Dr. Yan-Lin He

Time：2024-12-26Views：29

Dr. Yan-Lin He

Professor


Email: heyl@mail.buct.edu.cn

Background                                                                  

Yan-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. 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 was also listed in the global top 2% of scientists.

Areas of Research of Expertise

1. Computational Intelligence

Computational Intelligence uses natural-inspired methods to solve complex problems. Key techniques include:

Neural Networks: Models inspired by the brain for pattern recognition.

2. System Modeling and Optimization

System Modeling and Optimization create models of real-world systems and find the best solutions. Key techniques include:

Modeling: Describing and analyzing systems.

Optimization: Finding the best solution using mathematical methods (e.g., linear programming).

Multi-objective Optimization: Optimizing several goals at once.

3. Soft Sensing

Soft Sensing estimates variables that are hard to measure directly using available data. Key methods include:

Data Fusion: Combining data from multiple sensors for better accuracy.

Mathematical Modeling: Using models like neural networks to estimate unmeasurable variables.

4. Fault Diagnosis

Fault Diagnosis detects and locates problems in a system. Key methods include:

Condition Monitoring: Continuously monitoring system states with sensors.

Model-based Diagnosis: Comparing real data with expected values to detect faults.

Data-driven Diagnosis: Using machine learning to identify faults based on data patterns.

5. Machine Learning

Machine Learning enables systems to learn from data and make decisions. Key types include:

Supervised Learning: Learning from labeled data for prediction (e.g., decision trees).

Unsupervised Learning: Finding patterns in unlabeled data (e.g., clustering).

Deep Learning: Using complex neural networks for tasks like image and speech recognition.

These areas are often combined to solve complex problems, especially in automation and intelligent systems.

Ten Representative Publications                                           

  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.