Tabla de Contenidos: “…-- Chapter 3: Implementing DSLs -- Architecture of DSL Processing -- The Workings of a Parser -- Grammars, Syntax, and Semantics -- Parsing Data -- Macros -- Testing DSLs -- Handling Errors -- Migrating DSLs -- Chapter 4: Implementing an Internal DSL -- Fluent and Command-Query APIs -- The Need for a Parsing Layer -- Using Functions -- Literal Collections -- Using Grammars to Choose Internal Elements -- Closures -- Parse Tree Manipulation -- Annotation -- Literal Extension -- Reducing the Syntactic Noise -- Dynamic Reception -- Providing Some Type Checking -- Chapter 5: Implementing an External DSL -- Syntactic Analysis Strategy -- Output Production Strategy -- Parsing Concepts -- Mixing-in Another Language -- XML DSLs -- Chapter 6: Choosing between Internal and External DSLs -- Learning Curve -- Cost of Building -- Programmer Familiarity -- Communication with Domain Experts -- Mixing In the Host Language -- Strong Expressiveness Boundary -- Runtime Configuration -- Sliding into Generality -- Composing DSLs -- Summing Up -- Chapter 7: Alternative Computational Models -- A Few Alternative Models -- Chapter 8: Code Generation -- Choosing What to Generate -- How to Generate -- Mixing Generated and Handwritten Code -- Generating Readable Code -- Preparse Code Generation -- Further Reading -- Chapter 9: Language Workbenches -- Elements of Language Workbenches…”

Tabla de Contenidos: “…The properties of sound -- Microphone types -- Microphone pickup patterns -- Microphone placement -- Syncing audio -- Auto-syncing audio to video -- Manually syncing audio to video -- Fixing sound -- De-Esser -- Removing hum -- Changing stereo to mono -- Noise reduction -- Voice isolation -- Summary -- Questions -- Further reading -- Chapter 4: Adding Narration, Voice Dubbing, and Subtitles -- Technical requirements -- Welcome to the Edit page -- Edit Index -- Sound Library -- Metadata -- Mixer -- Importance of subtitles and closed captions -- Legal requirements to add subtitles to your video -- Subtitling and SEO -- Lower thirds -- Subtitles versus closed captions -- Creating and working with subtitles -- Creating your own subtitles -- Adding a new subtitle clip -- Adding a new subtitle track -- Renaming a subtitle track -- Changing default subtitle settings -- Formatting our subtitles -- Exporting and importing subtitles -- Exporting subtitles to upload to YouTube or Vimeo -- Exporting subtitles in the File menu -- Exporting subtitles in the Subtitle track header -- Exporting subtitles on the Deliver page -- Importing subtitles into Resolve -- Positioning subtitle clips on the Timeline -- Welcome to the Fairlight page -- Index -- Meters -- ADR -- Creating a new audio track -- Patching, arming, and recording -- Patching tracks -- Arming tracks -- Recording audio -- Voice dubbing in Fairlight using ADR -- Creating a new Timeline using selected footage -- Adding scene cuts -- Scene Detect -- Ripple deleting clips on the Edit page -- Ripple-deleting clips on the Cut page -- Smooth Cut -- The ADR interface -- Summary -- Questions -- Further reading -- Chapter 5: Creating Additional Sound -- Technical requirements -- Enhancing the vocals -- Pitch -- Vocal Channel -- Dialogue Processor -- Importing audio effects plug-ins…”

