AI Factory

AI Factory

by Ramin Karim, Diego Galar, Uday Kumar
Released May 2023
Publisher(s): CRC Press
ISBN: 9781000865073

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Book description

This book provides insights on how to approach and utilise data science tools, technologies/methodologies related to artificial intelligence in industrial context including their essence and inter-connections. Description of technology/methodology approaches, their purpose and benefits when developing AI-solution is given with case studies.

Table of contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Table of Contents
  7. About the Authors
  8. Foreword
  9. Preface
  10. Acknowledgements
  11. Prologue
  12. Chapter 1 Introduction
    1. 1.1 AI Factory
      1. 1.1.1 Artificial Intelligence
      2. 1.1.2 Industrial Automation
      3. 1.1.3 Engineering Knowledge
      4. 1.1.4 System Understanding
      5. 1.1.5 Context Adaptation
      6. 1.1.6 Connectivity
      7. 1.1.7 Information Logistics
      8. 1.1.8 In Summary
    2. 1.2 Artificial Intelligence-Empowered Analytics
    3. 1.3 AI Revolution in Industry
      1. 1.3.1 The AI Revolution is Happening Now
        1. 1.3.1.1 Healthcare
        2. 1.3.1.2 Education
        3. 1.3.1.3 Banking, Financial Services and Insurance (BFSI)
        4. 1.3.1.4 Retail and e-Commerce
        5. 1.3.1.5 Gaming and Entertainment
      2. 1.3.2 The Road to Superintelligence
    4. 1.4 AI Winter, AI Spring
      1. 1.4.1 The First AI Spring
      2. 1.4.2 The First AI Winter
      3. 1.4.3 The Second AI Spring
      4. 1.4.4 The Second AI Winter
      5. 1.4.5 Another Spring: The DL Revolution
      6. 1.4.6 Predictions of Another AI Winter
      7. 1.4.7 Surviving the Next AI Winter: From Myths to Realities
    5. 1.5 The Value of AI
      1. 1.5.1 Challenges of AI
    6. 1.6 Power of AI vs. Human Intelligence
    7. 1.7 Technologies Powering AI: ML and DL
    8. 1.8 Perception and Cognition
    9. References
  13. Chapter 2 Digital Twins
    1. 2.1 Basic Concept of Digital Twin
    2. 2.2 History of DT
    3. 2.3 What is Digital Twin? Its Intrinsic Characteristics
      1. 2.3.1 Why Use Digital Twin?
      2. 2.3.2 How Does Digital Twin Work?
        1. 2.3.2.1 Digital Twin and Simulation
        2. 2.3.2.2 Digital Twin and Cyber-Physical Systems
    4. 2.4 The Evolution of Digital Twin
    5. 2.5 Data Twin and the Physical World
    6. 2.6 Data Twin and the Digital World
    7. 2.7 Useful Terms and Classifications
      1. 2.7.1 Prototypes, Instances, and Aggregates
      2. 2.7.2 Digital Twin Classes
      3. 2.7.3 Digital Twin Categories
    8. 2.8 Level of Integration
      1. 2.8.1 Digital Model
      2. 2.8.2 Digital Shadow
      3. 2.8.3 Digital Twin
    9. 2.9 Main Characteristics of Digital Twin
    10. 2.10 Modelling Digital Twins
      1. 2.10.1 Systems Modelling Language (SysML)
      2. 2.10.2 Simulation as the Basis of Digital Twin Technology
      3. 2.10.3 The Connection Between MES-Systems and Digital Twins
      4. 2.10.4 Application Tools
    11. 2.11 Smart Manufacturing: An Example of Digital Twin Development and Operation
      1. 2.11.1 Development of The Smart Factory Cell
      2. 2.11.2 Operation of The Smart Factory Cell
    12. 2.12 Some Applications of Digital Twins
    13. 2.13 Uses of Digital Twin Technology
      1. 2.13.1 Current State of the Art
        1. 2.13.1.1 Components of DT
        2. 2.13.1.2 Properties of a DT
        3. 2.13.1.3 How DT Differs from Existing Technologies
        4. 2.13.1.4 A Brief Overview of Similar Concepts That Preceded DT
        5. 2.13.1.5 Added Value of Digital Twins
      2. 2.13.2 Specific Applications of Digital Twins in Maintenance
    14. 2.14 How Are Digital Twins Used in Maintenance?
