“…The sections include: 1) Software Engineering, 2) Applications: Semantic Cache, E-Health, Sport Video Browsing, and Power Grids, 3) Visualization, and 4) Natural Language Disambiguation. …”

“…Next, you will delve into detailed aspects of Ignite's data grid: web session clustering and querying data. You will learn how to process large volumes of data using compute grid and Ignite's map-reduce and executor service. …”

Tabla de Contenidos: “…Ortiz Grade estimation in a tabular deposit using unstructured grids M.A.A. Bassani, C.P. Araújo & J.F.C.L. Costa Influence of drilling spacing on the mineral resources uncertainty C.J.E. …”

Tabla de Contenidos: “…-- CONTRIBUTORS xi / /Introduction 1 /Bharath Balasubramanian, Mung Chiang, and Flavio Bonomi / /I.1 Summary of Chapters 5 / /I.2 Acknowledgments 7 / /References 8 / /I COMMUNICATION AND MANAGEMENT OF FOG 11 / /1 ParaDrop: An Edge Computing Platform in Home Gateways 13 /Suman Banerjee, Peng Liu, Ashish Patro, and Dale Willis / /1.1 Introduction 13 / /1.1.1 Enabling Multitenant Wireless Gateways and Applications through ParaDrop 14 / /1.1.2 ParaDrop Capabilities 15 / /1.2 Implementing Services for the ParaDrop Platform 17 / /1.3 Develop Services for ParaDrop 19 / /1.3.1 A Security Camera Service Using ParaDrop 19 / /1.3.2 An Environmental Sensor Service Using ParaDrop 22 / /References 23 / /2 Mind Your Own Bandwidth 24 /Carlee Joe-Wong, Sangtae Ha, Zhenming Liu, Felix Ming Fai Wong, and Mung Chiang / /2.1 Introduction 24 / /2.1.1 Leveraging the Fog 25 / /2.1.2 A Home Solution to a Home Problem 25 / /2.2 Related Work 28 / /2.3 Credit Distribution and Optimal Spending 28 / /2.3.1 Credit Distribution 29 / /2.3.2 Optimal Credit Spending 31 / /2.4 An Online Bandwidth Allocation Algorithm 32 / /2.4.1 Estimating Other Gateways' Spending 32 / /2.4.2 Online Spending Decisions and App Prioritization 34 / /2.5 Design and Implementation 35 / /2.5.1 Traffic and Device Classification 37 / /2.5.2 Rate Limiting Engine 37 / /2.5.3 Traffic Prioritization Engine 38 / /2.6 Experimental Results 39 / /2.6.1 Rate Limiting 39 / /2.6.2 Traffic Prioritization 41 / /2.7 Gateway Sharing Results 41 / /2.8 Concluding Remarks 45 / /Acknowledgments 46 / /Appendix 2.A 46 / /2.A.1 Proof of Lemma 2.1 46 / /2.A.2 Proof of Lemma 2.2 46 / /2.A.3 Proof of Proposition 2.1 47 / /2.A.4 Proof of Proposition 2.2 48 / /2.A.5 Proof of Proposition 2.3 49 / /2.A.6 Proof of Proposition 2.4 49 / /References 50 / /3 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking 52 /Xu Chen, Junshan Zhang, and Satyajayant Misra / /3.1 Introduction 52 / /3.1.1 From Social Trust and Social Reciprocity to D2D Cooperation 54 / /3.1.2 Smart Grid: An IoT Case for Socially-Aware Cooperative D2D and D4D Communications 55 / /3.1.3 Summary of Main Results 57 / /3.2 Related Work 58 / /3.3 System Model 59 / /3.3.1 Physical (Communication) Graph Model 60 / /3.3.2 Social Graph Model 61 / /3.4 Socially-Aware Cooperative D2D and D4D Communications toward Fog Networking 62 / /3.4.1 Social Trust-Based Relay Selection 63 / /3.4.2 Social Reciprocity-Based Relay Selection 63 / /3.4.3 Social Trust and Social Reciprocity-Based Relay Selection 68 / /3.5 Network Assisted Relay Selection Mechanism 69 / /3.5.1 Reciprocal Relay Selection Cycle Finding 69 / /3.5.2 NARS Mechanism 70 / /3.5.3 Properties of NARS Mechanism 73 / /3.6 Simulations 75 / /3.6.1 Erdos / Renyi Social Graph 76 / /3.6.2 Real Trace Based Social Graph 78 / /3.7 Conclusion 82 / /Acknowledgments 82 / /References 83 / /4 You Deserve Better Properties (From Your Smart Devices) 86 /Steven Y. …”

