Dr. Long Wang, Master of Distinction, University College London, UK, Ph.D., City University of Hong Kong. Mainly engaged in research on machine learning, data mining, computer vision and its industrial applications. He is currently a member of IEEE, IEEE Industrial Electronics Society and China Computer Society, a member of the Computer Vision Committee of the Chinese Computer Society, and a 2014 Hong Kong PhD Fellowship recipient. Currently serving as the associate editor of SCI journals IEEE Access (IF: 4.098) and Canadian Journal of Electrical and Computer Engineering (IF: 1.53), academic editor and editorial board member of PLOS ONE (IF: 2.776), Intelligent Automation & Soft Computing (IF: 0.79) and Guest Editor of Water (2.524)

1.    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: 6.807)

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, June 2017. (IF: 7.377)

3.    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, 2017. (IF: 7.503)

4.    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, 2018. (IF: 10.486)

5.    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. (IF: 4.943)

Hong Kong Research Grants Council thematic research project "Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems", participated in
Hong Kong Research Grants Council Distinguished Young Scholars Program "Scheduling Power Production of Hybrid Power Systems with Data Mining and Computational Intelligence", participated.
Horizontal project:
Project "Wind Turbine Generation Performance Monitoring with Representation Learning" of Dong Energy, Denmark, hosted

2017 Outstanding Academic Performance Award, City University of Hong Kong
2014 Hong Kong PhD Fellowship (Hong Kong PhD Fellowship)
2014 Chow Yei Ching School of Graduate Studies Entrance Scholarships, City University of Hong Kong