Mastering Rubik's Cube

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Footsteps of the Unknown.

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Killer Endings (A-list Screenwriting Series).

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Signal Processing and Machine Learning with Applications by Richter, Michael M., Paul, Sheuli, Këpuska, Veton, Silaghi, Marius

Table of Contents: “…Part I Realms of Signal Processing -- 1 Digital Signal Representation -- 1.1 Introduction -- 1.2 Numbers -- 1.2.1 Numbers and Numerals -- 1.2.2 Types of Numbers -- 1.2.3 Positional Number Systems -- 1.3 Sampling and Reconstruction of Signals -- 1.3.1 Scalar Quantization -- 1.3.2 Quantization Noise -- 1.3.3 Signal-To-Noise Ratio -- 1.3.4 Transmission Rate -- 1.3.5 Nonuniform Quantizer -- 1.3.6 Companding -- 1.4 Data Representations -- 1.4.1 Fixed-Point Number Representations -- 1.4.2 Sign-Magnitude Format -- 1.4.3 One’s-Complement Format -- 1.4.4 Two’s-Complement Format -- 1.5 Fix-Point DSP’s -- 1.6 Fixed-Point Representations Based on Radix-Point -- 1.7 Dynamic Range -- 1.8 Precision -- 1.9 Background Information -- 1.10 Exercises -- 2 Signal Processing Background -- 2.1 Basic Concepts -- 2.2 Signals and Information -- 2.3 Signal Processing -- ix -- x Contents -- 2.4 Discrete Signal Representations -- 2.5 Delta and Impulse Function -- 2.6 Parseval’s Theorem -- 2.7 Gibbs Phenomenon -- 2.8 Wold Decomposition -- 2.9 State Space Signal Processing -- 2.10 Common Measurements -- 2.10.1 Convolution -- 2.10.2 Correlation -- 2.10.3 Auto Covariance -- 2.10.4 Coherence -- 2.10.5 Power Spectral Density (PSD) -- 2.10.6 Estimation and Detection -- 2.10.7 Central Limit Theorem -- 2.10.8 Signal Information Processing Types -- 2.10.9 Machine Learning -- 2.10.10Exercises -- 3 Fundamentals of Signal Transformations -- 3.1 Transformation Methods -- 3.1.1 Laplace Transform -- 3.1.2 Z-Transform -- 3.1.3 Fourier Series -- 3.1.4 Fourier Transform -- 3.1.5 Discrete Fourier Transform and Fast Fourier Transform -- 3.1.6 Zero Padding -- 3.1.7 Overlap-Add and Overlap-Save Convolution -- Algorithms -- 3.1.8 Short Time Fourier Transform (STFT) -- 3.1.9 Wavelet Transform -- 3.1.10 Windowing Signal and the DCT Transforms -- 3.2 Analysis and Comparison of Transformations -- 3.3 Background Information -- 3.4 Exercises -- 3.5 References -- 4 Digital Filters -- 4.1 Introduction -- 4.1.1 FIR and IIR Filters -- 4.1.2 Bilinear Transform -- 4.2 Windowing for Filtering -- 4.3 Allpass Filters -- 4.4 Lattice Filters -- 4.5 All-Zero Lattice Filter -- 4.6 Lattice Ladder Filters -- Contents xi -- 4.7 Comb Filter -- 4.8 Notch Filter -- 4.9 Background Information -- 4.10 Exercises -- 5 Estimation and Detection -- 5.1 Introduction -- 5.2 Hypothesis Testing -- 5.2.1 Bayesian Hypothesis Testing -- 5.2.2 MAP Hypothesis Testing -- 5.3 Maximum Likelihood (ML) Hypothesis Testing -- 5.4 Standard Analysis Techniques -- 5.4.1 Best Linear Unbiased Estimator (BLUE) -- 5.4.2 Maximum Likelihood Estimator (MLE) -- 5.4.3 Least Squares Estimator (LSE) -- 5.4.4 Linear Minimum Mean Square Error Estimator -- (LMMSE) -- 5.5 Exercises -- 6 Adaptive Signal Processing -- 6.1 Introduction -- 6.2 Parametric Signal Modeling -- 6.2.1 Parametric Estimation -- 6.3 Wiener Filtering -- 6.4 Kalman Filter -- 6.4.1 Smoothing -- 6.5 Particle Filter -- 6.6 Fundamentals of Monte Carl -- 6.6.1 Importance Sampling (IS) -- 6.7 Non-Parametric Signal Modeling -- 6.8 Non-Parametric Estimation -- 6.8.1 Correlogram -- 6.8.2 Periodogram -- 6.9 Filter Bank Method -- 6.10 Quadrature