Tabla de Contenidos: “…Infrastructure -- 2.14.1. Electric grid -- 2.15. Point-Of-Sale Systems -- 2.16. Social Engineering -- 2.16.1. …”

Tabla de Contenidos: “…10.4 Adapting IoT to New Needs: Challenges from Brazil -- 10.4.1 IoT RD&amp -- I Funding in Brazil -- 10.4.2 IoT Success Cases in Brazil -- 10.4.2.1 RFID/IoT Change of Paradigm -- 10.4.2.2 Smart Metering and Smart Grids -- 10.4.3 International Standardisation Related to IoT -- 10.4.4 EU-Brazil Collaboration on IoT -- 10.4.4.1 EU-Brazil Joint Call for IoT Pilots RIAs -- 10.4.4.2 EU-Brazil Mapping and Comparative Study -- 10.4.4.3 The EU-Brazil FUTEBOL Project -- 10.4.4.4 FurtherWork on EU-Brazil Cooperation -- 10.5 Do More with Less: Challenges for Africa. …”

Tabla de Contenidos: “…Machine generated contents note: Chapter 1: Introduction to High Performance Drives 1.1 Preliminary Remarks 1.2 General Overview of High Performance Drives 1.3 Challenges and Requirements for Electric Drives for Industrial Applications 1.4 Organization of the Book References Chapter 2: Mathematical and Simulation Models of AC Machines 2.1 Preliminary Remarks 2.2 DC Motors 2.2.1 Separately Excited DC Motor Control 2.2.2 Series DC Motor Control 2.3 Squirrel Cage Induction Motor 2.3.1 Space vector representation 2.3.2 Per unit model of Induction Motor 2.3.3 Double Fed Induction Generator (DFIG) 2.4 Mathematical Model of Permanent Magnet Synchronous Motor Problems References Chapter 3: Pulse Width Modulation of Power Electronic DC-AC Converter 3.1 Preliminary Remarks 3.2 Classification of Pulse Width Modulation Schemes for Voltage source inverter 3.3 Pulse Width Modulated Inverters 3.3.1 Single Phase Half Bridge Inverters 3.3.1.1 Matlab/Simulink Model of Half Bridge Inverter 3.3.2 Single Phase Full Bridge Inverters 3.3.2.1 Matlab/Simulink Model of Single-phase Full-Bridge Inverter 3.4 Three-phase PWM voltage source inverter 3.4.1 Carrier based Sinusoidal PWM 3.4.2 Third Harmonic Injection Carrier-based PWM 3.4.3 Carrier-based PWM With Offset Addition 3.4.4 Space Vector PWM 3.4.5 Discontinuous Space Vector PWM 3.4.6 Matlab/Simulink Model for space vector PWM 3.4.7 Space Vector PWM in Over-modulation Region 3.4.8 Artificial Neural Network Based PWM 3.5 Relationship Between Carrier-based PWM and Space Vector PWM 3.6 Multi-level Inverters 3.6.1 Diode Clamped Multi-level Inverters 3.6.2 Flying Capacitor Type Multi-level Inverter 3.6.3 Cascaded H-Bridge Multi-level Inverter 3.7 Impedance Source or Z-Source Inverter 3.7.1 Circuit Analysis 3.7.2 Carrier-based Simple Boost PWM control of a Z-source Inverter 3.7.3 Carrier-based Maximum Boost PWM control of a Z-source Inverter 3.7.4 Matlab/Simulink model of Z-source inverter 3.8 Quasi Impedance Source or qZSI Inverter 3.8.1 Matlab/Simulink model of qZ-source inverter 3.9 Dead Time Effect in a Multi-phase Inverter 3.10 Summary References Chapter 4: Field Oriented Control of AC Machines 4.1 Introduction 4.2 Induction Machines Control 4.2.1 Control of Induction Motor using V/f method 4.2.2 Vector Control of Induction Motor [4.1-4.16] 4.2.3 Direct and Indirect Field Oriented Control 4.2.4 Rotor and stator flux computation 4.2.5 Adaptive flux observer 4.2.6 Stator Flux Orientation 4.2.7 Field Weakening Control 4.3 Vector Control of Double Fed Induction Generator (DFIG) 4.3.1 Introduction 4.3.2 Vector Control of DFIG connected with the Grid (abModel) 4.3.3 Simulation Results 4.4 Control of Permanent Magnet Synchronous Machine 4.4.1 Introduction 4.4.2 Vector Control of PMSM in dq axis 4.4.3 Vector Control of PMSM in a-baxis using PI controller 4.4.4 Scalar Control of PMSM Exercises Additional tasks Possible tasks for DFIG References Chapter 5: XXXXXXXXXX Chapter 6: Nonlinear Control of Electrical Machines Using Nonlinear Feedback 6.1 Introduction 6.2 dynamic system linearization using non-linear feedback 6.3 Nonlinear Control of Separately Excited DC Motor 6.3.1 Matlab/Simulink Nonlinear Control Model 6.3.2 Nonlinear Control Systems 6.3.3 Speed Controller 6.3.4 Control for variable m 6.3.5 Field Current Controller 6.3.6 Simulation Results 6.4 Multiscalar model (MM) of induction motor 6.4.1 Multiscalar variables 6.4.2 Nonlinear linearization of induction motor fed by voltage controlled VSI 6.4.3 Design