Tabla de Contenidos: “…8.4 Results and Analysis -- 8.5 Conclusions -- References -- Chapter 9 Models for the Driver Assistance System -- 9.1 Introduction -- 9.2 Related Survey -- 9.3 Proposed Methodology -- 9.3.1 Proposed System -- 9.3.2 Data Acquisition -- 9.3.3 Noise Reduction -- 9.3.4 Feature Extraction -- 9.3.5 Histogram of Oriented Gradients -- 9.3.6 Local Binary Pattern -- 9.3.7 Feature Selection -- 9.3.8 Classification -- 9.4 Experimental Study -- 9.4.1 Quantitative Investigation on the NTHU Drowsy Driver Detection Dataset -- 9.5 Conclusion -- References -- Chapter 10 Control of Autonomous Underwater Vehicles -- 10.1 Introduction -- 10.2 Literature Review -- 10.3 Control Problem in AUV Control System -- 10.4 Methodology -- 10.5 Results -- References -- Chapter 11 Security and Privacy Issues of AI in Autonomous Vehicles -- 11.1 Introduction -- 11.2 Development of Autonomous Cars with Existing Review -- 11.3 Automation Levels of Autonomous Vehicles -- 11.4 The Architecture of an Autonomous Vehicle -- 11.5 Threat Model -- 11.6 Autonomous Vehicles with AI in IoT-Enabled Environments -- 11.7 Physical Attacks Using AI Against Autonomous Vehicles -- 11.8 AI Cybersecurity Issues for Autonomous Vehicles -- 11.9 Cyberattack Defense Mechanisms -- 11.9.1 Identity-Based Approach -- 11.9.2 Key-Based Solution -- 11.9.3 Trust-Based Solution -- 11.9.4 Solution Based on Behavior Detection -- 11.10 Solution Based on Machine Learning -- 11.11 Conclusion -- References -- Index -- EULA…”

Tabla de Contenidos: “…11-14 Diffraction -- 11-15 Mathematical Representation of a Traveling Wave -- Summary -- Questions -- MisConceptual Questions -- Problems -- General Problems -- Search and Learn -- Chapter 12: Sound -- 12-1 Characteristics of Sound -- 12-2 Intensity of Sound: Decibels -- Sound Level -- Intensity Related to Amplitude -- 12-3 The Ear and Its Response -- Loudness -- The Ear's Response -- 12-4 Sources of Sound: Vibrating Strings and Air Columns -- Stringed Instruments -- Wind Instruments -- 12-5 Quality of Sound, and Noise -- Superposition -- 12-6 Interference of Sound Waves -- Beats -- Interference in Space -- Beats-Interference in Time -- 12-7 Doppler Effect -- Doppler Effect for Light -- 12-8 Shock Waves and the Sonic Boom -- 12-9 Applications: Sonar, Ultrasound, and Medical Imaging -- Sonar -- Ultrasound Medical Imaging -- Summary -- Questions -- MisConceptual Questions -- Problems -- General Problems -- Search and Learn -- Chapter 13: Temperature and Kinetic Theory -- 13-1 Atomic Theory of Matter -- 13-2 Temperature and Thermometers -- Temperature Scales -- Standard Temperature Scale -- 13-3 Thermal Equilibrium and the Zeroth Law of Thermodynamics -- The Zeroth Law of Thermodynamics -- 13-4 Thermal Expansion -- Linear Expansion -- Volume Expansion -- Anomalous Behavior of Water Below 4°C -- Thermal Stresses -- 13-5 The Gas Laws and Absolute Temperature -- 13-6 The Ideal Gas Law -- 13-7 Problem Solving with the Ideal Gas Law -- 13-8 Ideal Gas Law in Terms of Molecules: Avogadro's Number -- 13-9 Kinetic Theory and the Molecular Interpretation of Temperature -- Kinetic Energy Near Absolute Zero -- 13-10 Distribution of Molecular Speeds -- 13-11 Real Gases and Changes of Phase -- 13-12 Vapor Pressure and Humidity -- Evaporation -- Vapor Pressure -- Boiling -- Partial Pressure and Humidity -- 13-13 Diffusion -- Summary -- Questions…”