Mirror Filter Bank (QMF) -- 6.11 Background Information -- 6.12 Exercises -- 7 Spectral Analysis -- 7.1 Introduction -- 7.2 Adaptive Spectral Analysis -- 7.3 Multivariate Signal Processing -- 7.3.1 Sub-band Coding and Subspace Analysis -- 7.4 Wavelet Analysis -- 7.5 Adaptive Beam Forming -- xii Contents -- 7.6 Independent Component Analysis (ICA) -- 7.7 Principal Component Analysis (PCA) -- 7.8 Best Basis Algorithms -- 7.9 Background Information -- 7.10 Exercises -- Part II Machine Learning and Recognition -- 8 General Learning -- 8.1 Introduction to Learning -- 8.2 The Learning Phases -- 8.2.1 Search and Utility -- 8.3 Search -- 8.3.1 General Search Model -- 8.3.2 Preference relations -- 8.3.3 Different learning methods -- 8.3.4 Similarities -- 8.3.5 Learning to Recognize -- 8.3.6 Learning again -- 8.4 Background Information -- 8.5 Exercises -- 9 Signal Processes, Learning, and Recognition -- 9.1 Learning -- 9.2 Bayesian Formalism -- 9.2.1 Dynamic Bayesian Theory -- 9.2.2 Recognition and Search -- 9.2.3 Influences -- 9.3 Subjectivity -- 9.4 Background Information -- 9.5 Exercises -- 10 Stochastic Processes -- 10.1 Preliminaries on Probabilities -- 10.2 Basic Concepts of Stochastic Processes -- 10.2.1 Markov Processes -- 10.2.2 Hidden Stochastic Models (HSM) -- 10.2.3 HSM Topology -- 10.2.4 Learning Probabilities -- 10.2.5 Re-estimation -- 10.2.6 Redundancy -- 10.2.7 Data Preparation -- 10.2.8 Proper Redundancy Removal -- 10.3 Envelope Detection -- 10.3.1 Silence Threshold Selection -- 10.3.2 Pre-emphasis -- Contents xiii -- 10.4 Several Processes -- 10.4.1 Similarity -- 10.4.2 The Local-Global Principle -- 10.4.3 HSM Similarities -- 10.5 Conflict and Support -- 10.6 Examples and Applications -- 10.7 Predictions -- 10.8 Background Information -- 10.9 Exercises -- 11 Feature Extraction -- 11.1 Feature Extractions -- 11.2 Basic Techniques -- 11.2.1 Spectral Shaping -- 11.3 Spectral Analysis and Feature Transformation -- 11.3.1 Parametric Feature Transformations and Cepstrum -- 11.3.2 Standard Feature Extraction Techniques -- 11.3.3 Frame Energy -- 11.4 Linear Prediction Coe_cients (LPC) -- 11.5 Linear Prediction Cepstral Coe_cients (LPCC) -- 11.6 Adaptive Perceptual Local Trigonometric Transformation -- (APLTT) -- 11.7 Search -- 11.7.1 General Search Model -- 11.8 Predictions -- 11.8.1 Purpose -- 11.8.2 Linear Prediction -- 11.8.3 Mean Squared Error Minimization -- 11.8.4 Computation of Probability of an Observation Sequence -- 11.8.5 Forward and Backward Prediction -- 11.8.6 Forward-Backward Prediction -- 11.9 Background Information -- 11.10Exercises -- 12 Unsupervised Learning -- 12.1 Generalities -- 12.2 Clustering Principles -- 12.3 Cluster Analysis Methods -- 12.4 Special Methods -- 12.4.1 K-means -- 12.4.2 Vector Quantization (VQ) -- 12.4.3 Expectation Maximization (EM) -- 12.4.4 GMM Clustering -- 12.5 Background Information -- 12.6 Exercises -- xiv Contents -- 13 Markov Model and Hidden Stochastic Model -- 13.1 Markov Process -- 13.2 Gaussian Mixture Model (GMM) -- 13.3 Advantages of using GMM -- 13.4 Linear Prediction Analysis -- 13.4.1 Autocorrelation Method -- 13.4.2 Yule-Walker Approach -- 13.4.3 Covariance Method -- 13.4.4 Comparison of Correlation and Covariance methods -- 13.5 The ULS Approach -- 13.6 Comparison of ULS and Covariance Methods -- 13.7 Forward Prediction -- 13.8 Backward Prediction -- 13.9 Forward-Backward Prediction -- 13.10Baum-Welch Algorithm -- 13.11Viterbi Algorithm -- 13.12Background Information -- 13.13Exercises -- 14 Fuzzy Logic and Rough Sets -- 14.1 