of system control 6.4.4 Nonlinear linearization of induction motor fed by current controlled VSI 6.4.5 Stator oriented nonlinear control system (based on Ys,is) 6.4.6 Rotor-Stator Fluxes based Model 6.4.7 Stator Oriented Multiscalar Model 6.4.8 Multiscalar Control of Induction Motor 6.4.9 Induction Motor Model 6.4.10 State Transformation 6.4.11 Decoupled IM Model 6.5 MM of double fed induction machine (DFIM) 6.6 Nonlinear Control of Permanent Magnet Synchronous Machine 6.6.1 Nonlinear Control of PMSM for a dq motor model 6.6.2 Nonlinear Vector Control of PMSM in a-baxis 6.6.3 PMSM in a-b(x-y) axis 6.6.4 Transformation 6.6.5 Control System 6.6.6 Simulation Results Problems References Chapter 7: Five-Phase Induction Motor Drive System 7.1 Preliminary remarks 7.2 Advantages and Applications of Multi-phase drives 7.3 Modelling and Simulation of a Five-phase Induction motor drive 7.3.1 Five-phase Induction motor model 7.3.1.1 Phase variable model 7.3.1.2 Model transformation 7.3.1.3 Machine model in an arbitrary common reference frame 7.3.1.4 Matlab/Simulink model of main fed five-phase induction motor drive 7.3.2 Five-phase Two-level Voltage source Inverter Model 7.3.2.A Ten-Step Mode of Operation 7.3.2.A.1 Fourier analysis of the five-phase inverter output voltages 7.3.2.A.2 Matlab/Simulink Modelling for Ten-step Mode 7.3.2.A.3 Prototype of a five-phase VSI for ten-step operation 7.3.2.A.4 Experimental Results for Ten-Step mode 7.3.2.A.5 PWM mode of operation of five-phase VSI 7.3.3 PWM Schemes of a Five-phase VSI 7.3.3.1 Carrier-based Sinusoidal PWM scheme 7.3.3.2 Matlab/Simulink simulation of Carrier-based sinusoidal PWM 7.3.3.3 5th Harmonic Injection Based pulse width modulation scheme 7.3.3.4 Matlab/Simulink simulation of 5th harmonic injection PWM 7.3.3.5 Offset addition based pulse width modulation scheme 7.3.3.6 Space Vector Pulse Width Modulation Scheme 7.3.3.7 Matlab/Simulink model of SVPWM 7.4 Indirect Rotor Field Oriented Control of Five-phase induction motor 7.4.1 Matlab/Simulink Model of Field oriented control of five-phase Induction machine 7.5 Field Oriented Control of Five-phase induction motor with current control in the Synchronous reference frame 7.6 Model Predictive Control (MPC) 7.6.1 MPC Applied to a Five-phase Two-Level VSI 7.6.2 Matlab/Simulink of MPC for Five-phase VSI 7.7 Summary 7.8 Bibliography Chapter 8: Sensorless Speed Control of AC Machines 8.1 Preliminary Remarks 8.2 Sensorless Control of Induction Motor 8.2.1 Observer 1 8.2.2 Observer 2 8.2.3 Observer 3 8.2.4 MRAS- (closed loop) speed estimator 8.2.5 The use of power measurements 8.3 Sensorless Control of PMSM 8.3.1 Control system of PMSM 8.3.2 Adaptive backstepping observer 8.3.3 Model Reference Adaptive System for PMSM 8.3.4 Simulation Results 8.4 MRAS-Based Sensorless Control of Five-Phase Induction Motor Drive 8.4.1 MRAS-BASED SPEED ESTIMATOR 8.4.2 Simulation Results References Chapter 9: Selected Problems of Induction Motor Drives with Voltage Inverter and Inverter Output Filters 9.1 Drives and filters - overview 9.2 Three phase to two phase transformations 9.3 Voltage and current common mode component 9.3.1 Matlab/Simulink model of induction motor drive with PWM inverter and common mode voltage 9.4 Induction motor common mode circuit 9.5 Bearing current types and reduction methods 9.5.1 Common mode choke 9.5.2 Common mode transformers 9.5.3 Common mode voltage reduction by PWM modifications 9.6 Inverter output filters 9.6.1 Selected structures of inverter output filters 9.6.2 Inverter output filters design 9.6.3 Motor choke 9.6.4 Matlab/Simulink model of induction motor drive with PWM inverter and differential mode (normal mode) LC filter 9.7 Estimation problems in the drive with filters 9.7.1 Introduction 9.7.2 Speed observer with disturbances model 9.7.3 Simple observer based on motor stator models 9.8 Motor control problems in the drive with filters 9.8.1 Introduction 9.8.2 Field oriented control 9.8.3 Nonlinear field oriented control 9.8.4 Nonlinear multiscalar control 9.9 Predictive current control in the drive system with output filter 9.9.1 Control system 9.9.2 Predictive current controller 9.9.3 EMF estimation technique 9.10 Problems 9.11 Questions 9.12 References Index…”