    15. 2.15 Digital Twins and Predictive Maintenance
    16. 2.16 A Digital Twin Maintenance Use Case: Point Machine for Train Switches
    17. 2.17 Planning the Digital Twin
    18. 2.18 Digital Twin During Operation Phase
    19. 2.19 Hybrid Analysis and Fleet Data
    20. 2.20 Steps to Ensure Widespread Implementation of Digital Twin
    21. 2.21 Digital Twin and Its Impact on Industry 4.0
    22. References
  14. Chapter 3 Hypes and Trends in Industry
    1. 3.1 Asset Management
      1. 3.1.1 Challenges to Asset Management
      2. 3.1.2 Intelligent Asset Management
      3. 3.1.3 Taxonomy of AAM
    2. 3.2 Tracking and Tracing in Asset Management
      1. 3.2.1 What can be Tracked and Traced?
      2. 3.2.2 Challenges of Tracking and Tracing
      3. 3.2.3 Benefits of Tracking and Tracing
    3. 3.3 Green Industry (Sustainable)
      1. 3.3.1 Sustainability Green Industry 4.0
        1. 3.3.1.1 Sustainable Green Industry Model
    4. 3.4 Industry 4.0
      1. 3.4.1 What is Industry 4.0?
      2. 3.4.2 Talking About a Revolution: What is New in Industry 4.0?
      3. 3.4.3 On the Path to Industry 4.0: What Needs to be Done?
      4. 3.4.4 Key Paradigms of Industry 4.0
      5. 3.4.5 Four Components of Networked Production
      6. 3.4.6 Connected Technologies
      7. 3.4.7 Nine Pillars of Technological Advancement
        1. 3.4.7.1 Big Data and Analytics
        2. 3.4.7.2 Autonomous Robots
        3. 3.4.7.3 Simulation
        4. 3.4.7.4 Horizontal and Vertical System Integration
        5. 3.4.7.5 Industrial Internet of Things (IIoT)
        6. 3.4.7.6 Cybersecurity
        7. 3.4.7.7 The Cloud
        8. 3.4.7.8 Additive Manufacturing
        9. 3.4.7.9 Augmented Reality
      8. 3.4.8 Other Industry 4.0 Components
        1. 3.4.8.1 Cyber-Physical Systems (CPS)
        2. 3.4.8.2 Internet of Things (IoT)
        3. 3.4.8.3 Internet of Services
        4. 3.4.8.4 Smart Factory
      9. 3.4.9 The Impact of Industry 4.0
        1. 3.4.9.1 Quantifying the Impact
        2. 3.4.9.2 Producers: Transforming Production Processes and Systems
        3. 3.4.9.3 Manufacturing-System Suppliers: Meeting New Demands and Defining New Standards
        4. 3.4.10 How Will Industry 4.0 Impact Equipment?
    5. 3.5 Digitalisation and Digitisation
    6. 3.6 Data, Models, and Algorithm
    7. 3.7 Transformative Technologies
      1. 3.7.1 Artificial Intelligence (AI)
      2. 3.7.2 The Internet of Things (IoT)
      3. 3.7.3 Blockchain
      4. 3.7.4 Some Implications
    8. 3.8 Artificial Intelligence vs Industrial Artificial Intelligence
      1. 3.8.1 Key Elements in Industrial AI: ABCDE
      2. 3.8.2 Industrial AI Ecosystem
        1. 3.8.2.1 Data Technology
        2. 3.8.2.2 Analytics Technology
        3. 3.8.2.3 Platform Technology
        4. 3.8.2.4 Operations Technology
    9. 3.9 Autonomy and Automation
      1. 3.9.1 Autonomy and Asset Management
      2. 3.9.2 Drones and Robots
        1. 3.9.2.1 Deploying Robots
      3. 3.9.3 Strong Automation Base Layer
      4. 3.9.4 Autonomy in Industry Today
        1. 3.9.4.1 Challenges of Autonomy
    10. 3.10 Digital Transformation
      1. 3.10.1 Defining Digital Transformation
      2. 3.10.2 Digital Transformation – The Future of Predictive Maintenance
        1. 3.10.2.1 Applying Digital Transformation in Maintenance
    11. References
  15. Chapter 4 Data Analytics
    1. 4.1 Data-Driven and Model-Driven Approaches
      1. 4.1.1 Data Mining and Knowledge Discovery
    2. 4.2 Types of Analytics
      1. 4.2.1 Descriptive Analytics
        1. 4.2.1.1 What is Descriptive Analytics?
        2. 4.2.1.2 How Does Descriptive Analytics Work?
        3. 4.2.1.3 How is Descriptive Analytics Used?
        4. 4.2.1.4 What Can Descriptive Analytics Tell Us?
