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Dr. CHU Yinghao (褚英昊博士)

PhD (University of California San Diego)
Bachelor (HKUST)

Assistant Professor

Contact Information

Office: G6607 YEUNG
Phone: 34424703
Fax: 34420173
Email: yinghchu@cityu.edu.hk

Research Interests

  • Artificial Intelligence
  • Deep Learning
  • Renewable Energy
  • Smart Manufacturing
Yinghao Chu received his Ph.D. from University of Califnornia, San Diego in 2015. Now Dr. Chu is an assistant professor working at Department of Advanced Design and Systems Engineering, City University of Hong Kong. Dr. Yinghao Chu has been working in the domain of Artificial Intelligence (AI) for real-world application since 2011. His research focuses on hybrid AI, which simulates the attention and coordination mechanism of human intelligence to solve application-orientated problems in real world, particularly in the areas of (1) renewable forecast and application, such as probabilistic forecasts of solar/load time series, and (2) smart manufacturing, such as operational computer vision system for robot guidance and surface quality inspection.


Publications Show All Publications Show Prominent Publications


Journal

  • Chu, Y. , Wang, Y. , Yang, D. , Chen, S. & Li, M. (2024). A review of distributed solar forecasting with remote sensing and deep learning. Renewable and Sustainable Energy Reviews. 198. 114391 .
  • Chu, Y. , He, Y. , Xiong, X. , Lou, Y. , Yu, C. & Duan, L. (2024). Augmented Hybrid Learning for Visual Defect Inspection in Real-World Hydrogen Storage Manufacturing Scenarios. IEEE Transactions on Industrial Informatics. 20. 8477 - 8487.
  • Chu, Y. , Yang, D. , Yu, H. , Zhao, X. & Li, M. (2024). Can end-to-end data-driven models outperform traditional semi-physical models in separating 1-min irradiance?. Applied Energy. 356. 122434 .
  • Yang, D. , Gu, Y. , Mayer, M.J. , Gueymard, C.A. , Wang, W. , Kleissl, J. , Li, M. , Chu, Y. & Bright, J.M. (2024). Regime-dependent 1-min irradiance separation model with climatology clustering. Renewable and Sustainable Energy Reviews. 189. 113992 .
  • Yu, H. , Chen, S. , Chu, Y.*. , Li, M. , Ding, Y. , Cui, R. & Zhao, X. (2024). Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems. Applied Energy. 374. 124007 .
  • Zhang, X. , Yang, D. , Zhang, H. , Liu, B. , Li, M. , Chu, Y. , Wang, J. & Xia, X. (2024). Spatial solar forecast verification with the neighborhood method and automatic threshold segmentation. Renewable and Sustainable Energy Reviews. 202. 114655 .
  • Chu, Y. , Wu, J. , Yan, Z. , Zhao, Z. , Xu, D. & Wu, H. (2024). Towards generalizable food source identification: An explainable deep learning approach to rice authentication employing stable isotope and elemental marker analysis. Food Research International. 179. 113967 .
  • Chu, Y. , Feng, D. , Liu, Z. , Zhang, L. , Zhao, Z. , Wang, Z. , Feng, Z. & Xia, X. (2023). A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance. IEEE Internet of Things Journal. 10. 1066 - 1081.
  • Zhao, Zizhou. , Lyu, J. , Chu, Y.*. , Liu, K. , Cao, D. , Wu, C. , Qin, L. & Qin, S. (2023). Toward Generalizable Robot Vision Guidance in Real-World Operational Manufacturing Factories: A Semi-Supervised Knowledge Distillation Approach. Robotics and Computer-Integrated Manufacturing. 86. 102639 .
  • Chu, Yinghao. , Li, M. , Pedro, H.T. & Coimbra, C.F. (2022). A network of sky imagers for spatial solar irradiance assessment. Renewable Energy. 187. 1009 - 1019.
  • Chu, Y. , Feng, D. , Liu, Z. , Zhao, Z. , Wang, Z. , Xia,, X. & Quek, T. (2021). Hybrid Learning Based Operational Visual Quality Inspection for Edge Computing Enabled IoT System. IEEE Internet of Things Journal. 9. 4958 - 4972.
  • Chu, Y. , Li, M. , Coimbra, C.F. , Feng, D. & Wang, H. (2021). Intra-Hour Irradiance Forecasting Techniques for Solar Power Integration: A Review. iScience. 103136.
  • Chu, Y. , Pedro, H.T.C. , Kaur, A. , Kleissl, J. & Coimbra, C.F.M. (2017). Net Load Forecasts for Solar-Integrated Operational Grid Feeders. Solar Energy. 158. 236 - 246.
  • Chu, Y. & Coimbra, C.F.M. (2017). Short-Term Probabilistic Forecasts for Direct Normal Irradiance. Renewable Energy. 101. 526 - 536.
  • Chu, Y. , Li, M. , Pedro, H.T.C. & Coimbra, C.F.M. (2016). Real-Time Prediction Intervals for Intra-hour DNI Forecasts. Renewable Energy. 83. 234 - 244.
  • Chu, Y. , Li, M. & Coimbra, C.F.M. (2016). Sun-Tracking Imaging System for Intra-hour DNI Forecasts. Renewable Energy. 96. 792 - 799.
  • Chu, Y. , Pedro, H.T.C. , Li, M. & Coimbra, C.F.M. (2015). Real-Time Forecasting of Solar Irradiance Ramps with Smart Image Processing. Solar Energy. 112. 68 - 77.
  • Chu, Y. , Nonnenmacher, L. , Inman, R.H. , Liao, Z. , Pedro, H.T.C. & Coimbra, C.F.M. (2014). A Smart Image-Based Cloud Detection System for Intra-Hour Solar Irradiance Forecasts. AMS Journal of Atmospheric and Oceanic Technology. 31. 1996 - 2008.
  • Chu, Y. , Urquhart, B. , Gohari, S.M.I. , Pedro, H.T.C. , Kleissl, J. & Coimbra, C.F.M. (2014). Short-Term Reforecasting of Power Output from a 48 MWe Solar PV Plant. Solar Energy. 112. 68 - 77.
  • Chu, Y. , Pedro, H.T.C. & Coimbra, C.F.M. (2013). Hybrid Intra-Hour DNI Forecasts with Sky Image Processing Enhanced by Stochastic Learning. Solar Energy. 98. 592 - 603.


Last update date : 04 Aug 2024