Rough Sets -- 14.2 Fuzzy Sets -- 14.2.1 Basis Elements -- 14.2.2 Possibility and Necessity -- 14.3 Fuzzy Clustering -- 14.4 Fuzzy Probabilities -- 14.5 Background Information -- 14.6 Exercises -- 15 Neural Networks -- 15.1 Neural Network Types -- 15.1.1 Neural Network Training -- 15.1.2 Neural Network Topology -- 15.2 Parallel Distributed Processing -- 15.2.1 Forward and Backward Uses -- 15.2.2 Learning -- 15.3 Applications to Signal Processing -- 15.4 Background Information -- 15.5 Exercises -- Part III Real Aspects and Applications -- Contents xv -- 16 Noisy Signals -- 16.1 Introduction -- 16.2 Noise Questions -- 16.3 Sources of Noise -- 16.4 Noise Measurement -- 16.5 Weights and A-Weights -- 16.6 Signal to Noise Ratio (SNR) -- 16.7 Noise Measuring Filters and Evaluation -- 16.8 Types of noise -- 16.9 Origin of noises -- 16.10Box Plot Evaluation -- 16.11Individual noise types -- 16.11.1Residual -- 16.11.2Mild -- 16.11.3Steady-unsteady Time varying Noise -- 16.11.4Strong Noise -- 16.12Solution to Strong Noise: Matched Filter -- 16.13Background Information -- 16.14Exercises -- 17 Reasoning Methods and Noise Removal -- 17.1 Generalities -- 17.2 Special Noise Removal Methods -- 17.2.1 Residual Noise -- 17.2.2 Mild Noise -- 17.2.3 Steady-Unsteady Noise -- 17.2.4 Strong Noise -- 17.3 Poisson Distribution -- 17.3.1 Outliers and Shots -- 17.3.2 Underlying probability of Shots -- 17.4 Kalman Filter -- 17.4.1 Prediction Estimates -- 17.4.2 White noise Kalman filtering -- 17.4.3 Application of Kalman filter -- 17.5 Classification, Recognition and Learning -- 17.5.1 Summary of the used concepts -- 17.6 Principle Component Analysis (PCA) -- 17.7 Reasoning Methods -- 17.7.1 Case-Based Reasoning (CBR) -- 17.8 Background Information -- 17.9 Exercises -- xvi Contents -- 18 Audio Signals and Speech Recognition -- 18.1 Generalities of Speech -- 18.2 Categories of Speech Recognition -- 18.3 Automatic Speech Recognition -- 18.3.1 System Structure -- 18.4 Speech Production Model -- 18.5 Acoustics -- 18.6 Human Speech Production -- 18.6.1 The Human Speech Generation -- 18.6.2 Excitation -- 18.6.3 Voiced Speech -- 18.6.4 Unvoiced Speech -- 18.7 Silence Regions -- 18.8 Glottis -- 18.9 Lips -- 18.10Plosive Speech Source -- 18.11Vocal-Tract -- 18.12Parametric and Non-Parametric Models -- 18.13Formants -- 18.14Strong Noise -- 18.15Background Information -- 18.16Exercises -- 19 Noisy Speech -- 19.1 Introduction -- 19.2 Colored Noise -- 19.2.1 Additional types of Colored Noise -- 19.3 Poisson Processes and Shots -- 19.4 Matched Filters -- 19.5 Shot Noise -- 19.6 Background Information -- 19.7 Exercises -- 20 Aspects Of Human Hearing -- 20.1 Human Ear -- 20.2 Human Auditory System -- 20.3 Critical Bands and Scales -- 20.3.1 Mel Scale -- 20.3.2 Bark Scale -- 20.3.3 Erb Scale -- 20.3.4 Greenwood Scale -- 20.4 Filter Banks -- 20.4.1 ICA Network -- 20.4.2 Auditory Filter Banks -- 20.4.3 Filter Banks -- Contents xvii -- 20.4.4 Mel Critical Filter Bank -- 20.5 Psycho-acoustic Phenomena -- 20.5.1 Perceptual Measurement -- 20.5.2 Human Hearing and Perception -- 20.5.3 Sound Pressure Level (SPL) -- 20.5.4 Absolute Threshold of Hearing (ATH) -- 20.6 Perceptual Adaptation -- 20.7 Auditory System and Hearing Model -- 20.8 Auditory Masking and Masking Frequency -- 20.…”
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Advanced control of power converters : techniques and MATLAB/Simulink implementation by Komurcugil, Hasan, Bayhan, Sertac, Guzman, Ramon (Professor), Malinowski, Mariusz (Electrical engineer), Abu-Rub, Haithem

