IEEE
ICIA 2 0 1 1 IEEE International C onference on I nformation and Automation
& International Symposium on Integration T echnology
June 6 – 8, 2011, Shenzhen, China
!
! Program! Digest
Sponsored by Technically Sponsored by
IEEE Robotics and Automation Society The Chinese University of Hong Kong
The CAS/CUHK Shenzhen Institutes of Shandong University
Advanced Technology The Chinese Association of Automation
The Robotics Society of Japan
The Japan Society of Mechanical Engineers
The Society of Instrument and Control Engineers
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Monday June 6, 2011
MA-1 Tracking Control
MA-2 Medical Robot
MA-3 Signal Processing
MA-4 ISIT Image and Video Processing
MP-1 Adaptive Control
MP-2 Robotics I
MP-3 Navigation System
MP-4 ISIT Pattern Recognition I
ME-1 Advanced Management I
ME-2 Robotics II
ME-3 Mechanism and Design
ME-4 ISIT Pattern Recognition II
1
MA-1: Tracking Control
Session Chairs: Puren Ouyang and Yuqing He Room Zhuhai, 10:20—12:00, Monday, 6 June 2011
MA-1 (1) 10:20—10:40 MA-1 (2) 10:40—11:00 Tracking Micro Reentering USV with TDRS and Position Domain PD Control: Ground Stations Using Adaptive IMM Method Stability and Comparison Li-Qiang Hou , Heng-Nian Li , Fu-Ming Huang and Pu Huang Puren Ouyang and Truong Dam State Key Laboratory of Astronautic Dynamics, Xi’an Satellite Control Center Department of Aerospace Engineering, Ryerson University Xi’an, China Toronto, Canada
• Tracking sub-orbit USV of wave-rider • Pos itio n Do ma in PD co ntro l: a Position domain control Cross coupled control Time domain control 1.5 1.5 1.5 shape with TDRS (Tracking and Data Target New approach for contour 1 1 1 Relay Satellite) and ground stations. 0.5 0.5 0.5 tracking. • Processing trajectory data and estimate 0 0 0 Desi red contour Desi red contour Desired contour
-0Y -a . x 5 is p o s i t io n(m ) -0.5Y -a x is p o s i t io n(m ) -0Y -a . x 5 is p o s i t io n(m ) aerodynamic parameters of the • Dynamic model represented in Real contour Real contour Real contour -1 -1 -1 complicated trajectory together. 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 position domain. X-axis position (m) X-axis position (m) X-axis position (m) • Processing data using different Position domain control Cross coupled control Ti me domain control • Stability analysis proved. 0.6 0.6 measurement models of TDRS and 0.5 0.5 0.5
0.4 0.4 0.4 ground tracking stations Attitude Reference Frame • Different contour tracking 0.3 0.3 0.3 Desired contour Desired contour Desired contour Real contour Real contour Real contour • Adaptive method for calculating 0.2 0.2 0.2 compared. (m) position Y-axis 0.1 (m) position Y-axis 0.1 (m) position Y-axis 0.1 transition probabilities of time -varying 0 0 0 Earth -0.1 -0.1 -0.1 IMM • Better contour tracking obtained. -0.5 0 0.5 -0.5 0 0.5 -0.5 0 0.5 X-axis position (m) X-axis position (m) X-axis position (m) • Iterated Sigma Point Kalman Filter Contour tracking results (ISPKF). Tracki ng USV with TDRS
MA-1 (3) 11:00—11:20 MA-1 (4) 11:20—11:40
Contour Tracking Control in Position Domain Input Assignability of Nonlinear System and for CNC Machines Its Applications in Robust/Tracking Control Truong Dam and Puren Ouyang Yuqing He and Jianda Han Department of Aerospace Engineering, Ryerson University State Key Laboratory of Robotics, Shenyang Institute of Automation, 3D t rajec tory Toronto, Canada Chinese Academy of Sciences, Shenyang, Liaoning 110016, China.
6 [email protected]; [email protected] • Pos itio n Do ma in co ntro l: a New 4 2 A new con ce p t of input assignability (I AS ) is
0 approach for contour tracking. Z-axis posit(m) ion introduced and used to design input
-2 3 assignability based co n trol (I ASC) in this • Application to a 3-DOF CNC Ideal 2 2 PD track ing 1 1.5 paper. the main advantages of the new PPD1 t racking 0 0.5 machine. -1 0 Y-axis posit ion (m) X-axis pos ition (m) designed me thod lies in: 1) the complete or 3D t rajec tory • Linear and circular motions in incomplete uncertainty information ca n be utilized and the co ns e rva ti venes s of the 3D. 0.6 0.4 traditional rob ust co n trol algorithm can be thus improved greatly; 2) with the IASC, the • Better contour tracking 0.2
0 influence of uncertainties on the cl osed loop performance compared to Z-axis posit(m) ion -0 .2 can be assigned to di ffe rent states in 0.6 Ideal traditional PD control. 0. 4 0.5 PD track ing di ffe ren t applications even for the same 0.2 PPD0 t racking 0 plants. -0.2 -0 .5 Y-axis posit ion (m) X-axis pos ition (m)
MA-1 (5) 11:40—12:00 Trajectory-Tracking and Discrete-Time Sliding- Mode Control of Wheeled Mobile Robots Adrian Filipescu, Viorel Minzu, Bogdan Dumitrascu and Adriana Filipescu Department of Automation and Electrical Enginering University “Dunarea de Jos” of Galati,Romania Eugenia Minca Department of Automation, Computer Science and Electrical Engineering University “Valahia” of Targoviste,Romania
• Discrete-time sliding mode control for the trajectory tracking problem of wheeled mobile robots is presented. • The wheeled mobile robot (WMR) taken into account was PowerBot. • The algorithm has been designed in discrete- time domain in order to avoid problems caused by the discretization of continuous- time controllers. • The simulation results and real time results prove the effectiveness of the proposed Mobile platform PowerBot controller.
3
MA-2: Medical Robot
Session Chairs: Dong Sun and Wei-Hsin Liao Room Hong Kong, 10:20—12:00, Monday, 6 June 2011
MA-2 (1) 10:20—10:40 MA-2 (2) 10:40—11:00 A Medical Robot System for Celiac Minimally Optimal Path Planning for Inserting a Invasive Surgery Steerable Needle into Tissue Mei Feng, Yili Fu, Bo Pan and Chang Liu Jianjun Wang, Dong Sun, Jinjin Zheng and Wen Shang State Key Laboratory of Robotics and System, Harbin Institute of Technology Joint Advanced Research Center of CityU-USTC China Suzhou, China • A medical robot for celiac MIS • The trajectory is combined by has been presented. smoothly connected arcs. • A new mechanism for romote • A method are proposed to center of motion was proposed. generate the shortest path with the • An improved surgical instrument least control effort. with wirst successfully solved the • 3D problem is transferred as two coupled motion. 2D problems. • A new method was proposed to • Case studies are performed to solve the master-slave inverse demonstrate the effectiveness of kinematics solution. the proposed approach. The Medical robot Different trajectories for 2D and 3D
MA-2 (3) 11:00—11:20 MA-2 (4) 11:20—11:40 Open-loop Control Experiment of Wireless Experimental Studies on Kinematics and Kinetics Capsule Endoscope Based on Magnetic Field of Walking with an Assistive Knee Brace Aaron See-Long Hung, Hongtao Guo, Wei-Hsin Liao, Daniel Tik-Pui Fong, and Kai-Ming Chan Cancheng Zhong, Chao Hu, and Fei Luo The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences • This study evaluated the interaction between the user and an Shenzhen, China assistive knee brace. • This paper presents an experiment of using • A gait analysis was performed to analyze the gait differences of external magnetic field to implement the walking with the knee brace. actuation of wireless capsule endoscope with open-loop control strategy. • Results showed that gait parameters, joint kinematics and joint • Besides, we improve the existed kinetics were not affected by the knee brace. mathematical model of the coil’s magnetic fie ld. The thic kness of the coils is taken into account. • Finally, MATLA B simu lation e xperiment is carried out to analyse the relation of the Gait Analysis coils’ sizes and the character of magnetic General experimental setup: tw o pairs field distribution. of coils and the Agilent power supply
MA-2 (5) 11:40—12:00 Optimized Design of Capsule Endoscopy Lens Based on ZEMAX Lilai Tang, Chao Hu, Kang Xie, Chang Cheng, Zhiyong Liu Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Shenzhen, China
• Overview of the development of capsule endoscopy lens.
• Specification and design procedure.
• Optimized design of CE lens.
• Simulation results and discussions. The Olympus CE
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MA-3: Signal Processing
Session Chairs: Liyang Zhou and Yuexian Zou Room Kowloon, 10:20—12:00, Monday, 6 June 2011
MA-3 (1) 10:20—10:40 MA-3 (2) 10:40—11:00 Direction Estimation Under Compressive Sensing A CDMA Acoustic Communication System for Framework: A Review and Experimental Results Multiuser Based on Sound Card Bo LI, Yuexian ZOU* and Yuesheng ZHU School of Computer & Information Engineering, Peking University Shenzhen Graduate School, Zixin Zhao and Shuxiang Guo Shenzhen, China Kagawa University, Japan • A CDMA acoustic communication • DOA estimation under compressive system for multiuser based on hardware sensing framework utilizing the spatial platform is developed. sparsity of the array signal has been • The sound card in the computer with studied comprehensively. sound box and microphone was used as 1 X: 50 • DOA estimation approaches using CS 0.9 Y: 1 the energy transduction to accomplish in time domain and spatial domain are 0.8 acoustic communication in the air X: 60 0.7 Y : 0.7704 instead of the acoustic transducer and reviewed and analyzed in details. 0.6 0.5 the hydrophone in the ocean for • Broadband source DOA estimation 0.4 convenient. Acoustic Communication Platform 0.3 using CS is proposed in spatial-domain. SpectrumNormalized Angular 0.2 • In the simulation and experiments, the signals transmitted from the 0.1 • Intensive experiments for speech DOA transmitting part could be received exactly in the receiving part which 20 40 60 80 100 120 140 160 180 estimation application show that DOA- (degree) Top: Two sources DOA estimation setup indicated good quality of the acoustic communication system. CS outperforms MUSIC algorithm. Bottom: DOA-CS results, SNR=10dB
MA-3 (3) 11:00—11:20 MA-3 (4) 11:20—11:40 A Modified ESPRIT Algorithm Based on A New An Improved Bistable Circuitry System for Weak SVD Method for Coherent Signals Signal Detection Liyang Zhou, Dengshan Huang， Hongliang Duan, Yulong Chen School of Electronics and Information， Northwestern Polytechnical University Daoyi Dai, Qingbo He*, Yongbin Liu, Jianjun Wang, and Chang Gong Xi’an, China Department of Precision Machinery and Precision Instrumentation • The new algorithm is based on a University of Science and Technology of China
new SVD method, the MSVD- 1.6 Hefei, Anhui 230026, China
SVD-ESPRIT ESPRIT algorithm for short. 1.4 MSVD-ESPRIT • Explore weak signal detection via • The main idea is to construct a 1.2 matrix with the maximum the SR effect with an improved eigenvector according to certain 1 bistable circuitry system. 0.8 rules, then fix this matrix and get RMSE • Use a filter array to realize multi- two signal subspaces by singular 0.6 value decomposition; at the last we 0.4 scale noise tuning. use the rotation invariant to get 0.2 • Main merits of the SR with multi-
DOA. 0 0 5 10 15 20 25 30 scale noise tuning : (1) the range of • This improved ESPRIT algorithm’s SNR/dB resolution and robustness is the noise density suited for SR is obviously better, especially in low RMSE for defferent SNR wider; (2) the output SNR of SR is Experimental setup of the SNR case. higher. bistable circuitry system
MA-3 (5) 11:40—12:00
A New Approach to the Diagnostic Quality Ambulatory ECG Recordings Tsau Y et al. DIMETEK Digital Medical Technologies, Ltd
A new approach to ECG acquisition with the diagnostic quality in various activity states from resting to strenuous exercises without missing ECG signals is proposed, which is based on the digital technologies of pure digital medical amplifier (PDMA) featuring great input signal dynamic range (ISDR) and high immunity to various noises, as well as extensive use of digital filtering and parallel processing. The device can monitor ECG traces with up-to-moment P-QRS-T waveform measurements. It can be used as an all-in-one ECG device for diagnostic, monitoring, ambulatory, and stress testing purposes, and so has many new potential applications.
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MA-4: ISIT Image and Video Processing
Session Chairs: Gang Wang and Qi Zhang Room Macau, 10:20—12:00, Monday, 6 June 2011
MA-4 (1) 10:20—10:40 MA-4 (2) 10:40—11:00 Information Reduction Based on Temporal Human Tracking in Thermal Catadioptric Similarity and Spatial Importance for Video Omnidirectional Vision Yazhe Tang, Youfu Li, Tianxiang Bai, Zhongwei Li, Xiaolong Zhou Transmission in Mobile Surveillance System Department of Manufacturing Engineering and Engineering Management, Yi-Chun Lin and Feng-Li Lian City University of Hong Kong Department of Electrical Engineering, National Taiwan University Hong Kong, China
foreman
256(kb) 512( kb) 1024( kb) 2048( kb) 4096( kb) 8192( kb) 10240 (kb ) 60 24. 12 / 3. 87 29. 81 / 3. 52 31. 50 / 1. 81 31. 95 / 1. 04 31. 95 / 1. 04 39. 66 / 1. 92 40. 41 / 1. 28 Taipei, Taiwan 0.00 / 1. 42 0.00 / 3. 76 16. 72 / 6. 99 22. 99 / 3. 84 30. 47 / 0. 65 35. 79 / 0. 60 39. 74 / 0. 76 • This paper introduces a novel super 50 0.00 / 0. 00 0.00 / 0. 00 0. 00 / 0. 00 0. 00 / 0. 00 0. 00 / 3. 02 33. 15 / 0. 34 33. 37 / 0. 57 • Temporal similarity sampling is used 40 surveillance system which has a 30 PS NR (d B ) to eliminate temporal redundancy. 20 global view and can work under all- 10
0 0 1000 2 000 3000 4 000 5000 6000 7000 8000 9000 10 000 • Spatial importance encoding is Bandwidth (kb) weather condition. hall
256(kb) 512( kb) 1024( kb) 2048( kb) 4096( kb) 8192( kb) 10240 (kb ) 60 23. 68 / 2. 07 31. 36 / 1. 44 32. 29 / 0. 67 32. 29 / 0. 67 32. 29 / 0. 67 41. 09 / 0. 60 41. 09 / 0. 53
0.00 / 0. 75 0.00 / 3. 19 16. 43 / 1. 29 21. 60 / 0. 43 31. 09 / 0. 18 37. 06 / 0. 17 40. 76 / 0. 12 utilized to maintain high importance 50 0.00 / 0. 00 0.00 / 0. 00 0. 00 / 0. 00 0. 00 / 0. 00 0. 00 / 0. 00 33. 22 / 0. 08 33. 84 / 0. 30 • The proposed tracking method
40
30 content. PS (dB NR ) integrates the SVM with particle
20 • Information Reduction based on 10 filter for effective tracking in 0 0 1000 2 000 3000 4 000 5000 6000 7000 8000 9000 10 000 Bandwidth (kb)
Client 2(1024k bps) thermal catadioptric omnidirectional Similarity and Importance (IRSI) Lossless Label 1 The Thermal Catadioptric 90 Label 2 80 Label 3 vision. algorithm is proposed. 70 Omnidirectional Vision 60
50
40
PSNR (dB) • The experiments verify the proposed • Experimental results demonstrate 30 20 10 algorithm is effective. excellent performance. 5 61 146 187 22 2 255 Frame (No.)
MA-4 (3) 11:00—11:20 MA-4 (4) 11:20—11:40 Online Image Classifier Learning for Google Efficient Registration Algorithm for UAV Image Image Search Improvement Sequence Fan baojie, Du yingkui, Tang yandong Yuchai Wan, Xiabi Liu and Jie Bing Yunpeng Chen State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, China School of Computer Science and Technology, The Middle School Attached to Northern Beijing Institute of Technology Jiaotong University Beijing, China Beijing, China • This paper presents a fast and efficient image registration • The images returned by Google are An example of top 10 images: used to learn a posterior pseudo- algorithm for UAV image sequence . The UAV and its vision probability function for measuring • The proposed algorithm consists of simulation motion platform their relevance to the query. three main steps: feature extraction, • All the images are re-ranked in feature point tracking, and From Google homography matrix estimation.. descending order of their posterior pseudo-probabilities. • Experiments on different image • The approach can bring better image sequences indicate that our method has satisfactory image registration retrieval precisions at top ranks than results with the average time 0.3s The results of image original Google results. Improved by using our approach registration in the sequence
MA-4 (5) 11:40—12:00 A Method Study of Generating Digital Terrain Map for Lunar Exploration based on the Stereo Vision Jianjun DU, Jinshou HE and Jianjun ZHU Shenzhen Graduate School Harbin Institute of Technology Shenzhen, Guangdong Province China • Construct the model and calibrate the camera. • Use the polar geometry of linear transformation theory to recalculate pixel coordinates to rectify the image. • Adopt the sum of absolute value of difference (SAD) method to achieve the image match. • Use based on grid to generate digital Left image and disparity map of landform reconstruction map of Mars-3 method to descript scene intuitively.
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MP-1: Adaptive Control
Session Chairs: Zhibin Li and Shaobo Kang Room Zhuhai, 14:00—15:40, Monday, 6 June 2011
MP-1 (1) 14:00—14:20 MP-1 (2) 14:20—14:40 The UUV Heading Control System Robust Adaptive Control of Piezo-actuated Based on Adaptive Robust PD Control Principle Positioning Stages with An Ellipse-based Li Xu1, Shijie Li1, Jian Xu1,2, Jie Zhao2 College o f Automation, Harbin Engineering University, Harbin, China Hysteresis Model Robotics Institute, Harbin Institute of Technology , Harbin, China Guo-Ying Guab, LiMin Zhua, and Chun-Yi Sub aSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China • A heading control method of UUV bDepartment of Mechanical and Industrial Engineering, Concordia University, Montreal, Canada based on adaptive robust PD control principle is presented. Controller Hysteresis Plant • Simulation research is carried out for typica l working cond itions of • Description of the piezo-actuated positioning stage system UUV. • Hysteresis modeling using an ellipse-based hysteresis model • The lake trial of “BSA-I” UUV in • Development of a discontinue-projection-based robust “xinan” river is carried out and the adaptive controller effectiveness of adaptive robust PD Lake Trial of “BSA-I”UUV • Simulation validation and conclusion control principle is proved.
