits of the parameters to be used by the clustering technique, and B. Accuracy in Propagation Scenarios in such case BICMC would be useful. Nevertheless, the Figure 4 shows the performance in terms of similarity similarity measures indicate that BICMC could indeed measures applied to the results of the clustering increase accuracy by selecting the value of the techniques with and without the use of BICMC parameter(s) for the clustering technique. (indicated as BIC for brevity). The values of the similarity measures are based on their mean. The results C. Overall Comparison indicate that the use of BICMC generally improved the Table 2 exhibits the accuracy performance of the performance of the clustering techniques. As can be clustering techniques. Also included is the time seen in Equations (5) to (7), Ja c encompasses both E ff complexity based on the literature. On the basis of andPur . The emphasis of is the number of BICMC, the clustering techniques that have the best multipath clusters in the reference multipath clusters performance in terms of accuracy are AHC, QC, and K- means. The accuracy results suggest that better whereas the significance of Pur is in the number of computed multipath clusters. So in this regard, is a multipath clustering techniques must be developed and evaluated with existing methods. Almost all literature in better similarity measure than andPur . In Figure 4, it is seen then that scores provide a good balance to applying clustering techniques to double-directional propagation channel estimates have used K-means and the scores given by andPur . The outcomes in Figures 3 and 4 combine both the its variant, KPowerMeans [1]. The propagation channel modeling community could benefit by using AHC and indoor and semi-urban environment scenarios from the QC since both could have better accuracy and less time COST 2100 reference channels. In order to see the complexity than K-means. In terms of robustness, which performance of the clustering techniques in these two was quantified using the standard deviation of , AHC scenarios, Figures 5 and 6 present their mean similarity scores. In the indoor environment channel scenarios, the and K-means vary the least, which indicates they could clustering techniques have higher performance than in have steady performance. the semi-urban environment channel scenarios. This IV. CONCLUSION difference is attributed to the higher multipath cluster count in the semi-urban scenarios in which the resulting AHC, QC, and K-means have the best accuracy and multipath cluster membership has higher chances of with statistically similar performance. For indoor being missed due to increased degrees of freedom to environment propagation scenarios of the COST 2100 which each multipath could be grouped to. On the other reference channel, which has less number of multipath hand, since there are few multipath clusters in the clusters, AHC, QC, K-means, and CNN have closed indoor scenarios, the likelihood of the multipaths getting accuracy performance, whereas for the semi-urban grouped into their correct multipath cluster is higher scenarios, which have more multipath clusters, AHC, than in the semi-urban scenarios. In the indoor QC, and K-means have the highest accuracy. The scenarios, multipath clusters are best identified using recommended clustering techniques (AHC, QC, and K- AHC, QC, K-means, and CNN, and for the semi-urban means) have also the least accuracy deviation among scenarios AHC, QC, and K-means. A variant of AHC the clustering techniques that have been evaluated. has been proposed in [9] and [11] for identifying Notwithstanding, better multipath clustering techniques multipath clusters. must be advanced and evaluated with existing methods. Moreover, in Figures 4 to 6, though it appears that the performance of BICMC for most clustering techniques is close to the one without BICMC, it is to be noted that

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vol. 21, no. 4, pp. 953–956, Apr. 2017. [13] H. Xiao, A. G. Burr, L. Hentila, and P. Kyosti, “Statistical Technique to Identify Clusters from Multi-Dimensional ACKNOWLEDGMENT Measurement Data,” Proc. 2nd European Conf. Antennas and Propag. (EuCAP ’07), 2007. DLSU URCO (FRF 64FU4TAY14-3TAY16) under [14] P. Hanpinitsak, K. Saito, J. Takada, M. Kim, and L. the office of the Vice Chancellor for Research and Materum, “Multipath Clustering and Cluster Tracking for Geometry-based Stochastic Channel Modeling,” IEEE Trans. Innovation and DOST ERDT are acknowledged for Antennas Propag., p. (accepted for publication), 2017. supporting this research work. Technical University of [15] F. Luan, A. F. Molisch, L. Xiao, F. Tufvesson, and S. Zhou, Malaysia Malacca is also acknowledged for their “Geometrical Cluster-Based Scatterer Detection Method with invitation and valuable comments. Any opinions, the Movement of Mobile Terminal,” in IEEE 81st Veh. Technol. Conf. 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A Similarity Measure Based on Rating Frequencies for Collaborative Filtering

Soojung Lee Gyeongin National Univ. of Education, 155 Sammak-ro, Anyang, Kyunggi-do, Korea 13910 [email protected]

Abstract—Traditional similarity measures for may be Amazon and Barnes and Noble. However, due collaborative filtering make use of user ratings to the possible scarcity of ratings data, this technique information to compute similarity. To enhance their may suffer from low recommendation qualities. As an performance, many approaches are developed to attempt to overcome this drawback inherent to the manipulate the ratings data to elicit the context memory-based CF methods, model-based CF information. This paper utilizes the frequency of ratings given to an item to estimate their importance which is then approaches have been developed [2]. Popular models of incorporated into a similarity measure. Furthermore, we these approaches include latent semantic models, employ a genetic algorithm to search for an optimal weight Bayesian belief networks, clustering, matrix of each component of the similarity measure. Performance factorization, and Markov decision process [2]. of the proposed measure is investigated to find that it This paper focuses on memory-based CF systems. To significantly outperforms existing similarity measures in compensate for data scarcity and yield more accurate terms of prediction accuracy and recommendation similarity, some of the previous similarity measures take qualities, especially with a sparser dataset. not only the ratings themselves but also their context

information. For instance, significance of ratings, Index Terms—Collaborative filtering; Genetic algorithm; Recommender system; Similarity measure deviation between user ratings, rating singularities, and rating differences between users are some of the context I. INTRODUCTION information reflected onto the similarity measures [6– 9]. These are reportedly useful ways to enhance Internet users nowadays have received great benefits performance of CF systems by simply manipulating of recommendation services provided by many ratings data without devising complicated techniques commercial systems. These services are available while such as those used by model-based CF approaches. people are looking for books, movies, restaurants, many This paper addresses the concept of singularity types of products, etc. Among several methods of presented by [7] to incorporate it into a similarity implementing recommender systems, collaborative measure in a more improved way. Moreover, we filtering (CF) has been most useful in real life [1,2]. Its employ a genetic algorithm to search for an optimal basic idea is recommending items that have been weight of each component of the similarity measure. preferred by other similar users. It uses ratings We investigated performance of the proposed measure information, either implicit or explicit, to determine through extensive experiments, showing a successful preference criteria. While explicit ratings are to be made achievement in terms of prediction accuracy and specifically by the users on a given scale, implicit recommendation qualities. ratings are inferred using mechanisms exploiting user The remainder of this paper is organized as follows: behavior such as product purchases and click-throughs In the next section, we discuss previous works related to [3]. this study. Section 3 presents the proposed similarity As a group of similar users plays a critical role in CF measure, followed by experimental results in Section 4. methods, many efforts have been made in literature to Section 5 concludes this paper. develop reliable similarity measures [2,4,5] for so-called memory-based CF systems that exploit user ratings II. RELATED WORKS information. These systems are known to be highly effective and easy to implement, thus being successfully Some of the well-known similarity measures in CF under service in commerce, the most famous of which systems are Pearson correlation, cosine similarity, mean