Table of Contents: “…About the Authors xiii -- List of Abbreviations xvii -- Preface xix -- Acknowledgment xxi -- About the Companion Website xxiii -- 1 Introduction 1 -- 1.1 General Remarks 1 -- 1.2 Basic Closed-Loop Control for Power Converters 3 -- 1.3 Mathematical Modeling of Power Converters 4 -- 1.4 Basic Control Objectives 6 -- 1.4.1 Closed-Loop Stability 6 -- 1.4.2 Settling Time 10 -- 1.4.3 Steady-State Error 11 -- 1.4.4 Robustness to Parameter Variations and Disturbances 12 -- 1.5 Performance Evaluation 12 -- 1.5.1 Simulation-Based Method 12 -- 1.5.2 Experimental Method 13 -- 1.6 Contents of the Book 13 -- References 15 -- 2 Introduction to Advanced Control Methods 17 -- 2.1 Classical Control Methods for Power Converters 17 -- 2.2 Sliding Mode Control 18 -- 2.3 Lyapunov Function-Based Control 22 -- 2.3.1 Lyapunov's Linearization Method 23 -- 2.3.2 Lyapunov's Direct Method 24 -- 2.4 Model Predictive Control 27 -- 2.4.1 Functional Principle 27 -- 2.4.2 Basic Concept 28 -- 2.4.3 Cost Function 29 -- References 30 -- 3 Design of Sliding Mode Control for Power Converters 33 -- 3.1 Introduction 33 -- 3.2 Sliding Mode Control of DC-DC Buck and Cuk Converters 33 -- 3.3 Sliding Mode Control Design Procedure 44 -- 3.3.1 Selection of Sliding Surface Function 44 -- 3.3.2 Control Input Design 46 -- 3.4 Chattering Mitigation Techniques 48 -- 3.4.1 Hysteresis Function Technique 48 -- 3.4.2 Boundary Layer Technique 49 -- 3.4.3 State Observer Technique 50 -- 3.5 Modulation Techniques 51 -- 3.5.1 Hysteresis Modulation Technique 51 -- 3.5.2 Sinusoidal Pulse Width Modulation Technique 52 -- 3.5.3 Space Vector Modulation Technique 53 -- 3.6 Other Types of Sliding Mode Control 54 -- 3.6.1 Terminal Sliding Mode Control 54 -- 3.6.2 Second-Order Sliding Mode Control 54 -- References 55 -- 4 Design of Lyapunov Function-Based Control for Power Converters 59 -- 4.1 Introduction 59 -- 4.2 Lyapunov-Function-Based Control Design Using Direct Method 59 -- 4.3 Lyapunov Function-Based Control of DC-DC Buck Converter 62 -- 4.4 Lyapunov Function-Based Control of DC-DC Boost Converter 67 -- References 71 -- 5 Design of Model Predictive Control 73 -- 5.1 Introduction 73 -- 5.2 Predictive Control Methods 73 -- 5.3 FCS Model Predictive Control 75 -- 5.3.1 Design Procedure 76 -- 5.3.2 Tutorial 1: Implementation of FCS-MPC for Three-Phase VSI 80 -- 5.4 CCS Model Predictive Control 86 -- 5.4.1 Incremental Models 86 -- 5.4.2 Predictive Model 88 -- 5.4.3 Cost Function in CCSMPC 92 -- 5.4.4 Cost Function Minimization 93 -- 5.4.5 Receding Control Horizon Principle 96 -- 5.4.6 Closed-Loop of an MPC System 97 -- 5.4.7 Discrete Linear Quadratic Regulators 97 -- 5.4.8 Formulation of the Constraints in MPC 99 -- 5.4.9 Optimization with Equality Constraints 103 -- 5.4.10 Optimization with Inequality Constraints 105 -- 5.4.11 MPC for Multi-Input Multi-Output Systems 108 -- 5.4.12 Tutorial 2: MPC Design For a Grid-Connected VSI in dq Frame 109 -- 5.5 Design and Implementation Issues 112 -- 5.5.1 Cost Function Selection 112 -- 5.5.1.1 Examples for Primary Control Objectives 113 -- 5.5.1.2 Examples for Secondary Control Objectives 114 -- 5.5.2 Weighting Factor Design 114 -- 5.5.2.1 Empirical Selection Method 115 -- 5.5.2.2 Equal-Weighted Cost-Function-Based Selection Method 116 -- 5.5.2.3 Lookup Table-Based Selection Method 117 -- References 118 -- 6 MATLAB/Simulink Tutorial on Physical Modeling and Experimental Setup 121 -- 