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Enterprise Information Systems

The Role of Intelligent Visualization in Smart Manufacturing

About the issue

Advances in information and communication technologies (ICT) have continuously progressed in various sectors and play a significant role in the manufacturing industry. The current development of information technologies, smart and intelligent technologies, and the cyber system have given rise to the Internet of Things (IoT), big data, cloud computing, digital twin, artificial intelligence (AI), machine learning, and cyber-physical systems. The introduction of these technologies into the manufacturing industry addresses various critical challenges while transforming traditional manufacturing into smart manufacturing. These technologies enables the acquisition and sharing of real-time manufacturing data, helping for fast and precise decision-making, and deviating considerable attention towards the next-generation industrial revolution. However, smart manufacturing needs to manage a large amount of industrial data generated from various manufacturing equipment and communicating devices with different data formats. In this scenario, intelligent visualization plays a significant role in understanding and explaining large and complex data for analysing manufacturing data, empowering people's insight into process innovation and productivity improvement. It can efficiently integrate machine-intelligence with human-intelligence to understand insight from the industrial data to support effecient decision-making under complex scenarios.

With the development of Industry 4.0, machine intelligent visual simulation and data processing grow exponentially in various industrial sector, including aerospace manufacturing, food processing, nuclear fuel processing, automotive manufacturing etc. Intelligent visualization can be applied at different stages of the manufacturing lifecycle like production planning, simulation, production monitoring, and testing. Hence with the use of smart technologies, the manufacturing data can be transformed into intelligent visualization.

This special issue focuses on exploring the application of emerging information technologies for visualization and analysis of industrial data at different manufacturing stages to build a smart manufacturing model with an intelligent decision support system. We welcome genuine and original research article that discovers an analytical solution to various industrial and manufacturing issue using intelligent and smart technologies.

Topic of interest that can be included but not restricted are:

  • AI-based intelligent visualization for smart manufacturing

  • Big data analysis and intelligent visualization for smart manufacturing.

  • Intelligent visualization integrated smart manufacturing based on big data analytics and machine learning.

  • Importance of Big Data visualization towards next-generation manufacturing.

  • AI-based Intelligent manufacturing with visualization technology in Industry 4.0

  • IT-oriented intelligent visualization for smart manufacturing.

  • IoT assisted real-time industrial data analysis using artificial intelligence approaches for smart manufacturing

  • Digital twin-driven smart manufacturing with visualization

  • Challenges and opportunities of AI technologies for visualization in smart manufacturing

  • Machine intelligent based data processing and visualization from smart manufacturing

  • Production monitoring based on intelligent visualization for smart manufacturing

  • AI based visual simulation of real-time industrial data for smart manufacturing

  • Human intelligent based visual monitoring in smart manufacturing

  • Evolution of AI in decentralized control of manufacturing enterprise

  • The recent developments in digital manufacturing to enable manufacturing enterprise smarter

  • Impact of smart technologies in the evolution of manufacturing enterprise

 

Timeline:

  • Last Date for Manuscript Submission: 15.11.2021

  • Notification to Authors: 25.12.2021

  • Revised Manuscript Due: 21.02.2022

  • Decision Notification: 25.04.2022

 

Guest Editor Details:

Dr. Ching-Hsien Hsu [Managing Guest Editor],

Fellow of IET

Chair Professor and Dean,

College of Information and Electrical Engineering

Department of Computer Science,

Asia University, Taiwan,

Email: robertchh@asia.edu.tw

GoogleScholar: https://scholar.google.co.in/citations?user=VfjoNfkAAAAJ&hl=en

Dr. Ching-Hsien Hsu is Chair Professor and Dean of the College of Information and Electrical Engineering, Asia University, Taiwan; His research includes high performance computing, cloud computing, parallel and distributed systems, big data analytics, ubiquitous/pervasive computing and intelligence. He has published 200 papers in top journals such as IEEE TPDS, IEEE TSC, ACM TOMM, IEEE TCC , IEEE TETC, IEEE System, IEEE Network, top conference proceedings, and book chapters in these areas. Dr. Hsu is the editor-in-chief of International Journal of Grid and High Performance Computing, and International Journal of Big Data Intelligence; and serving as editorial board for a number of prestigious journals, including IEEE Transactions on Service Computing, IEEE Transactions on Cloud Computing, International Journal of Communication Systems, International Journal of Computational Science, AutoSoft Journal. He has been acting as an author/co-author or an editor/co-editor of 10 books from Elsevier, Springer, IGI Global, World Scientific and McGraw-Hill. Dr. Hsu was awarded six times talent awards from Ministry of Science and Technology, Ministry of Education, and nine times distinguished award for excellence in research from Chung Hua University, Taiwan. Since 2008, he has been serving as executive committee of IEEE Technical Committee of Scalable Computing; IEEE Special Technical Co mmittee Cloud Computing; Taiwan Association of Cloud Computing. Dr. Hsu is a Fellow of the IET (IEE); Vice Chair of IEEE Technical Committee on Cloud Computing (TCCLD), IEEE Technical Committee on Scalable Computing (TCSC), a Senior member of IEEE.

 

 

Dr. Ying Mao 

Fordham University, New York, USA

Email: ymao41@fordham.edu  

GoogleScholar: https://scholar.google.com/citations?user=s_oeuQUAAAAJ&hl=en

Dr. Ying Mao is currently a tenure-track Assistant Professor in the Department of Computer and Information Science at Fordham University in the New York City. He received his Ph.D. in Computer Science from University of Massachusetts Boston in Spring 2016 and obtained his M.S. degree in Electrical Engineering from University at Buffalo in Fall 2011.  Dr. Mao was a Fordham-IBM Research Fellow in 2019 and received Google exploreCSR Award in 2020. His research interests mainly focus on the fields of big data systems, cloud resource management, and deep learning optimization.

 

 

Dr. Celimuge Wu       

The University of Electro-Communications, Japan         

Email: celimuge@uec.ac.jp

GoogleScholar: https://scholar.google.com/citations?user=u6MC8koAAAAJ&hl=en

 

Dr. Celimuge Wu received his Ph.D. degree from The University of Electro- Communications, Japan in 2010. He is currently an associate professor with the Graduate School of Informatics and Engineering, The University of Electro-Communications. His current research interests include IoT, big data, AI, and mobile edge computing. He serves as an associate editor of IEEE Transactions on Network Science and Engineering, IEEE Transactions on Green Communications and Networking, China Communications, IEEE Access, Wireless Networks, IEICE Transactions on Communications, International Journal of Distributed Sensor Networks, and MDPI Sensors. He has been a guest editor of IEEE Transaction on Intelligent Transportation Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Computational Intelligence Magazine, ACM/Springer MONET etc. He is the chair of IEEE TCBD SIG on Big Data with Computational Intelligence and IEEE TCGCC SIG on Green Internet of Vehicles. He received IEEE Computer Society 2019 Best Paper Award Runner-Up. He is a senior member of IEEE.

 

 

Dr. Yingyuan Xiao     

Tianjin University of Technology, China

Email: yyxiao@tjut.edu.cn

GoogleScholar: https://scholar.google.com/citations?user=SAHHQM0AAAAJ&hl=en

Dr. Yingyuan Xiao received the PhD degree in computer science from Huazhong University of Science and Technology, P.R. China, in 2005. He is currently a professor in the School of Computer Science and Engineering, Tianjin University of Technology, P.R. China. He was a visiting scholar in the School of Computing at the National University of Singapore from March 2009 to April 2010. His research interests include personalized recommender systems, advanced databases, and machine learning. He has published more than 120 journal and conference papers in these areas, including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, Future Generation Computer Systems, Engineering Applications of Artificial Intelligence, Information Processing Letters, Journal of Classification, Soft Computing, WWW, DASFAA, ICWS, DEXA, etc. He has served as a program chair of WAIM 2013 International Workshop (International Workshop on Location-based Query Processing in Mobile Environments) and the program committee member for a number of international conferences, including APWeb, APSCC, FSKD, IEEE CloudCom, etc.