Tabla de Contenidos: “…6.9 Markov Sequence -- 6.10 Markov Process -- 6.11 Random Sequences -- 6.11.1 Bernoulli Sequence -- 6.11.2 Bernoulli Scheme -- 6.11.3 Independent Sequences -- 6.11.4 Bernoulli Random Walk -- 6.11.5 Binomial Counting Sequence -- 6.12 Random Processes -- 6.12.1 Poisson Counting Process -- 6.12.2 Random Telegraph Signal -- 6.12.3 Wiener Process -- 6.12.4 Gaussian Process -- 6.12.5 Pulse Amplitude Modulation -- 6.12.6 Random Sine Signals -- Problems -- Further Reading -- 7 Stochastic Convergence, Calculus, and Decompositions -- 7.1 Introduction -- 7.2 Stochastic Convergence -- 7.3 Laws of Large Numbers -- 7.4 Central Limit Theorem -- 7.5 Stochastic Continuity -- 7.6 Derivatives and Integrals -- 7.7 Differential Equations -- 7.8 Difference Equations -- 7.9 Innovations and Mean-Square Predictability -- 7.10 Doob-Meyer Decomposition -- 7.11 Karhunen-Lo`eve Expansion -- Problems -- Further Reading -- 8 Systems, Noise, and Spectrum Estimation -- 8.1 Introduction -- 8.2 Correlation Revisited -- 8.3 Ergodicity -- 8.4 Eigenfunctions of RXX(τ) -- 8.5 Power Spectral Density -- 8.6 Power Spectral Distribution -- 8.7 Cross-Power Spectral Density -- 8.8 Systems with Random Inputs -- 8.8.1 Nonlinear Systems -- 8.8.2 Linear Systems -- 8.9 Passband Signals -- 8.10 White Noise -- 8.11 Bandwidth -- 8.12 Spectrum Estimation -- 8.12.1 Periodogram -- 8.12.2 Smoothed Periodogram -- 8.12.3 Modified Periodogram -- 8.13 Parametric Models -- 8.13.1 Autoregressive Model -- 8.13.2 Moving-Average Model -- 8.13.3 Autoregressive Moving-Average Model -- 8.14 System Identification -- Problems -- Further Reading -- 9 Sufficient Statistics and Parameter Estimation -- 9.1 Introduction -- 9.2 Statistics -- 9.3 Sufficient Statistics -- 9.4 Minimal Sufficient Statistic -- 9.5 Exponential Families -- 9.6 Location-Scale Families -- 9.7 Complete Statistic -- 9.8 Rao-Blackwell Theorem…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright Page -- Contents -- Dedication -- Preface -- Author Biography -- Chapter 1 Concepts and Foundations Automation and Emerging Technologies -- 1.1 Introduction -- 1.2 Structure and Framework of Automation and Key Performance Indexes (KPIs) -- 1.3 Advanced Image Processing Techniques -- 1.4 Fuzzy and Its Recent Advances -- 1.5 Automatic Detection and Its Applications in Infrastructure -- 1.6 Feature Extraction and Fragmentation Methods -- 1.7 Feature Prioritization and Selection Methods -- 1.8 Classification Methods and Its Applications in Infrastructure Management -- 1.9 Models of Performance Measures and Quantification in Automation -- 1.10 Nature-Inspired Optimization Algorithms (NIOAS) -- 1.11 Summary and Conclusion -- 1.12 Questions and Exercise -- Chapter 2 The Structure and Framework of Automation and Key Performance Indices (KPIs) -- 2.1 Introduction -- 2.2 Macro Plan and Architecture of Automation -- 2.2.1 Infrastructure Automation -- 2.2.2 Importance of Infrastructure Automation Evaluation -- 2.3 A General Framework and Design of Automation -- 2.4 Infrastructure Condition Index and Its Relationship with Cracking -- 2.4.1 Road Condition Index -- 2.4.2 Bridge Condition Index -- 2.4.3 Tunnel Condition Index -- 2.5 Automation, Emerging Technologies, and Futures Studies -- 2.6 Summary and Conclusion -- 2.7 Questions -- Further Reading -- Chapter 3 Advanced Images Processing Techniques -- Introduction -- 3.1 Preprocessing (PPS) -- 3.1.1 Edge Preservation Index (EPI) -- 3.1.2 Edge-Strength Similarity-Based Image Quality Metric (ESSIM) -- 3.1.3 QILV Index -- 3.1.4 Structural Content Index (SCI) -- 3.1.5 Signal-To-Noise Ratio Index (PSNR) -- 3.1.6 Computational time index (CTI) -- 3.2 Preprocessing Using Single-Level Methods -- 3.2.1 Single-Level Methods -- 3.2.2 Linear Location Filter (LLF) -- 3.2.3 Median Filter…”