        5. 4.2.1.5 Steps in Descriptive Analytics
        6. 4.2.1.6 Benefits and Drawbacks of Descriptive Analytics
      2. 4.2.2 Diagnostic Analytics
        1. 4.2.2.1 Hypothesis Testing
        2. 4.2.2.2 Correlation vs. Causation
        3. 4.2.2.3 Diagnostic Regression Analysis
        4. 4.2.2.4 How Do You Get Started with Diagnostic Analytics?
      3. 4.2.3 Maintenance Predictive Analytics
        1. 4.2.3.1 What is Predictive Analytics?
        2. 4.2.3.2 How Does Predictive Analytics Work?
        3. 4.2.3.3 What Can Predictive Analytics Tell Us?
        4. 4.2.3.4 What Are the Advantages and Disadvantages of Predictive Analysis?
        5. 4.2.3.5 Predictive Analytics Techniques
        6. 4.2.3.6 How Can a Predictive Analytics Process be Developed?
        7. 4.2.3.7 Predictive Maintenance Embraces Analytics
        8. 4.2.3.8 Metrics for Predictive Maintenance Analytics
        9. 4.2.3.9 Technologies Used for Predictive Maintenance Analytics
        10. 4.2.3.10 Predictive Maintenance and Data Analytics
        11. 4.2.3.11 Predictive Asset Maintenance Analytics
      4. 4.2.4 Prescriptive Analytics
        1. 4.2.4.1 What is Prescriptive Analytics?
        2. 4.2.4.2 How Does Prescriptive Analytics Work?
        3. 4.2.4.3 What Can Prescriptive Analytics Tell Us?
        4. 4.2.4.4 What Are the Advantages and Disadvantages of Prescriptive Analytics?
        5. 4.2.4.5 Getting Started in Prescriptive Analysis
        6. 4.2.4.6 Maintenance Prescriptive Analytics: A Cure for Downtime
        7. 4.2.4.7 Prescription
        8. 4.2.4.8 Scale Out
        9. 4.2.4.9 The Need for Prescriptive Analytics In Maintenance: A Case Study
    3. 4.3 Big Data Analytics Methods
      1. 4.3.1 Defining Big Data Analytics
      2. 4.3.2 Defining Big Data Via the Three Vs
        1. 4.3.2.1 Data Volume as a Defining Attribute of Big Data
        2. 4.3.2.2 Data Type Variety as a Defining Attribute of Big Data
        3. 4.3.2.3 Data Feed Velocity as a Defining Attribute of Big Data
      3. 4.3.3 Text Analytics
      4. 4.3.4 Audio Analytics
      5. 4.3.5 Video Analytics
      6. 4.3.6 Social Media Analytics
    4. 4.4 Maintenance Strategies with Big Data Analytics
    5. 4.5 Data-Driven and Model-Driven Approaches
      1. 4.5.1 Data Mining and Knowledge Discovery
    6. 4.6 Maintenance Descriptive Analytics
    7. 4.7 Maintenance Diagnostic Analytics
    8. 4.8 Maintenance Predictive Analytics
    9. 4.9 Maintenance Prescriptive Analytics
    10. 4.10 Big Data Analytics Methods
      1. 4.10.1 Text Analytics
      2. 4.10.2 Audio Analytics
      3. 4.10.3 Video Analytics
      4. 4.10.4 Social Media Analytics
    11. 4.11 Big Data Management and Governance
    12. 4.12 Big Data Access and Analysis
    13. 4.13 Big Data Visualisation
    14. 4.14 Big Data Ingestion
    15. 4.15 Big Data Cluster Management
    16. 4.16 Big Data Distributions
    17. 4.17 Data Governance
    18. 4.18 Data Access
    19. 4.19 Data Analysis
    20. 4.20 Bid Data File System
      1. 4.20.1 Quantcast File System
      2. 4.20.2 Hadoop Distributed File System
      3. 4.20.3 Cassandra File System (CFS)
      4. 4.20.4 GlusterFS
      5. 4.20.5 Lustre
      6. 4.20.6 Parallel Virtual File System
      7. 4.20.7 Orange File System (OrangeFS)
      8. 4.20.8 BeeGFS
      9. 4.20.9 MapR-FS
        1. 4.20.9.1 Kudu
    21. References
  16. Chapter 5 Data-Driven Decision-Making
    1. 5.1 Data for Decision-Making
      1. 5.1.1 Data-Driven Decision-Making
      2. 5.1.2 The Process of Data-Driven Decision-Making
      3. 5.1.3 The Context of Data-Driven Decision-Making
      4. 5.1.4 The Importance of Data-Driven Decision-Making
