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Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah
| Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2022] |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 307.760285 |
| Soggetto topico |
Smart cities
Smart power grids |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-74832-1
1-119-74834-8 1-119-74831-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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). |
| Record Nr. | UNINA-9910555142703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah
| Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / / edited by M. Hadi Amini, Miadreza Shafie-khah |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, , [2022] |
| Descrizione fisica | 1 online resource (323 pages) |
| Disciplina | 307.760285 |
| Soggetto topico |
Smart cities
Smart structures Smart power grids |
| ISBN |
1-119-74832-1
1-119-74834-8 1-119-74831-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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). |
| Record Nr. | UNINA-9910830498703321 |
| Hoboken, New Jersey : , : John Wiley & Sons, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Flexible resources for smart cities / / Miadreza Shafie-khah and M. Hadi Amini
| Flexible resources for smart cities / / Miadreza Shafie-khah and M. Hadi Amini |
| Autore | Shafie-khah Miadreza |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (194 pages) |
| Disciplina | 004.6 |
| Soggetto topico |
Cooperating objects (Computer systems) - Automatic control
Telecommunication - Social aspects |
| ISBN | 3-030-82796-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910508454903321 |
Shafie-khah Miadreza
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| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Flexible resources for smart cities / / Miadreza Shafie-khah and M. Hadi Amini
| Flexible resources for smart cities / / Miadreza Shafie-khah and M. Hadi Amini |
| Autore | Shafie-khah Miadreza |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2021] |
| Descrizione fisica | 1 online resource (194 pages) |
| Disciplina | 004.6 |
| Soggetto topico |
Cooperating objects (Computer systems) - Automatic control
Telecommunication - Social aspects |
| ISBN | 3-030-82796-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464523003316 |
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Shafie-khah Miadreza
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||
| Cham, Switzerland : , : Springer International Publishing, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Foundations of blockchain : theory and applications / / Ahmed Imteaj, M. Hadi Amini, Panos M. Pardalos
| Foundations of blockchain : theory and applications / / Ahmed Imteaj, M. Hadi Amini, Panos M. Pardalos |
| Autore | Imteaj Ahmed |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (80 pages) |
| Disciplina | 005.74 |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Blockchains (Databases)
Internet of things |
| ISBN | 3-030-75025-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910495160903321 |
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Imteaj Ahmed
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Fundamentals of Brooks–Iyengar distributed sensing algorithm : trends, advances, and future prospects / / by Pawel Sniatala, M. Hadi Amini, Kianoosh G. Boroojeni
| Fundamentals of Brooks–Iyengar distributed sensing algorithm : trends, advances, and future prospects / / by Pawel Sniatala, M. Hadi Amini, Kianoosh G. Boroojeni |
| Autore | Sniatala Pawel |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XIX, 202 pages) : 46 illustrations, 42 illustrations in color) |
| Disciplina | 681.2 |
| Soggetto topico |
Electrical engineering
Computational intelligence Computer security Application software Communications Engineering, Networks Computational Intelligence Systems and Data Security Information Systems Applications (incl. Internet) |
| ISBN | 3-030-33132-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I Introduction -- Introduction to Sensor Networks -- Introduction to Algorithms for Wireless Sensor Networks -- Fault Tolerant Distributed Sensor Networks -- Part II Advances of Sensor Fusion Algorithm -- Theoretical Analysis of Brooks-Iyengar Algorithm: Accuracy and Precision Bound -- The Profound Impact of the Brooks-Iyengar Algorithm -- Part III Trends of Brooks-Iyengar Algorithm -- Robust Fault Tolerant Rail Door State Monitoring Systems -- Part IV Applications of Brooks-Iyengar Algorithm for The Next 10 Years -- Decentralization of Data-Source using Blockchain-based Brooks-Iyengar Fusion -- A Novel Fault-Tolerant Random Forest Model using Brooks-Iyengar Fusion -- Designing a Deep-Learning Neural Network chip to detect Hardware Errors using Brooks-Iyengar Algorithm -- Ubiquitous Brooks-Iyengars Robust Distributed Real-time Sensing Algorithm: Past, Present and Future -- Index. |
| Record Nr. | UNINA-9910377824403321 |
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Sniatala Pawel
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Optimization, Learning, and Control for Interdependent Complex Networks / / edited by M. Hadi Amini
| Optimization, Learning, and Control for Interdependent Complex Networks / / edited by M. Hadi Amini |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (X, 304 p. 90 illus., 67 illus. in color.) |
| Disciplina | 003 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Electrical engineering
Power electronics Computational intelligence Application software Communications Engineering, Networks Power Electronics, Electrical Machines and Networks Computational Intelligence Information Systems Applications (incl. Internet) |
| ISBN | 3-030-34094-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Interdependent Complex Networks: Tale of IoT-based Smart Cities -- Deep Learning Algorithms for Energy Systems -- Distributed Algorithms for Interdependent Networks -- Online Optimization Learning for Interdependent Complex Networks -- Deep Learning Algorithms for Ramp Rate Prediction in Unit Commitment -- Networked Control Systems: Case Study of Unmanned Aerial Vehicle -- Conclusion. |