MP-1 (3) 14:40—15:00 MP-1 (4) 15:00—15:20 Adaptive Control of a Class of Uncertain Nonlinear Adaptive Synchronization Between Two Systems with Unknown Input Hysteresis Delayed Complex Networks With Derivative Li Zhifu, Yuan Peng, Hu Yueming and Chen Tiemei Coupling And Non-identical Nodes Engineering Research Center for Precision Electronic Manufacturing Equipments of Ministry of Education, College of Automation Science and Engineering, South China University of Technology Wang Jian, Feng Lin and Li Shu-kai Guangzhou, China Institute of Systems Engineer ing, Tianjin University Tianjin, Ch ina • Krasnosel’skii-Pokrovkii model and its inversion • Complex networks. • Design of adaptive controllers for a •Synchronization. class of uncertain nonlinear systems •Time delay. preceded by unknown Krasnosel’skii- • Non-identical nodes. Pokrovkii hysteresis • Numerical simulation to verify the theoretical findings and show the effectiveness of the proposed scheme • Conc lusio ns Krasnosel’skii-Pokrovkii Hysteresis
MP-1 (5) 15:20—15:40 Application for Solving Angular Velocity with Adaptive Kalman Filtering in Chen Lei, Sun Shuguang, Cheng Z ijian, Jiang Mai, JIA Gang Heilongjiang East University，Harbin, China
• Based on a nine-accelerometer configuration, detailed analyzes the principle of GFSINS and gives the process for solving angular velocity. • Adaptive kalman filter is constructed equations of state and filtering equations for solving angular velocity. • Compare kalman filter and adaptive kalman filter for solving angular velocity. • Simulate and verify the results, and obtain results . Configuration of nine accelerometer
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MP-2: Robotics I
Session Chairs: Ying Hu and Xiaodong Wu Room Hong Kong, 14:00—15:40, Monday, 6 June 2011
MP-2 (1) 14:00—14:20 MP-2 (2) 14:20—14:40 Human-robot Collaborative Manipulation through Development of a Sensor-driven Snake-like Imitation and Reinforcement Learning Robot SR-I Ye Gu, Anand Thobbi and Weihua Sheng, Xiaodong Wu1, and Shugen Ma1,2 ASCC Lab, Oklahoma State University, 1.Department of Robotics, Ritsumeikan University, Japan Stillwater, USA. 2.Shenyang Institute of Automation, CAS, China • We propose a two-phase learning • To achieve self-adaptive locomotion, framework for human-robot a sensor-driven snake-like robot SR- collaborative manipulation tasks. I has been developed. • Phase I – To learn to grasp the table • The design of the mechanism and using imitation learning. control system of this sensor-driven • Imitating the human in the task snake-like robot is presented. space. • Based o n the sensory information, • Phase II – To learn the collaboration the implementations of terrain- behavior by reinforcement learning. adaptive locomotion and collision- Human-robot performing Snake-like Robot SR-I • Robot can acquire the skill in a short free locomotion are investigated the table lifting task time. respectively.
MP-2 (3) 14:40—15:00 MP-2 (4) 15:00—15:20 Hybrid Control Policy of Robot Arm Motion for A Novel High Adaptability Out-door Mobile Assistive Robots Robot with Diameter-variable Wheels Hsien-I Lin and Chi-Li Chen Lan Zheng1,2,3, Peng Zhang1,2, Ying Hu1, Gang Yu2, Zhangjun Song1, Jianwei Zhang1 Graduate Institute of Automation Technology National Taipei University of Technology 1.Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences Taipei, Taiwan 2. Mechanical Engineering and Automation, HIT Shenzhen Graduate School Performance comparison • Propose a hybrid control policy of Feature End-effect Tele- Hybrid 3.The Chinese University of Hong Kong, China operation • A novel high terrain adaptability out- robot arm motion to semi-automatically Obs tac le Fair Good Good control a remote robot arm for assisting avoidance door mobile robot with diameter- Accuracy Good Low Good the elderly in daily living. Speed Good Low Fair variable wheels was proposed. Human- Low Good Good • Integrate the advantages of end-effector like • The mechanism was described. The posture and tele-operation control modes. Safety Low Good Good obstacle climbing capability and • Validate the hybrid mode by showing stability of the robot were analyzed. its capability of obstacle avoidance and • The kinematic and dynamic models reaching to a target position quickly. were introduced. Simulations were An Out-door Mobile Robot • The average execution time of tele- carried out to show that the robot has with Diameter-variable Wheels operation mode: 40.46 sec.; hybrid high adaptability in unstructured (a) (b) environment. mode: 22.26 sec. in the task. The ta sk environment to va lidate the hy brid mode
MP-2 (5) 15:20—15:40 Simulation Study of Planetary Rover’s Static Model under Unstructured Terrain Condition
Ning Mao1,2, Bo su2, Qichang Yao2, Lei Jiang2, Hongji Xu1 1Changchun University of Science and Technology Changchun, Jilin Province, China 2China North Vehicle Research Institute Beijing, China • The static simulation of the rover with optimal power consumption is described. • It is based on kinematics and statics model, combined with experiment data. • This method can not only be seemed as an advance judgment of the trafficability, but also can be used as the design basis of the rover controller. Flow diagram of static simulation 8
MP-3: Navigation System
Session Chairs: Shulian Pan and Hongyang Bai Room Kowloon, 14:00—15:40, Monday, 6 June 2011
MP-3 (1) 14:00—14:20 MP-3 (2) 14:20—14:40 Calibration of Low Cost MEMS Inertial A useful Doppler Radar outlier elimination Measurement Unit for an FPGA-based Navigation algorithm Based on Orthogonality of Innovatione System Hongyang BAI1, Xiaozhong Xue2 1.National Key Laboratory of Transient Physics, 2.Department of Power Engineering Lei Wang Nanjing University of Science and Technology Nanjing, China Center of Micro-system Technology , Shenyang Ligong University Shenyang, China • An effective intelligent calibration method combining with Kalman Filter is proposed. • A prototype development board based on FPGA is implemented for experimental testing. • The significant error sources of IMU are estimated in virtue of static tests, rate tests, thermal tests. • The efficiency of proposed approach is The DFBINS System demonstrated by various experimental MEMS IMU and FPGA scenarios with real MEMS data. prototype
MP-3 (3) 14:40—15:00 MP-3 (4) 15:00—15:20 Average Speed Estimation Based on the Data of Application of the Adaptive Two-stage EKF Diverse Floating Car Algorithm in Geomagnetic Aided Inertial Shuliang Pan, Bo Jiang, Nan Zou, and Lei Jia School of Control Science and Engineering, Shandong University Navigation Jinan, China Ming Liu, Haijun Wang, Qingye Guo • Road section division Aviation Information Technology R&D Center, Binzhou University • Dynamic road section integration Binzhou, China • Fitting the multi-type floating car • The geomagnetic aided inertial speed into average car speed navigation is introduced to enhance using the least squares method the land vehicle navigation precision. • Average speed estimation model • Nonlinear model is developed, and based on the number of floating an adaptive two-stage EKF method cars The Curve of Estimated is proposed. • The average Error Rate is less Speed and Real Speed • Simulation results show the than seven percent by simulation efficiency of the new method. test using vissim The east velocity error's estimation errors
MP-3 (5) 15:20—15:40
Research of Strapdown Inertial Navigation System Monitor Technique Based on Dual-axis Consequential Rotation Jianhua Cheng, Mingyue Li and Daidai Chen College of Automation, Harbin Engineering University Harbin, China • Analysis the errors of SINS. • Display the theory of single-axis rotation monitor system. • Design the scheme of dual-axis consequential rotation monitor system which can modulate three errors including constant error, the scale error and the installation error. • Compare the modulation results Dual-axis rotation done by single-axis and dual-axis monitor system consequential rotation.
9
MP-4: ISIT Pattern Recognition I
Session Chairs: Hai Wang and Gang Zhou Room Macau, 14:00—15:40, Monday, 6 June 2011
MP-4 (1) 14:00—14:20 MP-4 (2) 14:20—14:40 Detecting Temporal Patterns using Reconstructed Scene text detection based on hierarchical MLP Phase Space and Support Vector Machine in the Gang Zhou, Yuehu Liu, and Jianji Wang Institute of AI and Robotics, Xi’an Jiaotong University Dynamic Data System Xi’an, China Wenjing Zhang, Student Member, IEEE, Xin Feng, Senior Member, IEEE, and Naveen Bansal Department of Electrical and Computer Engineering Marquette University, Milwaukee, WI 53201-1881, USA • Utilizing local information for • Detecting dynamic temporal patterns 260 image segmentation. that are characteristic of significant 240 220 • A novelty hierarchical architecture
events in a dynamic data system. 200 consisting of two MLP classifiers • Gaussian Mixture Model to cluster the 180 in tandem is utilized to analysis the 160 data sequence into three categories of SVI connected components. 140
signals, e.g. normal, patterns and events. 120 • 7 kinds of unary components • A hybrid method using Support Vector 100 feature and 4 kinds of binary Machines and Maximum a Posterior to 80 components feature are proposed 60 100 200 300 400 500 600 700 800 900 1000 for training hierarchical MLP. Application of Scene text detection classify temporal pattern signals in Tim e t Reconstructed Phase Space. Dynamic Temporal Patterns
MP-4 (3) 14:40—15:00 MP-4 (4) 15:00—15:20 Chinese Handwriting Quality Evaluation Based A matching algorithm on statistical properties of on Analysis of Recognition Confidence Harris corner Yan Gao and Lianwen Jin Baigen-He, Zhu Ming and Yajuan Wei Human-Computer Communication Intelligent Laboratory, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences South China University of Technology, Guangzhou, China • This paper describes the statistical properties of Harris • Recognize the Chinese handwriting corner. character by the MQDF classifier . • Compute the recognition confidence • A detailed analysis of the based on MQDF distance. independence of Harris corners’ statistical properties through • Rank the handwriting quality of some experiments. Chinese character based on the recognition confidence. • Statistical properties of Harris Matching result • The proposed method generally corner are applied to image coincides with the human scores. matching combined with BBF The Sony Aibo Dog algorithm.
MP-4 (5) 15:20—15:40 Multiple Binary Classifiers Fusion using Induced Intuitionistic Fuzzy Ordered Weighted Average Operator Hai Wang, Yan Zhang, Gang Qian School of Computer Science and Technology, Nanjing Normal University Nanjing, China
• We present a multiple binary classifiers fusion scheme which is achieved by the induced intuitionistic fuzzy ordered weighted average (I-IFOWA) operator. • With different manifestations of the weighting vector, we develop 9 specific I-IFOWA operators to weight and select base classifiers. • Comparable experiments are developed and we consequently clarify that the weight of a base classifier is not simply linear to its accuracy.
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ME-1: Advanced Management I
Session Chairs: Yanjiong Wang and Liling Xia Room Zhuhai, 16:00—17:40, Monday, 6 June 2011
ME-1(1) 16:00—16:20 ME-1 (2) 16:20—16: 40 The Design and Implementation of Feature-Grading The Design and Implementation of Recommendation System for E-Commerce Telemedical Consulting System for Luo Yi1 , Fan Miao 2, Zhou Xiaoxia 3 1 International School, 2 School of Software Engineering of Beijing University of Posts and Telecommunications Auscultation 3 School of Insurance and Economics University of International Business and Economics AUTHORS: Lisheng Xu,Ying Wang ,Yue Wang,etc • The overall process can be separated into 5 key steps. A telemedical auscultation consulting system based • Based on feature mining, sentimental on LAN is design ed an d implement ed. Besides analysis, and the records of customer transmitting data with high security, this system can also historical behaviors. sample, real-time-display, and store heart sound signals. • Providing both related and highly Furthermore, many ot her funct ions are achieved such as appreciated items. heart sound playback, co mputer-aided diagno sis, electronic medical record reporting, etc. By using this • We also introduce the prototype system, doct ors can communicat e wit h and diagnose for recommendation system we patientsremotely. developed on the basis of Feature- Grad ing Fig 1. The user interface
ME-1 (3) 16: 40—17:00 ME-1 (4) 17:00—17:20 The Design and Implementation of Distributed System Engineering Method for Naval Inventory Management System Based on the Ship Evaluation Liang Ge, Yuan-hang Hou Intranet Architecture College o f Ship Building Engineering, Harbin Engineering University Haibin, China Liling Xia Department of Information Engineering, Nanjing Institute Of Industry Technology Nanjing, Ch ina • A simplified warship index system including • A distributed inventory manage ment efficiency, risk and cost was built up. system based on intranet system • The group decision-making method improved by structure was put forward in this paper. optimized Hadamard bulge combination was utilized to concentrate experts’ opinions for the • It analyses and designs the function weights of bottom targets. model of distributed inventory • The whole process of naval ship evaluation was generalized based on system engineering management system, and introduces methodology. system design and implementation • A new naval ship evaluation model which could methods support both group decision and different kinds of attributes was constructed. • Implement the valid mana geme nt and Fig. 2 Distributed inventory Flowchart of naval ship evaluation fast and accurate retrieval of distributed management system based on inve ntory infor matio n. intranet architecture
ME-1 (5) 17:20—17:40 A Key Exchange Scheme Based-On Product Code and Performance Simulation Yanjiong Wang, Qiaoyan Wen State Key Laboratory of Networking and Switching Technology, Beijing University o f Posts and Telecommunica tions, China
• A key exchange scheme base-on Merkle’s Puzzle without trusted third party. • For any items with different product codes, different keys can be Original Merkle’s Scheme ne gotiated. • Comparing with origninal Merkle’s scheme, sec urity performa nce has been improved. • Performance simulation is also CB-Merkle Scheme given. 11
ME-2: Robotics II
Session Chairs: Qing He and Wei Liu Room Hong Kong, 16:00—17:40, Monday, 6 June 2011
ME-2 (1) 16:00—16:20 ME-2 (2) 16:20—16:40
Research of a Static Balance Method for a Type Synthesis and Kinematic Analysis of a New Quadruped Robot Walking on a Slope Class Schonflies motion Parallel Manipulator Zhang Wen-yu , Zhang Lei Zhibin Li, Yunjiang Lou, Zexiang LI Institute of Command Automation, PLA University of Science and Technology, Nanjing, China Shenzhen Graduate School, Harbin Institute of Technology • We propose a new stability Shenzhen, China criterion combined Sne and stability margin together. • A new class of spatial 4-DOF Schonflies motion parallel manipulator • Based on this criterion, the gaits transition is planned and with four identical chains is presented. the two unstable problems are • The solution of its inverse kinematics solved by dynamically moving and forward kinematics are all sixteen. the center of gravity • Two kinds of singularities and its • Through simulation, it is workspace are discussed. verified that the quadruped robot could move in all • The results show that the moving directions on a slope with high The Quadruped robot platform and the base should be in stability and rapid speed simulation platform dissimilar dimension. The Parallel Manipulator
ME-2 (3) 16:40—17:00 ME-2 (4) 17:00—17:20 Fuzzy Logic-based Multi-robot Cooperation for Map Building for Mobile Robot Based on Object-pushing Distributed Control Technology Jinbo Sheng Yifan Cai and Simon X. Yang Songmin Jia, Ke Wang, Xiuzhi Li, Wei Cui, Jinhui Fan Hitachi, Ltd School of Engineering, University of Guelph College of Electronic Infor mation & Con trol Engineering Guelph, Ontario, Canada Beijing, China Tokyo, Japan • A two-stage fuzzy logical controller • An efficient SLAM technique for is developed. indoor mobile robot navigation based on Laser Range Finder and • The controller inputs include Rao-Blackwellized Particle Filter previous robot velocities, distances (RBPF) was proposed. detected from the obstacles, and • By using RTM, we deve loped LRF robot members. data getting component, mobile • The multi-robot sys tem can work in robot control component, RBPF the environments with both static componentand etc. obstacles and dynamic obstacles. • Some experimenta l results verified the effectiveness of the proposed • The multi-robot cooperation is Multi-robot Cooperation for The components structure of mobile Object-pushing method. designed to be adaptive. robot SLAM based on RBPF
ME-2 (5) 17:20—17:40
A New Real-time Method for Distortion Correction in Surgical Robot Positioning Systems Tingfang Yan1,2, Ning Wei1, Qing He1, Wei Liu1, C henxi W ang 1, Jinglan Tian2, Chao Hu1 and Max Q.-H. Meng1,2,3 1Shenzhen Institutes of Adv anced Technology , Chinese Academy of Sciences, Shenzhen, China 2School of Control Science and Engineering, Shandong Univ ersity , Jinan, Shandong, China 3Department of Electronic Engineering, The Chinese Univ ersity of HongKong, HongKong, China The surgical robot positioning system is real-time, so the image distortion correction should be real-time as well.
Analyze the distortion model and porpose a new real-time distortion correction method based on fixed point theorem.
Experiments show that our new distortion correction method can correct any points The original distorted The corrected image directly and quickly, meaning that it can image meet the positioning system's real-time property well.
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ME-3: Mechanism and Design
Session Chairs: Zhigang Liu and Kai He Room Kowloon, 16:00—17:40, Monday, 6 June 2011
ME-3 (1) 16:00—16:20 ME-3 (2) 16:20—16:40 Sensitivity Analysis of Torsional Vibrations in Theoretical and Experimental Analysis on Mill Drive Train System Deformation of Sheet Metal under Singal Point Xingchun Yan, Yongqin Wang, Yuanxin Luo and Qing Wang The State Key Laboratory of Mechanical Transmission , Chongqing University Waterjet Impact Chongqing, China He Mao, Kai He, Qun Luo and Ruxu Du • The sensitivity can be easily Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
obtained by using our in-house J16 J program. 11 • This paper studied the distribution of J J J13 J12 15 14 axial dynamic waterjet pressure and the • The inertia and stiffness of the impact pressure on sheet metal surface components are sensitive to the by theoretical analysis. specific order natural frequency J26 J25 J J J 24 23 22 J in a mill drive system. Most 21 • FEA simulations were carried out to predict the sheet metal plastic sensitivity parameters of the J27 drive system can be obtained by deformation under different parameters comparing the sensitivity value. including waterjet pressure, nozzle diameter and sheet metal thickness. • This program should be The Mill Drive Train System improved by considering the • Experiments were designed and carried out, and the experimental results Waterjet incremental sheet structure parameter of the major metal forming components in the future. matched the simulation results well.