Tabla de Contenidos: “…III Case Studies -- 12.Exploring Control Systems Through Simulation -- The Case Studies -- Modeling Time -- Control Time -- Simulation Time -- The Simulation Framework -- Components -- Plants and Systems -- Controllers -- Actuators and Filters -- Convenience Functions for Standard Loops -- Generating Graphical Output -- 13.Case Study: Cache Hit Rate -- Defining Components -- Cache Misses as Manufacturing Defects -- Measuring System Characteristics -- Controller Tuning -- Simulation Code -- 14.Case Study: Ad Delivery -- The Situation -- Measuring System Characteristics -- Establishing Control -- Improving Performance -- Variations -- Cumulative Goal -- Gain Scheduling -- Integrator Preloading -- Weekend Effects -- Simulation Code -- 15.Case Study: Scaling Server Instances -- The Situation -- Measuring and Tuning -- Reaching 100 Percent With a Nonstandard Controller -- Dealing with Latency -- Simulation Code -- 16.Case Study: Waiting-Queue Control -- On the Nature of Queues and Buffers -- The Architecture -- Setup and Tuning -- Derivative Control to the Rescue -- Controller Alternatives -- Simulation Code -- 17.Case Study: Cooling Fan Speed -- The Situation -- The Model -- Tuning and Commissioning -- Closed-Loop Performance -- Simulation Code -- 18.Case Study: Controlling Memory Consumption in a Game Engine -- The Situation -- Problem Analysis -- Architecture Alternatives -- A Nontraditional Loop Arrangement -- A Traditional Loop with Logarithms -- Results -- Simulation Code -- 19.Case Study Wrap-Up -- Simple Controllers, Simple Loops -- Measuring and Tuning -- Staying in Control -- Dealing with Noise -- pt. IV Theory -- 20.The Transfer Function -- Differential Equations -- Laplace Transforms -- Properties of the Laplace Transform -- Using the Laplace Transform to Solve Differential Equations -- A Worked Example -- The Transfer Function -- Worked Example: Step Response -- Worked Example: Ramp Input -- The Harmonic Oscillator -- What If the Differential Equation Is Not Known? …”

Tabla de Contenidos: “…ElasticNet -- Robust regression -- RANSAC -- Huber regression -- Bayesian regression -- Polynomial regression -- Isotonic regression -- Summary -- Chapter 5: Linear Classification Algorithms -- Linear classification -- Logistic regression -- Implementation and optimizations -- Stochastic gradient descent algorithms -- Passive-aggressive algorithms -- Passive-aggressive regression -- Finding the optimal hyperparameters through a grid search -- Classification metrics -- Confusion matrix -- Precision -- Recall -- F-Beta -- Cohen's Kappa -- Global classification report -- Learning curve -- ROC curve -- Summary -- Chapter 6: Naive Bayes and Discriminant Analysis -- Bayes' theorem -- Naive Bayes classifiers -- Naive Bayes in scikit-learn -- Bernoulli Naive Bayes -- Multinomial Naive Bayes -- An example of Multinomial Naive Bayes for text classification -- Gaussian Naive Bayes -- Discriminant analysis -- Summary -- Chapter 7: Support Vector Machines -- Linear SVM -- SVMs with scikit-learn -- Linear classification -- Kernel-based classification -- Radial Basis Function -- Polynomial kernel -- Sigmoid kernel -- Custom kernels -- Non-linear examples -- ν-Support Vector Machines -- Support Vector Regression -- An example of SVR with the Airfoil Self-Noise dataset -- Introducing semi-supervised Support Vector Machines (S3VM) -- Summary -- Chapter 8: Decision Trees and Ensemble Learning -- Binary Decision Trees -- Binary decisions -- Impurity measures -- Gini impurity index -- Cross-entropy impurity index -- Misclassification impurity index -- Feature importance -- Decision Tree classification with scikit-learn -- Decision Tree regression -- Example of Decision Tree regression with the Concrete Compressive Strength dataset -- Introduction to Ensemble Learning -- Random Forests -- Feature importance in Random Forests -- AdaBoost -- Gradient Tree Boosting…”

