Preface
Editors Biography
List of Figures
List of Tables
List of Abbreviations
1 AI Automotive
1.0 AI Reshaping the Automotive Industry
1.0.1 Introduction and Background
1.0.2 AI Developments and Future Trends for AI Technologies
1.0.3 AI-Based Applications
1.1 AI for Inbound Logistics Optimisation in Automotive Industry
1.1.1 Introduction and Background
1.1.2 Requirements – User Journeys
1.1.3 Data Flow Principles and Architecture of the MPDSS
1.1.4 Preliminary Analysis of Data and Dataset
1.1.4.1 Data Pre-processing and Visualisation
1.1.4.2 Classification Models
1.1.5 Conclusion
1.2 State of Health Estimation using a Temporal Convolutional Network for an Efficient Use of Retired Electric Vehicle Batteries within Second-Life Applications
1.2.1 Retired Electric Vehicle Batteries for Second-Life Applications
1.2.2 State of Health of Lithium-Ion Batteries
1.2.3 Data Measurement Using the Open-Source BMS foxBMS
1.2.4 Temporal Convolutional Neural Network for State of Health Prediction
1.2.4.1 Causal Convolutions and Receptive Field
1.2.4.2 Dilated Convolutions
1.2.4.3 Residual Block
1.2.5 Results
1.2.6 Conclusion
1.3 Optimising Trajectories in Simulations with Deep Reinforcement Learning for Industrial Robots in Automotive Manufacturing
1.3.1 Introduction
1.3.2 Background
1.3.3 Methodology
1.3.4 Conclusion and Outlook
1.4 Foundations of Real Time Predictive Maintenance with Root Cause Analysis
1.4.1 Introduction and Background
1.4.2 Foundations
1.4.2.1 Model-based Diagnosis
1.4.2.2 Machine Learning Based Diagnosis
1.4.2.3 Artificial Neural Networks for Diagnostics
1.4.3 Related Research
1.4.4 Conclusion
1.5 Real-Time Predictive Maintenance – Model-Based, Simulation-Based and Machine Learning Based Diagnosis
1.5.1 Introduction and Background
1.5.2 Application of Diagnosis Systems Based on Simplified DC e-Motor Model
1.5.2.1 Simplified DC e-Motor Model With Fault Injection Capabilities
1.5.2.2 Model-based Diagnosis for Simplified DC e-Motor
1.5.2.3 Simulation-Based Diagnosis for Simplified DC e-Motor
1.5.2.4 Machine Learning for Diagnosis of Simplified DC e-Motor
1.5.2.5 Comparisons and Limitations
1.5.3 Conclusion
1.6 Real-Time Predictive Maintenance – Artificial Neural Network Based Diagnosis
1.6.1 Introduction and Background
1.6.1.1 AI-based Diagnosis of E-motors
1.6.1.2 Artificial Intelligence in Vibration Diagnosis
1.6.2 Artificial Neural Network for e-Motor Diagnosis
1.6.2.1 Acausal e-Motor Model with Faults Injection Capability
1.6.2.2 Artificial Neural Network for Inter-turn Short Circuit Detection
1.6.3 Artificial Neural Network based Vibration Diagnosis
1.6.3.1 Vibration Diagnosis of Rotating Machines
1.6.3.2 AI Approaches in Vibration Diagnosis
1.6.3.3 MLP implementable in device at the edge
1.6.4 Conclusion
2 AI Semiconductor
2.0 AI in Semiconductor Industry
2.0.1 Introduction and Background
2.0.2 AI Developments in Semiconductor Industry
2.0.3 Future Trends for AI Technologies and Applications in Semiconductor Industry
2.0.4 AI-Based Applications
2.1 AI-Based Knowledge Management System for Risk Assessment and Root Cause Analysis in Semiconductor Industry
2.1.1 Introduction and Background
2.1.2 Research Areas
2.1.2.1 FMEA and FMEA Consistency Improvement
2.1.2.2 Causal Information Extracting from Free Text