ME-3 (3) 16:40—17:00 ME-3 (4) 17:00—17:20 A New Type of Four Supporting Points Parallel Digital Design of Low-cost 3-DOF Prosthetic Redundant Action Mechanism for Attitude Hand Adjustment Xi Tang, Changjie Luo, Kai He, and Ruxu Du Yong Nie, Zhigang Liu, Jinhua Zhang and Dichen Li Precision Engineering Center, Shenzhen Institutes of Advanced Technology Stake Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University Shenzhen, China Xi’an,China • Complete the structural design • The designing idea of this parallel of prosthetic hand according to the redundant action mechanism. anthropometric data . • The working principle of this • Develope a kind of digital design attitude adjustment mechanism. software of prosthetic hand. Two The 3-DOF Prosthetic Hand • The parts digital assembly with this function modules are included, quick mechanism. design module and customizable • The parts’ trajectory planning during module. the attitude adjustment process. • Make prototypes and do some tests. The Attitude Adjustment Mechanism
ME-3 (5) 17:20—17:40
Dynamic Optimum Design and Analysis of Cam Wave Generator Qianjin Xiao1,2, Hongguang Jia1 and Xuefeng Han1,2 1.Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences Changchun, China 2. Graduate School of Chinese Academy of Sciences Beijing, China
• The mathematical optimization model of the cam wave generator were established. • Topology optimization and size optimization were performanced, and the optimal results were obtained. • The analysis based on the static and dynamic performance was carried out in order to check The topology density the performance of the optimal cam. distribution of cam
13
ME-4: ISIT Pattern Recognition II
Session Chairs: Chao Wang and Panhong Wang Room Macau, 16:00—17:40, Monday, 6 June 2011
ME-4 (1) 16:00—16:20 ME-4 (2) 16:20—16:40 Robust Abnormal Wireless Capsule Endoscopy Vehicle Detection Based on Spatial-Temporal Connection Background Subtraction Frames Detection Based on Least Chao Wang, Zhan Song Guilin University of Electronic Technology Squared Density Ratio Algorithm Guilin, Guangxi, China Haibin Wang, Dongmei Chen, Max Q.-H. Meng, Chao Hu, Zhiyong Liu Shenzhen Institutes of Advanced Technology Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, China Shenzhen, Guangdong, China The Chinese University of Hong Kong, Hong Kong, Shatin, China • This paper proposes a new framework by defining Frame • The spatial contour information Abnormality Index using the ratio of training and testing data extracted to detect the vehicles. densities. • The GMM is used to establish the • We use Least Square-based background model for subtraction. algorithm to estimate density • The stability of the background ratio parameters without model is considered. involving density estimation. • Different methods are analysed and Actual clinical patient frames the stability is better than GMM including various abnormal method. frames are used to evaluate The strategy to detect abnormal video frames • Pre-process method is proposed. the performance of the Background Stability proposed method.
ME-4 (3) 16:40—17:00 ME-4 (4) 17:00—17:20 A Human Identification Method Learning Mahalanobis Distance for DTW based Online Based on Dynamic Plantar Pressure Distribution Signature Verification Yu Qiao1,2, Xingxing Wang1, and Chunjing Xu1,2 Yong Feng, Yunjian Ge, Quanjun Song 1. Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, China Department of Automation, University of Science and Technology of China 2. The Chinese University of Hong Kong, Hong Kong, China Hefei, China • Propose Mahalanobis distance (MD) for • Dynamic plantar pressure includes online signature verification. anatomical and behavioral characteristic • Estimate covariance matrix in MD of human. – Minimize signature difference • We established an in-shoe plantar for the same writer. pressure measure system for collecting Alignment pressure information. • Some methods were used for data – Maximize signature difference preprocessing. for different writers • SVM was used for classification, and the recognition rate reached 96% in our Technical flow chart • Achieve better performances than experiments. previous methods. Results
ME-4 (5) 17:20—17:40 A Method for HMM-Based System Calls Intrusion Detection Based on Hybrid Training Algorithm Panhong Wang, Liang Shi, Beizhan Wang, Yangbin Liu, Yuanqin Wu Software School of Ximen University, Ximen, China • HMM (Hidden Markov Model) is a very important intrusion detection tool. • The classical HMM training algorithm can only find a local optimal solution. • this paper introduces a hybrid algorithm into intrusion detection. • Experiments show that this algorithm can find a more accurate model. The Taring Model of HMM
14
Tuesday June 7, 2011
TA-1 Neural Network
TA-2 Vision I
TA-3 Intelligent Information
TA-4 ISIT Mechanics Design and Analysis
TP-1 Modeling and Control
TP-2 Vision II
TP-3 Computer and Application
TP-4 ISIT Engineering Optimization
TE-1 Stability Analysis and Control
TE-2 Embedded & FPGA System
TE-3 Network
TE-4 ISIT Reliable and Optimization
15
TA-1: Neural Network
Session Chairs:Chenn-Jung Huang and Yangze Dong Room Zhuhai, 10:20—12:00, Tuesday, 7 June 2011
TA-1 (1) 10:20—10:40 TA-1 (2) 10:40—11:00 Neural Network Based Edge Detection for RBF Neural Network Parameters Optimization Automated Medical Diagnosis based on Paddy Field Algorithm Sheng Wang1,2, Dawei Dai1,2,3, Huijuan Hu 4, Yen-Lun Chen1,2 ,and Xinyu Wu1,2 Dingran Lu(1), Xiao-Hua Yu(1), Xiaomin Jin(1), Bin Li(2,3), Quan Chen(2,3), Jianhua Zhu(4) 1.Shenzhen Institutes of Advanced Technology,Chinese Academy Sciences ,Shenzhen, China (1) Dept. of Electrical Eng., California Po lytechnic State University, SLO, Ca lifornia, USA 2.The Chinese University of Hong Kong Shatin, N.T., Hong Kong (2) Smar tbead Inc., San Luis Obispo, California, USA 3.South China University of Technology, Guangzhou, China (3) Health-co ming Co. Ltd, Haining, Zhejiang Province, China 4.China University of Mining and Technology, Xuzhou, China (4) Department of Orthopedics, Huzhou Central Hospital, Huzhou, Zhejiang Province, China Corresponding prediction output of various algorithms 1. 5 • With regard to the issue of selecting Radial Basis Ac tual output PSO-RBF Prediction Functions (RBF) neural network center 1 • Artificial neural network is employed to detect PFA-RBF Prediction parameters, this paper has introduced the paddy RBF Prediction edges in gray-scale images 0. 5 field algorithm (PFA) for its optimization.
• Fuzzy sets are introduced during the training phase y 0 • PFA had stronger global search capacity and to improve the generalization ability of neural higher convergence speed so as to better -0. 5 networks optimize RBF neural network. • The proposed approach is applied to the edge -1 • The experiment showed that all predicted errors -1. 5 detection of medical images for automated bladder 0 500 1000 1500 were lower than that of PSO predicted results. Sampling points x cancer diagnosis Edge detection for bladder cancer cell images Predicted output
TA-1 (3) 11:00—11:20 TA-1 (4) 11:20—11:40 Fuzzy Comprehensive Evaluation of Army KM Infrared Face Recognition Based on Local Binary Performance Based on Neural Network Pattern and Multi-objective Genetic Algorithm Identification Tu Wei, Xie Zhihua Jiang xi Sciences and Technology Normal University Xu Yuanlin, Gao Peng, Liu Zengliang and Xu Peng Nanchang, Jiangxi, China School of Management of Graduate School,The Chinese Academy of Sciences,Beijing,China • To get robust local features in infrared • Construct the army knowledge face, local binary pattern representation management perfor mance eva luatio n is applied to our method, instead of system ... holistic feature extraction method • Feature selection algorithm based on ...
• Fuzzy comprehensive evaluation ... multi-objective genetic algorithm (MOGA )
algorithm base on BPNN and ... is propos ed to analyse and discard patterns that are not relevant to the ... RBFNN ... recognition task. • Builde the evaluation model • The experimental results demonstrate the infrared face recognition method • Evaluated the knowledge management The features extraction process NN structure of determine the weight based on LBP+MOGA proposed of 4 corpses in certain division by outperforms the traditional methods based on LBP and NSGA using the above network structure and based on LBP or PCA+LDA.
TA-1 (5) 11:40—12:00
Vehicle-License-Plate Recognition Based on Neural Networks YiQing Liu，Dong Wei， Ning Zhang，MinZhe Zhao Control Theory and Control Engineering Beijing Institute of Civil Engineering and Architecture Xicheng Distrct,Beijing,China Artificial Intelligence Tianjing KuGe Technology Co.,LTD
• A license plate recognition system based on neural networks was designed and developed. The system used a neural-network chip（ CogniMem) to recognize license plates. • The c hip is a fully parallel silico n ne ural network with 1024 neurons inside.
17
TA-2: Vision I
Session Chairs: Baopu Li and Zhangjun Song Room Hong Kong, 10:20—12:00, Tuesday, 7 June 2011
TA-2 (1) 10:20—10:40 TA-2 (2) 10:40—11:00 3D TRACKING USING RECTANGULAR REGIONS Capsule Endoscopy Video Boundary IN STRUCTURED SCENES Detection Baopu Li, Max Q.-H. Meng, Kun Peng, Lulu Hou, Jing Kong, Ren Ren, Xianghua Ying, Hongbin Zha Department of Electronic Engineer ing, the Chinese University of Hong Kong Key Laboratory of Machine Perception (Ministry of Eduction) School of EEC S, Peking University, Beijing China • Capsule endoscopy (CE) is a new technology to diagnose the diseases for small • This paper presents a practical 3D intestine; tracking method using rectangular regions in structured scenes. • Reduction of the review time for a CE video is desired; • Dominant orthogonal vanishing • Color, textural and motion features are chosen to rep resent the frame content; points and some projections of rectangular regions (PRR) are • Boundary detection is obtained by localizing local maximal values along the feature detected from the first frame. distance curve;
• Full camera pose is tracked using 3D TRACKING USIN G REC TANGU LAR REGIONS IN STRUCTURED SCENES • Preliminary experiments show a promising performance of CE video boundary the intensity differences of PRR detection . among the frame sequences.
TA-2 (3) 11:00—11:20 TA-2 (4) 11:20—11:40
A Novel Strategy to Label Abnormalities for Wireless Real-time Pedestrian Detection Based on Edge Capsule Endoscopy Frames Sequence Dongmei Chen, Max Q.-H. Meng, Haibin Wang, Chao Hu, Zhiyong Liu Factor and Histogram of Oriented Gradient
No rmal Frames o n ly Normal& Abnormal Frame Guoqing Xu1,2.3 and Xiaocui Wu1,2, Li Liu1,2 and Zhengbin Wu1,2 • Wireless Capsule Endoscopy (WCE) is the most accurate, patient-friendly diagnostic tool 1 Shenzhen Institutes of Advanced Technology Chinese Academy of Science, that allows phy sicians to see the patient’s ShenZhen, China; whole gastrointestinal tract, especially the Pati en t W CE V id eo sma ll intestine . However, reviewing capsule Representative Group Under-Monitoring testing database 2 The Chinese University of Hong Kong, Hong Kong, China; endoscopic video is a labor intensive task and Pre-prepared Training Database very time consuming. Also the diagnosis process by WCE videos is not real-time. All Featu re Ex trac tio n above limitations motiva te us to develop an approach to automatically detect the
abnormalities in real tim e. In this paper we Training Testing propose a novel strategy to detect abnormal Samples Samples frame for WCE. D en sity • The key idea of the proposed stra tegy is to D en sity define the Fra me Abnormality Index (FAI)
using the ratio of training and testing data D en sity densities, where training dataset only consist of Ratio Frame Abnormality Index norm al samples and testing dataset consist of original image Gradient both normal and abnormal samples. We select Threshold training and testing database from several • Pre-process: convert to greyscale, zooming, set ROI; WCE video segments to do our pilot N o rmal Frame experiment. Experimental results show that the proposed strategy achieves promising • Coarse detection—edge factor; performances. Abnormal Frame
Approach to detect abnormal WCE frames • Fine detection—HOG/linSVM.
TA-2 (5) 11:40—12:00 Real-time Vehicle Detection Based on Haar Features and Pairwise Geometrical Histograms Xi Yong, Liwei Zhang, Zhangjun Song, Ying Hu, Lan Zheng, Jianwei Zhang Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences The Chinese University of Hong Kong, Hong Kong, China
• The PGH is a powerful shape descriptor applied to contours matching • In recent years, the Viola and Jones rapid object detection approach became very popular • Haar features are similar to the basis functions in Haarwavelet • We combine the Haar features Result of Vehicle Detection using Our Method and PGH together for vehicle detection
18
TA-3: Intelligent Information
Session Chairs: Xianhui Yang and Yu Mao Room Kowloon, 10:20—12:00, Tuesday, 7 June 2011
TA-3 (1) 10:20—10:40 TA-3 (2) 10:40—11:00 Automatic Extraction of The Lung Field from A new model updating approach of multivariate Volumetric Images for Statistical Anatomical statistical process monitoring Modeling: A Technical Approach Bo He, Xianhui Yang Department of Automation, T singhua University H ongliang Ren 1 and Max Q.-H. Meng 2 1 Children’s H ospital Boston, USA Beijing, China 2 Chinese University of Hong Kong • Proposed a new model updating • Fully automatic segmentation • Populational Statistical approach of multivariate statistical • Intensity Based Automatic Anatomical Shape Analysis process monitoring . Extraction of Lung-field • Statistical Atlas • Combining the information from quality information with real-time process measurements to monitoring. • An effective approach of calculating the upda te int erval has been d iscussed. • CSTR simulation to evaluated the proposed algorithms has been presented The Scheme of the new model updating approach
TA-3 (3) 11:00—11:20 TA-3 (4) 11:20—11:40 An Improved Method and Algorithm for An Alternative to Enhanced External Electromagnetic Localization Counterpulsation: A Pilot Study of ECG-driven Jinglan Tian1,2, Shuang Song1, Xiaojing Wa ng1, Tingfang Yan2,Chao Hu1, Ma x Q.‐H. Meng1,2,3 1) Shenzhen Instit utes of Advanced Technology, the Chinese Academy of Science, the Chinese University of Sequential Muscle Stimulation Hongkong, Shenzhen, China Ren Xu1, Xiaochang Liu1, Jia Liu1, Gang Dai2 and Guifu Wu2 2) School of Control Science and Engineering, Shandong University, Jina n, Shandong, China 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences / The Chinese 3) Department of Electronic Engineering, The Chinese University of Hong KongShatin, N.T. Hong Kong University of Hong Kong, Shenzhen, China 2 The Key Laboratory of Assisted Circulation, The Ministry of Health of China / The First • The ma gnetic field is ge nerated by Affiliated Hospital of Sun Yat-sun University, Guangzhou, China one 3-axe excitation source and one • We developed a system aiming at improving cardiac circulation similar to 2-axe sensor. EECP, using ECG-driven sequential • The two sensors are fixed with an muscle stimulation (ESMS). included angle (a constant va lue • A experiment on a beagle dog was less than 90 degree). carried out to find out its immediate hemodynamic effects . • Obtain the 6-D parameters(x, y, z, • We observed the augmented di astolic ψ, θ, φ) of position and blood pressure and blood flow induced or ie nta tio n informatio n. The Geometric Relationship by the proposed system, though D/S ESMS system ratio is much lower than the reported ( ) TA-3 (5) 11:40—12:00
An Analysis of the Effect of Bit Error Ratio on Signal Reconstruction Error Yu Miao, Haiyan Wang, Xuan Wang, Wanzheng Ning ,Yuan Zeng School of Marine Engineering, Northwestern Polytechnical University, Xi’an Shaanxi, China Abstract - When compressed sensing technology is applied to underwater acoustic communication system, signal reconstruction error will be affected more or les s by bit error ratio (BER). In this essay, random number of statistics, which obeys the law of standard normal distribution, is used to simulate the generation of bit error. Using the same reconstruction algorithm, reconstruction error with bit error and reconstruction error without bit error are compared. The result of the simulation shows that signal reconstruction error with a bit error ratio of 102 is 4 times as much as signal reconstruction error without bit error ratio.
Index Terms - Compressed Sensing; Bit Error Ratio; Reconstruction error.
19
TA-4: ISIT Mechanics Design and Analysis
Session Chairs: Jianing Liang and Jia Liu Room Macau, 10:20—12:00, Tuesday, 7 June 2011
TA-4 (1) 10:20—10:40 TA-4 (2) 10:40—11:00 Electric Air Conditioner System with On-board Charger for PHEV Performance Analysis of Two-way Cartridge Calve Based on MESim and Aorthogonal Test Jianing Liang12, Guoqing Xu23, Linni Jian12 and Liu Li12 Lei Tian Jinjin Guo and Rensheng Yu 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China Tianjin University 2The Chinese University of Hong Kong, Hong Kong, China 3Tongji University, Shanghai, • Proposed a novel topology of SRM converter. • Integrated a SRM driving function and battery charging function. • Analyzed the operation mode of proposed converter. • Proposed a control scheme for proposed converter. Proposed converter
TA-4 (3) 11:00—11:20 TA-4 (4) 11:20—11:40 Mechanical Designs and Control System of a novel instantaneous phase difference Throwable Miniature Reconnaissance Robot estimator:Piecewise Maximum cross-correlation Liancun Zhang1, Qiang Huang1, Liying Wu2,Yuancan Huang1, Yue Li1 and Wenhua Sang1 function 1. Beijing Institute of Technology, Beijing , China Xiaoan GU1, Jia Liu1, Xiping Gong2 2. Beijing University of Technology, Beijing, China 1Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences 2 • The robot can real-time feedback video and audio of target area. Department of Neurology, Beijing Tiantan Hospital, Capital Medical University • we took the complex Morlet wavelet • The robot has autonomous mode and remote control mode. method as an example to discuss the • The mechanical designs and control possible limitations system of the robot have been illustrated in the paper. • we proposed a new statistical approach PMCC based on time shift estimation. • ANSYS/LS-DYNA was applied to accomplish dynamic simulation • PMCC avoids the avoids the trade-off analysis of the robot. between time and frequency resolution and performs well in anti-noise . • Some experiments have been done to The Throwable Miniature • PMCC requires data to be mono-time- PMCC method validate the anti-impact ability and Reconnaissance Robot autonomous capabilities of the robot. shift signal.
TA-4 (5) 11:40—12:00 The Research and Design of ATM PIN Pad Based on Triple DES Wanping Wu1,2, Jianxun Jin1, and Jun Cheng2,3 1University of Electronic Science and Technology of China 2Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences. 3The Chinese University of Hong Kong ATM(Automatic Teller Machine) PIN (personal identification number) pad is essential in bank. In the paper, a PIN Pad system is designed based on 3DES encryption algorithm, and the exception handling is added in the PIN Pad (such as: jitter, command coverage, the wrong password more than three times). The experimental The system flow chart results demonstrate feasibility of designed system.