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squared differences [1,2]. These all compute similarity frequency of a rating given to an item. Their proposed from the user ratings of the items co-rated by two users. similarity measure incorporates these singularities into Pearson correlation is a representative metric in CF the mean squared differences. Our approach overcomes systems, but known to suffer from data sparsity problem limitations of the method in [7] by combining [2]. There are variants of correlation-based metrics singularities in an optimal manner using the genetic suggested for CF systems [2]. Instead of the means used algorithm, which is described in detail in the next by Pearson correlation, Constrained Pearson correlation section. uses midpoint ratings for measuring similarity. Other types of correlation-based metrics include Spearman III. PROPOSED SIMILARITY MEASURE rank correlation and Kendalls correlation. Cosine similarity treats the ratings of a user as a vector and Let IC be the set of all items commonly rated by both measures an angle between the vectors of two users. users u and v. Also let θ be a relevance threshold of Performance of these similarity measures as well as rating that is used for positive or negative discretization. their characteristics is studied in detail in literature [4,5]. Then we define IC,PP as the items positively rated by As data sparsity is a major problem in similarity both of the users. Similarly, IC,NN is defined as the item computation, simple but efficient solutions have been set negatively rated by both users and IC,PN as the set suggested. These can be classified into two types: one is positively rated by one user and negatively by the other incorporating a function of the number of co-rated items user. Formally, into the previous similarity measure and the other is 퐼퐶,푃푃 = { i ∈ 퐼퐶 | 푟푢,푖 ≥ θ, 푟푣,푖 ≥ θ}, manipulating raw ratings data to elicit some context 퐼퐶,푁푁 = { i ∈ 퐼퐶 | 푟푢,푖 < θ, 푟푣,푖 < θ}, information from them. Belonging to the first type, and Jamali and Ester proposed a sigmoid function with the 퐼퐶,푃푁 = 퐼퐶 − (퐼퐶,푃푃 ∪ 퐼퐶,푁푁), input of the number of common users and combined this where ru,i is the rating given by user u to item i. function with Pearson correlation to compute similarity In [7], two types of singularities are defined, one [10]. Ren et al. estimated the degree of rating overlap being the singularity of the relevant(or positive) rating which is then combined with Pearson correlation and on each item and the other that of the nonrelevant(or the cosine similarity [11]. They report that their negative) rating on each. Obviously the higher positive combined measure mitigates the sparsity problem, thus or negative singularities are assigned to those items improving performance of the corresponding traditional which are associated with the fewer positive or negative measures. Liu et al. adopted several metrics to merge ratings, respectively. For instance, assuming that 10 into a new similarity measure. As one of these metrics, ratings are given to an item x, among which only two Jaccard index measures the ratio of the number of items are above θ, the positive singularity on item x is 1 − co-rated by the two users [12]. Lee proposed an 2/10, i.e., 0.8. Based on this concept, the positive improved index of Jaccard that exploits the number of singularity on item i, denoted by Si,P is formally defined common items within each subrange of the rating scale as [13]. |{ 푢 ∈ 푈 | 푟푢,푖 ≥ θ}| As a second type of solutions discussed above, 푆 = 1 − , 푖,푃 { Bobadilla et al. compute similarity based on importance | 푢 ∈ 푈 | 푟푢,푖 ∈ [퐿, 퐻]}| of ratings [8]. They estimated the importance, so-called where U is the set of all users and [L,H] the range of significance, in three dimensions, i.e., that of the user, ratings allowed in the system. Likewise, the negative that of the item, and that of the rating. Instead of ratings singularity on item i themselves, these significances are taken into account in |{ 푢 ∈ 푈 | 푟푢,푖 < θ}| 푆푖,푁 = 1 − , determining similar users. Al-Shamri and Al-Ashwal |{ 푢 ∈ 푈 | 푟푢,푖 ∈ [퐿, 퐻]}| suggested fuzzifying rating values and rating deviation According to [7], similarity between users u and v is values to calculate similarity [6]. Their experiment computed for the three subsets of IC described above results showed that the fuzzy weighting based on the separately which are then combined using the arithmetic rating deviations generally outperformed the fuzzy average. The similarity is measured by the Mean weighting based on the raw ratings. The approach Squared Differences (MSD) weighted by the proposed by Desrosiers and Karypis [9] considers all the singularities. Specifically, similarity between users u available ratings of two users and defines a function of and v on the set IC,PP, denoted by MSDPP, is formulated differences between all pairs of items to compute as follows. similarity using the extension of the SimRank algorithm 1 2 2 [14]. They assert that their method contributes to 푀푆퐷푃푃 = ∑ [1 − (푟푢,푖 − 푟푣,푖) ]푆푖,푃 , |퐼퐶,푃푃| reducing the sensitivity to sparse data. All the available 푖∈퐼퐶,푃푃 ratings information is also exploited in [7]. They define Also, similarities on the set IC,NN and IC,PN are singularities of user ratings to reflect the relative analogously measured. Hence,

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1 2 2 threshold of rating(θ) and the weights used in formula 푀푆퐷푁푁 = ∑ [1 − (푟푢,푖 − 푟푣,푖) ]푆푖,푁 , |퐼퐶,푁푁| (3) ( α, β, and γ ).We choose MAE (Mean Absolute 푖∈퐼 퐶,푁푁 Error) for the fitness function, a well-known metric to 1 2 푀푆퐷푃푁 = ∑ [1 − (푟푢,푖 − 푟푣,푖) ]푆푖,푃 푆푖,푁, evaluate prediction accuracy of collaborative filtering |퐼퐶,푃푁| 푖∈퐼퐶,푃푁 systems. It is defined as the mean difference between Consequently, the similarity between users u and v the real rating of an item and the rating predicted by the proposed in [7] is system. 1 푆푀(푢, 푣) = (푀푆퐷 + 푀푆퐷 + 푀푆퐷 ) . The population of PS number of solutions is 3 푃푃 푁푁 푃푁 maintained constantly throughout the generations. (1) Initially, our genetic algorithm randomly creates each However, taking the average of the similarities as in gene which is composed of Nb bits to represent a real SM(u, v) does not reflect how many items out of all number within the designated range. The algorithm uses common items are co-rated with positive ratings or with two termination conditions. One is when a solution in negative ratings by both users. For instance, assume a the population yields a fitness less than a given case (a) of |IC,PP |/|IC| = 0.8, |IC,PN|/|IC| = 0.2 and a case threshold fth. The other is when the algorithm execution (b) of |IC,PP |/|IC| =0.2, |IC,PN|/|IC| = 0.8. Then the reaches a given number of generations Ngens. Either similarity corresponding to the case (a) should be condition upon satisfaction will terminate the run. measured normally higher than that for the case (b), In order to obtain an optimal solution, three popular although it depends on the relevance threshold of rating. operators, typically used in the genetic algorithm, are It is expected that reflecting this additional information utilized. They are selection, crossover, and mutation should refine the similarity metric. Thus we denote the operators. At each generation, two solutions are selected relative number of each subset as follows. with the probability of their associated fitnesses. That is, |퐼퐶,푃푃| |퐼퐶,푁푁| |퐼퐶,푃푁| 푅푃푃 = , 푅푁푁 = , 푅푃푁 = , a solution with a higher fitness has higher chance of |퐼퐶| |퐼퐶 | |퐼퐶| being selected. These two selected solutions are then Then the modified similarity measure is crossed over with crossover probability ProbC to 푆푀′(푢, 푣) = 푅푃푃푀푆퐷푃푃 + 푅푁푁푀푆퐷푁푁 + produce two new offsprings. The mutation operation is 푅푃푁푀푆퐷푃푁 (2) conducted on each of these offsprings, one of whose bits In addition, it is worthwhile to take further is randomly chosen and flipped with mutation consideration on the weight of RPP, RNN, and RPN. probability ProbM. The final offsprings are added to the Currently they all have the same weight of one as population. The algorithm repeats these three steps of defined in formula (1). This implies that two users genetic operation until the population size becomes the giving the same positive rating to an item is regarded as given number of solutions, PS. Table 1 lists up the having an equal similarity to that for those users giving parameters used in our genetic algorithm. the same negative rating to the item. However, this implication seems contradict to the work by [15]. This B. Design of Experiments work observed that users tend to give ratings higher Our experiments are conducted on two datasets with than the median and avoid the extreme values. Hence, it different rating ranges. These datasets are popularly is inferred that two users giving the same negative used in the related fields. Table 2 presents rating, especially a low rating, to an item should be characteristics of each dataset where the sparsity level treated as more similar than those giving the same represents how sparse the dataset is, defined by 1-(total positive rating to the item. To implement this idea, we number of ratings/matrix size). We utilized 80% of the propose the final similarity measure as follows. ratings data of each dataset to obtain similar users, 푆퐺퐺퐴(푢, 푣) = namely top nearest neighbors (topNN), and the rest to 1 훼 훽 evaluate the performance of the collaborative filtering 훽 훾 (푅푃푃푀푆퐷푃푃 + 푅 푀푆퐷푁푁 + 푅훼 +푅 +푅 푁푁 푃푃 푁푁 푃푁 system. The MovieLens 1M dataset originally has a 훾 푅푃푁푀푆퐷푃푁), (3) record of 6040 users, but due to the limitation of where α, β, and γ represent the weight values ranging computing capacity the PC to run our genetic algorithm, from wmin to wmax. We will use the genetic algorithm a subset of the original data is used. Using the two [16] to obtain optimal values of these weights. datasets, it is expected that the impact of the threshold θ can be investigated, since the rating scales of the IV. PERFORMANCE EXPERIMENTS datasets are different. In the literature, Pearson correlation is reportedly a A. Design of Genetic Algorithm best traditional similarity measure and most commonly In our genetic algorithm, the set of four weights used [2,7]. Furthermore, performance of the similarity comprises an individual of the population: the relevance measure SM described in the previous section is