6.1 Introduction 121 -- 6.2 Building Simulation Model for Power Converters 121 -- 6.2.1 Building Simulation Model for Single-Phase Grid-Connected Inverter Based on Sliding Mode Control 122 -- 6.2.2 Building Simulation Model for Three-Phase Rectifier Based on Lyapunov-Function-Based Control 126 -- 6.2.3 Building Simulation Model for Quasi-Z Source Three-Phase Four-Leg Inverter Based on Model Predictive Control 131 -- 6.2.4 Building Simulation Model for Distributed Generations in Islanded AC Microgrid 137 -- 6.3 Building Real-Time Model for a Single-Phase T-Type Rectifier 142 -- 6.4 Building Rapid Control Prototyping for a Single-Phase T-Type Rectifier 154 -- 6.4.1 Components in the Experimental Testbed 155 -- 6.4.1.1 Grid Simulator 155 -- 6.4.1.2 A Single-Phase T-Type Rectifier Prototype 156 -- 6.4.1.3 Measurement Board 157 -- 6.4.1.4 Programmable Load 158 -- 6.4.1.5 Controller 158 -- 6.4.2 Building Control Structure on OP- 5707 158 -- References 162 -- 7 Sliding Mode Control of Various Power Converters 163 -- 7.1 Introduction 163 -- 7.2 Single-Phase Grid-Connected Inverter with LCL Filter 163 -- 7.2.1 Mathematical Modeling of Grid-Connected Inverter with LCL Filter 164 -- 7.2.2 Sliding Mode Control 165 -- 7.2.3 PWM Signal Generation Using Hysteresis Modulation 168 -- 7.2.3.1 Single-Band Hysteresis Function 168 -- 7.2.3.2 Double-Band Hysteresis Function 168 -- 7.2.4 Switching Frequency Computation 170 -- 7.2.4.1 Switching Frequency Computation with Single-Band Hysteresis Modulation 170 -- 7.2.4.2 Switching Frequency Computation with Double-Band Hysteresis Modulation 171 -- 7.2.5 Selection of Control Gains 172 -- 7.2.6 Simulation Study 174 -- 7.2.7 Experimental Study 177 -- 7.3 Three-Phase Grid-Connected Inverter with LCL Filter 180 -- 7.3.1 Physical Model Equations for a Three-Phase Grid-Connected VSI with an LCL Filter 181 -- 7.3.2 Control System 182 -- 7.3.2.1 Reduced State-Space Model of the Converter 183 -- 7.3.2.2 Model Discretization and KF Adaptive Equation 187 -- 7.3.2.3 Sliding Surfaces with Active Damping Capability 188 -- 7.3.3 Stability Analysis 189 -- 7.3.3.1 Discrete-Time Equivalent Control Deduction 189 -- 7.3.3.2 Closed-Loop System Equations 191 -- 7.3.3.3 Test of Robustness Against Parameters Uncertainties 192 -- 7.3.4 Experimental Study 192 -- 7.3.4.1 Test of Robustness Against Grid Inductance Variations 192 -- 7.3.4.2 Test of Stability in Case of Grid Harmonics Near the Resonance Frequency 196 -- 7.3.4.3 Test of the VSI Against Sudden Changes in the Reference Current 196 -- 7.3.4.4 Test of the VSI Under Distorted Grid 198 -- 7.3.4.5 Test of the VSI Under Voltage Sags 198 -- 7.3.5 Computational Load and Performances of the Control Algorithm 199 -- 7.4 Three-Phase AC-DC Rectifier 200 -- 7.4.1 Nonlinear Model of the Unity Power Factor Rectifier 200 -- 7.4.2 Problem Formulation 202 -- 7.4.3 Axis-Decoupling Based on an Estimator 203 -- 7.4.4 Control System 205 -- 7.4.4.1 Kalman Filter 206 -- 7.4.4.2 Practical Considerations: Election of Q and R Matrices 208 -- 7.4.4.3 Practical Considerations: Computational Burden Reduction 208 -- 7.4.5 Sliding Mode Control 209 -- 7.4.5.1 Inner Control Loop 209 -- 7.4.5.2 Outer Control Loop 210 -- 7.4.6 Hysteresis Band Generator with Switching Decision Algorithm 212 -- 7.4.7 Experimental Study 215 -- 7.5 Three-Phase Transformerless Dynamic Voltage Restorer 224 -- 7.5.1 Mathematical Modeling of Transformerless Dynamic Voltage Restorer 