20
TP-1: Modeling and Control
Session Chairs: Zhangjun Song, Xinyu Miao Room Zhuhai, 14:00—15:40, Tuesday, 7 June 2011
TP-1 (1) 14:00—14:20 TP-1 (2) 14:20—14:40 Modeling of a Gyro-stabilized Helicopter Camera Research on Mathematical Model of Autonomous System Using Artificial Neural Networks Decentralized PMSM and Its Current Compensation Nicholas Layshot, Xiao-Hua Yu during Failure Department of Electrical Engineering, California Polytechnic State University Jizhu Liu, Shuanghui Hao, Shaohua Wang, Peng Zhang and Tao Chen San Luis Obispo, CA 93407, USA School of Mechanical and Elec tric Engineering, Soochow University Suzhou, China • The inertial characteristics of the Permanent magnetic synchronous motor (PMSM) with high speed, large torque and high power usually employs centralized high power, which increases the capacity of the inner gimba l in a mu lti-g imbal transformer, and reduces the reliability of the system, so the paper proposes a scheme system is modeled by artificial based on an autonomous decentralized architecture, in which the stator winding uses neural networks distributed network structure, each coil of the winding is controlled by a separate • The neural network is trained with driving controller, to improve the fault tolerance of the driving system. The paper time-domain data obtained from an deduces the formula for PMSM vector coordinate transformation, and the formula for motor self-inductance and mutual-inductance, as well as the motor voltage and actual gyro-stabilized camera system electromagnetic torque model in decentralized architecture, taking a 8-pole 12-slot • Computer simulation results show the neural PMSM as example, establishes the mathematical model of current compensation with network model fits well with the constant motor instantaneous electromagnetic torque before and after one or all coils of measurement data and significantly phase A are failed, finally tests the correctness of the current compensation model by outperforms the traditional model using finite element simulating analysis and concrete experiments .
TP-1 (3) 14:40—15:00 TP-1 (4) 15:00—15:20 Accurate Distortion Modeling of Active-RC Lossy Integrator by Volterra Series Method Generating Lane-change Trajectories using the Yingwu Miao and Yuxing Zhang Dynamic Model of Driving Behavior U niversity of Electronic Science and Technology of China Guoqing Xu1,2, Li Liu1, Zhangjun Song1, Yongsheng Ou1. Chengdu, C hina -20 1.Shenzhen Institutes of Advanced Technology, Chinesena Academy of Sciences.
• Accurate distortion modeling of active-RC -40 2.The Chinese University of Hong Kong, Shatin, Hong Kong.
integrator was proposed using the Volterra -60 dB 50 50 series method. -80 • In this paper, we propose a dynamic lane- • All the nonlinearity including nonlinear 40 -100 Spectre change model which reflects the driver 40 intermodulation, the loading effect of external Analysis -120 2 4 6 8 control strategy of adjusting longitude and configuration components and parasitic 10 10 10 10 30 30 frequency (Hz) capacitance are considered. -20 lateral acceleration. -40 • The proposed model is intuitive and can • Theoretical analyses are in accordance with 20 20 the transistor level simulation even up to 5 -60 clearly describe the habit and randomness of dB times gain bandwidth product. -80 lane-change behavior with limit parameters. 10 10 -100 • Analysis derived can be used to accelerate the -120 Spectre 0 0 design and avoid time-consuming transient Analysis 0 12 3 4567 0123 4567
-140 2 4 6 8 simulation. 10 10 10 10 frequency (Hz) Generated Lane-change Trajectories 2nd and 3rd order distortion
TP-1 (5) 15:20—15:40
Immune Evolution Algorithm for Iterative Learning Controller
Xiulan Wen, Hongsheng Li, Fulin Teng , JiaCai Huang, Li Fang Automation Department, Nanjing Institute of Technology Nanjing, Jiang su, China
• Immune evolution algorithm for iterative learning controller. • The proposed method is effective for both linear time invariable system and non- linear plant model • It has higher tracking accuracy and fast convergence speed. Tracking results in different iterations for non-linear plant model
21
TP-2: Vision II
Session Chairs: Qing He and Wei Liu Room Hong Kong, 14:00—15:40, Tuesday, 7 June 2011
TP-2 (1) 14:00—14:20 TP-2 (2) 14:20—14:40 Approach of Human Face Recognition Based on A New Calibration Method Used in the Infrared SIFT Feature Extraction and 3D Rotation Model Ray Environment Ran Zhou, Jie Wu, Qing He, Chao Hu and Zhuliang Yu Chenxi Wang2, Qing He 1,2, Ning Wei 2, Wei Liu 2, Chao Hu2,and Max Q.-H. Meng2,3 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 1 Institu te Microelectronics o f Chinese Acad emy of Sciences, Be ijing, China Shenzhen, China 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences,Shenzhen, China • This paper proposes a novel algorithm 3 Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, of human face recognition to overcome China the influences of varying poses and • Employed in the infrared ray illumination. environment • The structure of our face recognition • Design a move path to replace the system. gridiron pattern which is invisible in • SIFT feature extraction and matching. the infrared ray environment. • 3D rotation model. • The C.M.M control a visible ball to move along the designed path • Experiments and conclusions. Recognition rates • Intrinsic and extrinsicparameters computation Coordinate measuri ng machine
TP-2 (3) 14:40—15:00 TP-2 (4) 15:00—15:20 Design of an Embedded Vision System for the The Design of Infrared Touch Screen Rubik’s Cube Robot based on MCU Xin Hu, Xi Chen, Lei Nie, Zhan Song Zheng Wei, Wei Liu, Qing He, Ning Wei, Chenxi Wang, Tingfang Yan, Chao Hu, Max Q.-H. Meng Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences and The Province, China Chinese University of Hong Kong This paper introduces a touch screen technology based on infrared optical. This technology has apparent advantages in the large-size applications, which is simple, low cost, high feasibility.
Source Image Smooth Image •In the system, the detection of ambient light and the adaptive control of IR LED can effectively enhance the adaptability of the touch screen in Fig. 2 Before The Rubik’s Cube Restoration complex light environment via the MCU. •The image of foreground and background are Binary Image Result segmented by Otsu’s method with a coefficient. Integral projection is adopted to recognize the contact area. •Real time and accuracy. Result of Experiment Fig. 1 Hardware Structure Diagram Fig. 3 After The Rubik’s Cube Restoration
TP-2 (5) 15:20—15:40 Crowd Behavior Detection Based on the Energy Model Guogang Xiong, Xinyu Wu, Yen-Lun Chen and Yongsheng Ou Shenzhen Institutes of Advanced Technology ,Chinese Academy of Sciences Shenzhen, China • Two typical abnormal activities: pedestrain gathering and running. • Based on the potential energy and kinetic energy. • Reliable estimation of crowd density and crowd distribution are firstly introduced into the detection.
Pedestrain Gathering and Running
22
TP-3: Computer and Application
Session Chairs: Xuncai Zhang and Lanju Kong Room Kowloon, 14:00—15:40, Tuesday, 7 June 2011
TP-3 (1) 14:00—14:20 TP-3 (2) 14:20—14:40
A Metadata-driven Cloud Platform for Delivery of Grid Structures for Efficient Geometric SaaS Applications Algorithms KONG Lanju, LI Qingzhong Xiaodong Wang and Daxin ， School of Computer Science and Technology Shandong University Quanzhou Normal University Jinan, Ch ina Quanzhou, China • Designed and implemented a • This paper presents an efficient data structure for the on-line metadata-driven cloud platform for closest pair problem in $d$ dimensional space. The data delivery of SaaS applications. structure maintains the closest pair of the current point set in • Support development through \textit{d} dimensional space on- line in amortized time standard SQL and effectively $O(\log^2n)$, using $O(n)$ space. support the tenants’ customization. • In high-dimension cases, a data structure is given that • Easy to insure the data node maintains the minimal distance in amortized $O((\log n)^{d- stretching in the cloud. 1} )$ time, using $O(n)$ space. This leads to an $O(n\log ^{d- The SaaS Platform Model 1} n)$ time algorithm for the on-line closest pair problem.
TP-3 (3) 14:40—15:00 TP-3 (4) 15:00—15:20 Solving Minimum Vertex Cover Problems with Predicting Algorithm for RNA Pseudoknotted Microfluidic DNA Computer Structure Xuncai Zhang1, Ying Niu2, Fei Li1, Zuoxin Gan3 Zhendong Liu and Chuande Fu School of Electronics Engineering and Computer Science, Peking University ShandongJianzhu University and Shandong University Jinan, China Beijing China • Pseudoknots are a frequent RNA structure. Based on the relative stability of the s tems • Microflow Reactor in RNA mo lecules . • Selection Procedure and Principle of Operation • an algorithm is presented to predict RNA • Microfluidic Networks for Minimum Vertex Cover pseudoknotted structure,the introduced algorithm takes O(n3) time and O(n) space and outperforms other known algorithms in predicting accuracy. . • The algorithm not only reduces the time complexity to O(n3), but also widens the maximum length of the sequence. • The experimental results on the RNA sub- The architecture of a 6 bit configurable microfluidic computer sequences in PseudoBase indicate that the Complex Pseudoknotes algorithm has good accuracy, sensitivity and specificity.
TP-3 (5) 15:20—15:40 Semi-supervised Temporal-spatial Filter Based on MRP for Brain computer interfaces Lv Jun and Wang lei College of Automation, Guangdong University of Technology, Guangzhou, China
• In brain-computer interface (BCI) studies, if the number of training trails is small, the discriminative patterns of movement related potentials (MRPs) can not be appropriately extracted by temporal-spatial filter (TSF) algorithm.
• In this paper, we proposed a semi-supervised Fig .1 Av erag ed p red ict io n accu rac ies o f ssTSF acro ss 4 0 ti mes of random partitio n o n o r ig in al d a tas et, w i th d if f er en t it er at io n TSF (ssTSF) algorithm which emp loyed self- numbers and training sizes. The first iteration means that no unlabelled trial induced, viz. conventional TSF . Error bar training scheme to induce the unlabelled trails denotes stand error of mean. with high confidences and learn the discriminative patterns of MRPs iteratively. • TSF and ssTSF were evaluated on the data from BCI competition I. The results demonstrated the (a) TSF (b ) ssTSF
effectiveness of the ssTSF, especially for small Fig. 2 the standard deviation of spatial filter we ights for each channel obtained by TSF and ssTSF respectively (randomly training sets. choosing 80 training trials for 40 times).
23
TP-4: ISIT Engineering Optimization
Session Chairs: Lisheng Xu and Zhiwei Wang Room Macau, 14:00—15:40, Tuesday, 7 June 2011
TP-4 (1) 14:00—14:20 TP-4 (2) 14:20—14:40 Design of Frequency Invariant Response Array A Heuristic Task Scheduling for Multi-Pursuer Based on Steepest-descent algorithm Multi-Evader Games Wang Zhiwei1, Wang Dacheng1 and Liu Wenshuai2 Shiyuan Jin and Zhihua Qu 1.College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China Dept. of EECS, University of Central Florida 2.Dalian Test and Control Institute, Dalian, China Orlando, FL 32821, USA • A combination of Voronoi diagram • Model of signal received by 0 partitioning and negotiation-based
array. -10 mechanism is applied to the high- level for the scheduling of tasks • Building the target function. -20 between teams. • Derivation of weight vector -30 • A heuristic algorithm is applied to using steepest-descent -40 each team for position optimization
algorithm and simulation Normalizedoutput power/dB -50 of pursuers.
analysis. -60 • Guidelines for cooperation between evaders. -70 • The improved steepest-descent -40 -30 -20 -10 0 10 20 30 40 An g le /° • Simulation results show the algorithm and its simulation. Frequency invariant beam pattern Scheduling, surrounding and with the steepest-descent algorithm importance of team negotiation and the effectiveness of the heuristic capturing functions.
TP-4 (3) 14:40—15:00 TP-4 (4) 15:00—15:20 Intelligent Multi-Mode Energy-Refreshing Influence of the surface roughness on the micro- Station for Electric Vehicles within the vibrations of the aerostatic air film Framework of Smart Grid Yu Jing, Zhang Wen, Li Dongsheng, Xu Pengfei and Zhang Yubing Linni Jian,Guoqing Xu,Honghong Xue and Ming Chang College of metrology & measurement engineering Shenzhen Institutes of Advanced Technology China Jiliang University Chinese Academy of Sciences & Chinese University of Hong Kong 1068 Xueyuan Avenue,Shenzhen University Town,Shenzhen,P.R.China • aerostatic bearing system • aerostatic air film • surface roughness • frictional resistance • micro-vibration
Pressure distribution of the semicircular roughness model
Proposed intelligent multi-mode energy-refreshing station for EVs
TP-4 (5) 15:20—15:40 Multi-Gaussian Fitting for Digital Volume Pulse Using Weighted Least Squares Method Lisheng Xu1, 2, Shuting Feng1, Yue Zhong1, Cong Feng1, Max Q.-H. Meng3, Huaicheng Yan4 1Sino-Dutch Biomedical and Information Engineering School, 2Key Laboratory of Medical Image Computing, Northeastern University, Shenyang , China. 3The Chinese University of Hong Kong, Hong Kong, China. 4School of Information Science and Engineering, East China University of Science and Technology, Shanghai , China. • Decompose Digital Volume Pulse (DVP) using Multi-Gaussian (MG) model. • DVP is classified into four types and MG model can fit different types of DVP. • Component pulses of MG model may possess great signification in arterial parameter estimation. Pulse Plethysmogram
24
TE-1: Stability Analysis and Control
Session Chairs: Yu Wang and Shaobo Kang Room Zhuhai, 16:00—17:40, Tuesday, 7 June 2011
TE-1 (1) 16:00—16:20 TE-1 (2) 16:20—16:40
Optical Axis Stabilization of Semi-Strapdown Backstepping tracking control for a class of Seeker Based on Disturbance Observer discrete-time nonlinear systems in pure- Gao Sun, Mingchao Zhu, Shengli Yin, and Hongguang Jia feedback form Graduate University of Chinese Academy of Sciences Beijing, China; Changchun Institute of Hua Meng, Zhan Zhang and Shaoqing Wei Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China Hebei University of Science and Technology,Shijiazhuang, China • A new control scheme is proposed for a • Strapdown Stabilization Equation class of pure-feedback nonlinear and Control Principle. discrete-time systems by using backstepping technique. • Disturbance Observer Design. • The stability of the close-loop system is • Hardware-in-loop simulation shows proved based on Lyapunov theorem. effective function of the disturbance • Simulatio n stud ies are conducted to observer. illustrate the effectiveness of the proposed approach. Stabilization error compare at 5°1Hz missile disturbance
TE-1 (3) 16:40—17:00 TE-1 (4) 17:00—17:20
Delays-independent stability analysis of Delay-dependent Asymptotical Stability Analysis networked and quantized control system of Nonlinear Delay Neural Networks Feng Yi-wei , GuoGe School of Information Science and Technology, Dalian Maritime U niversity , Yuzhong Mo Dalian, China (Departm ent of Mathematics and Computer Science Liuzhou Teachers College ) 2 •In the note, the global asymptotic stability of nonlinear cellular 0 Stability analysis for System neur al network s with cons tant delay is studied. states networked and quantized -2 At first, a trans forma tion is made the nonlinear neura l networks
-4 control systems (NQCSs). 0 10 20 30 40 50 into the linear neural networks. Then the Lyapunov-Krasovskii A new delays-independent 2 stability theory for functional differential equations and the linear stability criterion is derived matrix inequality (LMI) approach are employed to investigate the Control fo r N QCSs . signal 0 problem. Present a guaranteed cost A novel sufficient condition is derived that is less conservative -2 0 10 20 30 40 50 controller for NQCSs. Times than the ones reported so far in the literature. N umerical examples illustrate the effectiveness of the method and State trajectory and control signal versus time improvement oversome existing methods.
TE-1 (5) 17:20—17:40
The Input Characteristic and Stability Analyse of Inverter with Induction Motor Shaobo Kang, Yaohua Luo, Jiang You and Shijia Lv College of Auto mation, Harbin Engineering University Harbin , China
The input and output impedance are e mployed to analyze the stability of t wo s tep cascaded system, and the input impedance of inverter was derived and analyzed in the paper
Motor controller • The d-a xis rotor flu x is inverse relationship controller with input impedance of inverter with IM.
Prime G IM mover • The input impedance would change with Line Propulsion capacitance the load torque. transformer rectifier filter inverter motor • The input impedance of inverter with IM is changed with DC-link voltage. The Two Step Cascaded System
25
TE-2: Embedded and FPGA Syetem
Session Chairs: Gang Wang and Jingsheng Liao Room Hong Kong, 16:00—17:40, Tuesday, 7 June 2011
TE-2 (1) 16:00—16:20 TE-2 (2) 16:20—16:40 FPGA-Based Parallel Calculation of Focus Hardware System Design of SD Card Reader and Function Image Processor on FPGA Yansi Yang, Yingyun Yang, Lipi Niu, Huabing Wang and Bo Liu Wenjia Ni, Jintao Liu, Shi Chen, Qing He, Wei Liu and Chao Hu Information Engineering Schoo, Communication University of China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Beijing, China Shenzhen, Guangdong Province, China • We designed a useful digital signal generating system which transforms various file data stored in SD card into SDI output • This paper proposes an adaptive signal based on the FPGA hardware platform. method with FPGA to design a new • This paper presents the hardware design and implementation auto-focus system. of the system, which includes two steps. • section 2 mainly describes the method • Include one picture/graph o f your work with >300 dpi of region selection and how to assign resolution. weights for each block in the system. • First step is the design of the NIOS II system, which includes • The discussion and experiment on SRAM controller and SD card controller IP core design. several classical focus functions are Second step is the generation of the whole functional SOPC presented in section 3. system which using Q uartus II de velop ment tool. NIOS II • Section 4 gives a parallel calculation system is integrated with the scrambling encoders in this step. method on the computing of the focus Defocus-Focus- And then the hardware system is implemented. function. Defocus Pictures
TE-2 (3) 16:40—17:00 TE-2 (4) 17:00—17:20 The Hardware System of Body Impedance Detection Equipment for Bottom Dead Jinhong Liao1, Zhiyuan Zhou2,Gang Wang3, Chao Hu4, Yong Yin5 Center of Punching Machine Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Derong Zhang Min Zheng Shenzhen, China Ningbo Institute of Technology, Zhejiang University Finance Bureau of Jiangdong District Ningbo, PR China Ningbo, PR China • The principle of Bio-impedance RC three components equivalent circuit model • Detection equipment of BDC. • Signal Generator choose MAX038 as the • Principle of Eddy Current Sensor. main chip to produce a sine signal, the range of output sine wave’s frequency is • Waveform of BDC Signal. 0.1Hz~20MHz which can be adjusted by the external resistor and capacitor. • Analog Filter , Digital Filter Design and Real-time Data • Voltage-controlled current source (VCCS) Circuit use AD844 to complete voltage into Acquisition. a current • LabVIEW Software Design. • Diffe rential a mp lifier c ircu it use AD620 to amplifier the voltage signals of measured body and precise resistor • Phase gain detected circuit use AD8302 to obtain the value of U1/U2 • Voltage uplifted circuit is designed for A/D The Block Diagram of the System sample ,to make sure the sampled signal’s voltage in the range 0~3.3V The system design diagram.