      5. 5.1.5 Common Challenges of Data-Driven Decision-Making
        1. 5.1.5.1 A Lack of Infrastructure and Tools
        2. 5.1.5.2 Poor Quality Data
        3. 5.1.5.3 Siloed Data
        4. 5.1.5.4 A Lack of Buy-In
        5. 5.1.5.5 Not Knowing How to Use Data
        6. 5.1.5.6 Being Unable to Identify Actionable Data
        7. 5.1.5.7 Too Much Focus on Data
      6. 5.1.6 Data-Driven Decision-Making for Industry 4.0 Maintenance Applications
        1. 5.1.6.1 Augmented Reality
        2. 5.1.6.2 Internet of Things
        3. 5.1.6.3 System Integration
        4. 5.1.6.4 Cloud Computing
        5. 5.1.6.5 Big Data Analytics
        6. 5.1.6.6 Cyber Security
        7. 5.1.6.7 Additive Manufacturing
        8. 5.1.6.8 Autonomous Robots
        9. 5.1.6.9 Simulation
      7. 5.1.7 Data-Driven Decision-Making Versus Intuition
    2. 5.2 Data Quality
      1. 5.2.1 eMaintenance and Data Quality
        1. 5.2.1.1 Problems in Data Quality
        2. 5.2.1.2 Data Quality in The Maintenance Phases
      2. 5.2.2 Data Quality Problems
    3. 5.3 Data Augmentation
      1. 5.3.1 Importance of Data Augmentation in Machine Learning
      2. 5.3.2 Advanced Models for Data Augmentation
      3. 5.3.3 Image Recognition and Natural Language Processing
        1. 5.3.3.1 Image Classification and Segmentation
        2. 5.3.3.2 Natural Language Processing
      4. 5.3.4 Benefits of Data Augmentation
      5. 5.3.5 Challenges of Data Augmentation
      6. 5.3.6 Data Augmentation Methods
        1. 5.3.6.1 Traditional Transformations
        2. 5.3.6.2 Generative Adversarial Networks
        3. 5.3.6.3 Texture Transfer
        4. 5.3.6.4 Convolutional Neural Networks
      7. 5.3.7 Data Augmentation for Data Management
        1. 5.3.7.1 Data Augmentation for Data Preparation
        2. 5.3.7.2 Data Augmentation for Data Integration
      8. 5.3.8 Advanced Data Augmentation
        1. 5.3.8.1 Interpolation-Based Data Augmentation
        2. 5.3.8.2 Generation-Based Data Augmentation
        3. 5.3.8.3 Learned-Data Augmentation
      9. 5.3.9 Data Augmentation with Other Learning Paradigms
        1. 5.3.9.1 Semi-Supervised and Active Learning
        2. 5.3.9.2 Weak Supervision
        3. 5.3.9.3 Pre-Training for Relational Data
    4. 5.4 Information Logistics
      1. 5.4.1 Information Logistics and eMaintenance
      2. 5.4.2 Information Logistics and Information Flow in an Era of Industry 4.0
      3. 5.4.3 Information Life Cycle
      4. 5.4.4 eMaintenance – Information Logistics for Maintenance Support
    5. 5.5 Data-Driven Challenges
    6. References
  17. Chapter 6 Fundamental in Artificial Intelligence
    1. 6.1 What is Decision-Making?
      1. 6.1.1 Importance of Decision-Making
      2. 6.1.2 Features or Characteristics of Decision-Making
      3. 6.1.3 Principles of Decision-Making
    2. 6.2 General Decision-Making Process
    3. 6.3 Problem-Solving Process in Industrial Contexts
      1. 6.3.1 Six-Step Problem-Solving Model
        1. 6.3.1.1 Step One: Identify the Problem
        2. 6.3.1.2 Step Two: Determine the Root Cause(s) of the Problem
        3. 6.3.1.3 Step Three: Develop Alternative Solutions
        4. 6.3.1.4 Step Four: Select a Solution
        5. 6.3.1.5 Step Five: Implement the Solution
        6. 6.3.1.6 Step Six: Evaluate the Outcome
    4. 6.4 System Thinking and Computer Science
    5. 6.5 Decision Support Systems
    6. 6.6 Data in a Decision-Making Process
    7. 6.7 Knowledge Discovery
      1. 6.7.1 Approaches to KDP Modelling
      2. 6.7.2 The Leading KDP Models
    8. 6.8 Business Intelligence
      1. 6.8.1 Business Intelligence on a Practical Level
      2. 6.8.2 What Does Business Intelligence Do?
