Series & Parallel Impedance Parameters
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Efficient Splitter for Data Parallel Complex Event Procesing
Institute of Parallel and Distributed Systems University of Stuttgart Universitätsstraße D– Stuttgart Bachelorarbeit Efficient Splitter for Data Parallel Complex Event Procesing Marco Amann Course of Study: Softwaretechnik Examiner: Prof. Dr. Dr. Kurt Rothermel Supervisor: M. Sc. Ahmad Slo Commenced: March , Completed: September , Abstract Complex Event Processing systems are a promising approach to detect patterns on ever growing amounts of event streams. Since a single server might not be able to run an operator at a sufficiently high rate, Data Parallel Complex Event Processing aims to distribute the load of one operator onto multiple nodes. In this work we analyze the splitter of an existing CEP framework, detail on its drawbacks and propose optimizations to cope with them. This yields the newly developed SPACE framework, which is evaluated and compared with an industry-proven CEP framework, Apache Flink. We show that the new splitter has greatly improved performance and is able to support more instances at a higher rate. In comparison with Apache Flink, the SPACE framework is able to process events at higher rates in our benchmarks but is less stable if overloaded. Kurzfassung Complex Event Processing Systeme stellen eine vielversprechende Möglichkeit dar, Muster in immer größeren Mengen von Event-Strömen zu erkennen. Da ein einzelner Server nicht in der Lage sein kann, einen Operator mit einer ausreichenden Geschwindigkeit zu betreiben, versucht Data Parallel Complex Event Processing die Last eines Operators auf mehrere Knoten zu verteilen. In dieser Arbeit wird ein Splitter eines vorhandenen CEP systems analysiert, seine Nachteile hervorgearbeitet und Optimierungen vorgeschlagen. Daraus entsteht das neue SPACE Framework, welches evaluiert wird und mit Apache Flink, einem industrieerprobten CEP Framework, verglichen wird. -
Phases of Two Adjoints QCD3 and a Duality Chain
Phases of Two Adjoints QCD3 And a Duality Chain Changha Choi,ab1 aPhysics and Astronomy Department, Stony Brook University, Stony Brook, NY 11794, USA bSimons Center for Geometry and Physics, Stony Brook, NY 11794, USA Abstract We analyze the 2+1 dimensional gauge theory with two fermions in the real adjoint representation with non-zero Chern-Simons level. We propose a new fermion-fermion dualities between strongly-coupled theories and determine the quantum phase using the structure of a `Duality Chain'. We argue that when Chern-Simons level is sufficiently small, the theory in general develops a strongly coupled quantum phase described by an emergent topological field theory. For special cases, our proposal predicts an interesting dynamical scenario with spontaneous breaking of partial 1-form or 0-form global symmetry. It turns out that SL(2; Z) transformation and the generalized level/rank duality are crucial for the unitary group case. We further unveil the dynamics of the 2+1 dimensional gauge theory with any pair of adjoint/rank-two fermions or two bifundamental fermions using similar `Duality Chain'. arXiv:1910.05402v1 [hep-th] 11 Oct 2019 [email protected] Contents 1 Introduction1 2 Review : Phases of Single Adjoint QCD3 7 3 Phase Diagrams for k 6= 0 : Duality Chain 10 3.1 k ≥ h : Semiclassical Regime . 10 3.2 Quantum Phase for G = SU(N)......................... 10 3.3 Quantum Phase for G = SO(N)......................... 13 3.4 Quantum Phase for G = Sp(N)......................... 16 3.5 Phase with Spontaneously Broken Partial 1-form, 0-form Symmetry . 17 4 More Duality Chains and Quantum Phases 19 4.1 Gk+Pair of Rank-Two/Adjoint Fermions . -
Admittance, Conductance, Reactance and Susceptance of New Natural Fabric Grewia Tilifolia V