Tabla de Contenidos: “…6.2 The Effect of Offering a Choice of Modes -- 6.3 Getting People to Respond Online -- 6.4 Sequencing Different Modes of Data Collection -- 6.5 Separating the Effects of Mode on Selection and Reporting -- 6.5.1 Conceptualizing Mode Effects -- 6.5.2 Separating Observation from Nonobservation Error -- 6.5.2.1 Direct Assessment of Measurement Errors -- 6.5.2.2 Statistical Adjustments -- 6.5.2.3 Modeling Measurement Error -- 6.6 Maximizing Comparability Versus Minimizing Error -- 6.7 Conclusions -- References -- Chapter 7 Mobile Web Surveys: A Total Survey Error Perspective -- 7.1 Introduction -- 7.2 Coverage -- 7.3 Nonresponse -- 7.3.1 Unit Nonresponse -- 7.3.2 Breakoffs -- 7.3.3 Completion Times -- 7.3.4 Compliance with Special Requests -- 7.4 Measurement Error -- 7.4.1 Grouping of Questions -- 7.4.1.1 Question-Order Effects -- 7.4.1.2 Number of Items on a Page -- 7.4.1.3 Grids versus Item-By-Item -- 7.4.2 Effects of Question Type -- 7.4.2.1 Socially Undesirable Questions -- 7.4.2.2 Open-Ended Questions -- 7.4.3 Response and Scale Effects -- 7.4.3.1 Primacy Effects -- 7.4.3.2 Slider Bars and Drop-Down Questions -- 7.4.3.3 Scale Orientation -- 7.4.4 Item Missing Data -- 7.5 Links Between Different Error Sources -- 7.6 The Future of Mobile web Surveys -- References -- Chapter 8 The Effects of a Mid-Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Famil... -- 8.1 Introduction -- 8.2 Literature Review: Incentives in Face-to-Face Surveys -- 8.2.1 Nonresponse Rates -- 8.2.2 Nonresponse Bias -- 8.2.3 Measurement Error -- 8.2.4 Survey Costs -- 8.2.5 Summary -- 8.3 Data and Methods -- 8.3.1 NSFG Design: Overview -- 8.3.2 Design of Incentive Experiment -- 8.3.3 Variables -- 8.3.4 Statistical Analysis -- 8.4 Results -- 8.4.1 Nonresponse Error -- 8.4.2 Sampling Error and Costs -- 8.4.3 Measurement Error…”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Basic Fundamentals -- Chapter 1 Introduction to Blockchain Technology for Smart Cities -- 1.1 Introduction -- 1.2 Smart City -- 1.3 Components of Smart City -- 1.3.1 Information and Communication Technology (ICT) Infrastructure -- 1.3.2 Internet of Things (IoT) and Sensors -- 1.3.3 Smart Mobility -- 1.3.4 Energy -- 1.3.5 Environmental Sustainability -- 1.3.6 Public Safety and Security -- 1.3.7 E-Governance and Citizen Services -- 1.3.8 Health and Well-Being -- 1.3.9 Education and Skill Development -- 1.3.10 Economic Development and Innovation -- 1.3.11 Urban Planning and Infrastructure -- 1.3.12 Data Analytics and Decision Support Systems -- 1.4 Blockchain Technology -- 1.5 Components of Blockchain in Smart Cities -- 1.5.1 Decentralized Ledger -- 1.5.2 Smart Contracts -- 1.5.3 Identity -- 1.5.4 Data Security and Integrity -- 1.5.5 Interoperability -- 1.5.6 Supply Chain -- 1.5.7 Payment and Transactions -- 1.5.8 Decentralized Energy Grids -- 1.5.9 Citizen Engagement and Governance -- 1.5.10 Data Analytics and Visualization -- 1.6 Types of Blockchain Architectures in Smart Cities -- 1.6.1 Public Blockchain -- 1.6.2 Private Blockchain -- 1.6.3 Consortium Blockchain -- 1.7 Layers of Blockchain Architecture Used in Smart Cities -- 1.7.1 Application Layer -- 1.7.2 Smart Contract and Business Logic Layer -- 1.7.3 Data and Transaction Layer -- 1.7.4 Network and Infrastructure Layer -- 1.8 Implementation of Blockchain in Smart Cities -- 1.8.1 Citizen Identity and Access Management -- 1.8.2 Public Utility and Infrastructure Management -- 1.8.3 Transportation and Mobility Solutions -- 1.8.4 Supply Chain Management and Logistics -- 1.8.5 Public Records and Compliance -- 1.8.6 Energy Trading and Renewable Energy Initiatives…”