      3. 6.8.3 Differences Between Artificial Intelligence and Business Intelligence
    9. 6.9 Database and Knowledge Base in Decision Support Systems
      1. 6.9.1 Differences Between a Database and a Knowledge Base
    10. 6.10 Inference Mechanisms in Artificial Intelligence
      1. 6.10.1 Deductive Reasoning
      2. 6.10.2 Inductive Reasoning
      3. 6.10.3 Adductive Reasoning
      4. 6.10.4 Case-Based Reasoning
      5. 6.10.5 Monotonic Reasoning and Non-Monotonic Reasoning
    11. 6.11 Knowledge Interpretation: The Role of Inference
      1. 6.11.1 Inference Engine Architecture
      2. 6.11.2 Inference Engine Implementations
    12. 6.12 From Data to Wisdom
      1. 6.12.1 Data, Information, Knowledge, and Wisdom
    13. 6.13 AI and Software Engineering
      1. 6.13.1 Artificial Intelligence and Software Engineering
        1. 6.13.1.1 Aspects of AI
        2. 6.13.1.2 Aspects of SE
      2. 6.13.2 The Role of Artificial Intelligence in Software Engineering
      3. 6.13.3 When Does Artificial Intelligence for Software Engineering Work Well?
      4. 6.13.4 Relationship Between Approaches to Artificial Intelligence for Software Engineering
      5. 6.13.5 Intersections Between Artificial Intelligence and SE
        1. 6.13.5.1 Agent-Oriented Software Engineering
        2. 6.13.5.2 Knowledge-Based SE
        3. 6.13.5.3 Computational Intelligence and Knowledge Discovery
        4. 6.13.5.4 Ambient Intelligence
    14. References
  18. Chapter 7 Systems Thinking and Systems Engineering
    1. 7.1 Definition of System
      1. 7.1.1 Characteristics of a System
        1. 7.1.1.1 System Structure
        2. 7.1.1.2 System Stakeholders
        3. 7.1.1.3 System Attributes
        4. 7.1.1.4 System Boundaries
        5. 7.1.1.5 System Needs
        6. 7.1.1.6 System Constraints
      2. 7.1.2 Systems Engineering
        1. 7.1.2.1 Holistic View
        2. 7.1.2.2 Interdisciplinary Field
        3. 7.1.2.3 Managing Complexity
        4. 7.1.2.4 Systems Engineering Processes
    2. 7.2 Systems-of-Systems
      1. 7.2.1 Manufacturing Supply Chains
      2. 7.2.2 Embedded Automotive Systems
      3. 7.2.3 Smart Grids
    3. 7.3 System of Interest
      1. 7.3.1 System of Interest Architectural Elements
        1. 7.3.1.1 Personnel System Element
        2. 7.3.1.2 Equipment System Element
        3. 7.3.1.3 Mission Resources System Element
        4. 7.3.1.4 Procedural Data System Element
        5. 7.3.1.5 System Reponse Element
        6. 7.3.1.6 Facilities System Element
    4. 7.4 Enabling Systems
    5. 7.5 System Lifecycle
      1. 7.5.1 Stages of a System Lifecycle
        1. 7.5.1.1 Conception
        2. 7.5.1.2 Design and Development
        3. 7.5.1.3 Production
        4. 7.5.1.4 Utilisation
        5. 7.5.1.5 Maintenance and Support
        6. 7.5.1.6 Retirement
        7. 7.5.1.7 Applications of the Six Lifecycle Stages
      2. 7.5.2 Defining Lifecycle Models
        1. 7.5.2.1 A Linear Lifecycle Model
        2. 7.5.2.2 An Iterative Lifecycle Model
        3. 7.5.2.3 An Incremental Lifecycle Model
    6. 7.6 Hierarchies
    7. 7.7 System Item Structure
      1. 7.7.1 Types of Systems
        1. 7.7.1.1 Open and Closed Systems
        2. 7.7.1.2 Deterministic and Probabilistic Systems
        3. 7.7.1.3 Physical and Abstract Systems
        4. 7.7.1.4 Man-Made Information Systems
      2. 7.7.2 Information Systems in the Organisational Context
        1. 7.7.2.1 Formal Information Systems
      3. 7.7.3 Informal Information Systems
      4. 7.7.4 Computer-Based Information Systems
    8. References
  19. Chapter 8 Software Engineering
    1. 8.1 Software Engineering Overview
    2. 8.2 From Programming Languages to Software Architecture
      1. 8.2.1 High-Level Programming Languages
      2. 8.2.2 Abstract Data Types
      3. 8.2.3 Software Architecture
    3. 8.3 System Software
      1. 8.3.1 Functions of System Software