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compared only with that of Pearson correlation in [7]. the other two. This implies that the sharp discretization Hence, we evaluate performance of the three similarity of ratings for singularity calculation may not give measures, Pearson correlation (PRS), similarity measure positive effect on performance. Another cause of this of formula (1) (SM), and the proposed similarity poor results of SM may be the strategy of imposing measure of formula (3) (SG_GA). equal importance of the three components in formula Performance is usually evaluated based on two well- (1). This conjecture is verified in the performance known standards, prediction quality and results of SG_GA. Notably, its performance is recommendation quality. MAE (Mean Absolute Error) significantly improved from that of SM. From the is a typical metric for prediction quality, where execution of our genetic algorithm, we obtained the prediction is made by accumulating ratings of similar parameters yielding the best results which are θ=0.75, users using their weighted average [17]. Hence, a α =2.08, β =9.92, and γ =0.06. That is, the relevance different group of similar users would result in different threshold of rating is equal to the one used for SM, but prediction of rating, which accounts for the importance each of the three weights, α, β, and γ, for the best of similarity measure. For measuring recommendation results is far different from one that is used by SM. quality, we use F1 to combine precision and recall Therefore, it can be concluded that the superior metrics into a harmonic mean [2]. We used the achievement of SG_GA is made by imposing different recommendation threshold of four for MovieLens. For weights to the components in formula (1). this dataset, the threshold θ used in similarity measures F1 results are also displayed in Figure 1. Observe that SM and SG_GA is also set to four. For BookCrossing SM demonstrates the worst performance as in MAE dataset, the recommendation threshold and θ are results, which are significantly outperformed by the initialized to seven and eight, respectively. other two. Specifically, SG_GA achieves approximately 13 to 28% higher F1 over SM. Another behavior to note is that SM seems to keep increasing as more neighbors Table 1 are consulted, while the other two look stable. Hence, Description of parameters used in the genetic algorithm although the maximum topNN is as many as 10% of the Parameter Description Value whole users in the experiments, more users for rating Nb number of bits composing a 10 consultation are to be included to investigate more PS gene 60 precise behavior of SM. It is also notable that SG_GA Ngens population size 20 produces better F1 results over PRS. This is due to the number of generations for fth algorithm termination 0.65/1.0 recall results with respect to which PRS is worse than fitness threshold SG_GA. (MovieLens/ Figure 2 pictures performance of the measures with BookCross 0.85 BookCrossing. This dataset is slightly sparser than ing) crossover probability 0.05 ProbC mutation probability 0 MovieLens, but with respect to each user, the number of ProbM the minimum weight value 10 ratings is at least 10, whereas it is 20 in case of wmin the maximum weight value (0, 1) MovieLens. Hence, it is more challenging to obtain wmax the normalized relevance similar users with BookCrossing, since common items θ threshold [wmin,wma of rating x] between two users should be much fewer. This accounts α, β, γ weights used in formula (3) for the lowest performance of PRS in both MAE and F1 as shown in the figure. All the three measures become Table 2 quickly stable as the number of topNN increasing, Characteristics of the datasets because with this sparse dataset consulting more neighbors than a given number, e.g., fifteen, for rating Matrix size Rating Sparsi (usersⅹratings) scale ty prediction is useless. It is notable that SM yields far level better results than PRS, contrary to the results with MovieLens 1000ⅹ3952 1~5(integer) 0.9610 MovieLens. This indicates that SM is more tolerable to BookCross 1014ⅹ883 1~10 0.9775 rating data sparseness of users. In fact, formula (1) ing (integer) corresponding to SM explains this phenomena in that a mean of each component, instead of each specific rating difference, is taken into the measure. The performance C. Performance Results of SM is further improved by applying our idea as seen Figure 1 shows performance results with MovieLens in the results of SG_GA in the figure. Although the best for varying number of nearest neighbors (topNN). PRS θ resulting from the genetic run turned out to be the is showing the best MAE results, confirming the same as that used by SM, the three weights yielding the statements of previous works [2,7]. It is observed that best results were quite different from one. That is, the results of SM is surprisingly very poor compared to

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α =0.058, β =5.41, and γ =0.68 with BookCrossing. 0.87 Hence, RPP is taken as the highest importance. 0.86 To conclude, our idea of imposing different weights PRS SM SG_GA onto the singularities is well verified through various experiments. The idea is more successful on a sparser F1 0.85 dataset and effective in both recommendation and prediction qualities. The rather poorer results of the 0.84 proposed measure with MovieLens compared to PRS 0.83

can be partly attributed to its base measure, MSD, since 5

10 25 15 20 30 35 40 45 50 55 60 65 70 75 80 85 90 95 performance of MSD is lower than that of PRS in our 100 topNN experiments which are not discussed due to the space limit. However, even with that dataset, recommendation Figure 2: MAE and F1 results with BookCrossing qualities of our measure turned out to be better than those of PRS. V. CONCLUSIONS

0.76 This paper proposed a novel similarity measure for 0.755 collaborative filtering systems. Our measure estimates 0.75 importance of each user rating in terms of its 0.745 frequencies associated with the corresponding item. It PRS

0.74 SM then employs a genetic algorithm to optimally combine MAE 0.735 SG_GA the importance of ratings with the mean squared 0.73 differences. Performance experiments showed that the

0.725 proposed measure improved both prediction and

0.72 recommendation qualities of previous similarity

10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

100 measures. The improvement was far significant with a topNN sparser dataset where each user has the fewer ratings. In spite of these achievements of the proposed measure, there remains more to be studied. One of the 0.62 considerations might be to choose various types of 0.6 previous measures other than the mean squared

0.58 PRS differences in the combination of the proposed measure. SM F1 0.56 Another is to make the number of components SG_GA comprising the proposed measure flexible according to 0.54 the data environment, while the suggested number in the 0.52 text is three regardless of the datasets.

0.5

30 50 10 15 20 25 35 40 45 55 60 65 70 75 80 85 90 95 100 topNN REFERENCES

Figure 1: MAE and F1 results with MovieLens [1] M. Aamir and M. Bhusry, “Recommendation system: State of the art approach,” International Journal of Computer Applications, vol. 120, no. 12, pp. 25-32, 2015. [2] X. Su and T. M. Khoshgoftaar, “A survey of collaborative 1.35 filtering techniques,” Advances in Artificial Intelligence, vol. 2009, 2009.

1.3 [3] B. N. Miller, J. A. Konstan, and J. Riedl, “PocketLens: toward a PRS personal recommender system,” ACM Transactions on SM Information Systems, vol. 22, no. 3, pp. 437-476, 2004. 1.25 SG_GA [4] H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, “A new user

MAE similarity model to improve the accuracy of collaborative filtering,” Knowledge-Based Systems, vol. 56, pp. 156-166, 2014. 1.2 [5] K. G. Saranya, G. S. Sadasivam, and M. Chandralekha, “Performance comparison of different similarity measures for

1.15 collaborative filtering technique,” Indian Journal of Science and

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10 Technology, vol. 9, no. 29, 2016. 100 topNN [6] M. Y. H. Al-Shamri and N. H. Al-Ashwal, “Fuzzy-weighted similarity measures for memory-based collaborative recommender systems,” Journal of Intelligent Learning Systems and Applications, vol. 6, pp. 1-10, 2014.

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[7] J. Bobadilla, F. Ortega, and A. Hernando, “A collaborative filtering similarity measure based on singularities,” Information Processing and Management, vol. 48, no. 2, pp. 204-217, 2012. [8] J. Bobadilla, A. Hernando, F. Ortega, and A. Gutierrez, “Collaborative filtering based on significances,” Information Sciences, vol. 185, pp. 1-17, 2012. [9] C. Desrosiers and G. Karypis, “A novel approach to compute similarities and its application to item recommendation,” in PRICAI 2010: Trends in Artificial Intelligence, Lecture Notes in Computer Science, vol. 6230/2010, pp. 39-51, 2010. [10] M. Jamali and M. Ester, “Trustwalker: A random walk model for combining trust-based and item-based recommendation,” in 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 397-406, 2009. [11] L. Ren, J. Gu, and W. Xia, “A weighted similarity-boosted collaborative filtering approach,” Energy Procedia, vol. 13, pp. 9060-9067, 2011. [12] G. Koutrica, B. Bercovitz, and H. Garcia, “FlexRecs: Expressing and combining flexible recommendations,” in Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 745-758, 2009. [13] S. Lee, “Improving Jaccard index for measuring similarity in collaborative filtering,” Lecture Notes in Electrical Engineering, vol. 424, pp. 799-806, 2017. [14] G. Jeh and J. Widom, “SimRank: a measure of structural-context similarity,” in Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538- 543, 2002. [15] J. Bobadilla, F. Serradilla, and J. Bernal, “A new collaborative filtering metric that improves the behavior of recommender systems,” Knowledge-Based Systems, vol. 23, no. 6, pp. 520-528, 2010. [16] G. Lv, C. Hu, and S. Chen, “Research on recommender system based on ontology and genetic algorithm,” Neurocomputing, vol. 187, pp. 92-97, 2016. [17] P. Resnick, N. Lakovou, M. Sushak, P. Bergstrom, and J. Riedl, “Grouplens: An open architecture for collaborative filtering of Netnews,” in Proc. the ACM Conference on Computer Supported Cooperative Work, pp. 175-186, 1994.