Sensors & Transducers Volume 119, Issue 8, www.sensorsportal.com ISSN 1726-5479 August 2010 Editors-in-Chief: professor Sergey Y. Yurish, tel.: +34 696067716, fax: +34 93 4011989, e-mail: [email protected] Editors for Western Europe Editors for North America Meijer, Gerard C.M., Delft University of Technology, The Netherlands Datskos, Panos G., Oak Ridge National Laboratory, USA Ferrari, Vittorio, Universitá di Brescia, Italy Fabien, J. Josse, Marquette University, USA Katz, Evgeny, Clarkson University, USA Editor South America Costa-Felix, Rodrigo, Inmetro, Brazil Editor for Asia Ohyama, Shinji, Tokyo Institute of Technology, Japan Editor for Eastern Europe Editor for Asia-Pacific Sachenko, Anatoly, Ternopil State Economic University, Ukraine Mukhopadhyay, Subhas, Massey University, New Zealand Editorial Advisory Board Abdul Rahim, Ruzairi, Universiti Teknologi, Malaysia Djordjevich, Alexandar, City University of Hong Kong, Hong Kong Ahmad, Mohd Noor, Nothern University of Engineering, Malaysia Donato, Nicola, University of Messina, Italy Annamalai, Karthigeyan, National Institute of Advanced Industrial Science Donato, Patricio, Universidad de Mar del Plata, Argentina and Technology, Japan Dong, Feng, Tianjin University, China Arcega, Francisco, University of Zaragoza, Spain Drljaca, Predrag, Instersema Sensoric SA, Switzerland Arguel, Philippe, CNRS, France Dubey, Venketesh, Bournemouth University, UK Ahn, Jae-Pyoung, Korea Institute of Science and Technology, Korea Enderle, Stefan, Univ.of Ulm and KTB Mechatronics GmbH, Germany -
Series Impedance and Shunt Admittance Matrices of an Underground Cable System
SERIES IMPEDANCE AND SHUNT ADMITTANCE MATRICES OF AN UNDERGROUND CABLE SYSTEM by Navaratnam Srivallipuranandan B.E.(Hons.), University of Madras, India, 1983 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE STUDIES (Department of Electrical Engineering) We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA, 1986 C Navaratnam Srivallipuranandan, 1986 November 1986 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of The University of British Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3 Date 6 n/8'i} SERIES IMPEDANCE AND SHUNT ADMITTANCE MATRICES OF AN UNDERGROUND CABLE ABSTRACT This thesis describes numerical methods for the: evaluation of the series impedance matrix and shunt admittance matrix of underground cable systems. In the series impedance matrix, the terms most difficult to compute are the internal impedances of tubular conductors and the earth return impedance. The various form u hit- for the interim!' impedance of tubular conductors and for th.: earth return impedance are, therefore, investigated in detail. Also, a more accurate way of evaluating the elements of the admittance matrix with frequency dependence of the complex permittivity is proposed. -
Impedance Matching
Impedance Matching Advanced Energy Industries, Inc. Introduction The plasma industry uses process power over a wide range of frequencies: from DC to several gigahertz. A variety of methods are used to couple the process power into the plasma load, that is, to transform the impedance of the plasma chamber to meet the requirements of the power supply. A plasma can be electrically represented as a diode, a resistor, Table of Contents and a capacitor in parallel, as shown in Figure 1. Transformers 3 Step Up or Step Down? 3 Forward Power, Reflected Power, Load Power 4 Impedance Matching Networks (Tuners) 4 Series Elements 5 Shunt Elements 5 Conversion Between Elements 5 Smith Charts 6 Using Smith Charts 11 Figure 1. Simplified electrical model of plasma ©2020 Advanced Energy Industries, Inc. IMPEDANCE MATCHING Although this is a very simple model, it represents the basic characteristics of a plasma. The diode effects arise from the fact that the electrons can move much faster than the ions (because the electrons are much lighter). The diode effects can cause a lot of harmonics (multiples of the input frequency) to be generated. These effects are dependent on the process and the chamber, and are of secondary concern when designing a matching network. Most AC generators are designed to operate into a 50 Ω load because that is the standard the industry has settled on for measuring and transferring high-frequency electrical power. The function of an impedance matching network, then, is to transform the resistive and capacitive characteristics of the plasma to 50 Ω, thus matching the load impedance to the AC generator’s impedance. -
