Advances in Intelligent Manufacturing and Robotics

Name: Advances in Intelligent Manufacturing and Robotics | Selected Articles from ICIMR 2023; 22-23 August, Suzhou, China
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Selected Articles from ICIMR 2023; 22-23 August, Suzhou, China

Andrew Tan Fan Zhu Haochuan Jiang Kazi Mostafa Eng Hwa Yap Leo Chen Lillian J. A. Olule Hyun Myung(Editor)

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1st Edition

Published on 26. February 2024

XII, 581 pages

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978-981-99-8498-5 (ISBN)

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Dr. Andrew Tan received his MEng in Mechanical Engineering in 2013 from The University of Nottingham. Developing a measurement tool for the Water-Energy-Food Nexus of Malaysia based on systems thinking, he has successfully obtained his PhD from the same university in 2018. Andrew was a manufacturing engineer in Agilent Technologies and Motorola Solutions, where he ensured the smooth development and sustenance of product manufacturing lines. In these positions, he has led extensive manufacturing projects across different teams, time zones, and varying concurrent engineering phases.

Dr. Fan Zhu received her Ph.D. degree in Computer Science from The University of Hong Kong in 2021. In 2016, she obtained her B.Eng. degree in Computer Science and Technology from Shandong University. She does research in intelligent robotic grasping and manipulation via sensor fusion. which includes robotic manipulation and human-robot interaction. Her current research interests include computer vision, AI-driven robotic manipulation, autonomous navigation, and sensor fusion in robotics.

Dr. Haochuan Jiang is currently an assistant professor at the School of Robotics in the Xi'an Jiaotong-Liverpool University Entrepreneur Collage (Taicang). He received his Ph.D. degree from the Department of Electrical Engineering and Electronics at the University of Liverpool. In 2013. Dr. Haochuan JIANG took the role as the new-tech researching engineer at the Commercial Robotic R&D Center of the ECOVACS Robotic (Suzhou) Co. Ltd., in 2013. He also served as an algorithm and model researcher in the Platform and Content Group of the Tencent Tech., Shenzhen in 2019. Before joining XJTLU, he worked as a post-doctoral research associate at the School of Engineering of the University of Edinburgh from 2019 to 2020.

Dr. Kazi Mostafa is an assistant professor in the School of Intelligent Manufacturing Ecosystem at Xi'an Jiaotong-Liverpool University, China. He received his BSc. in Mechanical Engineering from Chittagong University of Engineering and Technology, Bangladesh, in 2006 and his MEng degree in Manufacturing Management from the University of South Australia in 2009. He received his PhD (Mechanical and Electro-Mechanical Engineering) from National Sun Yat-sen University, Taiwan (ROC) in 2015. His research interests include image processing and robotics.

Dr. Eng Hwa Yap is the Dean of the School of Intelligent Manufacturing Ecosystem and the School of Robotics at Taicang Campus. He leads the strategic development of both schools. Eng Hwa also leads the initial School of CHIPS as Acting Dean. He was formerly an Associate Dean in the Faculty of Transdisciplinary Innovation (now TD School) at University of Technology Sydney in Australia, where he provided strategic leadership in, and oversight of, teaching and learning management and operations in the faculty. He received his PhD in Mechanical and Marine Engineering from University College London and completed his undergraduate degree in Marine Technology at the University of Plymouth, both in the United Kingdom. He has held academic leadership positions across several institutions in Australia and Malaysia. His research focuses on curated multidisciplinary and mixed approaches of inquiry to understand and address complex problems surrounding technology, environmental sustainability, and future energy systems.

Dr Leo Chen obtained his PhD in Mechanical Engineering from University of Glasgow, in 2010. He has been leading a few research grants in artificial intelligence, industry 4.0, high-performance computing, robotics and autonomous systems, and their applications in multi-disciplinary contexts. His research interests are in the areas of robotics, digital manufacturing, and industry 4.0, which demonstrates significant research and grant potential in engineering and cross-disciplinary applications.

Dr Lillian J. A. Olule received her PhD in Electrical and Electronic Engineering from University of Nottingham Malaysia. Her current research interests are in the areas of antennas and microwave systems and their energy harvesting applications, energy harvesting for biomedical applications and energy harvesting applications of sustainable structural materials.