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Parameter Estimation of Li-Po Battery using Nonlinear Feedback Structure Approximation

Mochammad Rusli, Unggul Wibawa, Rini Nur Hasanah and Akhmad Zainuri Electrical Engineering Department, Faculty of Engineering, Brawijaya University, Malang, Indonesia [email protected], [email protected], [email protected], [email protected]

Abstract—This paper presents the development of an The BMS should also be able to predict several main accurate battery model which can be implemented easily performances in the application of energy storage, in in the real time parameter estimation of an Li-Polymer which three outcomes of the battery are to be generated: cell. An equivalent circuit-based battery model with State of Charge (SOC), State of Health (SOH), and nonlinear feedback structure is proposed. The relationship State of Power (SOP). Because of the complexity of the between the open circuit voltage and the state-of-charge parameter is represented by some non-linear components. involved electrochemical processes, the battery The developed battery model is purposed to be used for conditions cannot be directly investigated using sensors. the implementation of a parameter estimation algorithm Several previous researchers have explored some on a microcontroller-based application. Parameters procedures, being referred as estimation algorithms, estimation consists of two execution processes, being aimed which are strongly influenced by the battery model. to find the time constant and the steady-state gain of the The most common implemented models are so far first-order parts and to find the parameters of the based on the electrochemical processes of the battery. relaxation effect of the battery. The parameters are High accuracy in the prediction of the battery states can updated continuously for each of the three input pulses of be obtained as it illustrates the actual principles of current. The proposed method and model are then verified in real time application of Li-Po batteries. electrochemistry processes. However, such type of estimation increases the complexity level, especially in Index Terms—Li-Po battery; Nonlinear feedback; the data processing. It needs a lot of storage space and Parameter estimation; Real-time estimation. more computing resources in order to get the solution of the partial differential equations and the predictable I. INTRODUCTION parameters. Therefore, such model is not realistic to be implemented with an algorithm in a microcontroller- A Lithium-Polymer battery (Figure 1) offers some great board based application. advantages for energy storage applications in smart-grid Another alternative is to use a model which is referred power generation. Being compared to other type of as an RC-equivalent electrical circuit. This model is electrical sources, the Li-Po energy storage is more mostly employed in real-time experiments level. It reliable with higher power density [1]. To achieve a safe offers simplicity and ease in battery modeling. In some and reliable operation of smart-grid power generations, references [2],[3] the researchers employed the a battery management system (BMS) becomes a parametric identification method to describe the tranfer fundamental requirement to check and to ensure the function of the input and output signals of the battery to essential performances of the battery packs. The three obtain some equivalent circuit model parameters. Figure most important variables which determine the 2 indicates an equivalent circuit of an Li-Po battery. fundamental performance of an electrical source and R I need to be measured are voltage, current and 0 C1 C 2 L temperature.

V = f (SOC) I L SOC O  Vbat R1 R2

Figure 2: An equivalent circuit of a second-order battery ECM- model

Figure 1: Li-Po battery (www.androidauthority.com) There is a second order model of the RC-equivalent

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electrical circuits. The second order of the RC networks R ( s +1) + R ( s +1) V (s) H(s) = 1 2 2 1 = (3) represents the relaxation effect of the battery, which ( s +1)( s +1) I(s) also represents the electrochemistry processes within the 1 2 batteries. The series resistance R0 represents the internal Equation 3 can be changed into a polynomial complex resistance of the battery [4]. equation Unfortunately, the above mentioned equivalent 2 2 (s + a1s + a0 )V (s) = (s + b1s + b0 )I(s) (4) electrochemical model (ECM) parameters vary when the SOC performs the fluctuating values. However, the  = (a0 a1 a2 b0 b1 b2 ) (5) parameters variation can be neglected if the SOC variation is small, especially with respect to the battery  = f (R , R ,C ,C ) (6) performance. The generation of battery performance 1 2 1 2 means the best estimation of the parameters at various where: different SOCs. This paper proposes a battery model by R , R : the resistance of relaxation proces; using a nonlinear feedback approximation (NLFA). 1 2  1 , 2 : the two time constants of relaxation process II. NONLINEAR STRUCTURE OF LI-POLYMER BATTERY  : vector-matrix of equation parameters MODEL The arrangement of a nonlinear structure

approximation of the battery model, which can be An Li-Po battery as an electrical source can be classified into the stiff system, contains two different approximated as a capacitor model. It has been referred time-constant parameters, as shown in Fig. 3. in some literatures that an Li-Po battery can save a very big amount of electrical energy during the retrieve process and deliver it during the discharging process. I/Q S IL For an Li-Po battery, the charging/discharging processes take place in a chemical process with the electrolyte, in which it can be figured out as a series RC circuit, where Voc Orde Ro VT the resistance with the capacitor represent the inter- Rc phase connection. The resistor is influenced directly and strongly by the SOC, the ambient temperature and the aging effect of the battery. The equivalent circuit of this Figure 3: A non-linear structure concept process can be represented as a first-order element with smaller time-constant. Figure 3 describes the dynamic of the battery The relaxation aspect is one of the factors which is characteristic when it is charged/discharged. It consists caused by the diffusion process and multi parts of the of two time conatants. The internal loop of the model charging/discharging effect [4]. This phenomenon can presents an integration of the current input dynamic of be approximated as some series-connected parallel RC the battery which can be symbolized as a first-orde circuits or can be modelled as a series of first-order transfer function. It describes an integration process of transfer functions. The compromise between the battery charging which is called as short time constant. accuracy and complexity can be chosen for the number The detailed block diagram of the proposed battery of a series of RC circuits. Figure 2 represents an Li-Po model is shown in Figure 4. battery model with consideration of the relaxation effect. The continuous-time model of an Li-Polymer battery V in the second-order electrochemical model (ECM) bat V - REF V o K I L K without internal resistance shown in Figure 2 can be I v S  vS +1 described mathematically as following - V (s) −V (s) V (s) H(s) = OC bat = (1) I(s) I(s) where VOC(s) is the open circuit voltage, Vbat(s) is the battery voltage and I(s) is the battery current. R R H(s) = 1 + 2 (2) 1s +1  2s +1 where 1=R1C1 is the first time-constant, and 2=R2C2 is the second time-constant. This equation can be represented using the Lapace equation as follows. Figure 4: The non-linear feedback structure Li-Pol battery

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III. DISCRETIZATION OF LI-PO BATTERY CONTINUOUS Figure 4 describes a detailed non-linear structure MODEL model of the Li-Po battery. It includes three diagram blocks: first-order transfer function, the outer first-order To build a continous battery model and to implement element and the non-linear element. The non-linear the related algorithm on a microcontroller-base element represents the relationship between the battery application, the time continous model should be output voltage and the SOC of the battery. The curve is derived into the discrete time equation. There are three approximated in 8-line segment equation, where each approximations to obtain the discrete equation: Pole- line is represented by a straight-line equation as Zero Mapping, Zero-Orde Hold and Bilinear follows. Transformation. In order to obtain the ease and simple algorithm on microcontroller board application, the V = f (SOC) = C SOC + C (7) o o 1 zero-order hold is suitable for this purposes. Figure 5 shows the relationship between the battery The discrete-time Li-Po battery model for the current output voltage and the state of charge (SOC). For input in experiment testbeds, is obtained by the zero- simplicity of analysis and ease to write a order hold (ZOH) method. The battery model is microcontroller algorithm, this curve is divided into clasified as a stiff system in which whoel tranfer only 4 sections. Each section describes a straight line function of battery have two time constants that they which is represented in Equation 7. The equation has have a large difference. For the short time constant can be approximated by the following steps. The first-order two parameters: C0 and C1 . Those parameters are transfer function can be written in form: shown in Table 1, being accompanied with the gradient G f of each straight line in the second column, and c1 values G f  f  in the third column. H(s) = = = G (8)  s + 1 1 f s +  f s +  f

The ZOH approximation uses the zero-order hold V

o element to modify the s-transfer function. For equation

(

volt shown by Equation 8, the zero-order hold can be written in the form:

) 1 − e −s  H(Z) = Z  H(s) (9)  s  The exponential term in Equation 9 can be replaced into: SOC(%) H(s) (10 H(Z) = Z(1 − Z −1 )Z Figure 5: A non-linear relationship between the battery output s ) voltage and the SOC With the z-transform of the second term of Equation 10, it can be changed into:  H(s) Z 1 − e − Z = G ( )   f − (11)  s  (Z − 1)(Z − e )

The z-transfer function can be transformed directly Table 1 The gradient value of each segment in Fig. 5 into the Equation 11 to form the difference equation: −1 −1 Y (Z ) 1 − b G f − G f b Z G f (1 − b)Z Y (Z ) = = • = = Segment Gradient value c1 X (Z ) Z − b Z − b Z −1 1 − bZ −1 X (Z ) (12) 1 1.2348 3.3861 2 0.3389 3.6407 3 0.7604 3.3746 The relationship between the input and output variables of Li-Po batterry model can be expressed as: The parameters shown in Table 1 are represented by −1 G f (1 − b)Z Y (Z ) the non-linear elements. Each non-linear element has a Y (Z ) − bZ −1Y (Z ) = = (13) 1 − bZ −1 X (Z ) gain that it shows the relationship between input with output as the output of non-linear element in on 1− e− H(Z) = G f (14) situation. Those magnitude-gain are obtained from Z − e− gradient values of the lines shown by figure 5. The − calculation results are shown on table 1. e = b (15) Equation 15 indicates the parameters to be estimated

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using this battery-model. The difference equation can between gain and derivative of the prediction error: be obtained based on Equation 13: ˆ(k +1) = ˆ(k) + F(k) 0 (k +1) (24) y(k +1) = by + G f (1− b)X (k) (16) The difference of estimated parameters can be approximated by the derivative of index quadratic error, c = G (1− b) f (17) in which can be formulated as: ˆ ˆ y(k +1) = by + cX (k) (18) (k +1) = y(k +1) − y(k +1) (25) Where it describes the a priori prediction which depends The estimation process will be found using two on the value of parameters estimated at the instant k, parameter b and c as shown by equation 18. and the estimated parameter vector is as follows.