2.1.2.3 Failure Analysis Process, Failure Analysis Reports, and Ontologies
2.1.2.4 Knowledge Representation
2.1.2.5 Refinement Algorithm
2.1.3 Reflections
2.1.4 Conclusion
2.2 Efficient Deep Learning Approach for Fault Detection in the Semiconductor Industry
2.2.1 Motivation: The Wafer Fault Classification Problem
2.2.2 Related Works
2.2.3 Target Platform Requirements
2.2.4 HW/SW System and Methodology
2.2.4.1 Industrial HW/SW System for On-Device Inference
2.2.4.2 Neural Network Building and Training Using N2D2
2.2.4.3 Neural Network Export and FPGA Implementation Used for Inference
2.2.5 Conclusion
2.3 Towards Fully Automated Verification of Semiconductor Technologies
2.3.1 Introduction
2.3.2 Background: Interpreting Semiconductor Technologies
2.3.2.1 Methodology: The Integrated Analysis Process
2.3.2.2 Example Analysis: From the Image to the Feature Extraction
2.3.3 Conclusion
2.4 Automated Anomaly Detection Through Assembly and Packaging Process
2.4.1 Introduction and Background
2.4.2 Dataset Description and Defect Types
2.4.3 Methodology
2.4.3.1 Anomaly Detection
2.4.3.2 Pseudo Anomaly Detection
2.4.3.3 Convolutional Neural Networks
2.4.4 Results and Discussion
2.4.5 Conclusion and Outlooks
3 AI Industrial Machinery
3.0 AI in Industrial Machinery
3.0.1 Introduction and Background
3.0.2 AI Developments and Future Trends in Industrial Machinery
3.0.3 AI-based Applications
3.1 AI-Powered Collision Avoidance Safety System for Industrial Woodworking Machinery
3.1.1 Introduction and Background
3.1.2 Review of Industrial-level Methods for Edge DNNs
3.1.2.1 Compression Techniques
3.1.2.2 Popular Frameworks and Tools
3.1.3 Materials and Methods
3.1.3.1 System Architecture
3.1.3.2 Dataset Collection
3.1.3.3 Detection Methods
3.1.3.4 Continual Learning Setup
3.1.4 Experimental Results
3.1.4.1 Evaluation Metrics
3.1.4.2 Continual Learning Scenario
3.1.4.3 Robustness Against Quantization
3.1.4.4 Latency, Energy and Memory Footprint on STM32H743ZI
3.1.5 Conclusion
3.2 Construction of a Smart Vision-Guided Robot System for Manipulation in a Dynamic Environment
3.2.1 Introduction and Background
3.2.2 Challenges of Enabling Robots to “See”
3.2.2.1 Modularity
3.2.2.2 Operability
3.2.2.3 Computer Vision Algorithms
3.2.2.4 Validation of Algorithms
3.2.3 Requirements
3.2.4 Proposed Solution
3.2.4.1 Hardware and Interface Components
3.2.4.2 Software Components
3.2.4.3 Hardware/Software Partitioning
3.2.5 Demonstrator Setup and Initial Results
3.2.6 Conclusion and Future Work
3.3 Radar-Based Human-Robot Interfaces
3.3.1 Introduction and Background
3.3.2 Gesture Recognition Using a Machine Learning Approach
3.3.2.1 Concept and Experimental Setup
3.3.2.2 Inference Pipeline, Training Algorithm
3.3.2.3 Data Recording and Results
3.3.3 Gesture Recognition Using a Spiking Neural Network
3.3.3.1 Concept and Experimental Setup
3.3.3.2 Inference Pipeline, Training Algorithm
3.3.3.3 Data Recording and Results
3.3.3.4 Discussion
3.3.4 Proof of Concept Demonstration
3.3.5 Comparison and Conclusion
3.4 Touch Identification on Sensitive Robot Skin Using Time Domain Reflectometry and Machine Learning Methods
3.4.1 Introduction and Background
3.4.2 State of the Art
3.4.3 Problem Definition
3.4.4 Concepts and Methods
