Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Newark
John Wiley & Sons, Incorporated
2022
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| Links: | https://onlinelibrary.wiley.com/doi/book/10.1002/9781119748342 https://doi.org/10.1002/9781119748342 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | 1 Online-Ressource (323 Seiten) |
| ISBN: | 9781119748311 9781119748342 |
| DOI: | 10.1002/9781119748342 |
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| 245 | 1 | 0 | |a Cyberphysical Smart Cities Infrastructures |b Optimal Operation and Intelligent Decision Making |
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions | |
| 505 | 8 | |a References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References | |
| 505 | 8 | |a Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking | |
| 505 | 8 | |a 8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion | |
| 505 | 8 | |a 9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV) | |
| 505 | 8 | |a 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850) | |
| 700 | 1 | |a Shafie-khah, Miadreza |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Amini, M. Hadi |t Cyberphysical Smart Cities Infrastructures |d Newark : John Wiley & Sons, Incorporated,c2022 |z 9781119748304 |
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Datensatz im Suchindex
| _version_ | 1818989465717178368 |
|---|---|
| any_adam_object | |
| author | Amini, M. Hadi |
| author_facet | Amini, M. Hadi |
| author_role | aut |
| author_sort | Amini, M. Hadi |
| author_variant | m h a mh mha |
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| bvnumber | BV048221482 |
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| contents | Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking 8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion 9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV) 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850) |
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| dewey-full | 307.76 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 307 - Communities |
| dewey-raw | 307.76 |
| dewey-search | 307.76 |
| dewey-sort | 3307.76 |
| dewey-tens | 300 - Social sciences |
| discipline | Soziologie |
| doi_str_mv | 10.1002/9781119748342 |
| format | Electronic eBook |
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code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 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| id | DE-604.BV048221482 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:38:55Z |
| institution | BVB |
| isbn | 9781119748311 9781119748342 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033602219 |
| oclc_num | 1289371334 |
| open_access_boolean | |
| owner | DE-Aug4 |
| owner_facet | DE-Aug4 |
| physical | 1 Online-Ressource (323 Seiten) |
| psigel | ZDB-30-PQE ZDB-35-WIC TUM_PDA_PQE ZDB-35-WIC FHA_PDA_WIC_Kauf |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | John Wiley & Sons, Incorporated |
| record_format | marc |
| spelling | Amini, M. Hadi Verfasser aut Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making Newark John Wiley & Sons, Incorporated 2022 ©2022 1 Online-Ressource (323 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking 8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion 9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV) 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850) Shafie-khah, Miadreza Sonstige oth Erscheint auch als Druck-Ausgabe Amini, M. Hadi Cyberphysical Smart Cities Infrastructures Newark : John Wiley & Sons, Incorporated,c2022 9781119748304 https://doi.org/10.1002/9781119748342 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Amini, M. Hadi Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making Cover -- Title Page -- Copyright -- Contents -- Biography -- List of Contributors -- Chapter 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications -- 1.1 Introduction -- 1.2 A Brief History of AI -- 1.3 AI in