Tabla de Contenidos: “…5.2 Modeling Structure -- 5.2.1 Single-Junction Solar Cell Model -- 5.2.2 Modeling of Multijunction Solar PV Cell -- 5.3 MPPT Design Techniques -- 5.3.1 Design of MPPT Scheme Based on P&amp -- O Technique -- 5.3.2 Design of MPPT Scheme Based on FLA -- 5.4 Results and Discussions -- 5.4.1 Single-Junction Solar Cell -- 5.4.2 Multijunction Solar PV Cell -- 5.4.3 Implementation of MPPT Scheme Based on P&amp -- O Technique -- 5.4.4 Implementation of MPPT Scheme Based on FLA -- 5.5 Conclusion -- References -- Chapter 6 Particle Swarm Optimization: An Overview, Advancements and Hybridization -- 6.1 Introduction -- 6.2 The Particle Swarm Optimization: An Overview -- 6.3 PSO Algorithms and Pseudo-Code -- 6.3.1 PSO Algorithm -- 6.3.2 Pseudo-Code for PSO -- 6.3.3 PSO Limitations -- 6.4 Advancements in PSO and Its Perspectives -- 6.4.1 Inertia Weight -- 6.4.2 Constriction Factors -- 6.4.3 Topologies -- 6.4.4 Analysis of Convergence -- 6.5 Hybridization of PSO -- 6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) -- 6.5.2 PSO Hybridization with Ant Colony Optimization (ACO) -- 6.5.3 PSO Hybridization with Genetic Algorithms (GA) -- 6.6 Area of Applications of PSO -- 6.7 Conclusions -- References -- Chapter 7 Application of Genetic Algorithm in Sensor Networks and Smart Grid -- 7.1 Introduction -- 7.2 Communication Sector -- 7.2.1 Sensor Networks -- 7.3 Electrical Sector -- 7.3.1 Smart Microgrid -- 7.4 A Brief Outline of GAs -- 7.5 Sensor Network's Energy Optimization -- 7.6 Sensor Network's Coverage and Uniformity Optimization Using GA -- 7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid -- 7.8 GA Versus Traditional Methods -- 7.9 Summaries and Conclusions -- References -- Chapter 8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber-Reinforced Polymer (CFRP) Drilling Process -- 8.1 Introduction…”