ˆ ˆ IV. THE LI-PO BATTERY ESTIMATION PROCESS  (k) = b(k),cˆ(k) (26) The recursive algorithm can be arranged by the To estimate the battery parameters, which can be current value of the predicted parameters, which are classified as a system with linear and time-invariant equal to the previous predicted parameters minus the characteristics, the mathematical relationship between multiplication gain of adaptation with the gradient of the the input and output of the system can be described into parameters variation.   a standard system-model. One of the standard model is ˆ(k +1) =  (k) + ˆ = ˆ(k)+ G  (k),(k), (k +1) (27) the autoregressive exogenous model. 0 The prediction of parameters variation has to be

directed to a right estimation, one method of it is the I(k=1 ...n). Current Ʈf gradient algorithm. The objective of the gradient First order Identi algorithm is to minimize a quadratic criterion in term of the prediction error, as shown by Equation (28). Voltage Gf v(k=1 .n) 2 SOC min J (k +1) =  (k +1) (28) SOC ESTIMATION J (k +1) Ʈf ˆ ˆ (k +1) =  (k)− G0 (29) ˆ(k +1) Relaxxation Identi Equation 29 indicates the estimation algorithm with

Gs gain adaptation variation and recursive processes.

Figure 6: The proposed identification process V. EXPERIMENT SET-UP

Generally, the continues- time model of a first-order The experiments have been conducted to verify the system should be transformed into discrete model for performance of the proposed modelling method. The Li- developing a microcontroller-based algorithm. The Po battery has been chosen due to its very high energy discrete-time model of the battery can be written as characteristics and smaller variation of power density. follows. The other positive advantages of this battery are that it y(k −1) = by(k)+ cu(k) (19can) deliver energy in a very low process rate and it has a where the parameters vector is as follows. very high efficiency for charge/discharge processes, T T being compared to the Lithium-ion battery technologies.  = b,c (k) = y(k) u(k) (20) Those characteristics are very attractive for the and the data which are obtained from the experiment performance improvement in the smart grid measurement is described into an observation vector as technologies. follows. The experiments have been conducted by testing the T (k) = − y(k) I L (k) (21) Li-Po batteries using the sampling and hold data devices, Lab-view and MATLAB-Simulink software. The prediction of the output voltage of the battery model consists of some parameters prediction which can The experiments were aimed at determining the battery be described into the following equations. characteristics during charging, discharging and   balancing.  0 (k +1) = y(k +1)− y0 (k +1) = y(k +1)−U T (k) (k ) (22) In this paper, the characteristics under study were The gradient of parameters variation can be directed those of the charging process. The method was applied by the derivative of prediction error: by recording the SOC of the battery during charging,  0 (k +1) and then storing it using a data logger. The data were = −(k) (23) ˆ(k) then processed to be displayed on the Lab-view graph. The prediction of parameters can be renewed from the On the other hand, the data were also processed with the previous parameters subtracted by the multiplication help of Matlab-Simulink software. Using equation 29, the parameters and the transfer function of the tested

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battery model is calculated. This process is called as the were then sent to a desktop computer to be processed by identification algorithm was then used to predict the Li- the Lab-View and Matlab Software, as indicated in Po battery parameters. The other battery parameters (for figure 9, It is the schematic diagram of the experimental long time constant) calcultaion were then derived based set-up for measuring the battery output voltage and for on a separate concept identification. The proposed generating an electrical current as the input variable. method is illustrated in the flowchart shown in figure 6.

Figure 9: The schematic circuit during data sampling and holding

Figure 10 shows the realization of the hardware module for experimental set-up. It is used for measuring the voltage and current of the battery and uses voltage and current data as input variables. The standard reference as comparison has been investigated using an AVO-meter and an oscilloscope connected to a desktop Figure 7: The method to characterize the battery computer.

To get the data to be processed in the LogView and Matlab, it requires the devices to sample and hold the data during the battery charging. The schema of the experimental test-bed is shown in figure 7.

Figure 8: The experimental set-up Figure 10: The experimental set-up module

The deatail hardware-realization of the experimental The measurement of the battery voltage was test-bed of the data sampling section being shown in supported using the signal condition devices. The figure 8 in which consists of a charger and a balancer, a current signals have been produced using a function series composition of three Li-Po batteries, and a digital generator being implemented with a microcontroller- voltmeter and ampere-meter. based algorithm. As indicated in Figure 10, the battery The monitored battery was a Li-Po of 2200 mAh. The output voltage has been investigated by using an voltmeter used in the experiment was functioning as a oscilloscope which was connected to a desktop voltage sensor being composed of voltage-divider computer. resistors. The current sensor used was ACS712-5A. The The measurement results of current and voltage of the outputs of both sensors were fed into the Arduino battery during the charging process are presented in analog input and were processed by the ADC and MCU Figure 12. The charging process of the other Li-PO so as to produce the current and voltage data. The data battery is shown by figure 11. For each pulse the

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battery-voltage should be increased until the maximal Figure 12: The parameter estimation results using the gain value of voltage is achieved. However in this figure 11, it is not 20 happen what should be take place on charging process. It can be observed that during the initial time of Therefore the data shown in figure 12 is used on this estimation the value was not fluctuating, and that the identification process. setady-state condition has been achieved after less than 100 sampling data. If it is assumed that the sampling time was 10 ms, then the complete estimation can be accomplised in not more than 1 s. When the gain adaptation value was set up in small value, in about 20 as seen in Fig. 12, some large variations of values can be observed during the starting of the estimation process. The steady situation of identification process is achieved at the time of about 100 ms. The corresponding estimation parameter results for each gain adaptation value are presented in Table 2.

Figure 11: The measurement results of current and voltage of the battery during the charging process Table 2 The parameters estimation results using Gain value of 20

VI. RESULTS AND DISCUSSION Parameters 1 2 3 4

f 1.5 1.10 0.051 0.053 The performance of the proposed model has been l 0.025 0.030 0.060 0.011 tested using the MATLAB-based simulation. The model Gf 0.0004 0.0364 0.009 0.0055 was implemented in MATLAB-Simulink to generate the Gl 0.003 0.065 0.006 0.005 characteristics of the battery. The simulation results were compared to the measurement data. In the simulation, the model under consideration was assumed Based on the parameters on table 2, the model has to have the parameter values shown in table 2. been simulated using MATLAB-Simulink. The simulation results is shown by figure 13. The increasing of voltage battery when it is charged from 3,32 volt until 3.37. If it is compared with the real data as in figure 12 which is ilustrated starting and ending time is not difference significantly.

Figure 12: Recorded measurement data Figure 13: The output battery of simualtion results It is important to notice that the simulated battery model is a typical stiff system. It contains a fast and a slow time-constants. The input excitation and the output VII. CONCLUSIONS voltage are shown in Figure 12. It consists of four pairs of discharge and charge pulses, each of which lasts for The proposed identification structure can predict the 10s. Referring to Figure 12, the amplitude of the pulse Li-Polymer battery parameters very well. Using current pairs are 1A and 0A, The parameter estimation MATLAB program, the implementation of the has been executed with implementation of various gain estimation algorithm in an array structures has been adaptation values. Figure 12 shows the parameter proven that such algorrithm is reliable and required less estimation with gain adaptation value of 20. Parameter A Metode RLS Gain Adaptation 1 memory space besides its duration process was much 1.5

1 shorter in time being compared the conventional 0.5 structure. The comparison of the battery output voltage

0 Theta -0.5 between the Simulink simulation results and the

-1 Parameter A1 Parameter A2 Parameter A3 laboratory experiment results indicates an error of less -1.5 Parameter A4

-2 0 50 100 150 200 250 300 350 400 450 500 than 10%. Waktu (s)