| Record Nr. | UNINA-9910377821703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Smart Grids: Security and Privacy Issues / / by Kianoosh G. Boroojeni, M. Hadi Amini, S. S. Iyengar
| Smart Grids: Security and Privacy Issues / / by Kianoosh G. Boroojeni, M. Hadi Amini, S. S. Iyengar |
| Autore | Boroojeni Kianoosh G |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XIV, 113 p. 26 illus., 25 illus. in color.) |
| Disciplina | 621.382 |
| Soggetto topico |
Electrical engineering
Power electronics Computer security Computational intelligence Application software Communications Engineering, Networks Power Electronics, Electrical Machines and Networks Systems and Data Security Computational Intelligence Information Systems Applications (incl. Internet) |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of the Security and Privacy Issues in Smart Grids -- I Physical Network Security -- Reliability in Smart Grids -- Error Detection of DC Power Flow using State Estimation -- Bad Data Detection -- II Information Network Security -- Cloud Network Data Security -- III Privacy Preservation -- End-User Data Privacy -- Mobile User Data Privacy. |
| Record Nr. | UNINA-9910135973503321 |
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Boroojeni Kianoosh G
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sustainable Interdependent Networks : From Theory to Application / / edited by M. Hadi Amini, Kianoosh G. Boroojeni, S.S. Iyengar, Panos M. Pardalos, Frede Blaabjerg, Asad M. Madni
| Sustainable Interdependent Networks : From Theory to Application / / edited by M. Hadi Amini, Kianoosh G. Boroojeni, S.S. Iyengar, Panos M. Pardalos, Frede Blaabjerg, Asad M. Madni |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (290 pages) |
| Disciplina | 004.678 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Electrical engineering
Computational intelligence Computer security Application software Energy security Environmental management Communications Engineering, Networks Computational Intelligence Systems and Data Security Information Systems Applications (incl. Internet) Energy Security Water Policy/Water Governance/Water Management |
| ISBN | 3-319-74412-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Panorama of Future Interdependent Networks: From Intelligent Infrastructures to Smart Cities -- Part 1. Strategic Planning of Developing Sustainable Interdependent Networks -- Calling For a Next Generation Sustainability Framework at MIT -- Towards A Smart City of Interdependent Critical Infrastructure Networks -- Interdependent Interaction of Occupational Burnout and Organizational Commitments: Case Study of Academic Institutions Located In Guangxi Province, China -- Part 2. Solutions to Performance and Security Challenges of Developing Interdependent Networks -- High Performance and Scalable Graph Computation on GPUs -- Security Challenges of Networked Control Systems -- Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership -- Part 3. Electric Vehicle: A Game-Changing Technology for Future of Interdependent Networks -- Barriers Towards Widespread Adoption Of V2G Technology in Smart Grid Environment: From Labs to Commercialization -- Plug-In Electric Vehicle Charging Optimization Using Bio-Inspired Computational Intelligence Methods -- Part 4. Promises of Power Grids for Sustainable Interdependent Networks -- Coordinated Management of Residential Loads in Large Scale Systems -- Estimation of Large-Scale Solar Rooftop PV Potential For Smart Grid Integration: A Methodological Review -- Optimal SVC Allocation in Power Systems for Loss Minimization and Voltage Deviation Reduction -- Decentralized Control of DR using a Multi-Agent Method -- Complex distribution networks: Case Study Galapagos Islands. |
| Record Nr. | UNINA-9910299945303321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sustainable Interdependent Networks II : From Smart Power Grids to Intelligent Transportation Networks / / edited by M. Hadi Amini, Kianoosh G. Boroojeni, S. S. Iyengar, Panos M. Pardalos, Frede Blaabjerg, Asad M. Madni
| Sustainable Interdependent Networks II : From Smart Power Grids to Intelligent Transportation Networks / / edited by M. Hadi Amini, Kianoosh G. Boroojeni, S. S. Iyengar, Panos M. Pardalos, Frede Blaabjerg, Asad M. Madni |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (316 pages) |
| Disciplina | 006.22068 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Electrical engineering
Power electronics Computational intelligence Application software Computer security Communications Engineering, Networks Power Electronics, Electrical Machines and Networks Computational Intelligence Information Systems Applications (incl. Internet) Systems and Data Security |
| ISBN | 3-319-98923-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- A System of Systems: Engineering Framework for Active Distribution Grids Operation -- Clustering Algorithms in Wireless Sensor Networks: Challenges, Solutions, and Future Research Trends -- Laboratory-Scale Microgrid System for Control of Power Distribution in Local Energy Networks -- Impact of Strategic Behavior of the Electrical Consumers on the Power System Reliability -- Reactive Power Dispatch Strategies for Loss Minimization in a DFIG based Wind Farm -- Distributed State Estimation and Energy Management in Smart Grids: A Consensus + Innovations Approach -- Promises of Intelligent Transportation Systems in Future Smart Cities -- High Performance and Scalable Graph Computation on GPUs for Smart Power Grids and Transportation System Applications -- A Comprehensive Review on Emerging Methods for Integration of Electric Vehicles into Power Systems -- A Comprehensive Overview of Distributed/Decentralized Control and Optimization Strategies of AC and DC Microgrids -- Hopf Bifurcation Control of Large-Scale Complex Nonlinear Dynamical Systems Via a Dynamic State Feedback Controller: The Tale of Power Networks -- Conclusion. |
| Record Nr. | UNINA-9910337469503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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