TE-2 (5) 17:20—17:40
Design of Medical Remote Monitoring System Base on Embedded Linux Ping Li, Jingsheng Liao, Chao Hu Shenzhen Institute of Advanced TechnologyShenzhen, Guangdong Province, China
• The remote wireless monitoring terminal use ARM microprocessor as its core controller. • The first is physical signal acquiring design as monitoring terminal. • The second section is a method to achieve the function of remote monitoring based on 3G wireless network and SSH (Secure Shell). • The whole software is run in embedded Linux system. The Remotoring System
26
TE-3: Network
Session Chairs: Guo Cui and Hongyu Shi Room Kowloon, 16:00—17:40, Tuesday, 7 June 2011
TE-3 (1) 16:00—16:20 TE-3 (2) 16:20—16:40 Information Reduction Based on Temporal A parking guidance and information system Similarity and Spatial Importance for Video based on wireless sensor network Mingkai Chen1,2,3 , Tianhai Chang1 Transmission in Mobile Surveillance System 1. School of Electronic and Information Engineering ,South China University of Technology ,Guangzhou,China Yi-Chun Lin and Feng-Li Lian 2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Department of Electrical Engineering, National Taiwan University foreman China 256 (kb ) 512 (kb) 1024 (kb ) 2048( kb) 4096( kb) 8192( kb) 10240( kb) 60 24.12 / 3.87 29 .81 / 3.52 31. 50 / 1.81 31. 95 / 1. 04 31. 95 / 1. 04 39. 66 / 1.92 40. 41 / 1. 28
Taipei, Taiwan 0.00 /1. 42 0. 00 / 3.76 16. 72 / 6.99 22. 99 / 3. 84 30. 47 / 0. 65 35. 79 / 0.60 39. 74 / 0. 76 50 0.00 /0. 00 0. 00 / 0.00 0. 00 / 0. 00 0. 00 / 0.00 0. 00 / 3.02 33. 15 / 0.34 33. 37 / 0. 57 3. The Chinese University of Hong Kong, Hong Kong, China • Temporal similarity sampling is used 40 30 PS (dB) NR to eliminate temporal redundancy. 20 • The communication between nodes 10
0 0 1000 2000 3000 4000 5000 6000 7 000 80 00 900 0 10000 Bandwidth (kb) is based on wireless sensor
• Spatial importance encoding is hall
256 (kb ) 512 (kb) 1024 (kb ) 2048( kb) 4096( kb) 8192( kb) 10240( kb) 60 23.68 / 2.07 31 .36 / 1.44 32. 29 / 0.67 32. 29 / 0. 67 32. 29 / 0. 67 41. 09 / 0.60 41. 09 / 0. 53
0.00 /0. 75 0. 00 / 3.19 16. 43 / 1.29 21. 60 / 0. 43 31. 09 / 0. 18 37. 06 / 0.17 40. 76 / 0. 12 network. utilized to maintain high importance 50 0.00 /0. 00 0. 00 / 0.00 0. 00 / 0. 00 0. 00 / 0.00 0. 00 / 0.00 33. 22 / 0.08 33. 84 / 0. 30
40
30 content. PS (dB) NR • For the new-coming car, PGIS will
20 • Information Reduction based on 10 calculate an ideal parking space. 0 0 1000 2000 3000 4000 5000 6000 7 000 80 00 900 0 10000 Bandwidth (kb)
Client 2(1024k bps) Similarity and Importance (IRSI) Lossless • PGIS will control the LED to show Label 1 90 Label 2 80 Label 3 algorithm is proposed. 70 the path to the parking space. 60
50
40 PSNR (dB) • Experimental results demonstrate 30
20
10
excellent performance. 5 61 146 187 222 255 Architecture of PGIS Frame (No.)
TE-3 (3) 16:40—17:00 TE-3 (4) 17:00—17:20
A New Hybrid Algorithm on TDOA A Prediction-Based Joint Bandwidth Allocation Localization in Wireless SensorNetwork Scheme for Heterogeneous Wireless Networks Chenn-Jung Huang, Ying-Chen Chen, Sheng-Chieh Tseng, Yu- Wu Wang and Chin-Fa Lin, HongyuShi Jianzhong C ao Department of Electronic Science Huizhou U niversity Huizhou, China Heng-Ming Chen and Chih-Tai Guan Intelligent System Laboratory, National Dong Hwa University Hualien, Taiwan • Abstract :A hybrid algorithm for TDOA localization is proposed in this paper. It has well combined the advantages of genetic algorithm and quasi-Newton algorithm. The hybrid algorithm has sufficiently displayed the characteristics of • Wireless network resource distribution genetic algorithm’s group searching and quasi-Newton method’s local strong is an important issue in recent years. searching. At the same time it effectively overcomes the problem of high sensitivity to initial point of quasi-Newton method and shortcoming of genetic • A user mobility prediction algorithm algorithm which reduces the searching efficiency in later period. The was proposed to improve it. experimental results show that if the parameters are assumed reasonably the • Hybrid genetic algorithm in o ur work hybrid algorithm has extremely stability, higher localization rate and localization is employed to allocate bandwidth precision than genetic algorithm and quasi-Newton algorithm. more effectively. • Keywords:Localization; Genetic Algorithm; Quasi _Newton Algorithm; TDOA • Results showed the effectiveness. Joint bandw idth allocation scheme
TE-3 (5) 17:20—17:40
Distributed Least Square Support Vector Regression for Environmental Field Estimation Bowen Lu, Dongbing Gu, and Huosheng Hu Robotics Group, Computer Science and Electronic Engineering, University of Essex, UK
•A distributed Least Square Support Vector Regression is applied on mobile sensor network for field function estimation.
•A gradient descending method (CVT) is used for sensor locational optimising.
Estimated result
27
TE-4: ISIT Reliable and Optimization
Session Chairs: Chaokun Yan and Ming Xu Room Macau, 16:00—17:40, Tuesday, 7 June 2011
TE-4 (1) 16:00—16:20 TE-4 (2) 16:20—16:40 Study on Delivery Reliabiltiy of Push-Pull Mixed Reliability Enhanced Grid Workflow Scheduling Supply Chain Algorithm with Budget Constraints Guohua Chen, Genbao Zhang and Jihong Pang Chaokun Yan, Zhigang Hu,Xi Li, Zhoujun Hu and Peng Xiao College of Mechanical Engineering Chongqing University, China Central South University
percent of pass 1 output time1 Changsha, China reject ratio 1
supplier's production supplier's inve ntory manufacturer's production order ratio 1 inspection ratio1 reject ratio 2 output ratio 1 delivery • Adopt M/M/C queuing system to reliability 1 inspection time 1 inspection ratio2 describe grid resource nodes’ manufac turer' s percent of pass 2 supplier's order target inve ntory workloads and service process. cycle time order 2 inspection time 2 • Proposed a reliability enhanced ditributor's production manufacturer's inventory grid workflow scheduling reject ratio3 order ratio 3 output ratio 2 ditributor's order delivery algorithm with budget constraint. inspection ratio3 percent of pass 3 order 3 cycle time reliability 2 market demand ditributor's target output time 3 • The main idea of this algorithm is inventory Smooth time ditributor's inventory Average demand to select the resource which can sales rate 3 maximize the reliability of task de livery re lia bility 3 execution. The dynamics model of push-pull mixed • The order of tasks in the queue is Mr.Yan supply chain operation sorted by Min-min Cost strategy.
TE-4 (3) 16:40—17:00 TE-4 (4) 17:00—17:20 Emulation Analysis on Space Gain of Frequency Multiple Sparse Tables Based On Pivot Table For Invariant Array Wang Zhiwei1, Wang Dacheng1 and Liu Wenshuai2 Multi-Tenant Data Storage In SaaS 1.College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin, China 2.Dalian Test and Control Institute, Dalian, China Wang Xue, Li Qingzhong and Kong Lanju School of Computer Science and Technology, Shandong University 0 Jinan, China • Calculation on spatial gain of frequency invariant. -10
• Proposes an index model called • Emulation analysis of the -20 Pivot Table influence of frequency-invariant -30 • Constructs respective index array error on spatial.
omliNormal zed out put-40 / power dB metadata for business data of the • Emulation analysis of the tenants, and achieves isolation of influence of array error to -50
-60 index data & customization frequency invariant feature. - 100 - 80 - 60 -40 - 20 0 20 40 60 80 100 angle / ° • Returns the tenants’ result sets more Beam pattern with errors quickly or updates index data on- demand The pivot table index model
TE-4 (5) 17:20—17:40 Cost-related importance measure Ming Xu, Wencong Zhao and Xianhui Yang Department of Automation, Tsinghua University Beijing, China
• A new importance measure (IM) - the cost-related importance measure (CIM) is introduced for system cost- risk analysis. • Different from other basic IMs, CIM takes into account both structure importance and cost-efficiency importance. • CIM is additive, which is easier for calculating groups of events or parameters than other basic IMs CIM for double-bride network
28
Wednesday June 8, 2011
WA-1 Control System I
WA-2 Mobil Robot
WA-3 Communication
WA-4 ISIT Serve Robot
WP-1 Control System II
WP-2 Special Design Robot
WP-3 Sensor
WP-4 ISIT Image
WE-1 Advanced Engineering Management II
WE-4 ISIT Material
29
WA-1: Control System I
Session Chairs: Guiyong Yang and Weimin Li Room Zhuhai, 10:20—12:00, Wednesday, 8 June 2011
WA-1 (1) 10:20—10:40 WA-1 (2) 10:40—11:00 Anti-skid for Electric Vehicles Based on Steering Law for Control Moment Sliding Mode Control with Novel Structure Gyroscopes Based on H∞ Theory Kun Xu1,2, Guoqing Xu2,3, Weimin Li1,2, Linni Jian1,2 and Zhibin Song1,2 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China Jingwen Yang, Shuai Tang, Li Zhang, Zhiqiang Zheng 2The Chinese University of Hong Kong, Hong Kong, China College of Machtronics and Automation , National University of Defense Technology 3Tongji University, Shanghai, China Changsha, China
• SMC based anti-skid control, with a athematical Modeling of Rigid friction coefficient estimator. Spacecraft with CMGs • Construct a novel structure to deal Novel Steering law for CMGs is with driver’s operation on foot pedal proposed and anti-skid control . esign the steering law with a control • Using a slip ratio selector instead of perspective the torque selector to improve the ased on H∞ Theory, get the dynamics. simulation for the new steefing law • Using co-simulation of SIMULINK Novel anti-skid controller and CarSim to show the effectiveness.
WA-1 (3) 11:00—11:20 WA-1 (4) 11:20—11:40 A Novel Method Using PFA in Parameters Design and Implementation of Model Predictive Control Turning of PID Controller of High-order System Algorithms for Small Satellite Three-Axis Stabilization Ming Cheng1,2,3, Dawei Dai1,2,3, Pandeng Zhang1,2, Jia Liu1,2, Fei Luo3 Xi Chen, Xiaofeng Wu 1Shenzhen Institutes of Advanced Technology, Chinese Academy Sciences, Shenzhen, China School of Aerospace, Mechanical and Mechatronic Engineering, 2The Chinese University of Hong Kong, Hong Kong, China The University of Sydney, Sydney, Australia 3South China University of Technology, Guangzhou, China
step response • The Paddy Field Algorithm (PFA) is 1.1 PSO-PID PFA-PID •Laguerre Functions are used to operated in the parametric space from PID 1 the initial scattering of seeds. simplify the traditional MPC 0.9 Algorithm
• We apply the PFA algorithm to design 0.8 the PID controller of a high-order Yout •A five-stage pipeline processor is 0.7 designed system. 0.6 •A novel methodolo gy for testing • Tested with the PID and PSO 0.5
5 10 15 20 25 30 35 40 45 50 the performance of the MPC- algorithms, the results show that the Ti m e(s e c) designed controller of PFA has a Step response curve of the dedicated embedded processor is better performance of overshoot and plant with different controller developed settling time.
WA-1 (5) 11:40—12:00
Nonlinear Parameter Prediction of Fossil Power Plant Based on OSC-KPLS Xi Zhang, Shihe Chen, Weiwu Yan, and Huihe Shao Guangdong Electric Power Research Institute Guangzhou,Guangdong, China
7 • A no vel p arameter p redic tion a nd Actual(red -) Predicted value Residual(green-) 6 estimation method based on Predicted(blue --) 5 orthogonal signal correction (OSC) Actual value 4
Training data Testing data and kernel partial least squares (KPLS) 3 Output is proposed. OSC-KPLS effectively 2 simplifies both the structure and 1 Residual v alue 0 interpretation of the resulting -1 0 20 40 60 80 100 120 140 160 180 200 regression model and shows superior Sam ple Number prediction performa- nce compared to OSC-KPLS prediction results PLS, OSC-PLS and KPLS.
31
WA-2: Mobil Robot
Session Chairs: Dan Liu and Bo Su Room Hong Kong, 10:20—12:00, Wednesday, 8 June 2011
WA-2 (1) 10:20—10:40 WA-2 (2) 10:40—11:00 Research on Algorithm of counter Target lost for Maneuvering Reentry Vehicle Trajectory Tracking and Attitude Identification of using Infrared Imaging Terminal Guidance Tao Xu, Xiao-ping Zhu, Xiao-feng Zhang the Lunar Rover Based on Computer Vision College of Astronautics, Northwestern Polytechnical University Jianjun DU, Dan HU and Jianjun ZHU Xi’an,,Shannxi, China Shenzhen Graduate School ,Harbin Institute of Technology Shenzhen, Guangdong Province 518055, China • Maneuvering Reentry Vehicle using Infrared(IR) imaging terminal guidance is Dun LIU vulnerable to interference which cause the seeker lost target. School of Astronautics, Harbin Institute of Technology Harbin, Heilongjiang Province 150001, China • According to this problem, first, this paper presents a new counter target lost Algorithm which combined extended state variable dimension passive location based on Extended Kalman Filter(EKF) and virtual guidance method to • Visual equipment and lunar rover rebuild guidance information. Then the passive location accuracy of extended • Velocity estimation based on state variable dimension EKF and normal EKF algorithm is compared Kalman filter throughout simulation, at last, guidance accuracy of fore mentioned two algorithm and acceleration command memory algorithm are compared by • Attitude identification of lunar rover circular error probable(CEP) simulation which based on six degree of freedom • Emulation of the visual servo system (6DoF) guidance and control model. • Experiment of visual servo track and • Simulation results show the validity of the algorithm presented in this paper. attitude identification
Diagram of visual sets and lunar rover
WA-2 (3) 11:00—11:20 WA-2 (4) 11:20—11:40 Research on Virtual Scene Simulation for Active Pedestrian Following Using Laser Range Planetary Rover Finder Chin-Lung Chen, Chih-Chung Chou, and Feng-Li Lian Ning Mao1,2, Bo su2, Qichang Yao2, Shuling Yang2, Hongji Xu1 Department of Electrical Engineering, National Taiwan University 1Changchun University of Science and Technology Taipei, Taiwan Changchun, Jilin Province, China 2China North Vehicle Research Institute • Detect and track the pedestrian Beijing, China target in a dynamic environment. • The kinematics equations of rover • Follow the pedestrian target by moving on uneven terrain are selecting the optimal trajectory deduced. using he uristic sear ch approac h. • The joint angles and some attitude • Avoid any static and dynamic angles can be got by solving obstacles by adopting DWA*, a nonlinear optimization equations. look-ahead algorithm. • The rover kine matic s simulatio n in Pioneer Robot Following a Pedestrian the virtual scene is realized. The rover in virtual environment
WA-2 (5) 11:40—12:00 Formation Control for Wheeled Mobile Robots Based on Consensus Protocol Shulin Feng , Huanshui Zhang School of Control Science and Engineering, Shandong University Jinan, China • In this paper, consensus protocol is presented for formation control of the mobile robots. In allusion to the mobile robot platform, a local computer which is used as controller and the AmigoBot mobile robots set up a wireless local area network (WLAN), transmitting data by means of wireless communication to implement the remote control of robots. The kinematics mathematical model of the mobile robot is presented, making use of consensus protocol to implement the column AmigoBot Robot and triangular formation control.
32
WA-3: Communication
Session Chairs: Zixin Zhao and Xuesen Lin Room Kowloon, 10:20—12:00, Wednesday, 8 June 2011
WA-3 (1) 10:20—10:40 WA-3 (2) 10:40—11:00 Highly Directional Emission from Multi- A Novel Method of Multipath Mitigation for C/A channel Photonic Crystal via Beam splitting Code Tracking Loop Based on Wavelet Qiong Wang1, Quanqiang Yu1, Lingling Zhang2 , Yiping Cui2, and Zhengbiao Ouyang1 Transform 1 College of Electronic Science and Technology,Shenzhen university,Shenzhen, China 2 School of Electronic Science and Engineering,Southeast University,Nanjing, China XUE Bing, GAI Meng, SHEN Feng, and LIU Na 407 Lab, Automation School, Harbin Engineering University • Efficient directional emission is Heilongjiang, China realized by using beam splitting in •Multipath is one of main errors multi-channel photonic crystal in GPS and other spread s ()n (MCPC). IF spectrum systems, it seriously I P • MCPC is introduced to create affects the performance of the multiple light beams on the surface navigatio n receiver. by coupling effect. Electric field amplitude distributions •A method of detecting • The interference of the light beams of (a) 1-PCW without multi-channel jumping-off point of emitted from the surface channels and (b) 1-PCW (c) 3-CPCWs autocorrelation function based leads to the directional emission. (d) 5-CPCWs with multi-channel on wavelet analysis is proposed.
WA-3 (3) 11:00—11:20 WA-3 (4) 11:20—11:40 A Multi-standard-supporting and General Survey on Cloud Based Mobile Security and A Communication Protocol Parsing System Design New Framework for Improvement Xiangtao Jiang, Jianbiao He Xuesen Lin School of Computer and Information Engineering, Central South University of Forest Computer Science and Engineering, The Chinese University of Hong Kong Technology, Changsha, China Hong Kong, China • The state-of-the-art of cloud based • Use descriptive and easy extension mobile security. way to define communication • The limitations of current protocol. frameworks. • Construct the universal sub-parsing • A new framework PCFC (Private engine of data item. Cloud and File Characteristic based) • decouple the protocol parsing module and protocol defining is proposed . module. • Provide uniform persistent interface. System component The architecture of PCFC structure diagram
WA-3 (5) 11:40—12:00 Development of Diabetics-oriented Telemedical Information System Zhao, Zhiqiong Wang, Yu Tang, Mengyu Zhao, Shuzhong Chen, Jingqi Hou, Meng Ke Sino-Dutch Biomedical and information College, Northeastern University Shenyang, China • A development of Diabetics- oriented Tele-medical Information System (DTMIS) is introduced. • Our software module is divided into patient-end, the doctor-end, and the server end. • The design of a self-monitoring center at the patient’s place is introduced. • We use wave let packet trans form Video Consultation Module (WPT) to remove the noise and baseline signal of ECG.
