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This work reviews recent advancements in research, new methods and techniques, and applications in computational techniques in intelligent manufacturing.
List of contents
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.
About the author
Kim Phuc Tran is a Senior Associate Professor of Artificial Intelligence and Data Science at the ENSAIT and the GEMTEX laboratory, University of Lille, France. He is an editor for several international journals such as
IEEE Transactions on Intelligent Transportation Systems and
Engineering Applications of Artificial Intelligence. His research interests include explainable and trustworthy Artificial Intelligence and its applications 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 in Computer Engineering, Automation and Signal Processing from University of Lille, France. His research focuses on digital twin, knowledge graph, modelling, simulation, and optimization via AI for sustainable manufacturing. He has published more than 30 papers in SCIE peer-reviewed international journals and conferences.
Summary
This work reviews recent advancements in research, new methods and techniques, and applications in computational techniques in intelligent manufacturing.
