Speakers

Biography: Distinguished Professor at Paris-Saclay University (Univ. Paris 11) France, Research Director of French National Research Center (CNRS). Pioneer and chairman of International Conference on Maximum Entropy and Bayesian Approaches (50 years); Top talent of Zhejiang Invited foreign-experts; Laureate of the Westlake Prize of Zhejiang for significate contribution of foreign experts; Laureate of the International scientific cooperation of Zhejiang.

He received the master and two PhD degrees from University of Paris 11 respectively in 1980, 1994 and 1998 respectively. His proposed Gaussian Porter image segmentation algorithm, fast Bayesian variational method, hyperparameter Bayesian inference method, etc., have been highly recognized by the international academic and industrial communities, and have been widely applied in the fields of non-destructive detection, mechanical fault diagnosis, medical image recognition, and industrial big data analysis. His methods and inventions have been directly adopted by Airbus, Thales, Dassault, CEA, etc. He has presided 31 projects (with 10 million euros funding), published over 300 papers, 2 monographs and 12 textbooks; He supervised 21 doctoral and 31 master's students.

Title: Bayesian Physics-Informed Neural Networks for Linear Inverse Problems: Industry Application to Infrared Image Processing

Abstract: Inverse problems are present across scientific and engineering domains, where we seek to infer hidden parameters or fields from indirect, noisy observations. Classical methods, such as regularization and Bayesian inference, provide theoretical foundations for addressing ill-posedness, but face limitations in high-dimensional or computationally expensive problems. Physics-Informed Neural Networks (PINNs) offer a promising data-driven approach by embedding physical laws within neural networks. This paper introduces a Bayesian PINN (BPINN) framework, extending the classical PINNs, accounting for modeling and measurement errors via priors and giving the possibility to quantify uncertainties in inverse problems. To show the performances of the proposed approach, we consider the inverse problems of infrared image processing, deconvolution and super-resolution, and show the results on some simulation and real industrial application cases.

Biography: 

Dr. Ning Chu, IEEE senior member, and vice-chairman of the Zhejiang Acoustic Society; Chief researcher with Zhejiang Shangfeng Special Blower Company Ltd., China. He received B.S. degree in information engineering from the National University of Defense Technology, Changsha, China in 2006, and the M.S. and Ph.D. in automatic, signal image processing from the Paris-Saclay University, France in 2010 and 2014, respectively, and the postdoc at EPFL Switzerland. His research interests are acoustic source imaging, infrared detection and Bayesian inference in machine fault prognosis. He has invented “Industrial Lung System” for green ventilation equipment, reported by CCTV2 in 2022, selected into the list of Zhejiang industrial Internet platform, and best cases of China intelligent manufacturing. In recent 5 years, he published more than 24 top journal papers and own 31 China invention patents, presided 3 national and provincial research projects.

Title: Multimodal sensing techniques and applications in wind turbines and ventilation

Abstract: In this presentation, we will show some challenging problem in modern manufacture of high-end equipment during its life-cycle health management. Then multimodal sensing techniques will be presented in details,  and various applications will be give for wind turbines and ventilation system. Finally, we would like to inspire the audience and students to think deeply the bottleneck technology in AI algorithm, sensors network, diagnosis and prognosis for industry equipment digitization.

Biography: 

Professor Yu Liang has published over 200 papers in the field of "Acoustic Sensing and Intelligent Information Processing for Mechanical Equipment," including more than 80 first/corresponding-author papers in internationally renowned SCI-indexed journals such as Mechanical Systems and Signal Processing (MSSP), Journal of Sound and Vibration (JSV), and IEEE Transactions on Instrumentation and Measurement (TIM). He has led three National Natural Science Foundation of China (NSFC) projects, over 20 provincial and ministerial-level projects, and industry collaboration initiatives. His accolades include two provincial/ministerial-level awards, seven conference Best Paper Awards, and editorship of two special issue proceedings. He has contributed to two NSFC Key Projects, two sub-projects under the National 973 Program, and other major national research initiatives.

Professor Yu serves on the organizing committees of four international conferences (ICICSP, MEAE, ICMIE, MAES, spanning nine sessions) and is the General Chair of the 2024 International Conference on Mechanical Engineering and Aerospace Engineering. He has delivered two plenary talks at international conferences and serves as a reviewer for 34 domestic and international journals and a guest editor for four special issues. Dedicated to advancing research in "Acoustic Sensing and Intelligent Information Processing for Mechanical Equipment," he has developed cutting-edge methodologies in acoustic measurement, fault diagnosis, and intelligent signal processing for mechanical systems, establishing a series of influential theoretical frameworks and technical approaches.