33
WA-4: ISIT Serve Robot
Session Chairs: Ying Liu and Yongsheng Ou Room Macau, 10:20—12:00, Wednesday, 8 June 2011
WA-4 (1) 10:20—10:40 WA-4 (2) 10:40—11:00 The Mobile Manipulation System of the Butler Robot Household Butler Robot based on Chunjie Chen, Xinyu Wu, Long Han, and Yongsheng Ou Shenzhen Institutes of Advanced Technology, CAS Multi-Monocular Cameras Shenzhen, China Long Hana, Xinyu Wua,b, Chunjie Chena and Yongsheng Oua I. INTRODUCTION a Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. II. ROBOT CONTROL SYSTEM bThe Chinese University of Hong Kong. III. APPLICATION OF NETWORK
IV. PROCESS CONTROL FOR BUTLER ROBOT • Describing a mobile manipulation V. SIMULATION AND EXPERIMENT system based on multi-view monocular. • Splitting the whole grasping problem into four steps in a "Look- Move-Look-Move" pattern and be solved step by • Implementing this algorithm on the Household Butler Robot (HBR). The HBR
WA-4 (3) 11:00—11:20 WA-4 (4) 11:20—11:40 Mechanical Design, Kinematic Modeling and Optimization on Multi-Robot Workcell Layout Simulation of a Robotic Dolphin in Vertical Plane Peng Liu, Kai He, Xiefeng Ou, and Ruxu Du Long Tao and Zhigang Liu Precision Engineering Center ,Shenzhen Institutes of Advanced Technology, Chinese State Key Laboratory for Manufacturing Systems Engineering Academy of Sciences Shenzhen, China Xi’an,Shannxi China • Designed the mechanical structure of robotic dolphin • Describe a potential application of multiple industrial robots in • Establish the motion equation aircraft fuselage riveting. of the robotic dolphin • Optimize multi-robot workcell • Achieve the motion simulation layout for avoiding collision during aircraft fuselage surface of the sinusoidal movement operation in vertical plane. mechanism and the caudal fin • Transform the problem of multi- robot workcell layout into a The Robotic Dolphin nonlinear programming problem. The Fuselage Assembly Site
34
WP-1: Control System II
Session Chairs: Feiping Wu and Xuejie Wang Room Zhuhai, 14:00—15:40, Wednesday, 8 June 2011
WP-1 (1) 14:00—14:20 WP-1 (2) 14:20—14:40 Using Reinforcement Learning for Agent-based Hybrid Flow-Shop Scheduling Method Based on Network Fault Diagnosis System Multi-agent Particle Swarm Optimization Fu Yue-wen, Zou Feng-xing, Xu Xiao-hong, Cui Qing-zhu and Wei Jia-hua Jingang Cao College of Mechatronics and Automation, National University of Defense Technology Department of Computer, North China Electric Power University Changsha, Hunan, China Baoding, China • Introduce the characteristics of • Present a hybrid integer program- Mobile Agent and Reinforcement ming model of HFSP. Learning. • Propose a MPSO algorithm with • Design and describe the Architecture the hybrid of MAS and PSO. of Network fault diagnosis system. • Design a random cycle topological • Depict the Reinforcement Learning structure related to MPSO. Algorithm of the fault diagnostic • The simulation shows that MPSO agent. can accelerate the evolution of the • Compared the system performance agents, improve the convergence through simulation and experiment, The Architecture of network precision and enhance the global The reconstruction process of optimum searching ability of PSO. and results show that the model has fault diagnosis system random cycle topological structure greater advantage.
WP-1 (3) 14:40—15:00 WP-1 (4) 15:00—15:20
Equipment Design of Linear Motor Driven A Design and Implementation of Edge Inverted Pendulum Based on cSPACE Controller for SPWM Waves 1,2 , 1, 1, 2 Rongmin Cao Huixing Zhou Ronghua Ma Ang Su Fan Lin, Kun Li and Yang Liu 1 School of Industry, China Agricultural University, Beijing 100083,China School of Aerospace Science, Beijing Institute of Technology 2 Beijing Information Science & Technology University, Beijing, China Beijing 100192,China
Inverted pendulum is a kind of typical platform for control theory verification. SPWM (Sinusoidal Pulse Width Modulation) waves are commonly used in the Noted as a non-linear, strong-coupling and natural instable system. In this paper, control of frequency conversion and test system. This paper presents a method ironless permanent magnet synchronous linear motor driven inverted pendulum which can control SPWM waves rising/falling time for the requirements of laboratory equipment is developed. The plant is hardware in the loop real time controlled edge. This function mainly for a high power device test systems. First, simulation control development system (Hardware-in-Loop, HIL) based on SPWM waves are generated by a generator programmed by LabVIEW, and the TMS320F2812DSP and MATLAB, it can use simple and efficient way to achieve SPWM waves’ dead time and frequency of carrier signals and modulation signals linear motor driven inverted pendulum real-time control. Control algorithm can be can be easily regulated by LabVIEW. Second, SPWM waves rising / falling time is investigated directly on MATLAB/Simulink, and can be generated automatically adjusted by edge controller. The experimental results indicate that SPWM waves control code to control inverted pendulum system. Long design time for edge time adjusts from 0.1μs to 12μs can be achieved using this design. programming and debugging difficulty are avoided for traditional programming language.The real performance of the driven inverted pendulum is researched in this paper, Based on cSPACE real time control system, the experiment results showed that the control ability of the system is fine.
WP-1 (5) 15:20—15:40 Pre-estimate Relative Intensity Noise Subtraction Performance of FOG by Using Signal Cross-Correlation
Yonggang Zhang, Honggang Chen, Tao Li and Deshuang Wang Automation College, Harbin Engineering University
Harbin, China Res ult of RIN reducti on with cros s-correlati on coeffic ient 0.91 0.05 interference si gnal b(n)
• A method of pre-estimate Relative Intensity Noise 0 (RIN) subtraction performance in IFOG is -0.05 introduced. 0 1000 2000 3000 4000 5000 6000 0.05 • Experiment shows that the noise subtraction result del ayed RIN sequenc e d(n) 0
depends on the cross-correlation coefficient voltage (v) -0.05 between FOG’s output and coupler’s free port 0 1000 2000 3000 4000 5000 6000 0.05 signal. FOG's output signal e(n) after nois e subtract ion 0 • This method can be used in high precision FOG, -0.05 0 1000 2000 3000 4000 5000 6000 and it can enhance the reliability of noise sampling point subtraction. Result of RIN reduction with coefficient 0.91
35
WP-2: Special Design Robot
Session Chairs: Xinyu Wu and Ying Liu Room Hong Kong, 14:00—15:20, Wednesday, 8 June 2011
WP-2 (1) 14:00—14:20 WP-2 (2) 14:20—14:40 On a Novel Wheeled Robot with Tumbler An Overview of the Underactuated Biped Characteristics Robots Liping Ouyang, Ying Liu, Ansi Peng, Xinyu Wu, Yongsheng Ou Zhensheng You Zhihuan Zhang Shenzhen Institutes of Advanced Technology, Chinese Academy Sciences, Shenzhen, China Zhejiang University Ningbo Institute of Technology, Zhejiang University The Chinese University of Hong Kong, Hong Kong, China • House environment challenges those • Introduction wheeled robots and it is a tough problem to keep balance in size, • Model Description intelligence, mobility and safety of a • STABILITY CRITERION ho usehold robot. Figure 1:Design concept • GAIT PLANNING • we proposed a novel wheeled robot • CONTROL STRATEGY with sp herical shape to conq uer those problems. This robot combines • CONCLUSION AND FUTURE DEVELOPMEN T wheeled robot with tumbler characteristics. • Mechanical design and experiment result is introduced. Figure 2: Prototype
WP-2 (3) 14:40—15:00 WP-2 (4) 15:00—15:20 Mechanical Structure of Intelligent A Screw Propelling Capsule Robot Underwater Dexterous Hand Huajin Liang1,2,3, Yisheng Guan1, Zhiguang Xiao1, Chao Hu2,3, Zhiyong Liu2,3 Xu De-Zhang,Yang Ming, Zhang Qing Wang Bu Yun,Ge Yun Jian 1Biomimetic and Intelligent Robotics Lab (BIRL), Anhui Polytechnic University WuHu City , China South China University of Technology, Guangzhou, China 2Shenzhen Institutes of Advanced Technology, CAS, Shenzhen, China • Introduction 3The Chinese University of Hong Kong, Hong Kong, China • The Design Goals of the • A novel approach to act ive Underwater Working capsule robot/ endoscopy. Dexterous Hand • A theoretical model for the robot is built, considering both • The Components of the the hydrodynamic effect and the Underwater Working direct contact. Dexterous Hand • The average speed of the • The Equipment of Sensors capsule robots in rubber pipe • Conclusion filled with water is evaluated as The screw propelling capsule robot IUDH Dexterous hand 60 mm/s.
WP-2 (5) 15:20—15:40 Heading Direction Computation Of Golf-Ball Collecting Robot Using Vanishing Points Zhiqiang Ma, Hyongsuk Kim Robot Vision Laboratory, Chonbuk National University Jeonju, Republic of Korea • Techniques of Navigation system for golf ball collecting robot is proposed using vanishing points of parallel lines contained in the facility buildings. • The inter-stair parallel lines of facility buildings are extracted and then the vanishing points are computed using Moore-Penrose inverse. Golf-Ball Collecting Robot • The proposed vanishing point method is proved to be time efficient and accurate via experiments.
36
WP-3: Sensor
Session Chairs: Zhang Qi and Bo Yang Room Kowloon, 14:00—15:40, Wednesday, 8 June 2011
WP-3 (1) 14:00—14:20 WP-3 (2) 14:20—14:40 Dynamic-Range Adjustable Pipelined ADC in A PVDF Micro-force Sensor Based on Inverse- CMOS Image Sensor with Black-Level and Offset model Algorithm and Its Applications Zhiyong Sun, Lina Hao, Shuai Li, and Jiawei Shen Noise Calibration School of mechanical Engineering and Automation, Northeastern University Ran Zheng, Tingcun Wei, Feng li and Deyuan Gao Shenyang, China Engineering Research Center of Embedded System Integration Ministry of Education, Northwestern Polytechn ical University, Xi’an, China • This paper gives a model of one • Proposed CIS Architecture. PVDF with its physical circuit • Sampling and calibration method system and gives its inverse-model. of black-level and offset noise. • This paper designs a static micro- force sensor based on the inverse- • An input-range adjustable pipelined model a lgorithm. ADC is necessary for noise sampl- • This paper sets up a micro-force- ing. tracking system and also gets some • The dynamic range is improved by good experimental results. 6dB. The force-tracking system
WP-3 (3) 14:40—15:00 WP-3 (4) 15:00—15:20 Research on Thermal Characteristics and On-Chip Temperature-Controlling Novel statistical technique of defective information for Silicon Micro-Gyroscope extraction in Pulsed eddy current NDE 1 2 Lu Xu, Bo Yang, Shourong Wang, Hongsheng Li and Libin Huang Guang Yang , Qi Zhang Key Laboratory of Micro Iner tial Instrument and Advanced Navigation Technology 1.Deparment of Electical and Computer, Michigan State University 2.Shenzhen Institutes of Advanced Technology, CAS of Ministry of Education Shenzhen, China Southeast University Nanjing 210096,China The pulsed eddy current (PEC) technique as complementary approach of traditional eddy current method has found increasing applications in deep flaw • Capacitive sensitivity has a variation detection and complex structure inspection. Considering the present PEC NDE of 13.5％ when gyroscope’s working (non-destructive evaluation) needs valid algorithms and techniques to implement temperature has a change of 50K. signal processing in time domain, the novel statistical technique based on • A gyroscope prototype with on-chip principle component analysis (PCA) and independent component analysis (ICA) temperature-controlling is given and is proposed to extract defective information from transient PEC signals and its performances are analyzed evaluate flaw inspection during the PEC detection. The presented results of defect theoretically and numerically. Cross sectional view of gyroscope classification associated with different flaw types validated the feasibility of • Fabrication process is designed proposed technique in this paper. with on-chip temperature-controlling based on Silicon-On-Glass (SOG) technology.
WP-3 (5) 15:20—15:40 Assessing Age‐Related Performance Decrements in User Interface Tasks Xiaolei Zhou Capital University of Economics and Business Manual dexterity Visual acuity Tasks Cognitive abilities Hearing … R How about elderly people W performance in common interface tasks? Conclusions The older subjects: performed less accurately faster speed at the cost of more loss of accuracy showed greater individual differences
37
WP-4: ISIT Image
Session Chairs: Yu Qiao and Yaoqin Xie Room Macau, 14:00—15:40, Wednesday, 8 June 2011
WP-4 (1) 14:00—14:20 WP-4 (2) 14:20—14:40 iGAPSearch: Using Phone Camera to Search Around A Video Quality Metric based on Frame the World Differencing Jiemin Wang1,2,3, Yuanhai He3, Yujie Zhou3, and Yu Qiao1,2 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen 518055, China Engin Mendi, Coskun Bayrak, Mariofanna Milanova 2. The Chinese University of Hong Kong, Hong Kong, China 3. Software Institute, Nanjing University, Nanjing, Jiangsu Province, China Computer Science Department, University of Arkansas at Little Rock, Little Rock, AR, USA Computer Engineering Department, Istanbul Kultur University, Istanbul, Turkey
PS NR ‐ wPSNR fo r Blurring • Identify buildings in certain area • Image - Video quality assessment. 100 filter size=6, std. dev=6 ‐ PSNR filter size=8, std. dev=8 ‐ PSNR using photos captured by phone 80 filter size=10, std. dev=10 ‐ PS NR • A simple and affordable objective filter size=6, std. dev=6 ‐ wPSNR filter size=8, std. dev=8 ‐ wPSNR wPSN R 60 filter size=10, std. dev=10 ‐ wPSN R & cameras. quality metric for tracking moving PSNR • Client part: Android phone platform. objects in video streams. 40
20 • The proposed metric particularly takes 3 6 9 12 15 • Server part: Linux OS, manage data Frame Ind ex the moving objects into account as SSIM ‐ wSSIM fo r Blurring and run a search. 1 visually important content. filter size=6, std. dev=6 ‐ PSNR • Image retrieval part: SIFT feature, filter size=8, std. dev=8 ‐ PSNR wSSIM • Foreground masks produced by frame filter size=10, std. dev=10 ‐ PS NR 0.9 & filter size=6, std. dev=6 ‐ wPSNR filter size=8, std. dev=8 ‐ wPSNR
K-means, frequency vectors and bag SSIM difference-based background filter size=10, std. dev=10 ‐ wPSN R of words method. subtraction are incorporated as 0.8 3 6 9 12 15 weighting function into the existing Frame Ind ex • Compare L1-norm, L2-norm, KL- divergence and χ 2 distance. iGAPSearch metrics. VQA plots
WP-4 (3) 14:40—15:00 WP-4 (4) 15:00—15:20 Using Neural Network to Combine Measures of A Feature-based Watermarking Algorithm Word Semantic Similarity for Image Annotation Resistant to Copy Attack Yue Cao, Xiabi Liu, Jie Bing and Li Song Junpeng Zhang , Qingfan ZhangShujuan Geng and Mingyu Zhang School of Computer Science and Technology, Beijing Institute of Technology School of Control Science and Engineering, Shandong University,Jinan, China Beijing, China • The Feed-forward Neural Network • A novel Feature-based watermarking algorithm for color image resistant to NC NC1 NC2 (FNN) is introduced to combine copy attack. Graph 1 1 0.5123 semantic similarity measures Graph 2 1 0.4782 • The feature is obtained from the Graph 3 1 0.4531 between words. chroma component of color host image • A Particle Swarm Optimization Graph 4 1 0.4510 and describes the image uniquely. Graph 5 1 0.4562 (PSO) algorithm is designed to train • DWT transform and Scrambling by Graph 6 1 0.4763 the FNN for achieving the optimal logistic chaotic sequence can enhance Graph 7 1 0.4209 image annotation accuracy. the security and robustness further. Graph 8 1 0.5067 • The annotations for testing images • The algorithm is robust against based on our hybrid measure are common image processing and is Nc Values Extracted From obviously better than those based on the annotations for the effectively robust against copy attack. Copy Attacked Images single measures. example images
WP-4 (5) 15:20—15:40 A Novel Video Object Segmentation Based on Recursive Kernel Density Estimation Qingsong Zhu Guanzheng Liu Zhen WangHao Chen and Yaoqin Xie Shenzhen Institutes of Advanced Technology, CAS Shenzhen, China This paper presents a novel recursive Kernel Density Estimation method for dynamic video segmentation. Mean shift method is used to approximate the peak values of the density function recursively. Components and parameters of mixture Gaussian distribution are adaptively selected via a proposed scheme and finally converge to a relative stable background distribution. In the segmentation, foreground is separated by simple background subtraction method firstly. And then, Bayes classifier is proposed to eliminate the misclassification points to refine the segmentation result.
