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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. , Feng, D. , Liu, Z. , Zhang, L. , Zhao, Z. , Wang, Z. , Feng, Z. & Xia, X. (2022). A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance. IEEE Internet of Things Journal.
  • 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 : 09 Feb 2023