Tabla de Contenidos: “…6.15.4 Machine Learning in Personalized Healthcare -- 6.15.5 Machine Learning in Outbreak Prediction -- 6.15.6 Machine Learning in Patient Risk Stratification -- 6.15.7 Machine Learning in Telemedicine -- 6.15.8 Multimodal Machine Learning for Data Fusion in Medical Imaging -- 6.16 Incorporating Expectations and Learning Significant Portrayals for the Space -- 6.17 Conclusion -- References -- 7 Demystifying the Capabilities of Machine Learning and Artificial Intelligence for Personalized Care -- 7.1 Introduction -- 7.2 Temporal Displacement of Care -- 7.3 AI/ML use in Healthcare -- 7.4 Wearable Health Devices -- 7.5 Conclusion -- References -- 8 Artificial Intelligence and the 4th Industrial Revolution -- 8.1 Introduction -- 8.2 The Industrial Revolutions -- 8.3 The Technologies of the 4th Industrial Revolution -- 8.3.1 Internet of Things -- 8.3.2 4th Industrial Revolution: New Technologies -- 8.3.3 Machine Learning and Artificial Intelligence -- 8.3.4 Internet of Things, Microelectro-sensors and Biosensor Tech -- 8.3.5 Robotics -- 8.3.6 Virtual Reality, Augmented Reality and Mixed Reality -- 8.3.7 3D Printing and Additive Manufacturing -- 8.3.8 Neuromorphic Computing -- 8.3.9 Biochips -- 8.4 AI Applications in the 4th Industrial Revolution -- 8.4.1 Gaming Industry -- 8.4.2 Surveillance and Human Behavioural Marketing -- 8.4.3 Identity Management -- 8.4.4 Chatbots -- 8.4.5 Healthcare -- 8.4.6 Wearable Wellbeing Monitors -- 8.4.7 Asset Monitoring and Maintenance -- 8.4.8 Monitoring Fake News on Social Media -- 8.4.9 Furniture Design -- 8.4.10 Engineering Design in Aeronautics -- 8.4.11 Self-Driving Vehicles -- 8.4.12 AI-enabled Smart Grids -- 8.5 Conclusion -- References -- 9 AI-Based Evaluation to Assist Students Studying through Online Systems -- 9.1 Problem Description -- 9.2 The Online Learning Environment -- 9.2.1 Content Delivery Process…”

