Workshops

Workshop1 : Industrial Big Data and Industrial Digitization

Title 1: Industrial Big Data and Industrial Digitization

Keywords: Industrial Big Data, Intelligent Scheduling, Industrial Digitization

Summary:

Big data-driven smart manufacturing makes factory operation transparent, workshop management accurate, product quality consistent, production line efficiency optimized, and equipment running smoothly, and it promotes collaborative optimization of the whole production life cycle. Big data-driven manufacturing model is the current research hotspot of intelligent manufacturing system. However, the main challenges are: the theoretical system of "big data-driven" scientific research paradigm is not yet complete, the enabling technologies of industrial big data are far from mature, and the industrial application scenarios on the ground are rare. The new generation of artificial intelligence technology will promote the analysis and application of industrial big data, comprehensively portray the system operation law, change the production operation mode, and create a new generation of intelligent manufacturing mode driven by big data.

This workshop aims to show the latest research results in the field of Industrial Big Data and Industrial Digitization. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Assoc. Prof. Hongtao Tang

Wuhan University of Technology, School of Mechanical and Electrical Engineering, China

Hongtao Tang was mainly engaged in digital design, digital manufacturing, intelligent manufacturing, intelligent optimization algorithm and application in manufacturing industry (automobile, hydraulic cylinder, mold, casting and other industries). He has developed the digital design software suitable for automobile, hydraulic cylinder, mold, casting and other industries, and developed the intelligent manufacturing PLM/ERP/MES/SCADA intelligent manufacturing system, which has been applied in many companies in different industries and achieved great economic benefits. He has presided over 2 national natural science general and youth funds, with more than 20 horizontal funds, and the total fund was over 8 million yuan. He has published more than 60 academic papers, including 20 SCI papers, 1 English monograph, 20 soft books, 6 invention patents, and has been reviewers of more than 20 international journals such as IJPR, JCLP, CAIE, JIM, ASOC and COR.

Workshop2 : Cloud-native Edge Computing for Intelligent Manufacturing

Title 1:Cloud Native Edge Computing for Intelligent Manufacturing

Keywords: Cloud-native Computing, Edge Computing, Intelligent Manufacturing

Summary:

With the vigorous development of industrial digitalization, the organic combination of cloud native and edge computing brings more innovation into enterprises, and becomes a key force to promote digitalization and intelligence, which has also attracted extensive attention from academia and industry. Cloud Native Edge Computing is committed to providing enterprises with a unified management and control platform for multidimensional workloads, realizing flexible definition of infrastructure and application platforms, and helping enterprises create greater business value. Recently, many advanced technologies have been explored to enable cloud native edge computing for intelligent manufacturing. These technologies mainly include container technology, edge-cloud collaborative orchestration, microservices, service grid, edge intelligence, etc.

This workshop aims to show the latest research results in the field of cloud native edge computing for intelligent manufacturing. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Prof. Saiqin Long

Jinan University, China

Saiqin Long received the PhD degree in computer applications technology from the South China University of Technology, Guangzhou, China, in 2014. She is currently a professor with the College of Information Science and Technology, Jinan University, China. Her research interests include cloud computing, edge computing, parallel and distributed systems, and Internet of things. She has published 20+ refereed papers in these areas, most of which are published in premium conferences and journals, including IEEE TSC, IEEE TPDS, IEEE TMC, etc. She has been a Publicity Chair or Technical Program Committee Member of many international conferences. She also serves as a reviewer for many international journals, such as IEEE JSAC, JSA, and JPDC. She is a member of Chinese Computer Federation (CCF).

Workshop3: Artificial Intelligence and Machine Learning

Title 1: The Eye of Artificial Intelligence: Machine Vision Technology

Keywords: Artificial Intelligence, Machine Learning, Machine Vision

Summary:

With the development of artificial intelligence technology, human beings have gradually entered the era of artificial intelligence. As the key technology to realize industrial automation and intellectualization, machine vision is becoming the fastest developing branch of artificial intelligence. The significance of machine vision to artificial intelligence is as important as the value of eyes to human beings. With the development of deep learning, three-dimensional vision technology, high-precision imaging technology and machine vision interconnection technology, the performance advantage of machine vision has been further improved, and its application field has also expanded to multi-dimensional, such as industrial detection, medical auxiliary diagnosis, traffic monitoring, bridge detection, etc, which greatly liberating the human labor force, and improving the level of automation and intelligence. Therefore, it has a broad application prospect and brings a new technological revolution to the development of society.

This workshop aims to show the latest research results in the field of enabling artificial intelligence and machine learning technology, especially machine vision technology. We encourage prospective authors to submit related distinguished research papers on the subject of both theoretical approaches and practical case reviews. 

