王龙博士，英国伦敦大学学院（University College London）杰出（Distinction）硕士，香港城市大学博士。主要从事机器学习、数据挖掘和计算机视觉及其工业应用等方面的研究。目前共发表SCI期刊论文60余篇，其中第一作者和通讯作者论文40余篇（含IEEE Transactions论文10篇、Journal论文6篇），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2022年度和2023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”（Outstanding Associate Editors of 2018）。

[1] 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.

[2] 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)

[3] 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.

[4] 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. 

[5] 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. 

[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] 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. 

[8] 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. 

[9] 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.

[10] 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.

[11] 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. 

[12] 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.

[13] 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.

[14] 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. 

[15] 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.

[16] 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.

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）

北京市“优秀人才培养资助计划”

北京市科协“青年人才托举工程”

斯坦福大学发布的2022年度和2023年度“全球Top 2%顶尖科学家（World’s Top 2% Scientists）”