王龍，男，北京科技大學計算機科學與技術系副教授，中國農業大學工學學士（2011）、碩士（2013），英國倫敦大學學院（University College London）傑出科學碩士（2014），香港城市大學博士（2017）。主要從事機器學習、計算智慧型和計算機視覺及其在電力市場、可再生能源和電力電子等領域套用的研究工作，是2014年香港政府博士獎學金（Hong Kong PhD Fellowship）獲得者。
科研方向,人物兼職,代表論文,科研業績,獲獎記錄,
科研方向
機器學習 數據挖掘 計算機視覺 計算智慧型 能源信息學
人物兼職
IEEE會員 IEEE Power and Energy Society會員 IEEE Council on RFID會員 IEEE Sensors Council會員 IEEE Systems Council 會員 中國電機工程學會會員 國際期刊IEEE Transactions on Cybernetics, IEEE Transactions on Network Science and Engineering, IET Electronics Letters, Journal of Systems
代表論文
1. 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, September 2017. (IF: 7.168(2016年))(JCR: Q1)
2. 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, July 2017. (IF: 5.680(2016年))(JCR: Q1)
3. 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, June 2017. (IF: 6.764(2016年))(JCR: Q1)
4. L. Wang, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Blade Breakage Monitoring with Deep Autoencoders,” IEEE Transactions on Smart Grid, in press, 2016. DOI: 10.1109/TSG.2016.2621135 (IF: 6.645(2016年))(JCR: Q1)
5. C. Huang and L. Wang, “Gaussian Process Regression-Based Modelling of Lithium-Ion Battery Temperature-Dependent Open-Circuit-Voltage,” Electronics Letters, vol. 53, no. 17, 2017. (IF: 1.155(2016年))(JCR: Q3)
6. C. Huang, L. Wang, R.S.C. Yeung, Z. Zhang, H.S.H. Chung, and A. Bensoussan, “A Prediction Model Guided Jaya Algorithm for the PV System Maximum Power Point Tracking,” IEEE Transactions on Sustainable Energy, in press, 2017. DOI: 10.1109/TSTE.2017.2714705 (IF: 4.909(2016年))(JCR: Q1)
7. S. Jang, K.S. Chin, L. Wang, G. Qu, and K.L. Tsui, “Modified Genetic Algorithm-based Feature Selection Combined with Pre-trained Deep Neural Network for Demand Forecasting in Outpatient Department,” Expert Systems with Applications, vol. 82, pp. 216-230, October 2017. (IF: 3.928(2016年))(JCR: Q1)
8. H. Long, L. Wang, Z. Zhang, Z. Song, and J. Xu, “Data-Driven Wind Turbine Power Generation Performance Monitoring,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6627-6635, June 2015. (IF: 7.168(2016年))(JCR: Q1)
9. L. Wang, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Blade Breakage Monitoring with Deep Autoencoders,” 2017 IEEE Power and Energy Society General Meeting, in press, 2017.
10. L. Wang, H. Long, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Gearbox Failure Monitoring Based on SCADA Data Analysis,” 2016 IEEE Power and Energy Society General Meeting, Boston, MA, July 2016.
2. 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, July 2017. (IF: 5.680(2016年))(JCR: Q1)
3. 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, June 2017. (IF: 6.764(2016年))(JCR: Q1)
4. L. Wang, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Blade Breakage Monitoring with Deep Autoencoders,” IEEE Transactions on Smart Grid, in press, 2016. DOI: 10.1109/TSG.2016.2621135 (IF: 6.645(2016年))(JCR: Q1)
5. C. Huang and L. Wang, “Gaussian Process Regression-Based Modelling of Lithium-Ion Battery Temperature-Dependent Open-Circuit-Voltage,” Electronics Letters, vol. 53, no. 17, 2017. (IF: 1.155(2016年))(JCR: Q3)
6. C. Huang, L. Wang, R.S.C. Yeung, Z. Zhang, H.S.H. Chung, and A. Bensoussan, “A Prediction Model Guided Jaya Algorithm for the PV System Maximum Power Point Tracking,” IEEE Transactions on Sustainable Energy, in press, 2017. DOI: 10.1109/TSTE.2017.2714705 (IF: 4.909(2016年))(JCR: Q1)
7. S. Jang, K.S. Chin, L. Wang, G. Qu, and K.L. Tsui, “Modified Genetic Algorithm-based Feature Selection Combined with Pre-trained Deep Neural Network for Demand Forecasting in Outpatient Department,” Expert Systems with Applications, vol. 82, pp. 216-230, October 2017. (IF: 3.928(2016年))(JCR: Q1)
8. H. Long, L. Wang, Z. Zhang, Z. Song, and J. Xu, “Data-Driven Wind Turbine Power Generation Performance Monitoring,” IEEE Transactions on Industrial Electronics, vol. 62, no. 10, pp. 6627-6635, June 2015. (IF: 7.168(2016年))(JCR: Q1)
9. L. Wang, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Blade Breakage Monitoring with Deep Autoencoders,” 2017 IEEE Power and Energy Society General Meeting, in press, 2017.
10. L. Wang, H. Long, Z. Zhang, J. Xu, and R. Liu, “Wind Turbine Gearbox Failure Monitoring Based on SCADA Data Analysis,” 2016 IEEE Power and Energy Society General Meeting, Boston, MA, July 2016.
科研業績
香港研究資助局主題研究計畫“Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems”，參與
香港研究資助局傑出青年學者計畫“Scheduling Power Production of Hybrid Power Systems with Data Mining and Computational Intelligence”，參與。
橫向項目：
丹麥Dong Energy公司項目“Wind Turbine Generation Performance Monitoring with Representation Learning”，主持
香港研究資助局傑出青年學者計畫“Scheduling Power Production of Hybrid Power Systems with Data Mining and Computational Intelligence”，參與。
橫向項目：
丹麥Dong Energy公司項目“Wind Turbine Generation Performance Monitoring with Representation Learning”，主持
獲獎記錄
2017年香港城市大學Outstanding Academic Performance Award
2014年香港政府博士獎學金（Hong Kong PhD Fellowship）
2014年香港城市大學Chow Yei Ching School of Graduate Studies Entrance Scholarships
2014年香港政府博士獎學金（Hong Kong PhD Fellowship）
2014年香港城市大學Chow Yei Ching School of Graduate Studies Entrance Scholarships