Tabla de Contenidos: “…; Investment casting ; Lost-PLA process ; Casting vs. printing in metal -- Large prints and post-processing: Printing computationally complex objects ; Printing physically big objects ; Objects that are too long for the build platform ; Objects that are too big in more than one dimension ; Gluing the pieces together ; Sanding, chemical smoothing, painting, and dyeing ; Sanding ; Smoothing and bonding ABS with acetone ; Painting ABS and PLA ; Dyeing nylon -- Troubleshooting: Clicking or grinding noises ; Environmental issues ; Drafts ; Ambient temperature ; Humidity ; Dust ; Printer internal alignment issues ; Prints not sticking to the build platform ; Clogged nozzle solutions ; Cold pull ; Wire brush bristle ; Extruder drive gear teeth clogged ; Eliminate stringing ; Software updates…”

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Tabla de Contenidos: “…Calculations -- 6.3 Sequential Circuits Vector Joystick Envelope Modulation -- 6.4 Linear Frequency Modulation with LFO or EG -- 6.5 BPM Sync -- 6.6 Quantizing -- 6.7 Ramp Modulation: Fade-in and Fade-out -- 6.8 Pitch Calculation -- 6.9 Pulse-Width Modulation (PWM) -- 6.10 Phase Distortion -- 6.11 Hard Sync -- 6.12 Filter Key Track Modulation -- Bibliography -- 7 Envelope Generators and DCA -- 7.1 Envelope Generator Fundamentals -- 7.2 EG Implementation: Finite State Machine -- 7.3 Digital EG Implementation: Rendering the Output -- 7.4 Biased EG Output -- 7.5 Analog EG Emulation -- 7.6 Synth Module: EnvelopeGenerator -- 7.7 EG Retrigger Modulation -- 7.8 EG Core Programming Notes -- 7.9 The Digitally Controlled Amplifier (DCA) -- 7.10 Exercises -- Bibliography -- 8 Low Frequency Oscillators -- 8.1 Noise Oscillators and Generators -- 8.2 Oscillator Clocking -- 8.3 LFO Waveforms and Rendering Equations -- 8.4 Render Modes -- 8.5 Waveform Shaping -- 8.6 Delay and Fade-in Times -- 8.7 Starting Phase -- 8.8 DC Offset -- 8.9 SynthLFO and Cores -- 8.10 LFO Core Programming Notes -- 8.11 Exercises -- Bibliography -- 9 Wavetable Oscillators -- 9.1 Wavetable Fundamentals: Table Lookup -- 9.2 Wavetable Objects and Database -- 9.3 Wavetable Sources and Database -- 9.4 WTOscillator and Cores -- 9.5 Wavetable Core Programming Notes -- 9.6 Exercises -- Bibliography -- 10 Virtual Analog Oscillators -- 10.1 VA Oscillator Fundamentals -- 10.2 Band Limited Impulse Train (BLIT) -- 10.3 Band Limited Step (BLEP) -- 10.4 Polynomial BLEP Approximation -- 10.5 Choosing the BLEP sinc Source -- 10.6 Other VA Algorithms -- 10.7 BLEP Square Wave -- 10.8 VAOscillator and Core -- 10.9 VA Core Programming Notes -- 10.10 Exercises -- Bibliography -- 11 PCM Sample Playback Oscillators -- 11.1 PCM Sample Playback Modes -- 11.2 PCM Sample Storage -- 11.3 Loading PCM Samples…”