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IEEE Transactions on Vehicular Technology, vol. 58, pp. 3905- ACKNOWLEDGMENT 16, 2009. [4] H. Rahimi-Eichi, F. Baronti, and M.-Y. Chow, “Online Adaptive Parameter Identification and State-of-Charge Coestimation for The authors would like to thank the Directorate Lithium-Polymer Battery Cells,” IEEE Transactions on General of Higher Education at the Ministry of Industrial Electronics, vol. 61, no. 4, pp. 2053-2061, 2014. Research, Technology, and Higher Education of the [5] B. Xia, X. Zhao, R. de Callafon, H. Garnier, T. Nguyen, and C. Republic of Indonesia for supporting the financial Mi, “Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods,” in 2016 Proc. aspect of this project. IEEE Energy Conversion Congress and Exposition (ECCE), pp. 1 – 8. [6] J.K. Barillas, J. Li, C. Günther, and M.A. Danzer, ” A comparative study and validation of state estimation algorithms REFERENCES for Li-ion batteries in battery management system,” Applied Energy, vol. 155, pp. 455-462, 1 October 2015. [1] W. Su, H. Eichi, W. Zeng, and M.-Yuen Chow,”A Survey on the [7] Yonhuang Li., R. Dyche Anderson, Jing Song, Anthony M. Electrification of Transportation in a Smart Grid Environment,” Phillips, Xu Wang, A Nonlinear Adaptive Observer Approach for IEEE Transactions on Industrial Informatics, vol. 8, no. 1, pp. 1- State of Charge Esti-mation of Lithium-Ion Batteriesrican , 10, 2012. Amerika Control Conference on O’Farrel Street, San Fransisco, [2] W. Su and M.-Y.Chow, “Sensitivity analysis on battery 2011. modeling to large-scale PHEV/PEV charging algorithms,” in [8] Habiballah Rahimi-Eichi, Federico Baronti, and Mo-Yuen Chow, 2011 Proc. IECON 2011 - 37th Annual Conference of the IEEE Online Adaptive Parameter Identification and State-of-Charge Industrial Electronics Society, pp. 3248 – 3253. oestimation for Lithium-Polymer Battery Cells, IEEE [3] C. R. Gould, C. M. Bingham, D. A. Stone, and P. Bentley, “ TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. New battery model and state-of-health determination through 61, NO. 4, APRIL 2014. subspace parameter estimation and state-observer techniques,”

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Emulation of Narrowband Power Line Communications Channel based on Transmission Line Model

Ann E. Dulay, Jethro Joseph S. Cadungon, Gicel Mari I. Oseña, John Justin V. Tan, Lawrence Y. Materum ECE Department, De La Salle University, Manila, Philippines [email protected]

Abstract - This research aims to create an emulator world PLC channels can be imitated by PLC channel that has the capability to replicate the behavior of an emulators. PLC channel emulators enable the testing indoor power line channel and use it as test bed for a of PLC transceivers without the real hazard posed by PLC modem. The emulator is implemented on Kintex-7 FPGA. FMC151 serves as the analog front end where the live network. The emulated channel uses the TL dual channels of ADC and DAC are mounted. The model and is intended for NBPLC devices. emulator’s response is based on the transmission line Narrowband power line communications (NBPLC) model that utilizes the ABCD matrix. Signal channel operates in the frequency range from 3 kHz convolution is done in the frequency domain whence to 500 kHz [6]. They are usually employed in input signal is multiplied with the channel transfer Automatic Meter Reading (AMR), home automation, function. The emulator’s output is compared with MATLAB generated results and the accuracy obtained energy management system, and other low-data rate is above 98%. The emulator features the addition of applications [7]. Noise is also emulated using the noise and various selections transfer functions that can noise models presented in [8] [9]. The PLC channel be selected by the user to improve realism. emulator in this study is implemented on Kintex-7 FPGA. Index Terms – narrowband PLC; PLC channel emulator; power line communication; transmission line Table 1 (TL) channel model. Communication Technologies for Smart Grid Home Auto- Industrial Automatic Communication mation Automation Meter PHEV I. INTRODUCTION Technologies and and Control Reading control Power Line Communication (PLC) is an emerging Power Line Y Y Y O technology that uses power lines as transmission Communications medium [1]. This technology has been around for Zigbee Y Y Y O five decades specifically for power plant monitoring Wi-fi Y Y Y O [2]. Renewed interest on this technology is spawned WiMAX N N O N by the introduction of the Smart Grid [3]. There are GSM/GPRS Y Y Y Y several communication technologies available for SG, Y – used, N – not used, O – ongoing research but PLC’s obvious advantage is its ubiquity and viable reuse of the same power cables intended for This paper is outlined as follows: Section 2 gives energy delivery [4]. Table 1 shows the comparison of the network topology and the generated transfer different communications technologies considered for functions, Sec. 3 provides the components of the SG applications in the consumer side. emulator, Sec. 4 discusses the results and analysis, One of the challenges faced by PLC designers is and Sec. 5 presents the conclusion. how to develop a PLC transceiver that would combat the effect of noise, load variations, and the frequency selective nature of the PLC channel [5]. PLC II. PLC NETWORK TOPOLOGY AND TRANSFER transceiver must undergo intensive testing before FUNCTION deploying to the real world. The behavior of the real-

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The transfer functions applied in this study are the transfer functions behave very much like a low generated using the PLC channel generator from [10]. pass filter, hence at narrowband range, the PLC’s It is based on the 2PN transmission line model where attenuation increases as the frequency increases. the parallel wires have a lumped-equivalent circuit comprising an R,G,L,C. The transfer function is generated using the ABCD matrix. The network topology of the PLC generator is shown in Fig. 1. The network comprises of 4 main path sections, Li (i { 1,2,3,4}) and three shunt sections, Si (i  {1,2,3}). The loads are represented as impedances Zi (i{1,2,3}). The modem is also represented as an impedance, ZG for the transmitting modem, and ZL for the receiving modem. The impedance of typical modem is 50 Ω. Eight transfer functions are generated from the PLC channel generator provided in [10]. The transfer functions comprise of topologies representing no load, single load (Z1, Z2, Z3), two loads (Z1 & Z2, Z1 & Z3, Z2 &Z3), and all three loads connected. The main path and shunt sections Fig. 2: Transmission line channel model transfer function plot use cable 1 whose specification are found Table 2. A for various topology scenarios constant load of 50 ohms is used for the generation of the transfer functions.

III. PLC CHANNEL EMULATOR

The PLC channel emulator simplified block diagram is shown on Fig. 3. The analog front end (FMC151) comprises of the ADC, which converts the analog signals to digital form to be processed correctly by the FPGA, and the DAC, which brings the signal back to its analog form. The FPGA contains the channel transfer function based on the Fig. 1:TL model topology [10] TL model, the signal processing block such as the Fast Fourier Transform block, multiplication block, and noise block. The following subsections discuss Table 2 briefly the function of each block. Electric cable data sheet Cable Type 0 1 2 3 4 Section (sq. m) 1.5 2.5 4 6 10

εeq 1.45 1.52 1.56 1.73 2

ZC (Ω) 270 234 209 178 143 C (pF/m) 15 17.5 20 25 33

L (µH/m) 1.08 0.96 0.87 0.78 0.68

RO 12 9.34 7.55 6.25 4.98 GO 030.9 34.7 38.4 42.5 49.3 Fig. 3: Block diagram of the narrowband channel emulator

In the generated transfer functions (Fig. 2), a A. Analog Front End (FMC151) heavily loaded network (ALL shunts are loaded with The FMC151 is an FPGA daughterboard which has 50 ohms) would tend to attenuate the signal more a dual channel ADC and dual channel DAC. The than an unloaded network. For the single load case, it does not matter much where the load is connected. ADC has two 14-bit channels and the DAC has two 16-bit channels which can operate up to 250 Msps This is also true with two loads scenario, however, as and 800 Msps, respectively. FMC 151 is interfaced to can be observed, the two load scenario has a higher the LPC mezzanine card of Kintex-7. Micro- attenuation than the single load scenario. The highest miniature coaxial (MMCX) and subminiature version attenuation is observed to be at -23dB at 500 kHz. All C coaxial (SSMCX) connectors were used in

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connecting the board to external inputs/outputs [11]. 푌− = 512푥 + 29360128 (2) B. Channel Transfer Function The channel transfer function (CTF) is represented as a fixed-point number in the FPGA. Since the Where Y+ and T- are the positive and negative numbers are smaller than 1, a good number of bits are output equations of the linear regression block and x allocated to the fractional part. Since the input is is the ADC output. The LR output is fed to the FFT represented as 3,11 FP number where 11bits is the block. allocation for the fraction, and the FFT uses pipelined streaming where the fractions bits do not change, the E. Fast Fourier Transform Block CTF fraction part is also allocated with 11 bits. Fig. 4 Fast Fourier Transform (FFT) is an algorithm that shows that the 11 fractional bits is sufficient to computes for the discrete Fourier transform (DFT) produce exactly the same CTF as that generated by and/or its inverse to transform signals from time the simulator. Only the CTF with the lowest value is domain to frequency domain. Finite discrete-time validated since the other CTFs have higher fraction signals are converted to frequency domain by taking values. a finite or discrete number of frequencies. Eqns. 3 and 4 shows the mathematical function of the FFT and IFFT respectively.