38
WE-1: Advanced Management II
Session Chairs: Dang Li and Liang Ge Room Zhuhai, 16:00—17:40, Wednesday, 8 June 2011
WE-1 (1) 16:00—16:20 WE-1 (2) 16:20—16:40 Vendor independent Control Database for Virtual Functional Requirement and Realization of Preparation and Formal Verification Regional Smart Grid Energy Management Petter Falkman, Jonathan Hedvall, Anders Holmblad, Bengt Lennartson System Control and Automation Laboratory Department of Signals and Systems Chalmers University of Technology Xilin Zhang1, Xiaojuan Han2,Xiaohua Wan3 and Shuo Wang4 Göteborg, Sweden Dispatching Center, Changchun Power Supply Company, JiLin, China • Virtual techniques for testing and developing software systems. • Power Quality On-Line Monitoring of EMS for • Formal verification techniques in District Intelligent Grid order to guarantee a correct system • The Setting Calibration Conditions About behavior. Extinction Arc coil • Present paper presents a method for • SCADA Interface Project Design with Integrated sharing information between the virtual development tools and formal Control verification tools. The Sony Aibo Dog
WE-1 (3) 16:40—17:00 WE-1 (4) 17:00—17:20 Design the principal dimensions of ships based Research on Premature Ventricular Contraction on a fuzzy hybrid operator Real-time Detection Based Support Vector Liang Ge, Yuan-hang Hou, Xiang-yin Meng Machine College of Ship Building Engineering, Harbin Engineering University Haibin, China Zhao Shen, C hao Hu, Ping Li and M ax Q.-H Meng School of Automation, Northwestern Polytechnical University • The uniform design method was used to Xi’an, Shaanxi, China provide a series versions of an original Shenzhen Institutes of Adv anced Technology project for experts to choose from. Shenzhen, Guangdong, C hina 2.5 • Their score of each version’s attributes was • The three types feature extracted by gathered, and then entered in a new morphology and spectrum method. 2 intuitionistic fuzzy matrix that was used to • The SVM is used to classified the PVC from 1.5 construct an overall expert opinion. other beats. 1 • The intuitionistic fuzzy hybrid geometric • Real-time detection method is proposed. 0.5 operator processed the evaluating matrix, • Different types of PVC is analysed and the result is more than 97% by using MIT-BIH 0 ensuring the weights of experts’ attitudes database. were counted in. • Pre-process method is proposed. -0.5
-1 • In this way a final decision on ship’s 0 500 1000 1500 2000 2500 3000 3500 principal dimension was finally obtained. Premature Contraction the basic optimize model
WE-1 (5) 17:20—17:40
Study of EMS Statements production and Dispatcher Simulation Training for Regional Smart Grid Xilin Zhang1, Fan Zhang2, Xiaojuan Han3 and Shuo Wang4 Dispatching Center, Changchun Power Supply Company, JiLin, China • Introduction • Regional Power Dispatching DTS Function Design • Regional Power Dispatch DTS Function Design • Conclusion
39
WE-4: ISIT Material
Session Chairs: Rong Sun and Kai Gong Room Macau, 16:00—17:40, Wednesday, 8 June 2011
WE-4 (1) 16:00—16:20 WE-4 (2) 16:20—16:40 Preparation and Magnetic Properties of Silane Modeling Asymmetric and Large-displacement Modified Ni-Zn Ferrite and its Epoxy Composites Hysteresis in Piezoelectric Actuators Yanmin Wu1, Pengli Zhu1, Rong Sun1,2 Guilin ZHANG, Chengjin ZHANG 1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen School of Control Science and Engineering, Shandong University 518055, China; 2. The Chinese University of Hong Kong, Hong Kong Jinan, China • Ni-Zn ferrite was prepared by a • Hysteresis is the main form of coprecipitation method ,and then nonlinearity in piezoelectric modified with KH-560. actuators. • It was proved that permeability • Introduce the inflexion point of the of this ferrite was higher . ascending curve. • The study of magnetic properties • Describe the ascending curve and of saline-modified Ni-Zn ferrite/ descending curve separately. epoxy stated that it kept stable permeability and had lower magnetic loss. Magnetic properties
WE-4 (3) 16:40—17:00 WE-4 (4) 17:00—17:20
The Impact of Laser Drilling on AlN Ceramics Multi-physics Analysis of Heat-Structure Chongyu Xie, Xizhao Lu, Haihe Xie,Wenqi Ding on Surface Resistance School of Physics and Mechanical & Electrical Engineering, Xiamen University Xiamen, China Jianxu Hu, Maozhou Meng Shenzhen Institutes of Advanced Technology Shenzhen, Guangdong, China • AlN has been used in printing Graduate University of Chinese Academy of Sciences circuit board for a long time, • Surface resistance was chosen to explain what and we need to explore the relationship among the electric field, heat transfer, and structure expansion by using the drilling conditions of it. multi-physics modeling and simulation software, COMSOL Multiphysics. • By performing experiments on • Model these three physical fields in fully AlN by laser drilling, we get to coupling. The components of the electric field, temperature field and stress field distribution know there’s little impact when were extracted to explain the interaction among laser drilling on the AlN. these three physics fields. • According to the results of numerical • At last, We get the way to simulation, the engineer will be inspired to Drilling on the AlN obtain an optimization structure and process. improve the quality of the laser drilling on AlN. Temperature distribution at steady state
40
Session Chair Index 2011 IEEE ICIA SESSION CHAIR INDEX
Bai,Hongyang ………………… ………… ………… ………… ………… ………… MP-3 Cui,Guo ………………………… ………… ………… ………… ………… ………… TE-3 Dong,Yangze ……… ………… ………… ………… ………… ………… ………… TA-1 Ge,Liang …………… ………… ………… ………… ………… ………… ………… WE-1 Gong,Kai ……………………… ………… ………… ………… ………… ………… WE-4 He,Kai ……………… ………… ………… ………… ………… ………… ………… ME-3 He,Qing ………………………… ………… ………… ………… ………… ………… ME-2 He,Qing ………………………… ………… ………… ………… ………… ………… TP-2 He,Yuqing ……………………… ………… ………… ………… ………… ………… MA-1 Hu,Ying ………………………… ………… ………… ………… ………… ………… MP-2 Huang,Chenn-Jung …………… ………… ………… ………… ………… ………… TA-1 Kang,Shaobo ……… ………… ………… ………… ………… ………… ………… MP-1 Kang,Shaobo ……… ………… ………… ………… ………… ………… ………… TE-1 Kong,Lanju ………… ………… ………… ………… ………… ………… ………… TP-3 Li,Baopu …………… ………… ………… ………… ………… ………… ………… TA-2 Li,Dang ………………………… ………… ………… ………… ………… ………… WE-1 Li,Weimin ……………………… ………… ………… ………… ………… ………… WA-1 Li,Zhibin ………………………… ………… ………… ………… ………… ………… MP-1 Liang,Jianing ……… ………… ………… ………… ………… ………… ………… TA-4 Liao,Jingsheng ………………… ………… ………… ………… ………… ………… TE-2 Liao,Wei-Hsin ………………… ………… ………… ………… ………… ………… MA-2 Lin,Xuesen ………… ………… ………… ………… ………… ………… ………… WA-3 Liu,Dan ………………………… ………… ………… ………… ………… ………… WA-2 Liu,Jia…………………………… ………… ………… ………… ………… ………… TA-4 Liu,Wei ………………………… ………… ………… ………… ………… ………… ME-2 Liu,Wei ………………………… ………… ………… ………… ………… ………… TP-2 Liu,Ying ………………………… ………… ………… ………… ………… ………… WA-4 Liu,Ying ………………………… ………… ………… ………… ………… ………… WP-2 Liu,Zhigang …………………… ………… ………… ………… ………… ………… ME-3 Miao,Yingwu ……… ………… ………… ………… ………… ………… ………… TP-1 Miao,Yu …………… ………… ………… ………… ………… ………… ………… TA-3 Ou,Yongsheng ………………… ………… ………… ………… ………… ………… WA-4 Ouyang,Puren ………………… ………… ………… ………… ………… ………… MA-1 Pan,Shulian …………………… ………… ………… ………… ………… ………… MP-3 Qi,Zhang ……………………… ………… ………… ………… ………… ………… WP-3 Qiao,Yu ………………………… ………… ………… ………… ………… ………… WP-4 Shi,Hongyu …………………… ………… ………… ………… ………… ………… TE-3 Song,Zhangjun ………………… ………… ………… ………… ………… ………… TA-2 Song,Zhangjun ………………… ………… ………… ………… ………… ………… TP-1 Su,Bo …………………………… ………… ………… ………… ………… ………… WA-2 Sun,Dong ……………………… ………… ………… ………… ………… ………… MA-2 Sun,Rong ……………………… ………… ………… ………… ………… ………… WE-4 Wang,Chao …………………… ………… ………… ………… ………… ………… ME-4 Wang,Gang …………………… ………… ………… ………… ………… ………… MA-4 2011 IEEE ICIA SESSION CHAIR INDEX
Wang,Gang …………………… ………… ………… ………… ………… ………… TE-2 Wang,Hai ……………………… ………… ………… ………… ………… ………… MP-4 Wang,Panhong …… ………… ………… ………… ………… ………… ………… ME-4 Wang,Xuejie …………………… ………… ………… ………… ………… ………… WP-1 Wang,Yanjiong ………………… ………… ………… ………… ………… ………… ME-1 Wang,Yu ……………………… ………… ………… ………… ………… ………… TE-1 Wang,Zhiwei …………………… ………… ………… ………… ………… ………… TP-4 Wu,Feiping ………… ………… ………… ………… ………… ………… ………… WP-1 Wu,Xiaodong ……… ………… ………… ………… ………… ………… ………… MP-2 Wu,Xinyu ……………………… ………… ………… ………… ………… ………… WP-2 Xia,Liling …………… ………… ………… ………… ………… ………… ………… ME-1 Xie,Yaoqin ……………………… ………… ………… ………… ………… ………… WP-4 Xu,Lisheng ………… ………… ………… ………… ………… ………… ………… TP-4 Xu,Ming ………………………… ………… ………… ………… ………… ………… TE-4 Yan,ChaoKun ………………… ………… ………… ………… ………… ………… TE-4 Yang,Bo ………………………… ………… ………… ………… ………… ………… WP-3 Yang,Guiyong ………………… ………… ………… ………… ………… ………… WA-1 Yang,Xianhui ……… ………… ………… ………… ………… ………… ………… TA-3 Zhang,Qi ……………………… ………… ………… ………… ………… ………… MA-4 Zhang,Xuncai ………………… ………… ………… ………… ………… ………… TP-3 Zhao,Zixin ……………………… ………… ………… ………… ………… ………… WA-3 Zhou,Gang ………… ………… ………… ………… ………… ………… ………… MP-4 Zhou,Liyang …………………… ………… ………… ………… ………… ………… MA-3 Zou,Yuexian …………………… ………… ………… ………… ………… ………… MA-3
Author Index Chen,Yen-Lun ……………… TP-2.5 B Chen,Ying-Chen …………… TE-3.4 Chen,Yulong ……… ……… MA-3.3 Bai,Hongyang ……………… MP-3.2 Chen,Yunpeng ……………… MA-4.3 Bai,Tianxiang ……………… MA-4.2 Cheng,Chang ……………… MA-2.5 Bansal,Naveen …… ……… MP-4.1 Cheng,Jianhua……………… MP-3.5 Bayrak,Coskun …… ……… WP-4.2 Cheng,Jun ………… ……… TA-4.5 Bing,Jie……………………… MA-4.3 Cheng,Ming ………………… WA-1.3 Bing,Jie WP-4.3 Cheng,Zijian………………… MP-1.5 Chignell,Mark ……………… WP-3.5 C Chou,Chih-Chung ………… WA-2.4 Cui,Qing-zhu………………… WP-1.2 Cai,Yifan …………………… ME-2.3 Cui,Wei ……………………… ME-2.4 Cao,Jianzhong ……………… TE-3.3 Cui,Yiping …………………… WA-3.1 Cao,Jin-gang ……………… WP-1.1 Cao,Rongmin ……………… WP-1.3 D Cao,Yue …………… ……… WP-4.3 Chan,Kai-Ming ……………… MA-2.3 Dai,Daoyi …………………… MA-3.4 Chang,Ming ………………… TP-4.3 Dai,Dawei …………………… TA-1.2 Chang,Tianhai ……………… TE-3.2 Dai,Dawei WA-1.3 Chen,Chi-Li………………… MP-2.3 Dai,Gang …………………… TA-3.4 Chen,Chin-Lung …………… WA-2.4 Dam,Truong ………………… MA-1.2 Chen,Chunjie ……………… WA-4.1 Dam,Truong MA-1.3 Chen,Chunjie WA-4.2 Ding,Wenqi ………………… WE-4.3 Chen,Daidai………………… MP-3.5 Dong,Yangze ……………… TE-3.1 Chen,Dongmei ……………… TA-2.3 Du,Jianjun ………… ……… MA-4.5 Chen,Dongmei ME-4.2 Du,Jianjun WA-2.2 Chen,Guohua ……………… TE-4.1 Du,Ruxu …………… ……… ME-3.2 Chen,Hao…………………… WP-4.5 Du,Ruxu ME-3.4 Chen,Heng-Ming …………… TE-3.4 Du,Ruxu WA-4.3 Chen,Honggang …………… WP-1.5 Du,Yingkui ………… ……… MA-4.4 Chen,Jian-fang …… ……… MA-3.5 Duan,Hongliang …………… MA-3.3 Chen,Lei…………… ……… MP-1.5 Dumtrascu,Bogdan ………… MA-1.5 Chen,Li……………………… MP-3.5 Chen,Mingkai ……………… TE-3.2 F Chen,Quan ………………… TA-1.1 Chen,Shi …………………… TE-2.1 Falkman,Petter …… ……… WE-1.2 Chen,Shihe ………………… WA-1.5 Fan,Baojie ………… ……… MA-4.4 Chen,Shuanshuan ………… TP-2.4 Fan,Jinhui …………………… ME-2.4 Chen,Shuzhong …………… WA-3.5 Fan,Miao …………………… ME-1.1 Chen,Tao …………………… TP-1.1 Fang,Li……………………… TP-1.5 Chen,Tiemei ………………… MP-1.4 Feng,Cong…………………… ME-1.2 Chen,Xi ……………………… WA-1.4 Feng,Cong TP-4.5 Chen,Yen-Lun ……………… TA-1.2 Feng,Lin …………… ……… MP-1.3 Feng,Mei …………………… MA-2.1 Han,Long …………………… WA-4.2 Feng,Shulin ………………… WA-2.5 Han,Xiaojuan ……………… WE-1.1 Feng,Shuting………………… TP-4.5 Han,Xiaojuan WE-1.5 Feng,Xin …………………… MP-4.1 Han,Xuefeng ……… ……… ME-3.5 Feng,Yiwei ………………… TE-1.3 Hao,Lina …………………… WP-3.2 Feng,Yong ………… ……… ME-4.3 Hao,Shuanghui……………… TP-1.1 Filipescu,Adrian …………… MA-1.5 Hao,Yongping ……………… MP-3.1 Filipescu,Adriana …………… MA-1.5 He,Baigen …………………… MP-4.4 Fong,Daniel Tik-Pui ……… MA-2.3 He,Bo ……………… ……… TA-3.2 Fu,Chuande ………………… TP-3.4 He,Jianbao ………………… WA-3.4 Fu,Yili ……………… ……… MA-2.1 He,Jinshou ………………… MA-4.5 Fu,Yue-wen………………… WP-1.2 He,Kai ……………………… ME-3.2 He,Kai ME-3.4 G He,Kai WA-4.3 He,Qing ……………………… ME-2.5 Gai,Meng …………………… WA-3.2 He,Qing TP-2.1 Gan,Zuoxin ………………… TP-3.3 He,Qing TP-2.2 Gao,Deyuan ………………… WP-3.1 He,Qing TP-2.3 Gao,Peng…………………… TA-1.3 He,Qing TE-2.1 Gao,Yan …………………… MP-4.3 He,Qingbo ………… ……… MA-3.4 Ge,Guo ……………………… TE-1.3 He,Yuanhai ………………… WP-4.1 Ge,Liang …………………… ME-1.4 He,Yuqing …………………… MA-1.4 Ge,Liang WE-1.3 Hedvall,Jonathan … ……… WE-1.2 Ge,YunJian………………… WP-2.3 Holmblad,Anders …………… WE-1.2 Ge,Yunjian ME-4.3 Hou,Jingqi ………… ……… WA-3.5 Geng,Ning…………………… ME-1.2 Hou,Li-Qiang ……… ……… MA-1.1 Geng,Shujuan ……………… WP-4.4 Hou,Lulu …………………… TA-2.1 Gong,Chang ………………… MA-3.4 Hou,Yuanhang……………… ME-1.4 Gong,Xiping ………………… TA-4.4 Hou,Yuan-hang WE-1.3 Gu,Dongbing ……… ……… TE-3.5 Hu,Chao……………………… MA-2.4 Gu,Guo-Ying ……… ……… MP-1.1 