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Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making /

"This book introduces novel algorithms and solutions to real-world problems under the umbrella of cyberphysical systems. It is organized in two sections: the first covers optimization algorithms for large-scale decision-making and the second covers intelligent decision-making in cyberphysical s...

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Detalles Bibliográficos
Clasificación:	Libro Electrónico
Otros Autores:	Amini, M. Hadi (Editor ), Shafie-khah, Miadreza (Editor )
Formato:	Electrónico eBook
Idioma:	Inglés
Publicado:	Hoboken, NJ : John Wiley & Sons, Inc., 2022.
Temas:	Smart cities. Smart structures. Smart power grids. Villes intelligentes. Structures intelligentes. Réseaux électriques intelligents.
Acceso en línea:	Texto completo (Requiere registro previo con correo institucional)

Tabla de Contenidos:

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.
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).

Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making /

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