푁−1 푗푛푘2휋 − 푋(푘) = ∑ 푥(푛)푒 푁 푘 (3) 푛=0 = 0, … , 푁 − 1

푁−1 1 푗푛푘2휋 푥(푛) = ∑ 푋(푘)푒 푁 푛 = 0, … , 푁 − 1 (4) 푁 푘=0 Fig. 4: Transfer Function (MATLAB vs Xilinx) – using 11 FP bits

C. Kintex-7 The narrowband PLC has a frequency range of up Hardware description languages such as Verilog or to 500 kHz and a subcarrier spacing of 488 Hz for PRIME standard and 1562.5 Hz for G3 standard [12]. VHDL are used to program the FPGA. In this study, The following specifications for the FFT operation VHDL was extensively used because the FMC 151 are based on the PRIME standard: interface code is based on VHDL. Selection of clock • 2048 transform Points for the operation of the FPGA was one of the crucial • Fixed Point Data factors in the design. Kintex-7 offers two clock • 1 MHz Clock rate sources: the system clock source, which was used as • Pipelined, Streaming I/O Architecture the FMC 151 clock source, and the Programmable User Clock Source that has 156.25 MHz default Fixed point format is chosen over floating point frequency, which was used as clock source on due to its lesser bit allocation. Floating point’s different operations. minimum input bits is 32 bits while for the fixed- point representation used in this study, 14 input bits, D. Linear Regression Block comprising 1 sign bit, 3 integer bits, and 11 fractional Linear regression transforms the sampled binary bits already suffice. gathered from the ADC to fixed point format Pipelined streaming I/O architecture is one of the identical to a sinusoidal waveform. It helps translate four choices in the FFT core’s algorithm. This the MSB of the output binary values from ADC to architecture produces lowest latency among the other smoothen the waveform. The binary output is choices. Although it handles more FPGA resources, it translated to its equivalent 2’s complement. The MSB generates the highest speed compared to the other for positive values is 0 (e.g. 0 to 7 is equivalent to architectures [13]. 0000 to 01111) and the MSB for negative values is 1 (e.g. -0 to -7 is equivalent to 1000 to 1111). It F. Complex Multiplier Block produces the expression The multiplier block multiplies the CTF with the output generated by the FFT. It emulates the passing 푌+ = 512푥 − 1279 (1) of the signal to the channel. This is equivalent to the

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convolution process in the time domain. The multiplier uses the FOIL approach in generating the product [14]. The product is then mixed with noise through a summing function before it is passed to the IFFT block. The IFFT block has the same functionality as the FFT block except that a 1/N is put into effect. Dividing by any number with power of 2 is easily implemented in FPGA using right shift. In this study, N = 2048, hence 11 bits are shifted to the right. Fig. 6: Emulator Test Set-up

G. Noise Generator Block A. Emulator Test without Noise There are three types of PLC noise models based In this test, only the CTFs are loaded in the FPGA. on [15] and [8] used in this study, colored Several frequencies are applied but for purposes of background noise, impulsive noise, and the presenting the results compactly, only two narrowband noise. Fig. 5 presents all the noise frequencies are presented. Fig. 7 shows the input- output waveforms for different transfer function emulated in the FPGA. All of them are converted to conditions at 488 Hz while Fig. 8 shows input-output the frequency domain and added to the product of the waveforms for a 4.88 kHz input signal. The output FFT and the CTF. over input ratio is calculated and compared with the corresponding attenuation obtained from the MATLAB simulation for each frequency. The percentage error between MATLAB simulation and the emulator is calculated and presented in Tables 3 and 4 for the 488 Hz and 4.88 kHz measurements respectively. It is observed that the error is very minimal for output values of 0.5 and logs around 12% (b) Periodic Impulsive Noise (a) Colored Background Noise when values go below 0.2. This is attributable to the allocated bits for the fraction in the FFT and CTF.

(d) Periodic Impulsive (c) Periodic Impulsive Asynchronous 2 (a). No Load Condition (b). One Load Condition (Z1) Asynchronous 1 Fig. 5: Noise models of the Channel Emulator

IV. RESULTS AND DISCUSSION

There are two tests conducted to verify the (c). Two Loads Condition (Z (d). All Loads Condition performance of the emulator, the first is the emulator 2 and Z3) test without noise, the second is the emulator test Fig. 7: Emulator performance at 488 Hz on different TF conditions with the addition of noise. Input signal from the generator is applied to the ADC of the FMC 151 and Table 3 this is viewed in Channel 1 of the oscilloscope (top Percent error of MATLAB simulation versus emulator output @ waveform), then the DAC output is connected to 488 Hz Channel 2. User switches are provided to select the TF MATLAB Emulator Percent corresponding CTF and/or noise models. The test set- Condition Simulation Output Difference up is shown in Fig. 6. None 0.5336 0.54 1.1931 %

Z1 0.3556 0.38 6.628 %

Z2 and Z3 0.2666 0.28 4.9056 % All 0.2131 0.24 11.874 %

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FPGA. The number of bits used to allocate for the fraction bits in a fixed-point format is also crucial in ensuring higher accuracy. The 11 bits in this case sufficed to provide an accuracy of 98% for values in the range 0.5 and higher. For values in the range 0.2, (a). No Load Condition (b). All Loads Condition an error of 12% is logged. This value though is Fig. 8: Emulator performance at 4.8 kHz on different TF considered acceptable since the attenuation of the conditions power line channel is not fixed, thus the 0.24 produced by the emulator versus 0.21 produced by Table 4 MATLAB is not critical in the operation of the Percent error of MATLAB simulation versus emulator output @ emulator. The sensitivity of PLC receivers is in the 4.8 kHz TF MATLAB Emulator Percent micro volt level. Thus, the above error will only be an Condition Simulation Output Difference issue if the output already goes below the sensitivity of the PLC receiver. The Fast Fourier Transform None 0.5309 0.54 1.6971 % architecture used here is the pipelined streaming All 0.2108 0.24 12.9002 % architecture, which allows for continuous processing

of the input signal, However, pipelined streaming B. Emulator Test with Noise architecture is highly resource intensive. This is In this test, the noise is added to the product of the CTF and the input signal. There are several possible where designer should strike a balance between speed combinations of the addition of noise which include, and resource utilization. Overall, the successful no noise, narrowband noise only, background noise emulation of NBPLC would make bench testing of only (any of the three), impulsive noise only or all of PLC devices much cheaper and easier. the noise added. There are 32 noise combinations used. Fig. 9 presents the resulting input-output ACKNOWLEDGMENT waveforms for various input frequencies muddled with different types of noise. This is validated with This work was supported by Engineering Research MATLAB simulation results. and Development for Technology (ERDT) under the faculty research grant.

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Potential Natural Product Inhibitor For Ebola Virus Glycoprotein

R.Nakrumpai1, 2 1Department of Biochemistry, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand 2Centre of Excellent in Medical Biotechnology, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand. [email protected]

Abstract— Ebola virus disease has caused a lot of deaths protein VP40 [4]. VP35 regulates the virus nucleocapsid in its latest outbreak in Africa. The need to search for formation [5]. drugs that can effectively treat the disease is still ongoing. A family of ion channels controlling the conductance Some drug lead compounds were reported to specifically of Ca2+ and Na+, two-pore channels (TPC), is suggested inhibit Ebola virus glycoprotein and highly reduce the to be exploited by Ebola during its infection to avoid virus infectivity. This work investigated these inhibitions in structural term by studying the interactions between endocytosis by escaping the endolysosome [6]. these drug lead compounds and Ebola virus glycoprotein Ebola virus glycoprotein (Ebola GP) is spiked on structure. It was found that interacting types and Ebola virus lipid envelope [7]. It mediates the interacting residues between Ebola GP and these drug lead attachment of the virus to its target host cell and its compounds impacted their inhibitory effects. Also this entry [7]. Ebola GP can be cleaved by its host cell work reported a natural product with potential inhibitory cysteine proteases cathepsin B and L enabling its effects on Ebola virus glycoprotein using related activation and its cellular entry [8]. bioinformatics and cheminformatics methods. Ebola GP in its trimeric form is spiked on the surface