Uprating of Electrolytic Capacitors
White Paper Uprating of Electrolytic Capacitors By Joelle Arnold Uprating of Electrolytic Capacitors Aluminum electrolytic capacitors are comprised of two aluminum foils, a cathode and an anode, rolled together with an electrolyte-soaked paper spacer in between. An oxide film is grown on the anode to create the dielectric. Aluminum electrolytic capacitors are primarily used to filter low-frequency electrical signals, primarily in power designs. The critical functional parameters for aluminum electrolytic capacitors are defined as capacitance (C), leakage current (LC), equivalent series resistance (ESR), impedance (Z), and dissipation factor (DF). These five parameters are interrelated through the following schematic. Physically, leakage current (LC) tends to be primarily driven by the behavior of the dielectric. ESR is primarily driven by the behavior of the electrolyte. Physically, impedance (Z) is a summation of all the resistances throughout the capacitor, including resistances due to packaging. Electrically, Z is the summation of ESR and either the capacitive reactance (XC), at low frequency, or the inductance (LESL), at high frequency (see Figure 1). Dissipation factor is the ratio of ESR over XC. Therefore, a low ESR tends to give a low impedance and a low dissipation factor. 1.1 Functional Parameters (Specified in Datasheet) The functional parameters that can be provided in manufacturers’ datasheets are listed below 1.1.1 Capacitance vs. Temperature It can be seen that the manufacturer’s guarantee of ±20% stability of the capacitance is only relevant at room temperature. While a source of potential concern when operating aluminum electrolytic capacitors over an extended temperature range, previous research has demonstrated that the capacitance of this capacitor is extremely stable as a function of temperature (see Figure 2 and Figure 4). -
Equivalent Series Resistance (ESR) of Capacitors
Application Note 1 of 5 Equivalent Series Resistance (ESR) of Capacitors Questions continually arise concerning the correct definition of the ESR (Equivalent Series Resistance) of a capacitor and, more particularly, the difference between ESR and the actual physical series resistance (which we'll call Ras), the ohmic resistance of the leads and plates or foils. The definition and application of the term ESR has often been misconstrued. We hope this note will answer any questions and clarify any confusion that might exist. Very briefly, ESR is a measure of the total lossiness of a capacitor. It is larger than Ras because the actual series resistance is only one source of the total loss (usually a small part). The Series Equivalent Circuit At one frequency, a measurement of complex impedance gives two numbers, the real part and the imaginary part: Z = Rs + jXs. At that frequency, the impedance behaves like a series combination of an ideal resistance Rs and an ideal reactance Xs (Figure 1). Rs RS XS CS Figure 1: Equivalent series circuit representation If Xs is negative, the impedance is capacitive, and the general reactance can be replaced with a capacitance of: −1 Cs = ωXs We now have an equivalent circuit that is correct only at the measurement frequency. The resistance of this equivalent circuit is the equivalent series resistance ESR = Rs = Real part of Z © IET LABS, Inc., September 2019 Printed in U.S.A 035002 Rev. A4 1 of 5 Application Note 2 of 5 Add the dissipation FACTOR energy lost Real part of Z If we define the dissipation factor D as D = = energy stored (− Imaginary part of Z) Rs Then D = =Rsω C = (ESR) ω C (− )Xs If one took a pure resistance and a pure capacitance and connected them in series, then one could say that the ESR of the combination was indeed equal to the actual series resistance. -
Basic Electrical Engineering
