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...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons
[2022]
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009724229206719 |
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.
- 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).
