主要从事机器学习、计算机视觉、数据挖掘以及其在工业领域应用的研究工作，取得了一定的研究成果，目前共发表SCI期刊论文70余篇，其中第一作者和通讯作者论文40余篇（含IEEE Transactions论文12篇、Journal论文6篇），Google Scholar引用达5000余次，包含3篇ESI高被引论文，曾获得香港政府博士奖学金（HKPFS）等多项荣誉，入选北京市“优秀人才培养资助计划”和北京市科协“青年人才托举工程”，入围斯坦福大学发布的2021年度至2025年度“全球Top 2%顶尖科学家（World’s Top 2% Scientists）”榜单。现担任国际SCI期刊《IEEE Access》和《PLoS One》的编委，《Renewable and Sustainable Energy Reviews》、《Measurement Science and Technology》、《Frontiers in Neurorobotics》、《Frontiers in Energy Research》和《Intelligent Automation and Soft Computing》的客座编委以及《中南大学学报（英文版）》的青年编委，并受邀担任2021、2022年IEEE IAS Industrial and Commercial Power System Asia Technical Conference (I&CPS Asia)，World Automation Congress 2021的程序委员会成员和第五届亚洲人工智能技术大会组委会成员，入选2018年《IEEE Access》“优秀副编辑”，现为中国计算机学会计算机视觉专委会执行委员、北京物联网学会理事。

主持科研项目如下：

• 国家自然科学基金1项（62202044）

• 北京市自然科学基金1项（4232040）

• 广东省基础与应用基础基金2项（2020A1515110431，2022A1515240044）

• 北京市优秀人才培养资助计划项目1项（BJSQ2020008）

• 佛山市科技创新专项资金项目1项（BK22BF009）

• 广东省新能源和可再生能源研究开发与应用重点实验室开放基金项目1项（Y807S61001）

• 流体及动力机械教育部重点实验室开放课题项目1项（szjj2019-011）

部分代表性论文如下：

[1]       X. Ye, L. Wang(通讯作者), C. Huang, and X. Luo, “UAV-Taken Wind Turbine Image Dehazing with a Double-Patch Lightweight Neural Network,” IEEE Internet of Things Journal, vol. 11, no. 13, pp. 22843-22852, 2024.

[2]       Y. Zhang, L. Wang(通讯作者), C. Huang, and X. Luo, “Wind Turbine Blade Defect Detection Based on the Genetic Algorithm-Enhanced YOLOv5 Algorithm Using Synthetic Data,” IEEE Transactions on Industry Applications, vol. 61, no. 1, pp. 653 – 665, 2025.

[3]       L. Wang, Z. Zhang, and X. Luo, “A Two-stage Data-driven Approach for Image based Wind Turbine Blade Crack Inspections,” IEEE-ASME Transactions on Mechatronics, vol. 24, no. 3, pp. 1271-1281, 2019.

[4]       L. Wang, and Z. Zhang, “Automatic Detection of Wind Turbine Blade Surface Cracks Based on UAV-taken Images,” IEEE Transactions on Industrial Electronics, vol. 64, no. 9, pp. 7293-7303, 2017.

[5]       C. Huang, Z. Zhang, and L. Wang(通讯作者), “PSOPruner: PSO-Based Deep Convolutional Neural Network Pruning Method for PV Module Defects Classification,” IEEE Journal of Photovoltaics, vol.12, no. 6, pp. 1550-1558, 2022.

[6]       L. Yang, L. Wang(通讯作者), Z. Zheng, and Z. Zhang, “A Continual Learning-based Framework for Developing a Single Wind Turbine Cybertwin Adaptively Serving Multiple Modeling Tasks,” IEEE Transactions on Industrial Informatics, vol.18, no.7, pp. 4912-4921, 2021.

[7]       C. Huang, Z. Ge, L. Wang(通讯作者), D. Zhang, H. Long and X. Luo, "Multi-Agent Deep Reinforcement Learning-Based Cooperative Optimal Operation for Building Integrated Energy Systems With Nash–Harsanyi Bargaining Game-Driven Cost Allocation," IEEE Transactions on Consumer Electronics, vol. 71, no. 2, pp. 4759-4770, 2025.

[8]       Z. Wang, L. Wang(通讯作者), and C. Huang, “A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine,” IEEE Transactions on Instrumentation and Measurement, vol. 70, article no. 5006512, 2021.

[9]       X. Liu, L. Yang, Z. Wang, L. Wang(通讯作者), C. Huang, Z. Zhang, and X Luo, “UAV-assisted Wind Turbine Counting with an Image-level Supervised Deep Learning Approach,” IEEE Journal on Miniaturization for Air and Space Systems, vol. 4, no. 1, pp. 18-24, 2023.

[10]      L. Wang, J. Yang, C. Huang, and X. Luo, “An Improved U-Net Model for Segmenting Wind Turbines From UAV-Taken Images,” IEEE Sensors Letters, vol. 6, no. 7, article no. 6002404, 2022.

[11]      Z. Wang, L. Wang(通讯作者), C. Huang, and X. Luo, “A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images,” IEEE Transactions on Industry Applications, vol. 59, no. 2, pp. 2041-2049, 2023.

[12]      L. Wang, Z. Zhang, H. Long, J. Xu, and R. Liu, “Wind Turbine Gearbox Failure Identification with Deep Neural Networks,” IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1360-1368, 2017. (现为ESI高被引论文)

[13]      L. Wang, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Blade Breakage Monitoring with Deep Autoencoders,” IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 2824-2833, 2018.

[14]      Z. Wang, L. Wang(通讯作者), R. M, C. Huang, and X. Luo, “Short-Term Wind Speed and Power Forecasting for Smart City Power Grid with a Hybrid Machine Learning Framework,” IEEE Internet of Things Journal, vol. 10, no.21, pp. 18754-18765, 2023.

[15]      C. Huang, L. Wang(通讯作者), and L.L. Lai, “Data-driven Short-term Solar Irradiance Forecasting Based on Information of Neighboring Sites,” IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9918-9927, 2019.

[16]      C. Huang, J. Zhang, L. Cao, L. Wang(通讯作者), X. Luo, J.H. Wang, and A. Bensoussan, “Robust Forecasting of River-flow Based on Convolutional Neural Network,” IEEE Transactions on Sustainable Computing, vol. 5, no. 4, pp. 594-600, 2020.

[17]      L. Wang, Z. Zhang, and J. Chen, “Short-term Electricity Price Forecasting with Stacked Denoising Autoencoders,” IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 2673-2681, 2017

[18]      Z. Wang, L. Wang(通讯作者), C. Huang, Z. Zhang, and X. Luo, “Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification with a Hybrid Symbolic Aggregate Approximation Algorithm,” IEEE Internet of Things Journal, vol. 8, no. 18, pp. 14003–14012, 2021.

[19]      X Ye, L. Wang(通讯作者), C Huang, X Luo, “Wind Turbine Blade Defect Detection with a Semi-Supervised Deep Learning Framework,” Engineering Applications of Artificial Intelligence, vol. 136, article no. 108908, 2024.

[20]      X. Pan, L. Wang(通讯作者), Z. Wang, and C. Huang, “Short-term Wind Speed Forecasting Based on Spatial-temporal Graph Transformer Networks,” Energy, vol. 253, article no. 124095, 2022.

[21]      M. Liu, Z. Cao, J. Zhang, L. Wang(通讯作者), C. Huang, and X. Luo, “Short-term Wind Speed Forecasting Based on the Jaya-SVM Model,” International Journal of Electrical Power and Energy Systems, vol. 121, article no. 106056, 2020.

暂无内容

暂无内容

暂无内容