BASIC ELECTRICAL ENGINEERING V.HimaBindu V.V.S Madhuri Chandrashekar.D GOKARAJU RANGARAJU INSTITUTE OF ENGINEERING AND TECHNOLOGY (Autonomous) Index: 1. Syllabus……………………………………………….……….. .1 2. Ohm’s Law………………………………………….…………..3 3. KVL,KCL…………………………………………….……….. .4 4. Nodes,Branches& Loops…………………….……….………. 5 5. Series elements & Voltage Division………..………….……….6 6. Parallel elements & Current Division……………….………...7 7. Star-Delta transformation…………………………….………..8 8. Independent Sources …………………………………..……….9 9. Dependent sources……………………………………………12 10. Source Transformation:…………………………………….…13 11. Review of Complex Number…………………………………..16 12. Phasor Representation:………………….…………………….19 13. Phasor Relationship with a pure resistance……………..……23 14. Phasor Relationship with a pure inductance………………....24 15. Phasor Relationship with a pure capacitance………..……….25 16. Series and Parallel combinations of Inductors………….……30 17. Series and parallel connection of capacitors……………...…..32 18. Mesh Analysis…………………………………………………..34 19. Nodal Analysis……………………………………………….…37 20. Average, RMS values……………….……………………….....43 21. R-L Series Circuit……………………………………………...47 22. R-C Series circuit……………………………………………....50 23. R-L-C Series circuit…………………………………………....53 24. Real, reactive & Apparent Power…………………………….56 25. Power triangle……………………………………………….....61 26. Series Resonance……………………………………………….66 27. Parallel Resonance……………………………………………..69 28. Thevenin’s Theorem…………………………………………...72 29. Norton’s Theorem……………………………………………...75 30. Superposition Theorem………………………………………..79 31. -
Application Note: ESR Losses in Ceramic Capacitors by Richard Fiore, Director of RF Applications Engineering American Technical Ceramics
Application Note: ESR Losses In Ceramic Capacitors by Richard Fiore, Director of RF Applications Engineering American Technical Ceramics AMERICAN TECHNICAL CERAMICS ATC North America ATC Europe ATC Asia [email protected] [email protected] [email protected] www.atceramics.com ATC 001-923 Rev. D; 4/07 ESR LOSSES IN CERAMIC CAPACITORS In the world of RF ceramic chip capacitors, Equivalent Series Resistance (ESR) is often considered to be the single most important parameter in selecting the product to fit the application. ESR, typically expressed in milliohms, is the summation of all losses resulting from dielectric (Rsd) and metal elements (Rsm) of the capacitor, (ESR = Rsd+Rsm). Assessing how these losses affect circuit performance is essential when utilizing ceramic capacitors in virtually all RF designs. Advantage of Low Loss RF Capacitors Ceramics capacitors utilized in MRI imaging coils must exhibit Selecting low loss (ultra low ESR) chip capacitors is an important ultra low loss. These capacitors are used in conjunction with an consideration for virtually all RF circuit designs. Some examples of MRI coil in a tuned circuit configuration. Since the signals being the advantages are listed below for several application types. detected by an MRI scanner are extremely small, the losses of the Extended battery life is possible when using low loss capacitors in coil circuit must be kept very low, usually in the order of a few applications such as source bypassing and drain coupling in the milliohms. Excessive ESR losses will degrade the resolution of the final power amplifier stage of a handheld portable transmitter image unless steps are taken to reduce these losses. -
7. Parallel Methods for Matrix-Vector Multiplication 7
7. Parallel Methods for Matrix-Vector Multiplication 7. Parallel Methods for Matrix-Vector Multiplication................................................................... 1 7.1. Introduction ..............................................................................................................................1 7.2. Parallelization Principles..........................................................................................................2 7.3. Problem Statement..................................................................................................................3 7.4. Sequential Algorithm................................................................................................................3 7.5. Data Distribution ......................................................................................................................3 