Computational Techniques for Smart Manufacturing in Industry 5.0

Auteur

Kim Phuc Tran is a Senior Associate Professor (Maître de Conférences HDR, equivalent to a UK Reader) of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France. He has a Master of Engineering in Automated Manufacturing. He obtained a Ph.D. in Automation and Applied Informatics at the University of Nantes, and an HDR (Doctor of Science or Dr. Habil.) in Computer Science and Automation at the University of Lille, France. He has published more numerous papers in peer-reviewed reputed journals and proceedings of international conferences. He edited 3 books. He is an Associate Editor, Editorial Board Member, and Guest Editor for several international journals such as IEEE Transactions on Intelligent Transportation Systems and Engineering Applications of Artificial Intelligence. Kim Phuc Tran has supervised 12 Ph.D. students and 3 Postdocs. In addition, as the project coordinator (PI), he conducted a national project about Healthcare Systems with Federated Learning. He has been or is involved (PI, co-PI, or member) in 13 national and European projects. He is an expert and an evaluator for the Public Service of Wallonia (SPW-EER), Belgium, the Natural Sciences and Engineering Research Council of Canada, the ARN (Agence Nationale de la Recherche), the ANRT (Association Nationale de la Recherche et de la Technologie), and the CY Cergy Paris University, France. He received the Award for Scientific Excellence (Prime d'Encadrement Doctoral et de Recherche) from by the Ministry of Higher Education, Research and Innovation, France for 4 years from 2021 to 2025 in recognition of his outstanding scientific achievements. From 2017 until now, he has been the Senior Scientific Advisor at Dong A University and the International Research Institute for Artificial Intelligence and Data Science (IAD), Danang, Vietnam where he holds the International Chair in Data Science and Explainable Artificial Intelligence. His research interests include Explainable Trustworthy, and Transparent Artificial Intelligence; Ethical, and Human-centered Artificial Intelligence; Safety and Reliability of Artificial Intelligence; Statistical Computing; Intelligent Decision Support Systems; Digital Twins; and Applications of AI, Edge Computing, and Data Science in Industry 5.0.

Zhenglei He is an Assistant Professor of Automation and Intelligent Manufacturing at the State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, China. He holds a Ph.D. degree of Computer Engineering, Automation and Signal Processing from University of Lille, France. His research focuses on digital twin, knowledge graph, and modelling, simulation and optimization of manufacturing process via computational techniques for industrial sustanability. He has published more than 30 papers in SCIE peer-reviewed international journals and proceedings of international conferences. He contributed 4 book chapters. He is a board member of Advanced Materials & Sustainable Manufacturing, and the guest editor of Applied Science, International Journal of Computational Intelligent Systems, and Journal of Smart Environments and Green Computing etc. He has chaired / co-chaired special sections of International conferences of FLINS 2022, DSBFI2023, ISKE2023, GCPC 2023 etc. He has co-supervised 13 postgraduate students.

Résumé
This work reviews recent advancements in research, new methods and techniques, and applications in computational techniques in intelligent manufacturing.

Contenu

Introduction to Computational Techniques for Smart 1 Manufacturing in Industry 5.0: Methods and Applications. Research and Application of Raw Paper Quality Prediction Model for Cardboard Papermaking Process. Kriging Model Based Greenhouse Gas Emissions Model of Papermaking Wastewater Treatment Process. Peculiarities of BPG-Based Automatic Lossy Compression of Noisy Images. Recommendation and Design of Personalized Garments based on Intelligent Human-Product Interaction. A Probabilistic Neural Network-based Approach to Garment Fit Level Evaluation in 3D Digitalized Environment. Explainable Machine Learning based Control Charts for High-Dimensional Non-Stationary Time Series Data in IoT Systems: Challenges, Methods, and Future Directions. Monitoring the Ratio of Two Normal Variables and Compositional Data: A Literature Review and Perspective. Energy Efficiency Scheduling of Flexible Flow Shop Using Group Technology. Optimal Operation of Wind-solar-thermal Synergy Considering Carbon Trading and Energy Storage Systems. Adaptive Dempster-Shafer Theory for Evidence-based Trust Models in Multiagent Systems. Optimization Model of Raw Material Selection for Construction Material Manufacturing. Research on Fault Diagnosis of Paper-making Industry based on Knowledge Graph. Research on the Construction of Papermaking Process Model Based on Digital Twin. Index.