Tabla de Contenidos: “…3.1 Introduction -- 3.2 Definitions -- 3.2.1 Sources -- 3.2.2 Attributes -- 3.2.2.1 Volume -- 3.2.2.2 Variety -- 3.2.2.3 Velocity -- 3.2.2.4 Veracity -- 3.2.2.5 Variability -- 3.2.2.6 Value -- 3.2.2.7 Visualization -- 3.2.3 The Making of Big Data -- 3.3 The Analytic Challenge: From Database Marketing to Big Data and Data Science -- 3.4 Assessing Data Quality -- 3.4.1 Validity -- 3.4.2 Missingness -- 3.4.3 Representation -- 3.5 Applications in Market, Opinion, and Social Research -- 3.5.1 Adding Value through Linkage -- 3.5.2 Combining Big Data and Surveys in Market Research -- 3.6 The Ethics of Research Using Big Data -- 3.7 The Future of Surveys in a Data-Rich Environment -- References -- Chapter 4 The Role of Statistical Disclosure Limitation in Total Survey Error -- 4.1 Introduction -- 4.2 Primer on SDL -- 4.3 TSE-Aware SDL -- 4.3.1 Additive Noise -- 4.3.2 Data Swapping -- 4.4 Edit-Respecting SDL -- 4.4.1 Simulation Experiment -- 4.4.2 A Deeper Issue -- 4.5 SDL-Aware TSE -- 4.6 Full Unification of Edit, Imputation, and SDL -- 4.7 ``Big Data ́ ́ Issues -- 4.8 Conclusion -- Acknowledgments -- References -- Section 2 Implications for Survey Design -- Chapter 5 The Undercoverage-Nonresponse Tradeoff -- 5.1 Introduction -- 5.2 Examples of the Tradeoff -- 5.3 Simple Demonstration of the Tradeoff -- 5.4 Coverage and Response Propensities and Bias -- 5.5 Simulation Study of Rates and Bias -- 5.5.1 Simulation Setup -- 5.5.2 Results for Coverage and Response Rates -- 5.5.3 Results for Undercoverage and Nonresponse Bias -- 5.5.3.1 Scenario 1 -- 5.5.3.2 Scenario 2 -- 5.5.3.3 Scenario 3 -- 5.5.3.4 Scenario 4 -- 5.5.3.5 Scenario 7 -- 5.5.4 Summary of Simulation Results -- 5.6 Costs -- 5.7 Lessons for Survey Practice -- References -- Chapter 6 Mixing Modes: Tradeoffs Among Coverage, Nonresponse, and Measurement Error Roger Tourangeau -- 6.1 Introduction…”

Tabla de Contenidos: “…Introduction -- 9.2. Overview of noise reduction techniques -- 9.3. Monaural beamforming -- 9.4. …”

Tabla de Contenidos: “…Reducing the signal-to-noise ratio -- Our first custom wrapper method - global.log() -- Rules to enforce coding standards -- Strict mode -- Turning on individual TypeScript subset rule checks -- CodeGPT -- Summary -- Chapter 4: Super Speed - Time-Travel Paradoxes and Broken Promises -- Technical requirements -- The time-travel dilemma -- Schrödinger and the quantum mechanics of test automation -- Callbacks, promises, and async/await -- The death of fibers and synchronous mode -- Keep it simple with async and await -- Summary -- Chapter 5: Alter Egos - The ClickAdv Wrapper -- Adding a helpers file -- The "Hello, World!" …”

