Dr. Yan-Lin He

Office Address

Kejilou, room 607, Beisanhuan east road No.15, Choayang district, Beijing, China.

Mailing Address

Mail Box 4, No.15 Beisanhuan East Road, Chaoyang District, Beijing 100029, P.R. China

Email

heyl@mail.buct.edu.cn

Phone


Research Interests

1.Artificial intelligence
2.Data mining
3.Soft sensor
4.Neural networks
5.Machine learning
6.Fault diagnosis

Working/Education Experience

2007.9-20011.7 Bachelor of Computer science and technology, Beijing University of Chemical Technology, Beijing, China.
2011.9-2016.6 Doctor of Control Theory and Control Engineering, Beijing University of Chemical Technology, Beijing, China.
2015.2-2016.2 Joint scholar of University of California, Davis, CA, USA.
2016.7- Associate Professor, College of Information Science & Technology, Beijing University of Chemical Technology. Beijing, China.

Teaching Courses

1.Artificial intelligence and its applications
2.Intelligent manufacturing system
3.System engineering theory
4.Fault detection and diagnosis technology

Projects

1.Participating the key research project supported by National Natural Science Foundation——《Big data and knowledge based energy efficiency assessment and system optimization in complex chemical process》
2.Direct National Natural Science Foundation——《Modeling complex chemical processes based on virtual sample generation and extreme learning machine》

Publications

[1]Energy modeling using an effective latent variable based functional link learning machine ,Energy ,2018,162: 883-891 ,SCI
[2]A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry ,Energy ,2018, 162: 593-602 ,SCI
[3]A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction ,Energy ,2018, 164: 137-146 ,SCI
[4]A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry ,Energy ,2018, 147: 418-427 ,SCI
[5]Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry ,Applied Energy ,2018, 213: 322-333 ,SCI
[6]An improved extreme learning machine integrated with nonlinear principal components and its application to modeling complex chemical processes ,Applied Thermal Engineering ,2018, 130: 745-753 ,SCI
[7]A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries ,Applied Energy ,2017, 197: 405-415 ,SCI
[8]Novel Multidimensional Feature Pattern Classification Method and Its Application to Fault Diagnosis ,Industrial & Engineering Chemistry Research ,2017, 56(31): 8906-8916 ,SCI
[9]Novel Causal Network Modeling Method Integrating Process Knowledge with Modified Transfer Entropy: A Case Study of Complex Chemical Processes ,Industrial & Engineering Chemistry Research ,2017, 56(48): 14282-14289 ,SCI
[10]A Novel Hybrid Method Integrating ICA-PCA With Relevant Vector Machine for Multivariate Process Monitoring ,IEEE Transactions on Control Systems Technology ,2018 (99): 1-8 ,SCI
[11]A PSO based virtual sample generation method for small sample sets: Applications to regression datasets ,Engineering Applications of Artificial Intelligence ,2017, 59: 236-243 ,SCI
[12]A novel robust regression model based on functional link least square (FLLS) and its application to modeling complex chemical processes ,Chemical Engineering Science ,2016, 153: 117-128 ,SCI
[13]A novel nonlinear functional expansion based PLS (FEPLS) and its soft sensor application ,Chemometrics and Intelligent Laboratory Systems ,2017, 161: 108-117 ,SCI
[14]An effective high-quality prediction intervals construction method based on parallel bootstrapped RVM for complex chemical processes ,Chemometrics and Intelligent Laboratory Systems ,2017, 171: 161-169 ,SCI
[15]An improved multi-kernel RVM integrated with CEEMD for high-quality intervals prediction construction and its intelligent modeling application ,Chemometrics and Intelligent Laboratory Systems ,2017, 171: 151-160 ,SCI
[16]A novel ensemble model using PLSR integrated with multiple activation functions based ELM: Applications to soft sensor development ,Chemometrics and Intelligent Laboratory Systems ,Accepted ,SCI
[17]基于特征提取的函数连接神经网络研究及其化工过程建模应用 ,化工学报 ,2018, 69(3): 907-912 ,EI
[18]基于FEEMD-AE与反馈极限学习机组合模型预测研究与应用 ,化工学报 ,2017, 69(3): 1064-1070 ,EI
[19]基于改进 ELM 的递归最小二乘时序差分强化学习算法及其应用 ,化工学报 ,2016, 68(3): 916-924 ,EI
[20]Research and application of KICA-AROMF based fault diagnosis ,2017 6th International Symposium on Advanced Control of Industrial Processes ,2017 ,EI
[21]A bootstrap based virtual sample generation method for improving the accuracy of modeling complex chemical processes using small datasets ,2017 6th Data Driven Control and Learning Systems ,2017 ,EI
[22]A novel EFSM-based ELM double-faults identification approach and its application to non-linear processes ,2017 11th Asian Control Conference ,2017 ,SCI
[23]An enhanced extreme learning machine with a double parallel structure and its application to modeling complex chemical processes ,2017 11th Asian Control Conference ,2017,SCI
[24]A novel intelligent faults diagnosis approach based on Ada-REIELM and its application to complex chemical processes ,2018 Tenth International Conference on Advanced Computational Intelligence ,2018 ,EI
[25]Research and application of causal network modeling based on process knowledge and modified transfer entropy ,IFAC-Papers On-Line ,2018: 303-308 ,EI
[26]A novel nonlinear virtual sample generation approach integrating extreme learning machine with noise injection for enhancing energy modeling and analysis on small data: Application to petrochemical industries ,2018 5th International Conference on Control, Decision and Information Technologies ,2018 ,EI
[27]Soft-sensing development using Adaptive PSO Optimization based Multi-Kernel ELM with Error Feedback ,2018 7th Data Driven Control and Learning Systems ,2018 ,EI
[28]Energy efficiency analysis using a novel VSG based DEA : A case study of ethylene production plants ,Chinese Automation Congress ,2018 ,EI
[29]Research on public opinion warning based on analytic hierarchy process integrated back propagation neural network ,Chinese Automation Congress ,2017 ,EI
[30]Early warning modeling and application based on analytic hierarchy process integrated extreme learning machine ,Intelligent Systems Conference ,2017 ,EI
[31]PID control loop performance assessment and diagnosis based on DEA-related MCDA ,2017 6th International Symposium on Advanced Control of Industrial Processes ,2017 ,EI