      2. 8.3.2 Application Software
      3. 8.3.3 Engineering/Scientific Software
      4. 8.3.4 Embedded Software
      5. 8.3.5 Web Applications
      6. 8.3.6 Artificial Intelligence Software
    4. 8.4 Software Evolution
      1. 8.4.1 Software Evolution Laws
        1. 8.4.1.1 Static-Type (S-Type)
        2. 8.4.1.2 Practical-Type (P-Type)
        3. 8.4.1.3 Embedded-Type (E-Type)
    5. 8.5 Paradigms of Software Engineering
      1. 8.5.1 Traditional Software Engineering Paradigms
        1. 8.5.1.1 Classical Lifecycle Development Paradigms
        2. 8.5.1.2 Incremental Development Paradigms
        3. 8.5.1.3 Evolutionary Paradigms
      2. 8.5.2 Advanced Software Engineering Paradigms
        1. 8.5.2.1 Agile Development Paradigm
        2. 8.5.2.2 Aspect-Oriented Development Paradigm
        3. 8.5.2.3 Cleanroom Development Paradigm
        4. 8.5.2.4 Component-Based Development Paradigm
    6. 8.6 Software Architecture Models
      1. 8.6.1 Layered Architecture
      2. 8.6.2 Event-Driven Architecture
      3. 8.6.3 Microkernel Architecture
      4. 8.6.4 Microservices Architecture
      5. 8.6.5 Space-Based Architecture
    7. 8.7 Software Systems and Software Engineering Processes
      1. 8.7.1 Software System Components
      2. 8.7.2 Properties of Software
      3. 8.7.3 The Importance of Software Engineering
      4. 8.7.4 The Software Engineering Process
        1. 8.7.4.1 The Layers of Software Engineering
        2. 8.7.4.2 A Generic Framework of the Software Process
    8. 8.8 Component-Based Software Engineering
      1. 8.8.1 Construction-Based Software Engineering Processes
      2. 8.8.2 Characteristics of Component-Based Software Engineering
      3. 8.8.3 Evolution of Component-Based Software Engineering
        1. 8.8.3.1 Preparations
        2. 8.8.3.2 Definitions
        3. 8.8.3.3 Progression
        4. 8.8.3.4 Expansion
      4. 8.8.4 Componentisation
    9. 8.9 Software Maintenance Overview
      1. 8.9.1 Types of Maintenance
      2. 8.9.2 Cost of Maintenance
      3. 8.9.3 Maintenance Activities
      4. 8.9.4 Software Re-Engineering
        1. 8.9.4.1 Reverse Engineering
        2. 8.9.4.2 Programme Restructuring
        3. 8.9.4.3 Forward Engineering
      5. 8.9.5 Component Reusability
        1. 8.9.5.1 Reuse Process
    10. 8.10 Applications of AI in Classical Software Engineering
      1. 8.10.1 AI in The Software Engineering Lifecycle
        1. 8.10.1.1 AI in Software Project Planning
        2. 8.10.1.2 AI at the Stage of Problem Analysis
        3. 8.10.1.3 AI at the Stage of Software Design
        4. 8.10.1.4 AI at the Stage of Software Implementation
        5. 8.10.1.5 AI at the Stage of Software Testing and Integration
        6. 8.10.1.6 AI at the Stage of Software Maintenance
    11. References
  20. Chapter 9 Distributed Computing
    1. 9.1 Cloud Computing
      1. 9.1.1 Advantages of Cloud Computing
      2. 9.1.2 Challenges of Cloud Computing
      3. 9.1.3 Relationship of Cloud Computing with Other Technologies
      4. 9.1.4 Cloud Computing Service Models
        1. 9.1.4.1 Software as a Service
        2. 9.1.4.2 Benefits of Software as a Service
        3. 9.1.4.3 Platform as a Service
        4. 9.1.4.4 Infrastructure as a Service
    2. 9.2 Cloud Computing Types
      1. 9.2.1 Advantages of Hybrid Clouds
      2. 9.2.2 Cloud Computing Architecture
    3. 9.3 Fog Computing
      1. 9.3.1 Benefits of Fog Computing
      2. 9.3.2 Disadvantages of Fog Computing
      3. 9.3.3 The Fog Paradigm
    4. 9.4 Edge Computing
      1. 9.4.1 Advantages of Edge Cloud Computing
      2. 9.4.2 How Edge Computing Works
      3. 9.4.3 Edge Computing and Internet of Things
    5. 9.5 A Comparative Look at Cloud, Fog, and Edge Computing
    6. 9.6 Data Storage
      1. 9.6.1 Relational Databases
      2. 9.6.2 Non-Relational Databases
      3. 9.6.3 Other Database Structures
      4. 9.6.4 Message Brokers
