文龙

文龙，bat365在线平台登录（武汉）机械与电子信息院，教授、博导，1988年12月生，中共党员，汉族。

联系方式：wenlong@cug.edu.cn

办公室：教二楼343

研究方向：

主要从事工业人工智能、深度学习、3D视觉、自动机器学习、智能故障诊断方面的研究。主持国家自然科学基金、中国博士后科学基金等项目6项，参与国家科技创新2030“新一代人工智能”、国家重点研发计划、湖北省科技重大专项、装备预研等项目10余项。在IEEE Transactions等期刊上发表SCI论文40余篇，入选ESI热点论文3篇、ESI高被引论文5篇，Google学术引用超过3200余次，最高单篇（一作）引用1270余次。出版教材《深度学习》1部，获2022年教育部自然科学奖一等奖（排4），学术专著1部，授权发明专利5项。

主要学术兼职：

中国机械工程学会工业大数据与智能系统分会委员

《IET Collaborative Intelligent Manufacturing》副主编（ESCI检索）

《Journal of Dynamics, Monitoring and Diagnostics》青年编委

《Entropy》《Sensors》《CMES-Computer Modeling in Engineering & Sciences》《Measure and Control》等SCI期刊上担任相关方向的客座编辑。

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、Journal of Intelligent Manufacturing等期刊审稿人

主要经历：

2019.07-至今       bat365在线平台登录（武汉），教授

2016.06-2019.07     华中科技大学，博士后

2015.01-2016.04     中国船舶重工集团第七二三研究所

2010.09-2014.12     华中科技大学，机械科学与工程学院，获工学博士学位

2006.09-2010.06     华中科技大学，机械科学与工程学院，获工学学士学位

主要研究方向：

招生信息：

本课题组欢迎热爱科学研究、对工业人工智能感兴趣的同学加盟，具有良好编程能力，实验动手能力，机械电子工程、工业工程、自动化、计算机等相关理工科背景的本科生和研究生加入课题组。招收硕士生和博士生。

科研项目：

[1] 国家重点研发计划课题，2019YFB1704603，基于数字孪生的电子产品生产调度与物料传输协同优化及决策技术，2019/12-2022/11，参与

[2] 现代制造技术教育部重点实验室（贵州大学），基于物理模型约束的复杂装备智能故障诊断与寿命预测方法，2023/1-2025/1，主持

[3] 企业横向课题，日用玻璃智能装备关键技术开发（视觉检测），2021/12-2023/12，主持

[4] 湖北省重点研发计划，2020AEA009，多模态异构医学大数据构建与智能分析云平台系统及示范应用，2020/07-2023/06，参与

[5] 国家重点实验室开放基金，高速传动齿轮健康状态监测与智能诊断，2023/01-2024/12，主持

[6] 国家自然科学基金，51805192，基于深度学习的智能车间机器故障状态预测方法研究，2019/01~2021/12，主持

[7] 中国博士后科学基金，2017M622414，基于深度迁移学习的在轨航天设备故障预测方法研究，主持

代表性论著与成果：

[1] 2022年度教育部自然科学奖一等奖（排4）

[2] 文龙，李新宇，深度学习，清华大学出版社，2022.

[3] 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热点论文，ESI高被引论文，Google学术引用1270余次)

[4] 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热点论文，ESI高被引论文，Google学术引用730余次)

[5] 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热点论文，ESI高被引论文，Google学术引用300余次)

[6] 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高被引论文，Google学术引用90余次)

[7] 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高被引论文)

[8] 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)

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] X Ye, L Gao, XY Li, L Wen*, “A New Hyper-Parameter Optimization Method for Machine Learning in Fault Classification,” Applied Intelligence, 2022.

[14] 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.

[15] 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.

[16] 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.

[17] 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.

[18] 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.

[19] 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.

[20] 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.

[21] 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.

[22] 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.

[23] 高亮，李新宇，文龙，《排序与调度：工艺规划与车间调度的智能算法》，清华大学出版社（书号：978-7-302-51964-5，十三五国家重点图书，排序与调度丛书）

[24] 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.

指导学生成果：

1) 王优，硕士生，2022年度研究生国家奖学金.

2) 苏少权，硕士生，2022年第五届大数据驱动的智能制造学术会议优秀论文.

3) Y Wang (王优，硕士生), 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, 中科院一区)

4) L Wen, Y Wang (王优，硕士生) 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. (中科院一区)

5) L Wen, Y Wang (王优，硕士生), XY Li, “A New Automatic Convolutional Neural Network Based on Deep Reinforcement Learning for Fault Diagnosis,” Frontiers of Mechanical Engineering, 17:17, Jun 2022.

6) X Ye (叶星辰，硕士生), L Gao, XY Li, L Wen, “A New Hyper-Parameter Optimization Method for Machine Learning in Fault Classification,” Applied Intelligence, 2022. (中科院二区)

7) Y Zhang (张杨，硕士生), 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.