Speakers

Prof. Jianguo Ma

IEEE Fellow

Zhongyuan University of 

Technology, China

Biography: Jianguo Ma was admitted to Lanzhou University in 1977 and graduated with a bachelor's degree in Radio Physics in 1982. In 1996, he obtained his Ph.D. in Electronic Engineering from the University of Duisburg-Essen in Germany and completed his postdoctoral research in Canada. From 1997 to 2005, he served as an Assistant Professor and Associate Professor at Nanyang Technological University (NTU) in Singapore. Additionally, he assumed the role of Director at NTU's Integrated Circuit Research Center during this period. In early 2006, he returned to China to pursue dedicated research endeavors. To date, he has published 237 SCI journal articles, two English books, and two Chinese monographs in his research field. Moreover, he is the head for several national science and technology projects, including the 03 project of the National Major Science and Technology Projects, key projects of the National Natural Science Foundation, National Key Research and Development Programs (formerly 973 programs), and international cooperation projects of the Ministry of Science and Technology. Additionally, He has served as an editor for the Proceedings of the IEEE (2013.1-2018.12) and was the first Asian scholar to serve as the Associate Editor of IEEE Microwave and Wireless Components Letters (2003.1-2005.12). Since 2018, he has also served as the Associate Editor of IEEE Microwave Magazine. He is the first scholar from outside the United States and Europe to serve as the Editor-in-Chief of IEEE Transactions on Microwave Theory and Techniques in nearly 70 years since its founding.

Professor Ma's work “The Design Method of High-bandwidth and Miniaturized Microwave Communication Integrated Circuits”, where he serves as the first author, won the first prize of the Technical Invention Award of the China Institute of Communications. Since 2019, he has obtained 53 international patents and 80 Chinese invention patents as the first inventor. Furthermore, Professor Ma has held pivotal roles in various academic institutions. He has served as the dean of the School of Electronic and Information Engineering at Tianjin University, the vice dean of the College of Integrated Circuits at Zhejiang University, and the director of the Intelligent Chip and Device Research Center (Research Center for Novel Computing Sensing and Intelligent Processing) at the Zhejiang Lab. In April 2024, he joined Henan University of Electronic Science and Technology (Zhongyuan University of Technology).

Speech Title: Internet of things and intelligent manufacturing

Abstract: My definition of  the Internet of Things (IoT) is "making othings talk." IoT has numerous applications, yet none are as crucial and urgent as its application in the manufacturing sector. Hence, IoT has always been more popular abroad than domestic unfortunately. This is because manufacturing is the cornerstone of a nation's economic development and sustainable existence, and industrial processes urgently need to 'talk', the manufacturing process of products urgently needs to 'talk', and the entire lifecycle status of products urgently needs to 'talk'! These aspects form the core of Industry 4.0, or industrial intellectualization and smart manufacturing. Moreover, the popularity of smart manufacturing abroad far exceeds that domestically. This report uses facts to explore whether our country still qualifies as a 'manufacturing powerhouse'. As a side note: it points out the pitifully low number of scientific papers published in our country, highlighting the need for a massive increase in publication.

Prof. Tianhong Pan

IEEE Senior Member

Anhui University, China

Biography: His research interests include multiple model approach and its application, machine learning, virtual metrology, predictive control, and run-to-run control theory and practice.

Speech Title: Run-to-Run Control for Semiconducotr Manufacturing Process

Abstract: The semiconductor wafer manufacturing process is a typical batch process with multiple variables and a wide range of operating conditions. With the development of manufacturing technology, higher requirements have been put forward for the utilization of equipment. Therefore, the same machine can simultaneously fabricate wafers with different specifications, and products with the same specification may appear on different machines, i.e., mixed product processes. The research on this process control problem has also received a large number of scholars' attention. In the past three decades, the Run-to-Run (R2R) control theory has made significant development. This report summarizes and analyzes several important strategies such as JADE, ANOVA, G&P-EWMA, d-EWMA collaborative design, ESO-EWMA, and DRL-EWMA from the perspective of optimized control, disturbance state estimation and measurement delay compensation in high-mixed product processes.