    7. 9.7 Information Management
      1. 9.7.1 Centralised Databases
      2. 9.7.2 Decentralised Databases
      3. 9.7.3 Web Databases
      4. 9.7.4 Cloud Database
      5. 9.7.5 Data Lakes
      6. 9.7.6 Object Storage
    8. 9.8 Data Fusion and Integration
      1. 9.8.1 Problems with Data Fusion
    9. 9.9 Data Quality
    10. 9.10 Communication
      1. 9.10.1 Machine Learning in Communications
        1. 9.10.1.1 Routing in Communication Networks
        2. 9.10.1.2 Wireless Communications
        3. 9.10.1.3 Security, Privacy, and Communications
        4. 9.10.1.4 Smart Services, Smart Infrastructure, and Internet of Things
        5. 9.10.1.5 Image and Video Communications
    11. 9.11 Cognitive Computing
      1. 9.11.1 Theoretical Foundations for Cognitive Computing
        1. 9.11.1.1 Cognitive Informatics for Cognitive Computing
        2. 9.11.1.2 Neural Informatics for Cognitive Computing
        3. 9.11.1.3 Denotational Mathematics for Cognitive Computing
      2. 9.11.2 Models of Cognitive Computing
        1. 9.11.2.1 Abstract Intelligence Model of Cognitive Computing
        2. 9.11.2.2 Computational Intelligence Model of Cognitive Computing
        3. 9.11.2.3 Behavioural Model of Cognitive Computing
      3. 9.11.3 Applications of Cognitive Computing
        1. 9.11.3.1 Autonomous Agent Systems
        2. 9.11.3.2 Cognitive Search Engines
    12. 9.12 Distributed Ledger
      1. 9.12.1 Distributed Ledger and Blockchain
        1. 9.12.1.1 How Blockchain Works
        2. 9.12.1.2 Types of Blockchain
        3. 9.12.1.3 Advantages of Blockchain
        4. 9.12.1.4 Uses of Blockchain
        5. 9.12.1.5 Limitations of Blockchain
        6. 9.12.1.6 Blockchain and Smart Contracts
        7. 9.12.1.7 Blockchain Frameworks in Use
        8. 9.12.1.8 Bitcoin
        9. 9.12.1.9 Ethereum
        10. 9.12.1.10 R3 Corda
        11. 9.12.1.11 Hyperledger Fabric
        12. 9.12.1.12 Comparison of Distributed Ledger Technologies
    13. 9.13 Information Security
    14. 9.14 Cybersecurity
      1. 9.14.1 Challenges and Responses
      2. 9.14.2 A Cybersecurity Framework
      3. 9.14.3 Access Control Security
      4. 9.14.4 Information Transmission Security
      5. 9.14.5 Data Storage Security
    15. References
  21. Chapter 10 Case Studies
    1. 10.1 Case Study 1 – AI Factory for Railway
      1. 10.1.1 AI Factory for Railway
        1. 10.1.1.1 Technology Platform (AI Factory)
        2. 10.1.1.2 Digital Governance Platform (eGovernance)
        3. 10.1.1.3 Communication Platform
        4. 10.1.1.4 Coordinating Platform
    2. 10.2 Case Study 2 – AI Factory for Mining
      1. 10.2.1 AIF/M Concept
      2. 10.2.2 Expected Results and Effects
      3. 10.2.3 Four Pillars of AIF/M
        1. 10.2.3.1 Technology Platform (AI Factory)
        2. 10.2.3.2 Digital Governance Platform (eGovernance)
        3. 10.2.3.3 Communication Platform
        4. 10.2.3.4 Coordinating Platform
    3. 10.3 Case Study 3 – AI Factory – Augmented Realtiy and Virtual Reality Services in the Railway
      1. 10.3.1 Data Fusion and Integration
      2. 10.3.2 Data Modelling and Analysis
        1. 10.3.2.1 Point Cloud Pre-Processing
        2. 10.3.2.2 Classification
        3. 10.3.2.3 Labelling
        4. 10.3.2.4 Object Extraction
        5. 10.3.2.5 Model Creation
      3. 10.3.3 Context Sensing and Adaptation
    4. 10.4 Case Study 5 – AI Factory – Cybersecurity Services in the Railway
      1. 10.4.1 Railway System Stakeholders
        1. 10.4.1.1 Infrastructure Manager
        2. 10.4.1.2 Maintainer
        3. 10.4.1.3 Passenger Operator
        4. 10.4.1.4 Interaction with Other Operators
        5. 10.4.1.5 Dependencies with External Stakeholders
        6. 10.4.1.6 Interaction in Public Areas
        7. 10.4.1.7 Interactions in Operational Environments
    5. References
  22. Chapter 11 AI Factory: A Roadmap for AI Transformation
    1. 11.1 What is the AI Factory?