224 -- 7.5.2 Design of Sliding Mode Control for TDVR 225 -- 7.5.3 Time-Varying Switching Frequency with Single-Band Hysteresis 227 -- 7.5.4 Constant Switching Frequency with Boundary Layer 229 -- 7.5.5 Simulation Study 231 -- 7.5.6 Experimental Study 233 -- 7.6 Three-Phase Shunt Active Power Filter 240 -- 7.6.1 Nonlinear Model of the SAPF 240 -- 7.6.2 Problem Formulation 242 -- 7.6.3 Control System 243 -- 7.6.3.1 State Model of the Converter 243 -- 7.6.3.2 Kalman Filter 245 -- 7.6.3.3 Sliding Mode Control 246 -- 7.6.3.4 Hysteresis Band Generator with SDA 247 -- 7.6.4 Experimental Study 248 -- 7.6.4.1 Response of the SAPF to Load Variations 249 -- 7.6.4.2 SAPF Performances Under a Distorted Grid 253 -- 7.6.4.3 SAPF Performances Under Grid Voltage Sags 254 -- 7.6.4.4 Spectrum of the Control Signal 254 -- References 257 -- 8 Design of Lyapunov Function-Based Control of Various Power Converters 261 -- 8.1 Introduction 261 -- 8.2 Single-Phase Grid-Connected Inverter with LCL Filter 261 -- 8.2.1 Mathematical Modeling and Controller Design 261 -- 8.2.2 Controller Modification with Capacitor Voltage Feedback 264 -- 8.2.3 Inverter-Side Current Reference Generation Using Proportional- Resonant Controller 264 -- 8.2.4 Grid Current Transfer Function 266 -- 8.2.5 Harmonic Attenuation and Harmonic Impedance 267 -- 8.2.6 Results 270 -- 8.3 Single-Phase Quasi-Z-Source Grid-Connected Inverter with LCL Filter 277 -- 8.3.1 Quasi-Z-Source Network Modeling 277 -- 8.3.2 Grid-Connected Inverter Modeling 280 -- 8.3.3 Control of Quasi-Z-Source Network 281 -- 8.3.4 Control of Grid-Connected Inverter 281 -- 8.3.5 Reference Generation Using Cascaded PR Control 282 -- 8.3.6 Results 283 -- 8.4 Single-Phase Uninterruptible Power Supply Inverter 287 -- 8.4.1 Mathematical Modeling of Uninterruptible Power Supply Inverter 287 -- 8.4.2 Controller Design 288 -- 8.4.3 Criteria for Selecting Control Parameters 290 -- 8.4.4 Results 292 -- 8.5 Three-Phase Voltage-Source AC-DC Rectifier 298 -- 8.5.1 Mathematical Modeling of Rectifier 298 -- 8.5.2 Controller Design 301 -- 8.5.3 Results 304 -- References 307 -- 9 Model Predictive Control of Various Converters 309 -- 9.1 CCS MPC Method for a Three-Phase Grid-Connected VSI 309 -- 9.1.1 Model Predictive Control Design 310 -- 9.1.1.1 VSI Incremental Model with an Embedded Integrator 310 -- 9.1.1.2 Predictive Model of the Converter 311 -- 9.1.1.3 Cost Function Minimization 312 -- 9.1.1.4 Inclusion of Constraints 313 -- 9.1.2 MATLAB ? …”
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Triumph of the egg by Anderson, Sherwood, 1876-1941

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Illuminated poems by Ginsberg, Allen, 1926-1997

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Women in science now : stories and strategies for achieving equity by Munoz, Lisa M. P.

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You should smile more : how to dismantle gender bias in the workplace by Hudson, Dawn (Public speaker), Krembs, Angelique Bellmer, Lacey, Katie, Marcus, Lori Tauber, Nicholson, Cie, Short, Mitzi

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US policy in southwest Asia : a failure in perspective by Lawrence, Robert G.

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Dear white people. Season one

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Evergreen review reader, 1967-1973

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