7.6. Matrix-Vector Multiplication in Case of Rowwise Data Decomposition ...................................4 7.6.1. Analysis of Information Dependencies ...........................................................................4 7.6.2. Scaling and Subtask Distribution among Processors.....................................................4 7.6.3. Efficiency Analysis ..........................................................................................................4 7.6.4. Program Implementation.................................................................................................5 7.6.5. Computational Experiment Results ................................................................................5 -
Chapter 5 Capacitance and Dielectrics
Chapter 5 Capacitance and Dielectrics 5.1 Introduction...........................................................................................................5-3 5.2 Calculation of Capacitance ...................................................................................5-4 Example 5.1: Parallel-Plate Capacitor ....................................................................5-4 Interactive Simulation 5.1: Parallel-Plate Capacitor ...........................................5-6 Example 5.2: Cylindrical Capacitor........................................................................5-6 Example 5.3: Spherical Capacitor...........................................................................5-8 5.3 Capacitors in Electric Circuits ..............................................................................5-9 5.3.1 Parallel Connection......................................................................................5-10 5.3.2 Series Connection ........................................................................................5-11 Example 5.4: Equivalent Capacitance ..................................................................5-12 5.4 Storing Energy in a Capacitor.............................................................................5-13 5.4.1 Energy Density of the Electric Field............................................................5-14 Interactive Simulation 5.2: Charge Placed between Capacitor Plates..............5-14 Example 5.5: Electric Energy Density of Dry Air................................................5-15 -
Parallel Methods for Matrix Multiplication
University of Nizhni Novgorod Faculty of Computational Mathematics & Cybernetics IntroductionIntroduction toto ParallelParallel ProgrammingProgramming Section 8. Parallel Methods for Matrix Multiplication Gergel V.P., Professor, D.Sc., Software Department Contents Problem Statement Sequential Algorithm Algorithm 1 – Block-Striped Decomposition Algorithm 2 – Fox’s method Algorithm 3 – Cannon’s method Summary Nizhni Novgorod, 2005 Introduction to Parallel Programming: Matrix Multiplication ©GergelV.P. 2 → 50 Problem Statement Matrix multiplication: C = A⋅ B or ⎛ c , c , ..., c ⎞ ⎛ a , a , ..., a ⎞ ⎛ b , b , ..., a ⎞ ⎜ 0,0 0,1 0,l−1 ⎟ ⎜ 0,0 0,1 0,n−1 ⎟ ⎜ 0,0 0,1 0,l−1 ⎟ ⎜ ... ⎟ = ⎜ ... ⎟ ⎜ ... ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝cm−1,0 , cm−1,1, ..., cm−1,l−1 ⎠ ⎝am−1,0 , am−1,1, ..., am−1,n−1 ⎠ ⎝bn−1,0 , bn−1,1, ..., bn−1,l−1 ⎠ ª The matrix multiplication problem can be reduced to the execution of m·l independent operations of matrix A rows and matrix B columns inner product calculation n−1 T cij = ()ai ,b j = ∑aik ⋅bkj , 0 ≤ i < m, 0 ≤ j < l k=0 Data parallelism can be exploited to design parallel computations Nizhni Novgorod, 2005 Introduction to Parallel Programming: Matrix Multiplication ©GergelV.P. 3 → 50 Sequential Algorithm… // Algorithm 8.1 // Sequential algorithm of matrix multiplication double MatrixA[Size][Size]; double MatrixB[Size][Size]; double MatrixC[Size][Size]; int i,j,k; ... for (i=0; i<Size; i++){ for (j=0; j<Size; j++){ MatrixC[i][j] = 0; for (k=0; k<Size; k++){ MatrixC[i][j] = MatrixC[i][j] + MatrixA[i][k]*MatrixB[k][j]; } } } Nizhni Novgorod, 2005 Introduction to Parallel Programming: Matrix Multiplication ©GergelV.P.