      1. 11.1.1 Infrastructure of the AI Factory
      2. 11.1.2 Becoming an AI Company
    2. 11.2 Mastering AI in Manufacturing: The Three Levels of Competency
      1. 11.2.1 AI Competency
        1. 11.2.1.1 The Apprentice
        2. 11.2.1.2 The Journeyman
        3. 11.2.1.3 The Master
    3. 11.3 Is AI Ready to Run a Factory?
      1. 11.3.1 Cyber-Physical Systems
        1. 11.3.1.1 Manufacturing and Cyber-Physical Systems
      2. 11.3.2 Artificial Intelligence
        1. 11.3.2.1 Industrial AI
        2. 11.3.2.2 Machine Learning
        3. 11.3.2.3 Why is the Adoption of AI Accelerating?
        4. 11.3.2.4 Digital Twins in Industry
    4. 11.4 The Data-Driven Approach and AI
      1. 11.4.1 Pros of AI and a Data-Driven Approach
      2. 11.4.2 Cons of AI and a Data-Driven Approach
      3. 11.4.3 Implementing the Data-Driven Approach
        1. 11.4.3.1 Governance
        2. 11.4.3.2 Business
        3. 11.4.3.3 Technology
    5. 11.5 Data-Driven Approach in Production: Six Implementation Stages and Failure Factors
      1. 11.5.1 Six Implementation Steps
      2. 11.5.2 Factors of Failure
    6. 11.6 Data-Driven Policymaking
      1. 11.6.1 Innovations in Data-Driven Policymaking
        1. 11.6.1.1 Use of New Data Sources in Policymaking
        2. 11.6.1.2 Co-Creation of Policy
        3. 11.6.1.3 Government Policymaking in the Transport Sector
    7. 11.7 Sustainability: The Triple Bottom Line
      1. 11.7.1 Economic Dimension
      2. 11.7.2 Social Dimension
      3. 11.7.3 Environmental Dimension
      4. 11.7.4 Other Sustainability Dimensions
      5. 11.7.5 The Triple Bottom Line Implementation
      6. 11.7.6 Measuring Sustainability
    8. References
  23. Chapter 12 In Industrial AI We Believe
    1. 12.1 Industrial AI
      1. 12.1.1 Industrial AI Versus Other AI Applications
      2. 12.1.2 Industrial AI and Levels of Autonomy
      3. 12.1.3 Data and Training
      4. 12.1.4 Using Trained Industrial AI
      5. 12.1.5 Conceptual Framework for Industrial AI
      6. 12.1.6 Categories of Industrial AI
        1. 12.1.6.1 Product Applications
        2. 12.1.6.2 Process Applications
        3. 12.1.6.3 Insight Applications
      7. 12.1.7 Why Industrial AI?
      8. 12.1.8 Challenges and Opportunities
        1. 12.1.8.1 Data Availability
        2. 12.1.8.2 Data Quality
        3. 12.1.8.3 Cybersecurity and Privacy
      9. 12.1.9 Industrial AI Application Case Examples
        1. 12.1.9.1 Monitoring
        2. 12.1.9.2 Optimisation
        3. 12.1.9.3 Control
      10. 12.1.10 Monitoring, Optimisation, and Control as an AI Maturity Model
    2. 12.2 Industrial AI in Action
      1. 12.2.1 The Future of Industry: The Self-Optimising Plant
    3. 12.3 Applying Industrial AI
      1. 12.3.1 Industrial AI Requirements
      2. 12.3.2 Industrial AI Solutions Landscape
        1. 12.3.2.1 Point Solutions
        2. 12.3.2.2 Pre-Trained AI Models and Services
        3. 12.3.2.3 Development Platforms
        4. 12.3.2.4 Developer Libraries
        5. 12.3.2.5 Statistical Packages
    4. 12.4 The IMS Architecture for Industrial AI
      1. 12.4.1 Data Technology
      2. 12.4.2 Analytic Technologies
      3. 12.4.3 Platform Technologies
        1. 12.4.3.1 Operations Technologies
    5. 12.5 Visible and Invisible Issues
    6. 12.6 Building the Future with AI
      1. 12.6.1 AI in Industry 4.0
        1. 12.6.1.1 Predictive Analytics
        2. 12.6.1.2 Predictive Maintenance
      2. 12.6.2 Industrial Robotics
        1. 12.6.2.1 Computer Vision
        2. 12.6.2.2 Inventory Management
    7. 12.7 We Believe in Industrial AI
    8. References
  24. Index

Product information

  • Title: AI Factory
  • Author(s): Ramin Karim, Diego Galar, Uday Kumar
  • Release date: May 2023
  • Publisher(s): CRC Press
  • ISBN: 9781000865073