Healthcare -- 1.4 Morality and Ethical Association of AI in Healthcare -- 1.5 Cybersecurity, AI, and Healthcare -- 1.6 Future of AI and Healthcare -- 1.7 Conclusion -- References -- Chapter 2 Data Analytics for Smart Cities: Challenges and Promises -- 2.1 Introduction -- 2.2 Role of Machine Learning in Smart Cities -- 2.3 Smart Cities Data Analytics Framework -- 2.3.1 Data Capturing -- 2.3.2 Data Analysis -- 2.3.2.1 Big Data Algorithms and Challenges -- 2.3.2.2 Machine Learning Process and Challenges -- 2.3.2.3 Deep Learning Process and Challenges -- 2.3.2.4 Learning Process and Emerging New Type of Data Problems -- 2.3.3 Decision‐Making Problems in Smart Cities -- 2.3.3.1 Traffic Decision‐Making System -- 2.3.3.2 Safe and Smart Environment -- 2.4 Conclusion -- References -- Chapter 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review -- 3.1 Introduction -- 3.2 Rise of the Embodied AI -- 3.3 Breakdown of Embodied AI -- 3.3.1 Language Grounding -- 3.3.2 Language Plus Vision -- 3.3.3 Embodied Visual Recognition -- 3.3.4 Embodied Question Answering -- 3.3.5 Interactive Question Answering -- 3.3.6 Multi‐agent Systems -- 3.4 Simulators -- 3.4.1 MINOS -- 3.4.2 Habitat -- 3.5 Future of Embodied AI -- 3.5.1 Higher Intelligence -- 3.5.2 Evolution -- 3.6 Conclusion -- References -- Chapter 4 Analysis of Different Regression Techniques for Battery Capacity Prediction -- 4.1 Introduction -- 4.2 Data Preparation -- 4.2.1 Dataset -- 4.2.2 Feature Extraction -- 4.2.3 Noise Addition -- 4.3 Experiment Design and Machine Learning Algorithms -- 4.4 Result and Analysis -- 4.5 Threats to Validity -- 4.6 Conclusions References -- Chapter 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City -- 5.1 Smart Charging in Transportation -- 5.1.1 Available EV Charging Technologies -- 5.1.1.1 Inductive Charging -- 5.1.1.2 Battery Swapping -- 5.1.1.3 Automatic Robotic Charging Connector -- 5.1.1.4 Automatic Ground‐Based Docking Connector -- 5.1.2 Current Regulations on Smart Charging -- 5.2 Cyber‐Physical Aspects of EV Networks -- 5.2.1 Sensing and Cooperative Data Collection -- 5.2.2 Data‐Driven Control and Optimization -- 5.3 Charging of Electric Fleet Vehicles in Smart Cities -- 5.3.1 Intelligent Management of Fleets of Electric Vehicles -- 5.3.1.1 Charging of EV Fleets -- 5.3.1.2 Route Mapping with Charging -- 5.3.2 Electricity Grid Support Services -- 5.3.2.1 Demand Response -- 5.3.2.2 Frequency Response -- 5.3.2.3 Emergency Power -- 5.3.2.4 Emergency Response -- 5.4 Data and Cyber Security of EV Networks -- 5.4.1 Attack Schemes -- 5.4.1.1 Data Injection -- 5.4.1.2 Distributed Denial of Service -- 5.4.1.3 Data and Identity Theft -- 5.4.1.4 Man‐in‐the‐Middle Attack -- 5.4.2 Attack Detection Methods -- 5.4.2.1 Abnormal State Estimation -- 5.4.2.2 Message Encryption and Authentication -- 5.4.2.3 Denial‐of‐Service Attacks -- 5.4.3 Privacy Concerns and Privacy‐Preserving Methods -- 5.5 EV Smart Charging Strategies -- 5.5.1 Optimization Approaches -- 5.5.1.1 Future Scheduling -- 5.5.1.2 Battery Health Optimization -- 5.5.1.3 Energy Loss Minimization -- 5.5.2 Artificial Intelligence Approaches -- 5.5.2.1 Deep Learning for Smart Charging -- 5.5.2.2 Predicting Charging Profiles -- 5.5.3 Coordinated Charging -- 5.5.3.1 Centralized Optimization -- 5.5.3.2 Distributed Optimization -- 5.5.4 Population‐Based Approaches -- 5.5.4.1 Case Study -- 5.6 Conclusion -- Acknowledgments -- References Chapter 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities -- 6.1 Introduction -- 6.2 System Model -- 6.2.1 Communication Latency in Smart Grid Systems -- 6.2.2 Risk Model for Communication Links -- 6.2.3 History of Communication Links -- 6.3 Risk‐Aware Quality of Service Routing Using SDN -- 6.3.1 Constrained Shortest Path Routing Problem Formulation -- 6.3.2 SDN Architecture and Implementation -- 6.3.3 Risk‐Aware Routing Algorithm -- 6.4 Risk‐Aware Adaptive Control -- 6.4.1 Smart Grid Model -- 6.4.2 Parametric Feedback Linearization Control -- 