Dr. Hyun Myung obtained his PhD in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST). He was a Senior Researcher with the Electronics and Telecommunications Research Institute, Daejeon, from 1998 to 2002, CTO and the Director with the Digital Contents Research Laboratory, Emersys Corporation, Daejeon, from 2002 to 2003, and a Principle Researcher with the Samsung Advanced Institute of Technology, Yongin, Korea, from 2003 to 2008. Since 2008, he has been a Professor with the Department of Civil and Environmental Engineering, KAIST, and he has served as the Chief of the KAIST Robotics Program. From 2019, he is a Professor with the School of Electrical Engineering. His current research interests include autonomous robot navigation, SLAM (simultaneous localization and mapping), spatial AI, machine learning, and swarm robots.

Content

Intro
Preface
Contents
Springback Prediction Using Gated Recurrent Unit and Data Augmentation
1 Introduction
2 Related Work
3 Methodology
3.1 Background
3.2 Data Augmentation
3.3 Springback Prediction with GRU
3.4 Implementation Detail
4 Evaluation
4.1 Experiment Result
4.2 Evaluation Metrics
4.3 What Does the Proposed System Compare with the Previous Work?
4.4 What is the Best Step Value?
4.5 What is the Best Grid Size?
5 Conclusion
References
Road Signage and Road Obstacle Detection Using Deep Learning Method
1 Introduction
1.1 Related Works
2 Methodology
2.1 Data Collection
2.2 Image Annotation
2.3 Dataset Splitting
2.4 Transfer Learning
2.5 Evaluation
3 Result and Discussion
3.1 Object Detection Result
3.2 Confusion Matrix
3.3 Precision-Recall Curve
3.4 F1 Score Graph
3.5 Discussion
4 Conclusion
References
Advancing Mass Customization Through GPT Language Models: A Multidimensional Analysis of Market, Technological, and Managerial Innovations
1 Introduction
1.1 Introduction to Intelligent Manufacturing
1.2 Introduction to ChatGPT
1.3 Introduction to Manufacturing and Mass Customization
2 Concept of GPT Innovation in Mass Customization
2.1 Innovation in New Business Model
2.2 Innovation in New Production Environment
2.3 Innovation in New Business Process System
3 ChatGPT in Mass Customization
4 Mass Customization of Luxury Brands Products
5 Limitation of GPT Development in Mass Customization
6 Conclusion
7 Future Work
References
CenterNet: A Transfer Learning Approach for Human Presence Detection
1 Introduction
2 Methodology
2.1 Image Acquisition
2.2 Image Annotation
2.3 Dataset Preparation
2.4 Transfer Learning-Fine-Tuning
2.5 Learning Curve Inspection
2.6 Performance Evaluation
3 Result and Discussion
4 Conclusion
References
Deep Learning Algorithms for Recognition of Badminton Strokes: A Study Using SDNN, RNN, and RNN-GRU Models with Off-Court Video Capture
1 Introduction
2 Methodology
3 Results and Discussion
4 Conclusion
References
Bibliometric Analysis of Image Segmentation with Deep Learning: An Analytical Study
1 Introduction
2 Method
3 Results and Discussion
3.1 Analysis of Publication and Document Classifications
3.2 Analysis of Journals and Cited Articles
3.3 Analysis of Countries and Institutions
3.4 Popular Research Topics and Keyphrase Analysis
4 Conclusion
References
Unsupervised Learning of Time-Series Classification Using Machine Learning Through Fertigation System
1 Introduction
2 Materials and Methods
2.1 Data Monitoring Device
2.2 Feature Extraction
2.3 k-means Clustering
2.4 Event Identification
3 Results
3.1 k-means Clustering
3.2 Event Identification
3.3 Comparison of Machine Learning Models
4 Conclusion
References
Design Optimization Study of the Temperature Uniformity in Air-Cooled Freezers
1 Introduction
2 Methodology
2.1 Physical Model
2.2 Governing Equation
2.3 Solution Algorithm
2.4 Optimization Process
3 Results
3.1 Grid Independence
3.2 Problems of Temperature Uniformity
3.3 Optimization Result
4 Conclusion and Future Work
References
Enhancing Elderly Well-Being Through the Adoption of Medication Adherence System
1 Introduction
2 Literature Review and Hypothesis Development
2.1 Medication Adherence
2.2 Perceived Risk
2.3 Trust
2.4 Health Consciousness
2.5 Perceived Usefulness
2.6 Perceived Ease of Use
2.7 Perceived Behavioural Control
2.8 Attitudes
2.9 Subjective Norm
3 Methodology
