The Early Career Advisory Board (ECAB) of IEEE/CAA Journal of Automatica Sinica (JAS) is pleased to present the seventh Symposium Series in 2022 on Innovation and Development of Data-Driven Methods in Industrial Process Monitoring, to be held online on August 25, 2022.

The Symposium Series is technically sponsored by the IEEE/CAA JAS and aims to provide a research venue for researchers, students and control engineers to exchange ideas and discuss the technical trends and challenges in the area of automation science and engineering. The Series brings together a set of one day or half day symposia at a common site, currently being planned as an online event given the pandemic situation, but IEEE/CAA JAS will be monitoring the pandemic situation and will announce any modifications to this plan. The Series will run multiple times yearly and different symposium topics will be solicited from the current ECAB members.

The seventh Symposium Series is devoted to recent advances in industrial process monitoring, discussing the application and innovation of data-driven methods in industry, hosted by three ECAB members Prof. Youqing Wang, Prof. Ke Gu, and Dr. Xiaofeng Yuan.

Accepted papers will be distributed as widely as possible over the subfields of process monitoring. Most excitingly, all accepted papers will be recommended to and published in the IEEE/CAA JAS as Letter when passing peer review.

Call for Papers

Topics of contributing papers include, but are not limited to

● Summary and innovation of monitoring methods for dynamic, nonlinear, non-stationary and other characteristics

● Recursive and adaptive methods in industrial process monitoring

● Data-driven fault diagnosis, health maintenance and performance evaluation

● Statistical learning, machine learning, data mining and practical applications in automation field

● Data-driven control for practical complex processes

● Neural networks and deep learning approaches for process monitoring

● Data-driven industrial process intelligence optimization and decision-making

● Big data in industrial processes and its applications in modelling and control

● Data-driven soft sensors for quality prediction, monitoring and control

● Explainable machine learning and deep learning approaches in process monitoring and fault diagnosis

● Few-shot learning, meta learning and self-supervised learning for Industrial process monitoring

● Image quality assessment, enhancement and recognition in process monitoring and fault diagnosis

Important Dates

Contributed papers up to 3 to 4 pages  (double column with font size being 9; the template can be found at https://www.ieee-jas.net/news/Information%20for%20Authors.htm）should be submitted by July 25 2022 via online submission and review system: https://mc03.manuscriptcentral.com/ieee-jas

The submission should be noted on the first page with “Symposium Series 7”.

All the authors will receive the notification of acceptance/rejection by August 10, 2022.

Organizing Committee

General Co-Chairs

Youqing Wang (Beijing University of Chemical Technology, China), IEEE/CAA JAS ECAB Member

Ke Gu (Beijing University of Technology, China), IEEE/CAA JAS ECAB Member

Xiaofeng Yuan (Central South University, China), IEEE/CAA JAS ECAB Member

Sponsors

College of Information Science and Technology, Beijing University of Chemical Technology, China

School of Artificial Intelligence and Automation, Beijing University of Technology, China

School of Automation, Central South University, China

Technical Sponsor

IEEE/CAA Journal of Automatica Sinica