Prof. Hai-Jian Wang

Guilin University of Electronic Technology, China

Hai-Jian Wang received his Ph.D. degree in Mechanical Engineering from the Liaoning Technical University, China, in 2017. From July 2017 to November 2020, he was a Lecturer with the Guilin University of Electronic Technology, China, where he has been an Associate Professor (Master Supervisor) and the Assistant Dean since December 2020. Dr. Wang is currently serving as the contributing editor of Journal of Intelligent Mine. His research interest is machine vision and artificial intelligence. He has authored/co-authored over 40 journal/conference papers. Meanwhile, he presided over a National Natural Science Foundation, a provincial key R & D projects, 3 provincial Natural Science Foundation and four Key Laboratory open Foundation. Moreover, he obtained 16 provincial and ministerial progress awards in science and technology, 9 national invention patents, 22 practical new-type patents, 19 national software copyrights. In particular, he also edited 2 group standards and published a monograph.

Workshop4: Artificial Intelligence in Modern Industry

Title 1: Artificial Intelligence in Modern Industry

Keywords: Artificial Intelligence, Modern Industry, Machine Learning

Summary:

 Predictive Health Management (PHM) is critical to ensure the safe and reliable operation of industrial systems. With the rapid development of Industrial Internet-of-Things (IIoT) and artificial intelligence (AI) technologies, modern industry has drawn great attention to the intelligent fault diagnosis, remaining useful life prediction and condition monitoring methods. Advanced AI methods are suitable to improve the industrial data mining effectiveness, realize the domain adaption problem, and enhance the reliability with respect to various modern industry systems, including deep learning, federated learning, transfer learning, few/zero-shot learning, reinforcement learning, cross-modal information fusion, interpretability and explainable AI, computer vision, semantic reasoning and digital twins in industrial technology, Internet of Things, optimization and control, fuzzy-based system, etc.

This workshop aims to show the latest research results in the field of enabling technologies of Artificial Intelligence methods. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. 

Prof. Ruonan Liu

Tianjin University, China

Ruonan Liu received the B.S., M.S. and PhD degrees from Xi'an Jiaotong University, Xi'an, China, in 2013, 2015 and 2019, respectively. She was a postdoctoral researcher with the School of Computer Science, Carnegie Mellon University in 2019. She currently is an associate professor in the College of Intelligence and Computing, Tianjin University, Tianjin, China. Her research interests include artificial intelligence and machine vision systems. Dr. Liu is currently serving as the Associate Editor of Sustainable Energy Technologies and Assessments, Shock and Vibration, and Frontiers in Artificial Intelligence. She has been a Session Chair or Technical Program Committee Member of many international conferences. She also serves as a reviewer for many international journals, such as IEEE TNNLS, IEEE TIE, IEEE TII et. al.

Workshop5: Digital Twin Technologies for Discrete Manufacturing Scenarios

Title 1: Digital Twin Technologies for Discrete Manufacturing Scenarios

Keywords: Digital Twin, Discrete Manufacturing, Virtual Workshop

Summary:

Digital twin technology provides a new solution for the physical fusion of information in the manufacturing process. The digital twin workshop system technologies synchronizes the virtual model with the real state of the physical equipment by acquiring the dynamic and static information of the actual equipment or products in the physical workshop and mapping them to the corresponding digital twin virtual model. The digital twin workshop system achieves the goal of improving enterprise productivity, reducing production cost and improving product quality. Digital twin technologies for discrete manufacturing scenarios include virtual model construction, information model, self-mapping, data acquisition, big data, information fusion, cloud computing, edge computing, communication and sensing, artificial intelligence, unreal engine, cloud platform, man-machine interactive, computational service, etc.

This workshop aims to show the latest research results in the field of digital twin technologies for discrete manufacturing scenarios. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.  

Prof. Cong-bin Yang

Beijing University of Technology, China

Cong-bin Yang received his Ph.D. degree in School of Mechanical Engineering from Beijing Institute of Technology, Beijing, China, in 2015. He is currently a Full Professor at Faculty of Materials and Manufacturing, Beijing University of Technology, China. Prior to this position, he held lecturer in 2015 and associate professor in 2019. His current research interests include machine tool precision design, digital design and manufacture, advanced manufacturing technology, and automation. He has authored/co-authored over 30 journal/conference papers. Prof. Yang is currently serving as a guest editor of the journal METAL. He also serves as a reviewer for many international journals, such as MECHANISM AND MACHINE THEORY, SURFACE AND COATINGS TECHNOLOGY, and PROCEEDINGS OF THE IMECHE. He has won the first prize of the Beijing Science and Technology Progress Award and the first prize of the Ministry of Education's excellent achievements in scientific research of University.