4 Findings and Results
5 Discussion and Implications
6 Conclusion
References
Deep Learning-Based Silicon Wafer Defect Classification: A Performance Comparison of Pretrained Networks
1 Introduction
2 Materials and Methods
2.1 Data Acquisition and Preprocessing
2.2 Transfer Learning of Pretrained Networks
2.3 Hyperparameter Settings
2.4 Performance Evaluations
3 Performance Comparison Results
4 Conclusions
References
A Modified African Vultures Optimization Algorithm for Enhanced Feature Selection
1 Introduction
2 Feature Selection Optimization Problem
3 Proposed MCAVOA as Feature Selection Algorithm
4 Performance Analysis
4.1 Simulation Settings
4.2 Comparison Between Different MSAs in Feature Selection
5 Conclusion
References
Convolutional Neural Network-Based Identifying Gender of Kiwifruit Flowers in Autonomous Pollination for Future Farming
1 Introduction
1.1 Literature Review
1.2 Motivation, Aims, and Objective
2 Methodology
2.1 Process Brief
2.2 Methodology
3 Location Detection (YOLOv5)
3.1 Data Collection and Labelling
3.2 Model Training
3.3 Result
4 Gender Recognition (LeNet and AlexNet and ResNet)
4.1 Data Pre-processing
4.2 Model Comparison and Evaluation
5 Conclusion
References
Hyperparameter Optimization of Deep Learning Model: A Case Study of COVID-19 Diagnosis
1 Introduction
2 Related Works
3 COVID-19 Diagnosis with Proposed SSOCNN
3.1 Data Acquisition and Preprocessing
3.2 Network Selection and Training
3.3 Hyperparameter Optimization in SSOCNN
4 Performance Evaluation
5 Conclusion
References
Predicting the Impact of Restaurant Automation and Food Safety in China: Identifying Key Factors for Smart Dining Experience
1 Introduction
1.1 Problem Statements
1.2 Hypotheses
2 Literature Review
2.1 Smart Dining Experience
2.2 Intelligent Restaurant
2.3 Food Safety
2.4 Relationship Quality
3 Framework
4 Conclusion
References
Development of Intelligent EWC with Autonomous and Interactive Behavior
1 Introduction
2 Related Work
3 Methodology
3.1 Module Overview
3.2 Motor Closed Loop Control
3.3 Serial Interface
3.4 ROS Driver for Arduino
3.5 LiDAR
3.6 Real-Time SLAM
3.7 Navigation
3.8 Speech Recognition
4 Results
5 Conclusion
References
Character Recognition Based on k-Nearest Neighbor, Simple Logistic Regression, and Random Forest
1 Introduction
2 Literature Review
3 Design Methodology
3.1 Out-of-Bag Data Error from Random Forest
3.2 Slow Training Speed
3.3 Large Data Dimension
3.4 The Random Forest Algorithm Consumes an Excessive Amount of Memory and is Unable to Process Large Datasets
4 Results and Discussion
5 Conclusion
References
The Integration of Artificial Intelligence in the Fashion Industry and Its Impact on Sustainable Fashion: A Systematic Literature Review
1 Introduction
2 Literature Review
2.1 Understanding Artificial Intelligence in Modern Digital Era
2.2 Sustainability in Fashion
2.3 Integration of Artificial Intelligence in Fashion Industry
3 Methodology
3.1 Systematic Literature Review
3.2 Search Strategy and Selection Criteria
4 Result
4.1 Types of AI Technologies Utilized in the Fashion Industry and Their Applications
4.2 The Impact of AI on Sustainability Practices in the Fashion Industry
4.3 Challenges and Opportunities Associated with the Implementation of AI in the Fashion Industry
5 Conclusion
References
Technical, Environmental, and Economical Assessment of Photovoltaics Technologies in Malaysia
1 Introduction
2 Literature Review
2.1 Technical Aspects
2.2 Environmental Aspects
2.3 Economical Aspects
2.4 Schemes and Tools Established in Malaysia and Singapore
3 Methodology
3.1 Site Selection
3.2 Data Collection
3.3 Simulation Using PVsyst
4 Results
4.1 Tilt and Orientation Factor
4.2 PV System Energy Production
4.3 Performance Evaluation
4.4 Environmental Assessment
4.5 Economic Assessment
5 Proposed Initiatives
6 Conclusion
References
An Intelligent Robotic Grasping and Manipulation System with Sensor Fusion
1 Introduction
2 System Configuration
2.1 Control System
2.2 Experiment Objects
2.3 Object Detection and Pose Estimation
2.4 Grasping Pose Strategy
2.5 Safe Grasping Force Framework
3 Results
3.1 Object Detection and Pose Estimation
3.2 Grasping Pose Strategy
3.3 Safe Grasping Force Framework
4 Conclusion
References