Tabla de Contenidos: “…13.2.1 Creating and Displaying Image Histogram -- 13.2.2 Histogram Equalization -- 13.3 Operations on Images -- 13.3.1 Linear Spatial Filtering -- 13.3.2 Spatial Noise Filtering -- 13.3.3 Morphological Image Processing -- 13.4 Point, Line, and Edge Detection -- 13.4.1 Point Detection -- 13.4.2 Line Detection -- 13.4.3 Edge Detection -- 13.5 Thresholding -- 13.5.1 Global Thresholding -- 13.5.2 Local Thresholding -- Programming Tips and Pitfalls -- Summary -- Exercises -- Index…”

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Tabla de Contenidos:

Tabla de Contenidos: “…10.5.5 Isolating Box -- 10.5.6 Rules of Noise Control -- Bibliography -- 11 Hybrid Systems -- 11.1 Hybrid SEA Matrix -- 11.2 Twin Chamber -- 11.2.1 Step 1 - Setting up System Configurations -- 11.2.2 Step 2 - Setting up System Matrices and Coupling Loss Factors -- 11.2.3 Step 3 - External Loads -- 11.2.4 Step 4 - Solving System Matrices -- 11.2.5 Step 5 - Adding the Results -- 11.3 Trim in Hybrid Theory -- 11.3.1 The Trim Stiffness Matrix -- 11.3.2 Hybrid Modal Formulation of Trim and Plate -- 11.3.3 Modal Space -- 11.3.4 Plate Example with Trim -- Bibliography -- 12 Industrial Cases -- 12.1 Simulation Strategy -- 12.1.1 Motivation -- 12.1.2 Choice of Simulation Method -- 12.2 Aircraft -- 12.2.1 Excitation -- 12.2.2 Simulation Strategy -- 12.2.3 Fuselage Sidewall -- 12.2.4 SEA Model of a Fuselage Section -- 12.3 Automotive -- 12.3.1 Simulation Strategy -- 12.3.2 Excitation -- 12.3.3 Rear Carbody -- 12.3.4 Full Scale SEA Models -- 12.4 Trains -- 12.4.1 Structural Design -- 12.4.2 Interior Design -- 12.4.3 Excitation and Transmission Paths -- 12.4.4 Simulation Strategy -- 12.4.5 Applications to Rail Structures - Double Walls -- 12.4.6 Carbody Sections - High Speed Applications -- 12.5 Summary -- Bibliography -- 13 Conclusions and Outlook -- 13.1 Conclusions -- 13.2 What Comes Next? …”