3.4.5 Proof of Concept of the Novel Sensor System
3.4.5.1 Experimental Acquisition of Training Data
3.4.5.2 Training Procedure
3.4.6 Results
3.4.7 Conclusions
4 AI Food and Beverage
4.0 AI in Food and Beverage Industry
4.0.1 Introduction and Background
4.0.2 AI Developments in Food and Beverage Industry
4.0.3 Future Trends for AI Technologies and Applications
4.0.4 AI-Based Applications
4.1 Innovative Vineyards Environmental Monitoring System Using Deep Edge AI
4.1.1 Introduction
4.1.2 Related Work
4.1.3 Edge Intelligence
4.1.4 Communication Technology – LoRaWAN
4.1.5 Environmental Monitoring System
4.1.6 Conclusion
4.2 AI-Driven Yield Estimation Using an Autonomous Robot for Data Acquisition
4.2.1 Introduction
4.2.2 Artificial Intelligence for Grape Detection
4.2.3 Towards an Automated Protocol for Yield Estimation
4.2.4 Assessing the Vine Vitality Using an Embarked LiDAR
4.2.5 Conclusions
4.3 AI-Based Quality Control System at the Pressing Stages of the Champagne Production
4.3.1 Introduction and Background
4.3.2 Methodology
4.3.3 Results and Discussion
4.3.4 Conclusion
4.4 Optimisation of Soybean Manufacturing Process Using Real-time Artificial Intelligence of Things Technology
4.4.1 Introduction
4.4.2 Soybean Production Process Description
4.4.3 Overall Manufacturing System Architecture and Platform
4.4.4 Process Parameters Monitoring
4.4.5 Edge Processing and AI-based Framework for Real-Time Monitoring
4.4.6 Embedded Intelligent Vision and Multi-sensors Fusion Approach
4.4.6.1 Embedded Vision IIoT Systems Evaluation
4.4.7 Experimental Setup
4.4.7.1 Experimental Evaluation and Results
4.4.8 Summary and Future Work
4.5 AI and IIoT-based Predictive Maintenance System for Soybean Processing
4.5.1 Introduction
4.5.2 Maintenance Foundations
4.5.3 Principles of Predictive Maintenance
4.5.4 Soybean Production Process and Maintenance Policies
4.5.4.1 Vibration Analysis
4.5.5 AI-based Predictive Maintenance Framework Methodology
4.5.6 Industrial Integrated System for Soybean Production Equipment
4.5.7 Experimental Set-up and Implementation
4.5.8 Summary and Future Work
5 AI Transportation
5.0 Applications of AI in Transportation Industry
5.0.1 Introduction and Background
5.0.2 AI Developments in Transportation Industry
5.0.3 Future Trends for Applications in Transportation Industry
5.0.4 AI-Based Applications
5.1 AI-Based Vehicle Systems for Mobility-as-a-Service Application
5.1.1 Introduction and Background
5.1.2 AI-Based 3D Object Detection and Tracking for Automated Driving
5.1.2.1 Camera and LiDAR Sensor Data Fusion
5.1.2.2 Experiments and Results
5.1.2.3 Evaluation of the Algorithm and Vehicle Integration
5.1.3 Autonomous Control Prototyping in Simulated Environments
5.1.3.1 Reinforcement Learning Control for Mobile Vehicles
5.1.3.2 The Architecture – Immediate Actions Time-Horizon
5.1.4 Conclusion
5.2 Open Traffic Data for Mobility-as-a-Service Applications – Architecture and Challenges
5.2.1 Introduction and Background
5.2.2 Data Acquisition
5.2.2.1 Bus Traces
5.2.2.2 Traffic Cameras
5.2.2.3 Loop Detectors
5.2.3 Data Processing at the Edge
5.2.3.1 Object Detection
5.2.3.2 Bus GPS Trace
5.2.4 Data Processing in the Cloud
5.2.4.1 Data Quality Monitoring
5.2.4.2 Data Quality Observations
5.2.5 Conclusion
List of Contributors
Index