Deep Learning for Breast Cancer Detection from Mammograms Images
1 Introduction
2 Methodology
2.1 Dataset
2.2 Data Preprocessing
2.3 Transfer Learning
2.4 Model Training
3 Results
3.1 Model Evaluation
3.2 Discussion
4 Conclusion
References
Object Detection in Autonomous Vehicles: A Performance Analysis
1 Introduction
1.1 Object Detection
1.2 Autonomous Vehicles
1.3 Faster R-CNN
1.4 SSD
2 Methodology
2.1 Data Acquisition
2.2 Data Pre-processing
2.3 Data Augmentation
2.4 Simulation Settings
2.5 Model Selection
2.6 Model Training
2.7 Training Stage
2.8 Loss Function
2.9 Performance Evaluation
3 Results and Discussion
4 Conclusion
References
Enhancing E-commerce Recommendation Accuracy Using KNN and Hybrid Approaches: An Empirical Study
1 Introduction
2 Methods
2.1 Data Collection and Preprocessing
2.2 Feature Extraction
2.3 Model Building
2.4 Evaluation
3 Results and Analysis
3.1 Data Preprocessing and Feature Extraction
3.2 Building the KNN-Based Recommendation System
4 Conclusion
References
Devulcanizing Recycled Rubber by Thermochemical Method
1 Introduction
2 Methodology and Experimental Set-up
2.1 Materials
2.2 Thermochemical Devulcanization Process
2.3 Determination of Crosslink Density
2.4 Surface Morphology
3 Results and Discussion
3.1 Crosslink Density
3.2 Surface Morphology
3.3 Emission from Devulcanizing Process
4 Conclusion
References
Influence of Sediment Particle Size on Erosion Rate of AISI 304 Stainless Steel
1 Introduction
2 Experiment Procedure
2.1 Materials and Specimens
2.2 Erosion Test
2.3 Estimation Erosion Rate
2.4 Surface Observations
3 Results and Discussion
4 Conclusion
References
Optimization Strategies for Training Artificial Neural Network: A Case Study in Medical Classification
1 Introduction
2 Related Work
3 Proposed Methodology
3.1 Optimization Model for ANN Training
3.2 Proposed AOA-COIS as ANN Training Algorithm
4 Performance Analyses Using Medical Datasets
4.1 Simulation Settings
4.2 Comparisons of Classification Accuracies
5 Conclusions
References
Wrapper-Based Feature Selection Using Sperm Swarm Optimization: A Comparative Study
1 Introduction
2 Fitness Function of Feature Selection Problem
3 Proposed Wrapper-Based Feature Selection Using SSO
4 Performance Evaluation
4.1 Simulation Settings
4.2 Comparison of Different Wrapper-Based Feature Selection Techniques
5 Conclusions
References
Development of a Bionic Metastructure and Its Coupling to Sensor Fusion
1 Introduction
2 Metastructure and Sensor Fusion Design
3 Results and Discussion
3.1 Metastructure Results
3.2 Sensor Fusion Results
4 Conclusion
References
Development of a Wear Sensor for Monitoring Grinding Mill Shell Liners
1 Introduction
2 Liner Wear Digital IOT Sensor Development
3 Mill Operating Conditions and Experimental Scheme
4 Results and Discussion
4.1 Wear Sensor Results
4.2 Wear Evolution Modelling
5 Conclusion
References
Online Wear and Rheology Monitoring System for Backfill Paste Pipelines
1 Introduction
2 Liner Wear Digital IoT Sensor Development
3 Operating Conditions and CFD Modelling Scheme
4 Results and Discussion
4.1 Wear Sensor Results
4.2 CFD Modelling Results
4.3 Wear Reconstruction
5 Conclusion
References
Path Planning of Autonomous Pollination Using Heredity Algorithm for Intelligent Agriculture
1 Introduction
2 Materials and Methods
2.1 Materials
2.2 Methods
3 Simulation Environment
3.1 Environment Modelling of MPrs
3.2 Basic Method
3.3 Result
4 Optimization Algorithm
4.1 Hybrid Algorithm
4.2 Model Comparison and Evaluation
5 Conclusion
References
Evaluation of Hybrid Recommendation System and Machine Learning Algorithms for E-Commerce Platform
1 Introduction
2 Methods
2.1 Dataset
2.2 System Development
2.3 Data Pre-processing
2.4 Models' Development and Evaluation
3 Results and Discussion
3.1 Exploratory Data Analysis
3.2 Feature Engineering
3.3 LightFM Recommendation System
3.4 Recommendation System Using Spark
4 Conclusion
References
Evaluating Machine Learning and Deep Learning Analytics for Predicting Bankruptcy of Companies
1 Introduction
1.1 Corporate Bankruptcy
2 Methods
2.1 Dataset Description
2.2 Exploratory Data Analysis
2.3 Data Cleaning, Feature Selection, and Dimension Reduction
2.4 ML Models
3 Results and Discussion
