IntroductionYan-Lin He: Professor, Doctoral Supervisor, Head of the Department of Automation. He received his Ph.D. in Engineering (Control Science and Engineering) in June 2016 and was selected for the Youth Talent 100 Plan of Beijing University of Chemical Technology in May 2019. He currently serves as a director of the Beijing Automation Society, a member of the Applied Committee of the Chinese Automation Society, a member of the Data-Driven Control, Learning, and Optimization Committee of the Chinese Automation Society, a member of the Uncertainty in Artificial Intelligence Committee of the Chinese Association for Artificial Intelligence, a member of the Youth Work Committee of the Chinese Automation Society, and a youth member of the Information Technology Application Committee of the Chinese Chemical Society, among other roles. Over the past five years, he has published more than 70 SCI papers as the first author or corresponding author in authoritative journals such as IEEE Transactions on Industrial Informatics, IEEE Transactions on Control Systems Technology, IEEE Transactions on Instrumentation and Measurement, IEEE Transactions on Reliability, A/B journals recommended by the Chinese Automation Society, IFAC journals, and top international chemical engineering journals. He has also been granted more than 10 patents and applied for over 20 patents. He has led the Youth Project and General Project of the National Natural Science Foundation of China. He received the Youth Talent Support Plan Award from the Beijing Association for Science and Technology for the years 2020-2022 and the Youth Scientific and Technological Innovation Talent Award from the Beijing Automation Society in 2018. He was also listed in the global top 2% of scientists. Research Areas: Computational Intelligence, System Modeling and Optimization, Soft Sensing, Fault Diagnosis, Machine Learning, etc. EducationWork ExperienceSocial PositionSocial ActivitiesResearchResearch Areas: Computational Intelligence, System Modeling and Optimization, Soft Sensing, Fault Diagnosis, Machine Learning, etc. TeachingPostgraduatesFundingVertical ProjectHorizontal ProjectPublications[1]P. -F. Wang, Q. -X. Zhu and Yan-Lin He, Novel Multiscale Trend Decomposition LSTM Based on Feature Selection for Industrial Soft Sensing, in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2024.3444896. [2]Yan-Lin He, L. Chen and Q. -X. Zhu, Quality Regularization-Based Semisupervised Adversarial Transfer Model With Unlabeled Data for Industrial Soft Sensing, in IEEE Transactions on Industrial Informatics, vol. 20, no. 2, pp. 1190-1197, Feb. 2024, doi: 10.1109/TII.2023.3272690. [3]Yan-Lin He, J. -T. Liang, Y. Tian and Q. -X. Zhu, Novel Schur Decomposition Orthogonal Exponential DLPP With Mixture Distance for Fault Diagnosis, in IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5601-5608, April 2024, doi: 10.1109/TII.2023.3336766. [4]Yan-Lin He, P. -F. Wang and Q. -X. Zhu, Improved Bi-LSTM With Distributed Nonlinear Extensions and Parallel Inputs for Soft Sensing, in IEEE Transactions on Industrial Informatics, vol. 20, no. 3, pp. 3748-3755, March 2024, doi: 10.1109/TII.2023.3313631. [5]Yan-Lin He, S. -H. Lv, Q. -X. Zhu and S. Lu, Novel Multiattribute Space-Based LSTM for Industrial Soft Sensor Applications, in IEEE Transactions on Industrial Informatics, vol. 20, no. 3, pp. 4745-4752, March 2024, doi: 10.1109/TII.2023.3316289. [6]L. Chen, Y. Xu, Q. -X. Zhu and Yan-Lin He*, Adaptive Multi-Head Self-Attention Based Supervised VAE for Industrial Soft Sensing With Missing Data, in IEEE Transactions on Automation Science and Engineering, vol. 21, no. 3, pp. 3564-3575, July 2024, doi: 10.1109/TASE.2023.3281336. [7]Yan-Lin He, X. -Y. Li, Y. Xu, Q. -X. Zhu and S. Lu, Novel Distributed GRUs Based on Hybrid Self-Attention Mechanism for Dynamic Soft Sensing, in IEEE Transactions on Automation Science and Engineering, doi: 10.1109/TASE.2023.3309339. [8]Yan-Lin He, L. Chen, Y. Xu, Q. -X. Zhu and S. Lu, A New Distributed Echo State Network Integrated With an Auto-Encoder for Dynamic Soft Sensing, in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-8, 2023, Art no. 2500308, doi: 10.1109/TIM.2022.3228278. [9]He Yan-Lin, K. Li, L. -L. Liang, Y. Xu and Q. -X. Zhu, Novel Discriminant Locality Preserving Projection Integrated With Monte Carlo Sampling for Fault Diagnosis, in IEEE Transactions on Reliability, vol. 72, no. 1, pp. 166-176, March 2023, doi: 10.1109/TR.2021.3115108. [10]He Yan-Lin, Li Kun, Zhang Ning, Xu Yuan, Zhu Qun-Xiong, Fault Diagnosis Using Improved Discrimination Locality Preserving Projections Integrated With Sparse Autoencoder, IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-8, 2021. AwardsPatentHonor RewardAdmissions Information |