Prof. Lei Xie

IEEE Senior Member

Zhejiang University, China

Biography: Prof. Lei Xie obtained a B.S. degree in 2000 and a Ph.D. in 2005 from Zhejiang University, P.R. China. From 2005 to 2006, he served as a postdoctoral researcher at the Berlin University of Technology. He was an Assistant Professor from 2005 to 2008 and currently holds the position of full Professor at the Department of Control Science and Engineering, Zhejiang University. His research efforts have resulted in over 90 articles published in internationally renowned journals and conferences, 3 book chapters, and a book in the field of applied multivariate statistics and modeling. His research interests are centered on the interdisciplinary domain of statistics and system control theory.

Speech Title: Collaborative Control And Optimization Of Industrial Predictive Control Systems

Abstract: This report briefly reviews the research history and current situation of collaborative control and optimization of industrial predictive control systems. Firstly, the report excavates the association knowledge and dynamic characteristics in the complex process system, and forms a data-driven causal analysis coupling modeling method, which helps establish a good design and reasonable equipment association relationship, and solves the problems of disjointed, insufficient connection and low computational efficiency of process design and control optimization link models in the traditional optimization integration problem. Subsequently, from the perspective of actual production situation, considering the problem of decision-making status level between the performance indicators of each equipment production process, a reasonable online level adjustment strategy is designed based on the retreat mechanism to reasonably coordinate and balance the parameter indicators of each subsystem. In addition, considering the complex problem of steady-state coordination of multiple subsystems when global constraints exist, a collaborative control strategy based on the decomposition of artificial parameters and singular values is proposed, which can realize the collaborative control under the condition of multi-device and multi-level global constraints while ensuring the feasibility of terminal sets. Finally, from the perspective of optimization strategy, the uncertainty factors affecting process design and control are systematically analyzed, and a real-time optimization and control integration method based on extreme value search is proposed to compensate for the difference in optimization control cycle, which improves the flexibility and credibility of integrated optimization design decisions in an uncertain environment.

Prof. Jinfeng Liu

University of Alberta, Canada

Biography: Professor Jinfeng Liu is with the Department of Chemical and Materials Engineering at the University of Alberta. He received his PhD in Chemical Engineering from the University of California, Los Angeles (UCLA), and both his MSc and BSc from Zhejiang University. Prof. Liu’s research interests lie in process systems and control engineering. He has co-authored 3 books, published over 200 journal and conference papers, and edited a few special issues. Prof. Liu currently serves as the editor-in-chief for IChemE journal Digital Chemical Engineering. He also holds roles as an associate editor for several other journals, including IFAC Journal of Process Control, Control Engineering Practice, International Journal of Systems Science and MDPI journal Mathematics. 

Speech Title: Towards agricultural water sustainability through closed-loop irrigation

Abstract: The challenge of ensuring water and food security has been consistently recognized as one of the most significant global risks by the World Economic Forum. Agriculture accounts for approximately 70% of the world’s freshwater consumption. However, the average irrigation water use efficiency is low, estimated to be around 50% to 60%. This inefficiency is primarily due to the open-loop nature of the current irrigation strategy, which does not consider the real-time field conditions such as soil moisture when determining the amount and timing of irrigation. From a systems engineering perspective, improving water use efficiency requires closing the decision-support loop and forming a closed-loop system for precision irrigation. This involves using sensors to collect real-time field information, which is then fed back to a control algorithm to calculate the best irrigation commands. Closed-loop irrigation has been shown to bring significant water conservation and economic benefits in greenhouses and nurseries under controlled environments. However, it is unclear whether closed-loop irrigation can achieve the same benefits  in large-scale irrigated agriculture, which is subject to significant uncertainties such as weather and sensing conditions. 

To implement a closed-loop irrigation system, various sensing instruments such as soil moisture sensors, evapotranspiration gauges, and thermal cameras are used to collect real-time field information on soil moisture, temperature, and other factors. This information is then fused together to estimate the entire field’s conditions, which is fed back to a control system. The control system calculates the best irrigation commands for the next few hours or day based on a field model,  estimated field conditions, local weather forecast and other pre-specified irrigation requirements. However, implementing such a system for large-scale agricultural fields presents significant challenges due to nonlinearity, uncertainties, and the large size of fields. We have been working towards realizing a closed-loop irrigation system for large-scale fields by developing technologies in field modeling, soil moisture estimation, irrigation scheduling, and control. In this talk, I will share our achievements and lessons learned on this journey.