of the virus membrane [9]. It consists of GP1 subunit Index Terms— Ebola virus, glycoprotein, natural product, bioinformatics. (involved in the attachment of the virus to the host cell) and GP2 subunit (responsible for the virus fusion) I. INTRODUCTION cleaved from the Ebola GP precursor (GP0) by protease furin [9]. The GP1 subunit containing receptor binding Ebola is a disease belonging to viral hemorrhagic domain is located at the protein N-terminus [9]. The fevers (VHFs). High death rates in the infected patients GP2 subunit containing fusion domain is located at the has been reported [1]. The cause of this disease is Ebola protein C-terminus [9]. In the synthesis of Ebola GP, an virus [1]. The most deadly Ebola virus causing the latest uncomplete transcribing product, sGP, can also occur devastating outbreak of Ebola in African countries is and then secreted into the extracellular space [9]. The Zaire Ebola Virus [2]. Other existed species of Ebola suggested function of this truncated protein is about viruses (Sudan, Reston, Tai Forest, and Bundibugyo) assisting the virus to survive in its host though this is yet are not as virulent [2]. unconfirmed [9]. When infected with Ebola virus, various symptoms The presence of Ebola virus can be detected using would occur within 2 days to about 2 weeks [2]. These reverse transcription-PCR (qRT-PCR) to verify Ebola symptoms include fever, headache, fatigue, and myalgia virus RNA. It is found that Ebola virus replication is [2]. In severely ill patients, hemorrhagic symptoms highly related to the severity of the disease and the would also occur [2]. fatality of the infected patients [10]. Thus the use of si- Ebola virus genome is non-segmented negative sense RNA as a possible therapy for Ebola is also suggested single-stranded RNA [3]. The length of its genome is 19 [10]. kilobases [3]. It encodes following important proteins: Cellular receptors play important role in determining glycoprotein (GP), membrane protein VP24, host tropism for viruses [11]. By using human haploid polymerase VP30 and VP35, matrix protein VP40, cells genetic screening and bioinformatics analyses, it nucleoprotein (NP), and RNA polymerase (L). These was found that Ebola virus has broad cell tropism [11]. proteins are vital to Ebola virus life cycle [3]. As a Its intracellular receptors include T-cell Ig and mucin filoviruses, Ebola virus is lipid-enveloped and domain 1 (TIM-1) as well as Niemann–Pick C1 (NPC1) filamentous [4]. Its budding is controlled by matrix [11]. Target cells for Ebola virus include immune cells, hepatocytes, endothelial cells, fibroblast, and adrenal

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cells [12]. Recently, bats are suggested as natural 15k, reported to specifically inhibit Ebola GP (as reservoir hosts of Ebola virus [13]. previously introduced in the former section) were built Currently, potential treatments Ebola virus disease are using ACD/ChemSketch [23]. The resulted structures still in trial phrases [14]. The search for effective drugs were subsequently cleaned and 3D optimized by the for Ebola virus disease is needed. Natural products are program. Energy minimization by Yasara program [24] interesting choices as they are of diverse properties. was employed to check their molecular arrangements. They are natural chemical molecules from various Open Babel [25] was used to convert molecular formats species. In drug discovery, natural products provide of the molecules as necessary. AutoDock Vina [26] and invaluable resource [15] due to their diverse properties. PyRx [27] were then used to dock these chemical For example, several natural products have potential use molecules with Ebola GP. The interacted structures in treating cancer [16]. Almost a third of drugs are between these molecules and Ebola GP were estimated to be natural products or their derivatives investigated using Chimera [28], Discovery Studio 4.5 [16]. Visualizer [29], PyMol [30], and Rasmol [31, 32]. ZINC database is a public database providing Interactions between 8,658 natural products in ZINC information and structures of various chemical database and Ebola GP were investigated using molecules including natural products [17]. These AutoDock Vina and PyRx. The interacted structures molecules can be downloaded in various chemical between these molecules and Ebola GP were then formats. Information regarding vendors selling these investigated using Chimera, Discovery Studio 4.5 molecules are also provided. Many of these molecules Visualizer, PyMol, and Rasmol. are interlinked to PubChem database which is a public chemical database provided by the National Center for Biotechnology Information (NCBI), National Institutes III. RESULTS AND DISCUSSION of Health (NIH) [18]. It provides data of chemical molecules including PubChem CID, chemical vendors, In this work, the interaction area between Ebola GP molecular formula, molecular weight, 2D structure, and lead compound 8a was found to be at the their literatures, and their chemical and physical glycoprotein’s fusion domain (Figure 1 and Figure 2) properties [18]. important for Ebola GP functioning. This obstruction of Preventing Ebola GP from functioning would prevent Ebola GP by the bound lead compound seemed to be the the virus infection. Thus it is one interesting target for reason that the infectivity value for Ebola virus was Ebola virus disease drug development. Some lead greatly reduced by lead compound 8a (as Ebola GP is compounds were demonstrated experimentally to inhibit directly involved in the infection of the virus to its host Ebola virus by selectively inhibit its glycoprotein (GP)- cell). Details about the residues in the binding pocket mediated infection of human cells [19]. For example, area found in the binding between Ebola GP and lead lead compound 8a and its derivatives, 15b and 15k, compound 8a are shown in Figure 2. The binding were reported to specifically inhibit Ebola GP [19]. This energy value of lead 8a to Ebola GP was found to be - work further investigated these inhibitions in structural 7.3 kcal/mol (Table 1). term by studying the interactions between these Lead compound 15b bound to the inside of GP1 interesting lead compounds with Ebola GP structure. subunit of Ebola GP (Figure 3). Detailed of the The binding areas and residues in the binding pockets interactions and the residues involved as well as the were also examined. residues in the binding pocket area are also shown in Also in this work, the previous initial study [20] about Figure 4. This lead compound was reported to inhibit the residues of Ebola GP that interacted with lead Ebola virus infectivity even better than lead compound compound 8a was examined in further depth by 8a. The results in this work suggested that during the investigating the area of Ebola GP that interacted with it binding its insertion into Ebola GP would destabilize the as well as the surrounding residues in the binding trimeric assembly of Ebola GP rather than blocking its pocket area. This work also studied interesting functioning like some of the other lead compounds. Its interactions between Ebola GP and natural products in binding energy value to the glycoprotein was -6.9 ZINC database as a mean to examine potential natural kcal/mol (Table 1). product inhibitors to Ebola GP. Similarly, lead 15k also bound to the inside of GP1 subunit of Ebola GP (Figure 5). Detailed interactions and the residues involved as well as residues in the II. MATERIALS AND METHODS binding pocket area are also shown (Figure 6). This lead compound was also reported to greatly reduce Ebola A three-dimensional protein structure of Zaire Ebola virus infectivity by inhibiting Ebola GP. From the GP [21], 5JQ3, was downloaded from PDB [22]. Lead results in this work, this seemingly was because it compound 8a and its interesting derivatives, 15b and destabilized Ebola GP during the binding. Its binding 123

energy value to Ebola GP was -7.7 kcal/mol (Table 1). A natural product in ZINC database, ZINC98365632 (Figure 7), was found to interact with Ebola GP in similar binding areas to those found in the binding between Ebola GP and all the investigated lead compounds (8a, 15b, 15k). This natural product in ZINC database is N-[[(1S,9aR)-2,3,4,6,7,8,9,9a- octahydro-1H-quinolizin-1-yl]methyl]-3-(2- methoxyphenyl)-1H-pyrazole-4. It bound to Ebola GP at the inside of GP1 subunit of Ebola GP (Figure 8) with binding energy value of -8.1 kcal/mol (Table 1). Detailed interactions and the residues involved as well as residues in the binding pocket area are also shown (Figure 9). As lead compound 15b and 15k highly reduced Ebola virus infectivity, this natural product could potentially well inhibit Ebola GP by destabilizing it in similar manners to that of lead compound 15b and Figure 2: Residues and interaction types involved in the interactions 15k. between lead 8a and Ebola GP along with residues in the binding pocket area Table 1 Binding energies of lead compound 8a, lead compound 15b, lead compound 15k, and ZINC98365632 to Ebola GP. Ebola virus infectivity values in the presence of lead compound 8a, lead compound 15b, lead compound 15k [19] are also shown.

Binding Energy Molecules Infectivity (kcal/mol) Leads 8a 56 -7.3 Leads 15b 4 -6.9 Leads 15k 3 -7.7 ZINC9836563 unknown 2 -8.1

Figure 3: Interactions between lead 15b and Ebola GP

Figure 1: Interactions between lead 8a and Ebola GP.

Figure 4: Residues and interaction types involved in the interactions between lead 15b and Ebola GP along with residues in the binding pocket area

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Figure 5: Interactions between lead 15k and Ebola GP Figure 8: Interactions between a natural product, ZINC98365632, and Ebola GP

Figure 6: Residues and interaction types involved in the interactions between lead 15k and Ebola GP along with residues in the binding pocket area Figure 9: Residues and interaction types involved in the interactions between a natural product, ZINC98365632, and Ebola GP along with residues in the binding pocket area

IV. CONCLUSION

This study suggests the importance of interacting Figure 7: ZINC98365632 is a natural product from ZINC areas between Ebola GP and chemical molecules, as database well as the interaction types and residues involved, in preventing Ebola virus infection. Also from the result, the reported natural product could potentially be used or developed as Ebola GP inhibitor. Further experimental work could be done to confirm the use of these natural products in Ebola virus inhibition.

ACKNOWLEDGMENT

This work was supported by the Thailand Research Fund (TRF).

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