Keynote Speech 01——Professor Shervin Shirmohammadi
Quantifying and Communicating Uncertainty in Machine Learning-Based Measurement
Prof. Shervin Shirmohammadi
University of Ottawa, Canada
Abstract
Like any science and engineering field, Instrumentation and Measurement (I&M) is currently experiencing the impact of the recent rise of Applied AI and in particular Machine Learning (ML). In fact the relationship between I&M and ML has reached new levels: I&M is used to collect data, which is used to train an ML model, which is then used in a measurement system. Uncertainty is accumulated at every stage, and quantifying it is crucial. But I&M and ML use terminology that sometimes sound or look similar, though they might only have a marginal relationship or even be false friends. Therefore, understanding the terminology used by both communities is of crucial importance to understand the influences of ML and I&M in each other.
In this talk, we will give an overview of ML’s contribution to measurement error, and how to avoid confusion with the said terminology in order to better understand the application of ML in I&M. We then use that understanding and terminology to show how to quantify the uncertainty introduced by ML regression and classification in a measurement system, and we go over some specific examples in measurement.
Biographical Sketch
Shervin Shirmohammadi received his Ph.D. in Electrical Engineering in 2000 from the University of Ottawa, Canada, and after spending 3 years in the industry as a senior architect and project manager, joined as Assistant Professor the same University, where since 2012 he has been a Full Professor with the School of Electrical Engineering and Computer Science. He is the Director of the DISCOVER Lab, doing research in Applied AI and measurement methods for multimedia systems and networks. The results of his research, funded by more than $27 million from public and private sectors, include over 400 publications, 3 Best Paper awards, over 70 researchers trained at the postdoctoral, PhD, and Master’s levels, 30 patents and technology transfers to the private sector, and a number of awards. He is the founding Editor-in-Chief of the IEEE Open Journal of Instrumentation and Measurement, was the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement from 2017 to 2021, the Associate Editor-in-Chief of IEEE Instrumentation and Measurement Magazine in 2014 and 2015, and is currently on the latter’s editorial board.
He has been an IEEE Instrumentation and Measurement Society (IMS) AdCom member since 2014, served as the Vice President of its Membership Development Committee from 2015 to 2017, and was a member of the IEEE I2MTC Board of Directors from 2014 to 2016.
Dr. Shirmohammadi is an IEEE Fellow for contributions to multimedia systems and network measurements, winner of the 2019 George S. Glinski Award for Excellence in Research, winner of the 2021 IEEE IMS Distinguished Service Award, a Senior Member of the ACM, a University of Ottawa Gold Medalist, and a licensed Professional Engineer in Ontario.
Keynote Speech 02——Professor Mingjian Zuo
Machine Learning and its Applications in Prognosis and Health Management
Prof. Mingjian Zuo
University of Alberta, Canada
Abstract
Machine learning has great potential for reliability assurance through prognosis and health management (PHM) of engineering assets. It has been attracting attention from both academic and industrial sectors. Recent developments of machine learning, especially the evolving branches of deep learning, transfer learning, and reinforcement learning, bring new opportunities for effective PHM. This presentation will first introduce some general knowledge of machine learning and its applications in various disciplines. We will then introduce some fundamentals of deep learning, with emphasis on artificial neural networks. Our recent research work on developing machine learning techniques for PHM will be described. Finally, applications of PHM methodology to industrial settings will be covered.
Biographical Sketch
Dr. Mingjian Zuo received the Bachelor of Science degree in Agricultural Engineering in 1982 from Shandong Institute of Technology, China, and the Master of Science degree in 1986 and the Ph.D. degree in 1989 both in Industrial Engineering from Iowa State University, Ames, Iowa, U.S.A. He is Founder and CEO of Mingserve Technology Co. Ltd., China, Guest Professor of the University of Electronic Science and Technology of China and Full Professor of the University of Alberta, Canada. His research interests include system reliability analysis, maintenance modeling and optimization, signal processing, fault diagnosis, machine learning, and prognosis & health management. He served as Department Editor of IISE Transactions, Associate Editor of IEEE Transactions on Reliability, Associate Editor of Journal of Risk and Reliability, Associate Editor of International Journal of Quality, Reliability and Safety Engineering, Regional Editor of International Journal of Strategic Engineering Asset Management, and Editorial Board Member of Reliability Engineering and System Safety, Journal of Traffic and Transportation Engineering, and International Journal of Performability Engineering. He is Fellow of the Canadian Academy of Engineering, Fellow of the Institute of Industrial and Systems Engineers (IISE), Fellow of the Engineering Institute of Canada (EIC), Founding Fellow of the International Society of Engineering Asset Management (ISEAM), and Senior Member of IEEE.
To Be Continued ......
15th August 2022 25th September 2022 - Manuscript Submission
15th October 2022 - Acceptance Notification
1st November 2022 - Camera Ready Submission
1st November 2022 10th November 2022 - Early Bird Registration
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