Prof. Xiaofeng Yuan

Central South University, China

Biography: Xiaofeng Yuan received the B.Eng. and Ph.D. degrees from the Department of Control Science and Engineering, Zhejiang University, Hangzhou, China, in 2011 and 2016, respectively. From November 2014 to May 2015, he was a Visiting Scholar with the Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada. He is currently a Professor with the School of Automation, Central South University. His research interests include deep learning and artificial intelligence, industrial internet of things, process data analysis, and so on. He is a member of IFAC Industry Commettee, IFAC TC 1.1, etc. He also serves as an Associate Editor for IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Journal, etc.

Speech Title: Industrial big data analysis and system under the edge-cloud collaborative framework

Abstract: The Industrial Internet of things (IIoT) is significant for realizing industrial intelligence. It is a core component of IIoT to build big data intelligent analysis technologies and platforms that enable interconnected data and efficient business collaboration. Currently, big data analysis and applications for the IIoT face challenges such as insufficient data analysis capabilities and high latency in edge-cloud collaboration. This report will introduce the recent works of big data analysis and systems under the edge-cloud collaborative framework by our research group. It will mainly cover the construction of an integrated edge-cloud hardware platform based on edge development kits and cloud servers, a high-efficiency edge-cloud collaborative framework with triple effectiveness in data/parameters/inference, deep learning algorithms for complex industrial data processing, and an intelligent analysis software platform for edge-cloud industrial big data.

Prof. Pietro S. Oliveto

Southern University of Science and Technology, China

Biography: Pietro Oliveto holds a Laurea degree in computer science from the University of Catania, Italy, awarded in 2005, and a PhD degree from the University of Birmingham, UK, conferred in 2009. His academic journey has been marked by several prestigious fellowships, including the EPSRC PhD+ Fellowship (2009-2010) and EPSRC Postdoctoral Fellowship (2010-2013) at the University of Birmingham, followed by the Vice-Chancellor's Fellowship (2013-2016) and EPSRC Early Career Fellowship (2015-2020) at the University of Sheffield. Prior to joining SUSTech, he served as the Chair in Algorithms at the Department of Computer Science, University of Sheffield.

Professor Oliveto's primary research focus is on the performance analysis, particularly the time complexity, of bio-inspired computation techniques. These techniques include evolutionary algorithms, genetic programming, artificial immune systems, hyper-heuristics, and algorithm configuration. Currently, he is spearheading the establishment of a Theory of Artificial Intelligence Lab at SUSTech.

His contributions to the academic community extend beyond research, as he has guest-edited special issues for journals such as Computer Science and Technology, Evolutionary Computation, Theoretical Computer Science, IEEE Transactions on Evolutionary Computation, and Algorithmica. He has also co-chaired the IEEE symposium on Foundations of Computational Intelligence (FOCI) from 2015 to 2021 and served as co-program Chair for the ACM Conference on Foundations of Genetic Algorithms (FOGA 2021). Additionally, he has held the position of Theory Track co-chair at GECCO 2022 and GECCO 2023. Professor Oliveto is a member of the Steering Committee of the annual workshop on Theory of Randomized Search Heuristics (ThRaSH), served as the Leader of the Benchmarking Working Group for the COST Action ImAppNIO, is a member of the EPSRC Peer Review College, and serves as an Associate Editor for IEEE Transactions on Evolutionary Computation.

Speech Title: Computational Complexity Analysis of Sexual Evolution for the Design of Better General Purpose Algorithms for AI

Abstract: Large classes of the general-purpose optimisation algorithms at the heart of modern artificial intelligence and machine learning technologies are inspired by models of Darwinian evolution. In this talk we show how the foundational computational complexity analysis of such algorithms leads to an understanding of their behaviour and performance. Such understanding in turn allows informed decisions on how to set their many parameters and how to improve the algorithms to allow for the obtainment of better solutions in shorter time. We provide two concrete examples of how such analyses can lead to counter intuitive insights into how to design sexual evolution inspired algorithms (using populations and recombination) and how to set their parameters such that they can considerably outperform their single trajectory and mutation only (asexual) counterparts at hillclimbing unimodal functions, and at escaping from local optima. We conclude the talk by presenting experimental results that confirm the superiority of the designed algorithms that was proven for benchmark functions with significant structures, for classical combinatorial optimisation problems with practical applications.