2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems: DPTA 2020
Chuanchao Huang · Yu-Wei Chan · Neil Yen
Jun 2021 · Springer Nature
Ebook
1669
Pages
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About this ebook
This book covers cutting-edge and advanced research on data processing techniques and applications for cyber-physical systems, gathering the proceedings of the International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2020), held in Laibin City, Guangxi Province, China, on December 11–12, 2020. It examines a wide range of topics, including distributed processing for sensor data in CPS networks; approximate reasoning and pattern recognition for CPS networks; data platforms for efficient integration with CPS networks; machine learning algorithms for CPS networks; and data security and privacy in CPS networks. Outlining promising future research directions, the book offers a valuable resource for students, researchers, and professionals alike, while also providing a useful reference guide for newcomers to the field.
About the author
Chuanchao Huang obtained Ph.D. degree from the School of Management in Huazhong University of Science and Technology, China. His research interests include machine learning, artificial intelligence, simulation model under multimedia information, and computing organization. He is now the associate professor in Huazhong University of Science and Technology, China. He has authored or co-authored more than 30 publications including Neurocomputing, Multimedia Tools Appl., Neural Computing and Applications, and Physic Review Part A.
Yu-Wei Chan received the B.S. and M.S. degrees in Information Engineering from Tamkang University, Taiwan, in 1997 and 2001, respectively. He received the Ph.D. degree in Computer Science from National Tsing Hua University in 2013. He joined the Department of Information Management at Chung Chou University of Science and Technology as a lecturer in 2002. In 2016, he joined the Department of Computer Science and Information Management at Providence University as a professor. His current research interests include cognitive radio in 5G, game theoretic wireless networking, and cloud computing.
Neil Y. Yen received Ph.D. degrees at the Department of Human Informatics and Cognitive Sciences, Waseda University, Japan, and the Department of Computer Science and Information Engineering, Tamkang University, Taiwan, in the year of 2012, respectively. He is currently an associate professor at the School of Computer Science and Engineering, University of Aizu, Japan. His current interests include human-centric computing (i.e., user modeling and understanding, social web, collaborative economy), big data (i.e., data/web mining, deep learning, statistical analysis), and anticipatory computing (i.e., prediction, recommendation, and well-being services).
Yu-Wei Chan received the B.S. and M.S. degrees in Information Engineering from Tamkang University, Taiwan, in 1997 and 2001, respectively. He received the Ph.D. degree in Computer Science from National Tsing Hua University in 2013. He joined the Department of Information Management at Chung Chou University of Science and Technology as a lecturer in 2002. In 2016, he joined the Department of Computer Science and Information Management at Providence University as a professor. His current research interests include cognitive radio in 5G, game theoretic wireless networking, and cloud computing.
Neil Y. Yen received Ph.D. degrees at the Department of Human Informatics and Cognitive Sciences, Waseda University, Japan, and the Department of Computer Science and Information Engineering, Tamkang University, Taiwan, in the year of 2012, respectively. He is currently an associate professor at the School of Computer Science and Engineering, University of Aizu, Japan. His current interests include human-centric computing (i.e., user modeling and understanding, social web, collaborative economy), big data (i.e., data/web mining, deep learning, statistical analysis), and anticipatory computing (i.e., prediction, recommendation, and well-being services).
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