3.1 Initial Data Interpretation
3.2 Missing Data Imputation
3.3 Exploratory Data Analysis (EDA)
3.4 Data Skewness Management, Data Scaling, and Feature Selection
3.5 Class Balancing
3.6 Model Implementation and Evaluation
4 Conclusion
References
Vibration Condition Monitoring of Rotating Machinery with IoT and Smartphone Sensors
1 Introduction
2 Methodology
2.1 Application Building and Development
2.2 Data Collection
2.3 Data Validation
3 Results
3.1 Measurement Data
3.2 Real-Time Warning
4 Conclusion
References
Computer-Aided Diagnosis of Oral Squamous Cell Carcinoma: A Feature-Based Transfer Learning Approach
1 Introduction
2 Methods
3 Results and Discussion
4 Conclusion
References
The Classification of Badminton Strokes: A Feature Importance Investigation
1 Introduction
2 Methods
3 Results and Discussion
4 Conclusion
References
Feature-Based Transfer Learning for IoT-Enabled Defect Detection for Quality Control in Industrial Manufacturing Processes: A DenseNet Evaluation
1 Introduction
2 Literature Review
3 Methods
4 Results and Discussion
5 Conclusion
References
Predicting Physical Activity of Young Adults Based on Psychological Need Satisfaction in Exercise Using Explainable Decision Tree Model
1 Introduction
2 Methodology
2.1 Participants
2.2 Instrumentation for Data Collection
2.3 Model Development Pipeline
2.4 Features Importance Analysis Contributing to the Model Performance
3 Results and Discussion
4 Conclusion
References
Exploring the Impact of Psychological Needs on Physical Activity Using a Logistic Regression-Based Machine Learning Model
1 Introduction
2 Methodology
2.1 Participants
2.2 Physical Activity and Psychological Variables Assessments
2.3 K-Means Clustering Analysis
2.4 LR Model Development, Testing and Validation
3 Results and Discussion
4 Conclusion
References
Dilation and Erosion for Left Atrium Scar Segmentation
1 Introduction
2 Method
3 Evaluation Method
3.1 Dataset
3.2 Training Details
3.3 Loss Function
4 Results
5 Conclusion
References
Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works
1 Introduction
2 Review of Classification Works
3 Methodology
3.1 CNN and ResNet50 Models
3.2 YOLO Training Models
4 Results and Discussion
4.1 Deep Learning Methods Performance Evaluation
4.2 YOLO V3 and YOLO V4 Performance Evaluation
4.3 Real-Time Object Detection
5 Conclusion
References
A Novel Fall Detection System Using the AI-Enabled EUREKA Humanoid Robot
1 Introduction
1.1 Prevalence of Falls in Elderly People
1.2 Healthcare Technologies
1.3 Research Problem and Objectives
2 Methods and Design
2.1 Sensor Data and Machine Learning Model
2.2 System Architecture
3 Results
3.1 Fall Detection Model
3.2 Performance
4 Conclusion and Discussion
4.1 Practical Application
4.2 Limitations and Weakness
4.3 Future Research
References
Control and Gait Generation of Biped Robots: A Review
1 Introduction
2 Locomotion of a Biped Robot
2.1 Overview of the Gait Generation Techniques
2.2 Overview of the Control Techniques
References
Application of Pearson Diversity Entropy as Prognostic Measure of Rotating Machinery
1 Introduction
2 Methodology
2.1 1Diversity Entropy
3 2Pearson Similarity
3.1 Prognosis with PDE
4 Experimental Verification
5 Conclusion
References
The Research and Development of an Educational SLAM AVG Based on Modular Design Concept
1 Introduction
2 Methodology of AGV Design Concept
2.1 Modular Design Concept
3 Methodology of Specification AGV Mechanical Modules Design
3.1 Technical Parameters Requirements
3.2 AGV Body Structure Module Design
3.3 AGV Drive Unit Structure Module Design
3.4 Finite Element Analysis
3.5 Functional Electronic Component Selection
4 Results and Contributions
4.1 Research Outcomes
4.2 Research Contributions
Appendix of Full-Size Table
References
Autonomous Mobile Robot for Transporting Goods in Warehouse and Production
1 Introduction
2 Methodology
3 Results and Analysis
4 Conclusion
References
Evaluating Manufacturing Machines Using ELECTRE Method: A Decision Support Approach
1 Introduction
2 Methods
3 Results and Conclusion
4 Conclusion
References
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Advances in Intelligent Manufacturing and Robotics

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