Yuquan Meng received his B.S. in Mechanical Engineering with Guo Moruo Scholarship from the University of Science and Technology of China (USTC) in 2017, and his Ph.D in Mechanical Engineering from University of Illinois of Urbana-Champaign (UIUC) in 2023. Starting from 2024, he becomes an assistant professor at Department of Systems Engineering, City University of Hong Kong. His primary research interests lie in data-efficient learning, physics-informed machine learning, in-situ process monitoring and smart decision-making, manufacturing systems control and automation and 3D metrology.
Selected Publications
- Meng, Yuquan. , Dong, Zhiqiao. , Lu, Kuanchieh. , Li, Shichen. & Shao, Chenhui. (2024). Meta-Learning-Based Domain Generalization for Cost-Effective Tool Condition Monitoring in Ultrasonic Metal Welding. IEEE Transactions on Industrial Informatics. In press.
- Meng, Yuquan. , Lu, Kuanchieh. , Dong, Zhiqiao. , Li, Shichen. & Shao, Chenhui. (2023). Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations. Journal of Manufacturing Processes. 107. 345 - 355.
- Wu, Yulun.* , Meng, Yuquan*. & Shao, Chenhui. (2022). End-to-end online quality prediction for ultrasonic metal welding using sensor fusion and deep learning. Journal of Manufacturing Processes. 83. 685 - 694.
- Qu, Yingjie.* , Meng, Yuquan*. , Fan, Hua. & Xu, Ronald. (2022). Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia. Infrared Physics & Technology. 123. 104201.
- Meng, Yuquan. & Shao, Chenhui. (2022). Physics-informed ensemble learning for online joint strength prediction in ultrasonic metal welding. Mechanical Systems and Signal Processing. 181. 109473.
- Meng, Yuquan. , Rajagopal, Manjunath. , Kuntumalla, Gowtham. , Toro, Ricardo. & et(2020). Multi-objective optimization of peel and shear strengths in ultrasonic metal welding using machine learning-based response surface methodology. Mathematical Biosciences and Engineering. 17(6). 7411 - 7427.
- Chen, Siyuan.* , Meng, Yuquan.*, Tang, Haichuan. , Tian, Yin. , He, Niao. & Shao, Chenhui. (2020). Robust deep learning-based diagnosis of mixed faults in rotating machinery. IEEE/ASME Transactions on Mechatronics. 25(5). 2167 - 2176.
PhD Openings
- I am currently recruiting highly-motivated PhD students and RA in our lab at CityU HK. We will be working on developing physics-informed machine learning (PIML) models and intelligent monitoring/control of manufacturing systems using deep learning. Students from engineering, physics or related fields are welcome to apply. If interested, please send your CV (including GPA and average scores), transcript, and highlighted publications to me at (yuqumeng@cityu.edu.hk). Exceptional candidates are also encouraged to apply for Hong Kong PhD Fellowship Scheme (HKPFS).
Last update date :
14 Sep 2024