Tabla de Contenidos: “…-- 13.3 Corporations' Use Of Machine Learning To Strengthen Their Cyber Security Systems -- 13.4 Cyber Attack/Cyber Security Threats And Attacks -- 13.4.1 Malware -- 13.4.2 Data Breach -- 13.4.3 Structured Query Language Injection (SQL-I) -- 13.4.4 Cross-Site Scripting (XSS) -- 13.4.5 Denial-Of-Service (DOS) Attack -- 13.4.6 Insider Threats -- 13.4.7 Birthday Attack -- 13.4.8 Network Intrusions -- 13.4.9 Impersonation Attacks -- 13.4.10 DDoS Attacks Detection On Online Systems -- 13.5 Different Machine Learning Techniques In Cyber Security -- 13.5.1 Support Vector Machine (SVM) -- 13.5.2 K-Nearest Neighbor (KNN) -- 13.5.3 Naïve Bayes -- 13.5.4 Decision Tree -- 13.5.5 Random Forest (RF) -- 13.5.6 Multilayer Perceptron (MLP) -- 13.6 Application Of Machine Learning -- 13.6.1 ML in Aviation Industry -- 13.6.2 Cyber ML Under Cyber Security Monitoring -- 13.6.3 Battery Energy Storage System (BESS) Cyber Attack Mitigation -- 13.6.4 Energy-Based Cyber Attack Detection in Large-Scale Smart Grids -- 13.6.5 IDS for Internet of Vehicles (IoV) -- 13.7 Deep Learning Techniques In Cyber Security -- 13.7.1 Deep Auto-Encoder -- 13.7.2 Convolutional Neural Networks (CNN) -- 13.7.3 Recurrent Neural Networks (RNNs) -- 13.7.4 Deep Neural Networks (DNNs) -- 13.7.5 Generative Adversarial Networks (GANs) -- 13.7.6 Restricted Boltzmann Machine (RBM) -- 13.7.7 Deep Belief Network (DBN)…”

“…This book summarizes the found insights of grain growth behavior, of multidimensional decomposition for regular grids to efficiently parallelize computing and how to simulate recrystallization by coupling the finite element method with the phase-field method for microstructure texture analysis. …”

“…This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems…”

“…-Shortly: Responsive menu and a little JavaScript for some form validation and working with the DOM. -Testimonial Grid: A project where we focus on using Tailwind's grid classes. …”

“…As more and more alternative and distributed energy systems require grid hook-ups and on-site storage, a working knowledge of batteries, inverters and other power electronics components becomes requisite. …”

“…The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence…”

“…Therefore, a model is developed which optimises capacity expansion and hourly dispatch of both conventional and renewable power generation, transmission grids and storage facilities in all hours of the analysed years. …”

“…Examples of such infrastructures include utility networks (e.g., electrical power grids), ground transportation systems (automotives, roads, bridges and tunnels), airports…”

“…Telerik offers you solutions for the reports, grids, charts, and text-editing controls that you need but don’t want to build from scratch yourself—the options are endless for increasing the functionality of any of your web solutions…”