Tabla de Contenidos: “…Machine generated contents note: Preface vii Acknowledgements ix List of Abbreviations xi 1 Introduction 1 1.1 Analysis and Synthesis of Nonlinear Stochastic Systems 2 1.1.1 Nonlinear Systems 3 1.1.2 Stochastic Systems 4 1.2 Multi-Objective Control and Filtering with Variance Constraints 5 1.2.1 Covariance Control Theory 5 1.2.2 Multiple Performance Requirements 7 1.2.3 Design Techniques for Nonlinear Stochastic Systems with Variance Constraints 9 1.2.4 A Special Case of Multi-Objective Design: Mixed H2/H1 Control/Filtering 11 1.3 Outline 12 2 Robust H1 Control with Variance Constraints 17 2.1 Problem Formulation 18 2.2 Stability, H1 Performance and Variance Analysis 20 2.2.1 Stability, H1 Performance Analysis 21 2.2.2 Variance Analysis 23 2.3 Robust Controller Design 27 2.4 Numerical Example 30 2.5 Summary 33 3 Robust Mixed H2=H1 Filtering 41 3.1 System Description and Problem Formulation 42 3.2 Algebraic Characterizations for Robust H2=H1 Filtering 44 3.2.1 Robust H2 Filtering 44 3.2.2 Robust H1 Filtering 50 3.3 Robust H2=H1 Filter Design Techniques 51 3.4 An Illustrative Example 60 3.5 Summary 62 4 Filtering with Missing Measurements 63 4.1 Problem Formulation 64 4.2 Stability and Variance Analysis 67 4.3 Robust Filter Design 71 4.4 Numerical Example 75 4.5 Summary 78 5 Robust Fault-Tolerant Control 87 5.1 Problem Formulation 88 5.2 Stability and Variance Analysis 90 5.3 Robust Controller Design 92 5.4 Numerical Example 98 5.5 Summary 103 6 Robust H2 SMC 105 6.1 The System Model 106 6.2 Robust H2 Sliding Mode Control 107 6.2.1 Switching Surface 107 6.2.2 Performances of the Sliding Motion 108 6.2.3 Computational Algorithm 114 6.3 Sliding Mode Controller 115 6.4 Numerical Example 116 6.5 Summary 118 7 Dissipative Control with Degraded Measurements 125 7.1 Problem Formulation 126 7.2 Stability, Dissipativity and Variance Analysis 129 7.3 Observer-Based Controller Design 134 7.3.1 Solvability of Multi-Objective Control Problem 134 7.3.2 Computational Algorithm 139 7.4 Numerical Example 140 7.5 Summary 142 8 Variance-Constrained H1 Control with Multiplicative Noises 145 8.1 Problem Formulation 146 8.2 Stability, H1 Performance, Variance Analysis 147 8.2.1 Stability 148 8.2.2 H1 performance 150 8.2.3 Variance analysis 152 8.3 Robust State Feedback Controller Design 153 8.4 A Numerical Example 156 8.5 Summary 157 9 Robust Finite-Horizon H1 Control 159 9.1 Problem Formulation 160 9.2 Performance Analysis 162 9.2.1 H1 Performance 162 9.2.2 Variance Analysis 164 9.3 Robust Finite Horizon Controller Design 167 9.4 Numerical Example 171 9.5 Summary 173 10 Finite-Horizon Filtering with Degraded Measurements 177 10.1 Problem Formulation 178 10.2 Performance Analysis 181 10.2.1 H1 Performance Analysis 181 10.2.2 System Covariance Analysis 186 10.3 Robust Filter Design 187 10.4 Numerical Example 190 10.5 Summary 191 11 Mixed H2=H1 Control with Randomly Occurring Nonlinearities: the Finite-Horizon Case 197 11.1 Problem Formulation 199 11.2 H1 Performance 200 11.3 Mixed H2=H1 Controller Design 204 11.3.1 State-Feedback Controller Design 204 11.3.2 Computational Algorithm 207 11.4 Numerical Example 207 11.5 Summary 211 12 Finite-Horizon H2=H1 Control of MJSs with Sensor Failures 213 12.1 Problem Formulation 214 12.2 H1 Performance 216 12.3 Mixed H2=H1 Controller Design 220 12.3.1 Controller Design 220 12.3.2 Computational Algorithm 224 12.4 Numerical Example 224 12.5 Summary 227 13 Finite-Horizon Control with ROSF 229 13.1 Problem Formulation 230 13.2 H1 And Covariance Performances Analysis 234 13.2.1 H1 Performance 234 13.2.2 Covariance Analysis 238 13.3 Robust Finite-Horizon Controller Design 240 13.3.1 Controller Design 240 13.3.2 Computational Algorithm 243 13.4 Numerical Example 243 13.5 Summary 244 14 Finite-Horizon H2=H1 Control with Actuator Failures 247 14.1 Problem Formulation 248 14.2 H1 Performance 251 14.3 Multi-Objective Controller Design 253 14.3.1 Controller Design 253 14.3.2 Computational Algorithm 256 14.4 Numerical Example 257 14.5 Summary 259 15 Conclusions and Future Topics 261 References 263 …”

Tabla de Contenidos: “…The Nelder-Mead simplex method -- 1.7.2. The noising method -- 1.7.3. Smoothing methods -- 1.8. …”