6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme -- 6.5 Simulation Environment and Numerical Analysis -- 6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint -- 6.5.2 Algorithm Overhead Comparison -- 6.5.3 Impact of QoS Constraints -- 6.5.4 Impact on Distributed Control -- 6.6 Conclusions -- References -- Chapter 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia -- 7.1 Introduction -- 7.2 Data Collection and Method -- 7.2.1 Data Description -- 7.2.2 Robust Possibilistic C‐Regression Models -- 7.2.3 Wind Speed Data Analysis Procedure -- 7.3 Experiment and Discussion -- 7.4 Conclusion -- References -- Chapter 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection -- 8.1 Introduction -- 8.1.1 Introduction -- 8.1.2 Background -- 8.1.3 Problem Statement -- 8.1.3.1 Purpose of Research -- 8.1.3.2 Research Questions -- 8.1.3.3 Study Aim and Objectives -- 8.1.3.4 Significance and Structure of the Research -- 8.2 Literature Review -- 8.2.1 Introduction -- 8.2.2 Machine Learning, Deep Learning, and Computer Vision -- 8.2.2.1 Machine Learning -- 8.2.2.2 Deep Learning -- 8.2.2.3 Computer Vision -- 8.2.3 Object Recognition, Object Detection, and Object Tracking 8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion 9.5.1 Findings: Vehicle Detection Across Multiple Images -- 9.5.2 Findings: Vehicle Detection Performance on an MP4 File -- 9.5.3 Findings: Vehicle Detection on Livestream Camera -- 9.5.4 Findings: Iteration 3 -- 9.5.5 Addressing the Research Questions -- 9.5.6 Assessment of Suitability -- 9.5.7 Future Improvements -- 9.6 Conclusion -- References -- Chapter 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications -- 10.1 Introduction -- 10.2 Overview of IEC 61850 Standards -- 10.3 IEC 61850 Protocols and Substandards -- 10.3.1 IEC 61850 Standards and Classifications -- 10.3.2 Basics of IEC 61850 Architecture Model -- 10.3.3 IEC 61850 Class Model -- 10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850) -- 10.4 IEC 61850 Features -- 10.4.1 MMS -- 10.4.2 GOOSE -- 10.4.3 Sampled Measured Value (SMV) or SV -- 10.4.4 R‐GOOSE and R‐SV -- 10.4.4.1 Application in Transmission Systems -- 10.4.4.2 Application in Distribution Systems -- 10.4.5 Web Services -- 10.5 Relevant Application -- 10.5.1 Substation Automation System (SAS) -- 10.5.2 Energy Management System (EMS) -- 10.5.3 Distribution Management System (DMS) -- 10.5.3.1 Feeder Balancing and Loss Minimization Distribution -- 10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction -- 10.5.3.3 Fault Location, Isolation, and Service Restoration -- 10.5.4 Distribution Automation (DA) -- 10.5.4.1 Voltage/VAR Control -- 10.5.4.2 Fault Detection and Isolation -- 10.5.4.3 Service Restoration Use Case -- 10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER]) -- 10.5.5.1 Storage -- 10.5.5.2 Solar Panels -- 10.5.5.3 Wind Farm -- 10.5.5.4 Virtual Power Plant (VPP) -- 10.5.6 Advanced Metering Infrastructure (AMI) -- 10.5.7 Electric Vehicle (EV) 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850) |
| title | Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making |
| title_auth | Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making |
| title_exact_search | Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making |
| title_full | Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making |
| title_fullStr | Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making |
| title_full_unstemmed | Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making |
| title_short | Cyberphysical Smart Cities Infrastructures |
| title_sort | cyberphysical smart cities infrastructures optimal operation and intelligent decision making |
| title_sub | Optimal Operation and Intelligent Decision Making |
| url | https://doi.org/10.1002/9781119748342 |
| work_keys_str_mv | AT aminimhadi cyberphysicalsmartcitiesinfrastructuresoptimaloperationandintelligentdecisionmaking AT shafiekhahmiadreza cyberphysicalsmartcitiesinfrastructuresoptimaloperationandintelligentdecisionmaking |