Hu,Chao MA-2.5 Gu,Xiaoan ………… ……… TA-4.4 Hu,Chao ME-2.5 Gu,Ye………………………… MP-2.1 Hu,Chao ME-4.2 Guan,Chih-Tai ……………… TE-3.4 Hu,Chao TA-2.3 Guan,Yisheng ……………… WP-2.4 Hu,Chao TA-3.3 Guo,Hongtao ……………… MA-2.3 Hu,Chao TP-2.1 Guo,Jinjin …………………… TA-4.2 Hu,Chao TP-2.2 Guo,Qingye ………………… MP-3.4 Hu,Chao TP-2.3 Guo,Shuxiang ……………… MA-3.2 Hu,Chao TE-2.1 Hu,Chao TE-2.3 H Hu,Chao TE-2.5 Hu,Chao WP-2.4 Han,Jianda………… ……… MA-1.4 Hu,Chao WE-1.4 Han,Long …………………… WA-4.1 Hu,Dan ……………………… WA-2.2 Hu,Huijuan ………………… TA-1.2 Kim,Hyongsuk……………… WP-2.5 Hu,Huosheng ……………… TE-3.5 Kong,Jing …………………… TA-2.1 Hu,Jianxu …………………… WE-4.4 Kong,Lanju ………………… TP-3.1 Hu,Xin ……………………… TP-2.4 Kong,Lanju TE-4.3 Hu,Ying ……………………… MP-2.4 Hu,Ying TA-2.5 L Hu,Yueming ………………… MP-1.4 Hu,Zhigang ………………… TE-4.2 Layshot,Nicholas … ……… TP-1.2 Hu,Zhoujun ………………… TE-4.2 Lennartson,Bengt … ……… WE-1.2 Huang,Chenn-Jung ………… TE-3.4 Li,Baopu …………………… TA-2.2 Huang,Dengshan…………… MA-3.3 Li,Bin ………………………… TA-1.1 Huang,Fu-Ming …… ……… MA-1.1 LI,Bo………………………… MA-3.1 Huang,JiaCai ……………… TP-1.5 Li,Dichen…………… ……… ME-3.3 Huang,Libin ………………… WP-3.3 Li,Dongsheng……… ……… TP-4.4 Huang,Pu …………………… MA-1.1 Li,Fei………………………… TP-3.3 Huang,Qiang ……… ……… TA-4.3 Li,Feng……………………… WP-3.1 Huang,Yuancan …………… TA-4.3 Li,Heng-Nian ……… ……… MA-1.1 Hung,Aaron See-Lon……… MA-2.3 Li,Hongsheng……… ……… TP-1.5 Li,Hongsheng WP-3.3 J Li,Kun ……………… ……… WP-1.4 Li,Mingyue…………………… MP-3.5 Jia,Gang…………… ……… MP-1.5 Li,Nanxi ……………………… MP-4.3 Jia,Hong-guang…… ……… TE-1.1 Li,Ping ……………………… TE-2.5 Jia,Hongguang ME-3.5 Li,Ping WE-1.4 Jia,Lei ……………… ……… MP-3.3 Li,Qingzhong………………… TP-3.1 Jia,Songmin ………………… ME-2.4 Li,Qingzhong TE-4.3 Jian,Linni …………………… TA-4.1 Li,Shijie ……………………… MP-1.2 Jian,Linni TP-4.3 Li,Shuai……………………… WP-3.2 Jian,Linni WA-1.1 Li,Shu-kai …………………… MP-1.3 Jiang,Bo …………… ……… MP-3.3 Li,Tao ……………… ……… WP-1.5 Jiang,Lei …………………… MP-2.5 Li,Weimin…………………… WA-1.1 Jiang,Mai…………………… MP-1.5 Li,Xi…………………………… TE-4.2 Jiang,Xiangtao ……………… WA-3.4 Li,Xiuzhi……………………… ME-2.4 Jiang,Xian-liang …………… MA-3.5 Li,Youfu ……………………… MA-4.2 Jiang,Yao…………………… ME-1.2 Li,Yue ……………… ……… TA-4.3 Jin,Jianxun ………………… TA-4.5 Li,Zexiang …………………… ME-2.2 Jin,Lianwen ………………… MP-4.3 Li,Zhibin……………………… ME-2.2 Jin,Shiyuan ………………… TP-4.2 Li,Zhifu ……………………… MP-1.4 Jin,Xiaomin ………………… TA-1.1 Li,Zhongwei………………… MA-4.2 Lian,Feng-Li ………………… MA-4.1 K Lian,Feng-Li WA-2.4 Liang,Huajin ………………… WP-2.4 Kang,Shaobo ……………… TE-1.5 Liang,Jianing ……… ……… TA-4.1 Ke,Meng …………………… WA-3.5 Liao, Wei-Hsin ……………… MA-2.3 Liao,Jingsheng …… ……… TE-2.5 Liu,Zhigang WA-4.4 Liao,Jinhong ………………… TE-2.3 Liu,Zhiyong………… ……… ME-4.2 Lin,Chin-Fa ………………… TE-3.4 Liu,Zhiyong TA-2.3 Lin,Fan ……………………… WP-1.4 Liu,Zhiyong MA-2.5 Lin,Hsien-I ………… ……… MP-2.3 Liu,Zhiyong WP-2.4 Lin,XueSen ………………… WA-3.3 Lou,Yunjiang ……… ……… ME-2.2 Lin,Yi-Chun ………………… MA-4.1 Lu,Bowen …………………… TE-3.5 Liu,Bo ……………… ……… TE-2.2 Lu,Dingran ………… ……… TA-1.1 Liu,Chang…………………… MA-2.1 Lu,Xizhao …………………… WE-4.3 Liu,Dun ……………………… MA-4.5 Luo,Changjie ……… ……… ME-3.4 Liu,Dun WA-2.2 Luo,Fei ……………………… MA-2.4 Liu,Guanzheng……………… WP-4.5 Luo,Fei WA-1.3 Liu,Jia………………………… WA-1.3 Luo,Qun……………………… ME-3.2 Liu,Jia TA-3.4 Luo,Yaohua………………… TE-1.5 Liu,Jia TA-4.4 Luo,Yi ……………… ……… ME-1.1 Liu,Jiajin……………………… ME-1.2 Luo,Yuanxin ………………… ME-3.1 Liu,Jintao …………………… TE-2.1 Lv,Jun ……………………… TP-3.5 Liu,Jizhu …………………… TP-1.1 Lv,Shijia …………… ……… TE-1.5 Liu,Li ………………………… TA-2.4 Liu,Li TA-4.1 M Liu,Li TP-1.4 Liu,Ming …………… ……… MP-3.4 Ma,Ronghua ……… ……… WP-1.3 Liu,Na ……………… ……… WA-3.2 Ma,Shugen ………………… MP-2.2 Liu,Peng …………………… WA-4.3 Ma,Zhiqiang………………… WP-2.5 Liu,Pingxiang ……………… TE-3.1 Mao,He ……………………… ME-3.2 Liu,Shao-yang……………… MA-3.5 Mao,Ning …………………… MP-2.5 Liu,Wei……………………… ME-2.5 Mao,Ning WA-2.3 Liu,Wei TP-2.3 Mendi,Engin ………………… WP-4.2 Liu,Wei TE-2.1 Meng,Hua…………………… TE-1.2 Liu,Wei TP-2.2 Meng,Maozhou …… ……… WE-4.4 Liu,Wenshuai……… ……… TP-4.1 Meng,Max Q.-H.…………… ME-4.2 Liu,Wenshuai TE-4.4 Meng,Max Q.-H. TA-3.3 Liu,Xiabi …………… ……… MA-4.3 Meng,Max Q.-H. TP-2.2 Liu,Xiabi WP-4.3 Meng,Max Q.-H. WE-1.4 Liu,Xiaochang ……………… TA-3.4 Meng,Max Q.-H. ME-2.5 Liu,Yang …………………… WP-1.4 Meng,Max Q.-H. TA-2.2 Liu,Yangbin ………………… ME-4.5 Meng,Max Q.-H. TA-2.3 Liu,Ying ……………………… WP-2.2 Meng,Max Q.-H. TA-3.1 Liu,Yiqing …………………… TA-1.5 Meng,Max Q.-H. TP-2.3 Liu,Yongbin ………………… MA-3.4 Meng,Max Q.-H. TP-4.5 Liu,Yuehu …………………… MP-4.2 Meng,Max Q.-H. TE-2.5 Liu,zengliang ……… ……… TA-1.3 Meng,Xiang-yin …………… WE-1.3 Liu,Zhendong ……………… TP-3.4 Miao,Yingwu ……… ……… TP-1.3 Liu,Zhigang ………………… ME-3.3 Miao,Yu ……………………… TA-3.5 Milanova,Mariofanna ……… WP-4.2 Minca,Eugenia ……………… MA-1.5 R Minzu,Viorel ………………… MA-1.5 Mo,Yuzhong ………………… TE-1.4 Ren,Hongliang ……………… TA-3.1 Ren,Ren……………………… TA-2.1 N Ren,Xiangshi ……………… WP-3.5
Ni,Wenjia …………………… TE-2.1 S Nie,Lei ……………………… TP-2.4 Nie,Yong …………………… ME-3.3 Sang , Wenhua …… ……… TA-4.3 Ning,Wanzheng …………… TA-3.5 Shang,Wen ………………… MA-2.2 Niu,Lipi ……………………… TE-2.2 Shao,Huihe………………… WA-1.5 Niu,Ying …………… ……… TP-3.3 Shen,Feng ………… ……… WA-3.2 Shen,Jiawei ………………… WP-3.2 O Shen,Zhao ………… ……… WE-1.4 Sheng,Jinbo ………………… ME-2.4 Ou,Xiefeng ………………… WA-4.3 Sheng,Weihua ……………… MP-2.1 Ou,Yongsheng……………… TP-2.5 Shi,Bo ……………………… MA-3.5 Ou,Yongsheng TP-1.4 Shi,Hongyu………… ……… TE-3.3 Ou,Yongsheng WA-4.1 Shi,Junyu…………………… MP-3.5 Ou,Yongsheng WA-4.2 Shi,Liang …………………… ME-4.5 Ou,Yongsheng WP-2.2 Song,Li ……………………… WP-4.3 Ouyang,Liping ……………… WP-2.2 Song,Quanjun ……………… ME-4.3 Ouyang,Puren ……………… MA-1.2 Song,Shuang ……………… TA-3.3 Ouyang,Puren MA-1.3 Song,Zhan…………………… TP-2.4 Ouyang,Zhengbiao………… WA-3.1 Song,Zhan ME-4.1 Song,Zhangjun …… ……… MP-2.4 P Song,Zhangjun TA-2.5 Song,Zhangjun TP-1.4 Pan,Bo ……………………… MA-2.1 Song,Zhibin ………………… WA-1.1 Pan,Shuliang ……………… MP-3.3 Su,Ang ……………………… WP-1.3 Pang,Jihong ………………… TE-4.1 Su,Bo ……………… ……… MP-2.5 Peng,Ansi …………………… WP-2.2 Su,Bo WA-2.3 Peng,Kun …………………… TA-2.1 Su,Chun-Yi ………………… MP-1.1 Sun,Dong …………………… MA-2.2 Q Sun,Gao …………………… TE-1.1 Sun,Rong …………………… WE-4.2 Qian,Gang ………… ……… MP-4.5 Sun,Shuguang……………… MP-1.5 Qiao,Yu ……………………… ME-4.4 Sun,Zhiyong ………………… WP-3.2 Qiao,Yu WP-4.1 Qu,Zhihua …………………… TP-4.2 T
Tang,Lilai …………………… MA-2.5 Tang,Shuai ………………… WA-1.2 Tang,Xi ……………………… ME-3.4 Wang,Lei TP-3.5 Tang,Yandong ……………… MA-4.4 Wang,Panhong …… ……… ME-4.5 Tang,Yazhe ………………… MA-4.2 Wang,Qing ………………… ME-3.1 Tang,Yu …………… ……… WA-3.5 Wang,Qiong ………………… WA-3.1 Tao,Long …………………… WA-4.4 Wang,Shaohua …… ……… TP-1.1 Teng,Fulin…………………… TP-1.5 Wang,Sheng ……… ……… TA-1.2 Thobbi,Anand ……………… MP-2.1 Wang,Shourong …………… WP-3.3 Tian,GuiYun ………………… WP-3.4 Wang,Shuo ………………… WE-1.1 Tian,Jinglan ………………… ME-2.5 Wang,Shuo WE-1.5 Tian,Jinglan TA-3.3 Wang,Xiaodong …………… TP-3.2 Tian,Lei ……………………… TA-4.2 Wang,Xiaojing ……………… TA-3.3 Tsau,Young ………………… MA-3.5 Wang,Xingxing …… ……… ME-4.4 Tseng,Sheng-Chieh ……… TE-3.4 Wang,Xuan ………………… TA-3.5 Tu,Wei……………… ……… TA-1.4 Wang,Xue …………………… TE-4.3 Wang,Yanjiong……………… ME-1.5 W Wang,Ying ………… ……… ME-1.2 Wang,Yongqin ……………… ME-3.1 Wan,Xiaohua ……………… WE-1.1 Wang,Yue …………………… ME-1.2 Wan,Yuchai………………… MA-4.3 Wang,Yu-Wu ……………… TE-3.4 Wang,Beizhan ……………… ME-4.5 Wang,Zhen ………………… WP-4.5 Wang,BuYun………………… WP-2.3 Wang,Zhiqiong …… ……… WA-3.5 Wang,Chao ………………… ME-4.1 Wang,Zhiwei ……… ……… TP-4.1 Wang,Chenxi ……………… ME-2.5 Wang,Zhiwei TE-4.4 Wang,Chenxi TP-2.2 Wei,Dong …………………… TA-1.5 Wang,Chenxi TP-2.3 Wei,Jia-hua………………… WP-1.2 Wang,Dacheng …… ……… TP-4.1 Wei,Ning…………… ……… TP-2.3 Wang,Dacheng TE-4.4 Wei,Ning ME-2.5 Wang,Deshuang …………… WP-1.5 Wei,Ning TP-2.2 Wang,Fei …………………… MP-3.1 Wei,Shaoqing ……………… TE-1.2 Wang,Gang ………………… TE-2.3 Wei,Tingcun ………………… WP-3.1 Wang,Guanxiong…………… ME-1.2 Wei,Yajuan ………………… MP-4.4 Wang,Hai …………………… MP-4.5 Wei,Zheng ………… ……… TP-2.3 Wang,Haibin ……… ……… ME-4.2 Wen,Qiaoyan……… ……… ME-1.5 Wang,Haibin TA-2.3 Wen,Xiulan ………………… TP-1.5 Wang,Haijun ……… ……… MP-3.4 Wu, Liying …………………… TA-4.3 Wang,Haiyan ……………… TA-3.5 Wu,Guifu …………………… TA-3.4 Wang,Huabing ……………… TE-2.2 Wu,Jie ……………………… TP-2.1 Wang,Jian ………… ……… MP-1.3 Wu,Wanping ……… ……… TA-4.5 Wang,Jianji ………………… MP-4.2 WU,Xiaocui ………………… TA-2.4 Wang,Jianjun ……………… MA-2.2 Wu,Xiaodong ……………… MP-2.2 Wang,Jianjun MA-3.4 Wu,Xiaofeng ……… ……… WA-1.4 Wang,Jiemin ……… ……… WP-4.1 Wu,Xinyu …………………… TA-1.2 Wang,Ke …………………… ME-2.4 Wu,Xinyu TP-2.5 Wang,Lei …………………… MP-3.1 Wu,Xinyu WA-4.1 Wu,Xinyu WA-4.2 Xue,Xiaozhong …… ……… MP-3.2 Wu,Xinyu WP-2.2 Wu,Yanmin ………………… WE-4.2 Y Wu,Yingjie…………………… TP-3.2 Wu,Yuanqin ………………… ME-4.5 Yan,Chaokun ……………… TE-4.2 Wu,Zhengbin ……… TA-2.4 Yan,Huaicheng……………… TP-4.5 Yan,Tingfang ……………… ME-2.5 X Yan,Tingfang TA-3.3 Yan,Tingfang TP-2.3 Xia,Liling…………… ……… ME-1.3 Yan,Weiwu ………………… WA-1.5 Xiao,Peng …………………… TE-4.2 Yan,Xingchun ……………… ME-3.1 Xiao,Qianjin ………………… ME-3.5 Yang, Shuling ……………… WA-2.3 Xiao,Zhiguang ……………… WP-2.4 Yang,Bo …………… ……… WP-3.3 Xie,Chongyu ……… ……… WE-4.3 Yang,Guang ………………… WP-3.4 Xie,Haihe…………………… WE-4.3 Yang,Jingwen ……………… WA-1.2 Xie,Kang …………………… MA-2.5 Yang,Ming…………………… WP-2.3 Xie,Yaoqin ………… ……… WP-4.5 Yang,Shu …………………… MA-3.5 Xie,Zhihua…………………… TA-1.4 Yang,Simon X.……………… ME-2.3 Xiong,Guogang……………… TP-2.5 Yang,Xianhui………………… TE-4.5 Xu,Chunjing ………………… ME-4.4 Yang,Xianhui TA-3.2 Xu,DeZhang………………… WP-2.3 Yang,Yansi ………………… TE-2.2 Xu,Guoqing………………… TA-2.4 Yang,Yingyun ……………… TE-2.2 Xu,Guoqing TA-4.1 Yang,Yuning ……… ……… WP-3.4 Xu,Guoqing TP-1.4 Yao,Qichang ……… ……… MP-2.5 Xu,Guoqing TP-4.3 Yao,Qichang WA-2.3 Xu,Guoqing WA-1.1 Yin,Sheng-li………………… TE-1.1 Xu,Hongji …………………… MP-2.5 Yin,Yong …………………… TE-2.3 Xu,Hongji WA-2.3 Yin,Yong TE-2.5 Xu,Jian ……………………… MP-1.2 Ying,Xianghua ……………… TA-2.1 Xu,Kun ……………………… WA-1.1 Yong,Xi ……………………… TA-2.5 Xu,Li ………………………… MP-1.2 You,Jiang …………………… TE-1.5 Xu,Lisheng ………………… ME-1.2 You,Zhensheng …………… WP-2.1 Xu,Lisheng TP-4.5 Yu,Gang …………………… MP-2.4 Xu,Lu ………………………… WP-3.3 Yu,Jimin …………… ……… TE-1.4 Xu,Ming ……………………… TE-4.5 Yu,Jing……………………… TP-4.4 Xu,Peng……………………… TA-1.3 Yu,QuanQiang ……………… WA-3.1 Xu,Pengfei ………… ……… TP-4.4 Yu,Runsheng ……………… TA-4.2 Xu,Ren……………………… TA-3.4 Yu,Xiao-Hua………………… TA-1.1 Xu,Tao ……………………… MA-3.5 Yu,Xiao-Hua TP-1.2 Xu,Tao WA-2.1 Yu,Yue ……………………… MA-3.5 Xu,Xiao-hong……… ……… WP-1.2 Yu,Zhuliang ………………… TP-2.1 Xu,Yuanlin…………………… TA-1.3 Yuan,Peng ………………… MP-1.4 Xue,Bing …………………… WA-3.2 Xue,Honghong ……………… TP-4.3 Zhao,MinZhe ……… ……… TA-1.5 Z Zhao,Shengdong…………… WP-3.5 Zhao,Wencong …… ……… TE-4.5 Zeng,Yuan ………… ……… TA-3.5 Zhao,Yue …………………… WA-3.5 Zha,Hongbing ……………… TA-2.1 Zhao,Zixin …………………… MA-3.2 Zhang,Chengjin …………… WE-4.1 Zheng,Jinjin ………………… MA-2.2 Zhang,Derong……………… TE-2.4 Zheng,Lan ………… ……… MP-2.4 Zhang,Fan ………… ……… WE-1.5 Zheng,Lan TA-2.5 Zhang,Genbao ……………… TE-4.1 Zheng,Min…………………… TE-2.4 Zhang,Guilin ………………… WE-4.1 Zheng,Ran ………………… WP-3.1 Zhang,Huanshui …………… WA-2.5 Zheng,Zhiqiang……………… WA-1.2 Zhang,Jianwei ……………… MP-2.4 Zhong,Cancheng …………… MA-2.4 Zhang,Jianwei TA-2.5 Zhong,Yue…………………… TP-4.5 Zhang,Jinhua ……………… ME-3.3 Zhou,Gang ………………… MP-4.2 Zhang,Junpeng …………… WP-4.4 Zhou,Huixing ……… ……… WP-1.3 Zhang,Lei …………………… ME-2.1 Zhou,Liyang ………………… MA-3.3 Zhang,Li …………… ……… WA-1.2 Zhou,Ran …………………… TP-2.1 Zhang,Liancun ……………… TA-4.3 Zhou,Xiaolei ………………… WP-3.5 Zhang,Lingling ……………… WA-3.1 Zhou,Xiaolong ……………… MA-4.2 Zhang,Liwei ………………… TA-2.5 Zhou,Xiaoxia………………… ME-1.1 Zhang,Mingyu……………… WP-4.4 Zhou,Yujie ………… ……… WP-4.1 Zhang,Ning ………………… TA-1.5 Zhou,Zhiyuan ……………… TE-2.3 Zhang,Pandeng …………… WA-1.3 Zhu,Daxin…………………… TP-3.2 Zhang,Peng………………… TP-1.1 Zhu,Jianhua ………………… TA-1.1 Zhang,Peng MP-2.4 Zhu,Jianjun ………………… MA-4.5 Zhang,Qi …………………… WP-3.4 Zhu,Jianjun WA-2.2 Zhang,Qing………………… WP-2.3 Zhu,LiMin …………………… MP-1.1 Zhang,Qingfan ……………… WP-4.4 Zhu,Ming …………………… MP-4.4 Zhang,Weng………………… TP-4.4 Zhu,Ming-chao ……………… TE-1.1 Zhang,Wenjing …… ……… MP-4.1 Zhu,Pengli ………… ……… WE-4.2 Zhang,Wenyu……………… ME-2.1 Zhu,Qingsong……………… WP-4.5 Zhang,Xi …………………… WA-1.5 Zhu,Xiao-ping ……………… WA-2.1 Zhang,Xiao-feng …………… WA-2.1 Zhu,Yuesheng……………… MA-3.1 Zhang,Xilin………… ……… WE-1.5 Zou,Feng-Xing……………… WP-1.2 Zhang,Xilin WE-1.1 Zou,Nan …………… ……… MP-3.3 Zhang,Xuncai ……………… TP-3.3 Zou,Yuexian ………………… MA-3.1 Zhang,Yan ………… ……… MP-4.5 Zhang,Yonggang …………… WP-1.5 Zhang,Yubing……………… TP-4.4 Zhang,Yuxing ……………… TP-1.3 Zhang,Zhan ………………… TE-1.2 Zhang,Zhihuan …… ……… WP-2.1 Zhao,Jie …………… ……… MP-1.2 Zhao,Mengyu ……………… WA-3.5
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