WEN Long, born in December 1988, CPC member, Han nationality, is a professor and doctoral supervisor of the School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan).
Contact information: wenlong@cug.edu.cn
Office: Room 343, No. 2 Teaching Building
Research Fields:
He is mainly engaged in the studies of industrial artificial intelligence, deep learning, 3D vision, automatic machine learning and intelligent fault diagnosis, has presided over 6 projects, including the National Natural Science Foundation of China and China Postdoctoral Science Foundation, and participated in more than 10 projects, including the National 2030 “new generation artificial intelligence” project for science and technology innovation, National Key R&D Program, Major Science and Technology Projects of Hubei Province, and equipment pre-research. He has published more than 40 SCI papers in IEEE Transactions and other journals, including 3 ESI hot papers and 5 ESI highly cited papers, with more than 3200 Google Scholar citations and the highest single (first author) citation of more than 1270. He has published one textbook titled “Deep Learning”, won the first prize (ranked fourth) of the Natural Science Award of the Ministry of Education in 2022, published one academic monograph and obtained 5 invention patents.
Concurrent Academic Posts:
Member of the Industrial Big Data and Intelligent Systems Branch of the Chinese Mechanical Engineering Society
Associate editor of IET Collaborative Intelligent Manufacturing (indexed by ESCI)
Young editorial board member of Journal of Dynamics, Monitoring and Diagnostics
Guest editor in corresponding fields of the SCI journals such as Entropy, Sensors, CMES-Computer Modeling in Engineering & Sciences, and Measure and Control
Reviewer of the journals such as IEEE Transactions on Artificial Intelligence, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Industrial Informatics, IEEE Transactions on Reliability, Engineering Applications of Artificial Intelligence and Journal of Intelligent Manufacturing
Main Experiences:
2019/07-present: Professor, China University of Geosciences (Wuhan)
2016/06-2019/07: Postdoctoral, Huazhong University of Science and Technology
2015/01-2016/04: The 723rd Research Institute of CSIC
2010/09-2014/12: Doctor of Engineering, School of Mechanical Science & Engineering, Huazhong University of Science and Technology
2006/09-2010/06: Bachelor of Engineering, School of Mechanical Science & Engineering, Huazhong University of Science and Technology
Main Research Fields:
Admission Information:
Welcome the undergraduates and postgraduates who love scientific research, are interested in industrial artificial intelligence, have good programming ability and experimental ability, and graduated from mechanical and electronic engineering, industrial engineering, automation, computer and other related science and engineering majors. Admissions for master’s and doctoral students.
Scientific Research Programs:
Project of National Key R&D Program, 2019YFB1704603, Collaborative optimization and decision-making technology for production scheduling and material transmission of electronic product based on digital twin, 2019/12-2022/11, participant
Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education (Guizhou University), Intelligent Fault Diagnosis and Life Prediction Method for Complex Equipment Based on Physical Model Constraints, 2023/01-2025/01, PI
Enterprise-sponsored program, Key Technology Development of the Intelligent Equipment for Daily Glass (Visual Inspection), 2021/12-2023/12, PI
Key R&D Plan of Hubei Province, 2020AEA009, Multimodal heterogeneous medical big data construction and intelligent analysis cloud platform system and demonstration application, 2020/07-2023/06, participant
Open Foundation of State Key Laboratory, Health condition monitoring and intelligent diagnosis of high-speed transmission gears, 2023/01-2024/12, PI
National Natural Science Foundation of China, 51805192, Research on the method for predicting the machine fault state in intelligent workshop based on deep learning, 2019/01-2021/12, PI
China Postdoctoral Science Foundation, 2017M622414, Research on the method for predicting the in-orbit space equipment failure based on deep transfer learning, PI
Selected Publications and Achievements:
The first prize (ranked fourth) of the Natural Science Award of the Ministry of Education in 2022
L Wen, XY Li, Deeping Learning, Tsinghua University Press, 2022.
L Wen, XY Li, L Gao, YY Zhang, “A New Convolutional Neural Network based Data-Driven Fault Diagnosis Method,” IEEE Transactions on Industrial Electronics, 65(7): 5990-5998, 2018. (ESI hot paper, ESI highly cited paper, over 1,270 Google Scholar citations)
L Wen, L Gao and XY Li, “A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(1): 136-144, 2019. (ESI hot paper, ESI highly cited paper, over 730 Google Scholar citations)
L Wen, XY Li, L Gao, “A Transfer Convolutional Neural Network for Fault Diagnosis Based on ResNet-50”, Neural Computing and Applications. 32(10), 6111-6124, 2020. (ESI hot paper, ESI highly cited paper, over 300 Google Scholar citations)
L Wen, XY Li, L Gao, “A New Two-level Hierarchical Diagnosis Network based on Convolutional Neural Network,” IEEE Transactions on Instrumentation and Measurement, 69(2): 330-338, 2020. (ESI highly cited paper, over 90 Google Scholar citations)
L Wen, X Xie, XY Li, L Gao, “A New Ensemble Convolutional Neural Network with Diversity Regularization for Fault Diagnosis,” Journal of Manufacturing Systems. 62: 964-971, 2020. (ESI highly cited paper)
Y Wang, Wentao Hu, L Wen and L Gao, “A New Foreground-perception Cycle-consistent Adversarial Network for Surface Defect Detection with Limited High-noise Samples,” IEEE Transactions on Industrial Informatics. (Accept, 2023)
L Wen, Y Wang and X Li, “A New Cycle-consistent Adversarial Networks with Attention Mechanism for Surface Defect Classification with Small Samples,” IEEE Transactions on Industrial Informatics, 18(12): 8988-8998, 2022.
L Wen, L Gao, XY Li and H Li, “A New Genetic Algorithm Based Evolutionary Neural Architecture Search for Image Classification,” Swarm and Evolutionary Computation, 75: 101191, December 2022.
L Wen, Z Chen, A Fuentes-Aznar, “Computerized Design, Simulation of Meshing and Stress Analysis of Non-Generated Double Circular-Arc Helical Gear Drives with Different Combinations of Transverse Pressure Angle,” Mechanism and Machine Theory, 170:104683, Apr 2022.
L Wen, Y Wang, XY Li, “A New Automatic Convolutional Neural Network Based on Deep Reinforcement Learning for Fault Diagnosis,” Frontiers of Mechanical Engineering, 17:17, Jun 2022.
X Ye, L Gao, XY Li, L Wen*, “A New Hyper-Parameter Optimization Method for Machine Learning in Fault Classification,” Applied Intelligence, 2022.
Y Zhang, LR Qiu, YK Zhu, L Wen*, XP Luo X, “A new childhood pneumonia diagnosis method based on fine-grained convolutional neural network,” Computer Modeling in Engineering & Sciences, 133(3), 873-894, 2022.
XY Li, Z Zhang, L Gao, and L Wen*, “A New Semi-Supervised Fault Diagnosis Method via Deep CORAL and Transfer Component Analysis,” IEEE Transactions on Emerging Topics in Computational Intelligence, 6(3), 690-699, 2022.
L Wen, X Li and L Gao, “A New Reinforcement Learning Based Learning Rate Scheduler for Convolutional Neural Network in Fault Classification,” IEEE Transactions on Industrial Electronics, 68(12): 12890-12900, Dec. 2021.
L Wen, L Gao, X Li and B Zeng, “Convolutional Neural Network with Automatic Learning Rate Scheduler for Fault Classification,” IEEE Transactions on Instrumentation and Measurement, 70: 1-12, 2021, Art no. 3509912.
L Wen, N Bo, XC Ye, XY Li, “A Novel Auto-LSTM Based State of Health Estimation Method for Lithium-Ion Batteries,” Journal of Electrochemical Energy Conversion and Storage, 18(3): 030902, 2021.
M Li, T Yan, C Mao, L Wen*, X Zhang, T Huang, “Performance‐enhanced iterative learning control using a model‐free disturbance observer,” IET Control Theory & Applications, 15(7), 978-988, 2021.
L Wen, XC Ye, L. Gao, “A New Automatic Machine Learning based Hyperparameter Optimization for Workpiece Quality Prediction,” Measurement and Control, 53(7-8): 1088-109, 2020.
L Wen, Y Dong and L Gao, “A New Ensemble Residual Convolutional Neural Network for Remaining Useful Life Estimation”, Mathematical Biosciences and Engineering, 16(2): 862-880, 2019.
L Wen, L Gao, Y Dong and Z Zhu, “A Negative Correlation Ensemble Transfer Learning Method for Fault Diagnosis based on Convolutional Neural Network,” Mathematical Biosciences and Engineering, 16(5):3311-3330, 2019.
Liang Gao, Xinyu Li, Long Wen, Sequencing and Scheduling: An Intelligent Algorithm for Process Planning and Shop Scheduling, Tsinghua University Press (ISBN: 978-7-302-51964-5, The 13th Five-year Plan National Key Book, Series of Sequencing and Scheduling)
XY Li, SC Cao, L Gao, L Wen*, “A threshold-control generative adversarial network method for intelligent fault diagnosis,” Complex System Modeling and Simulation, 1(1), 55-64, 2021.
Supervised Student Outcomes:
WANG You, master, 2022 National Scholarship for Postgraduates.
SU Shaoquan, master, Excellent Paper at the 5th Academic Conference on Big Data Driven Smart Manufacturing in 2022.
Y Wang (master), Wentao Hu, L Wen and L Gao, “A New Foreground-perception Cycle-consistent Adversarial Network for Surface Defect Detection with Limited High-noise Samples,” IEEE Transactions on Industrial Informatics. (Accept, CAS I)
L Wen, Y Wang (master) and X Li, “A New Cycle-consistent Adversarial Networks with Attention Mechanism for Surface Defect Classification with Small Samples,” IEEE Transactions on Industrial Informatics, 18(12): 8988-8998, 2022. (CAS I)
L Wen, Y Wang (master), XY Li, “A New Automatic Convolutional Neural Network Based on Deep Reinforcement Learning for Fault Diagnosis,” Frontiers of Mechanical Engineering, 17:17, Jun 2022.
X Ye (master), L Gao, XY Li, L Wen, “A New Hyper-Parameter Optimization Method for Machine Learning in Fault Classification,” Applied Intelligence, 2022. (CAS II)
Y Zhang (master), LR Qiu, YK Zhu, L Wen, XP Luo X, “A new childhood pneumonia diagnosis method based on fine-grained convolutional neural network,” Computer Modeling in Engineering & Sciences, 133(3), 873-894, 2022.