Yunchuan Liu


Email: yliu3 at govst dot edu

Office Room: D34150C

I am now an assistant professor at the Governors State University. My research interests are Machine Learning, Data Mining, Embedded System, and Power System.

I received my Ph.D. degree from University of Nevada, Reno and my major is Computer Science and Engineering. My advisors is Prof. Lei Yang. I received my Bachelor and Master degree from Shenzhen University.

News

Mar 2025: Our drone successfully completed its test flight and our internship work was covered by the media.

Oct 2024: Our proposal 'MediValAir: Innovative Drone Delivery System for Hospitals' is awarded by Illinois Innovation Voucher grant.

Jun 2024: Our paper 'Related Technological Density and Regional Industrial Upgrading from Perspective of Product Space Theory: Evidence from China' has been accepted by Applied Economics.

Feb 2023: Our paper 'Towards distributed learning of PMU data: A federated learning based event classification approach' has been accepted by IEEE PES General Meeting 2023.

Jan 2023: Our paper 'Drifting Streaming Peaks-over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast' has been published in Future Internet.

Aug 2022: Yunchuan joined the Division of Science Mathematics and Technology at Governors State University as an Assistant Professor.

Jun 2022: Our paper 'Weakly supervised event classification using imperfect real-world PMU data with scarce labels' has been selected as one of the Best Conference Papers by IEEE PES General Meeting 2022.

May 2022: Our paper 'Robust event classification using imperfect real-world PMU data' has been accepted by IEEE Internet of Things Journal.

Feb 2022: Our paper 'Real-time event detection using rank signatures of real-world PMU data' has been accepted by IEEE PES General Meeting 2022.

Apr 2021: Our paper 'Event Cause Analysis in Distribution Networks using Synchro Waveform Measurements' has been accepted by North American Power Symposium (NAPS) 2021.

Feb 2021: Our papers 'PMU-data-driven event classification in power transmission grids and Low-rank tensor completion for PMU data recovery' have been accepted by IEEE PES ISGT NA 2021.

Nov 2020: Our paper 'Seasonal self-evolving neural networks based short-term wind farm generation forecast' has been accepted by IEEE SmartGridComm 2020.

Oct 2020: Our paper 'A Regularized Tensor Completion Approach for PMU Data Recovery' has been accepted by IEEE Transactions on Smart Grid.

Teaching

University of Nevada, Reno

CS135 COMPUTER SCIENCE I
CS202 COMPUTER SCIENCE II

Governors State University

CPSC-6790 Data Mining and Business Intelligence
CPSC-8985 Grad Seminar

Publications

Journals

  • [J07] Cheng, H., Song, M., & Liu, Y. (2024). Related technological density and regional industrial upgrading from perspective of product space theory: evidence from China. Applied Economics, 56(48), 5774-5788.
  • [J06] Liu, Y., Ghasemkhani, A., & Yang, L. (2023). Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast. Future Internet, 15(1), 17.
  • [J05] Liu, Y., Yang, L., Ghasemkhani, A., Livani, H., Centeno, V. A., Chen, P. Y., & Zhang, J. (2022). Robust Event Classification Using Imperfect Real-world PMU Data. IEEE Internet of Things Journal.
  • [J04] Ghasemkhani, A., Niazazari, I., Liu, Y., Livani, H., Centeno, V. A., & Yang, L. (2020). A regularized tensor completion approach for pmu data recovery. IEEE Transactions on Smart Grid, 12(2), 1519-1528.
  • [J03] Liu, Y., & Gong, X. (2013). Processing and Hardware Implementation of BT. 656 Digital Video Stream. Chinese Journal of Liquid Crystals and Displays, 28(2), 238-243.
  • [J02] Cheng,Z. , Liu, Y., & Gong, X. Design of USB Transmission System for Microprojection Video Signal. Chinese Journal of Liquid Crystals and Display. 2012, 27(1) 81-86.
  • [J01] Liu, Y., Gong, X.,& Wu, Q. SPI IP Core and Its Application in Microprojection System. Microcontroller and Embedded Systems. 2011,(2): 27-30.

Conferences (Full Paper Refereed)

  • [C09] Mohammadabadi, S. M. S., Liu, Y., Canafe, A., & Yang, L. (2023, July). Towards distributed learning of pmu data: A federated learning based event classification approach. In 2023 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1-5). IEEE..
  • [C08] Ghasemkhani, A., Liu, Y., & Yang, L. Real-time event detection using rank signatures of real-world PMU data. In 2022 IEEE Power & Energy Society (PES) General Meeting (pp. 1-5). IEEE.
  • [C07] Liu, Y.,& Yang, L. Weakly supervised event classification using imperfect real-world PMU data with scarce labels. In 2022 IEEE Power & Energy Society (PES) General Meeting (pp. 1-5). IEEE.(Best Paper Award)
  • [C06] Canafe, A., Liu, Y., Yang, L., & Livani, H. (2022, March). DCCA Enhanced Forced Oscillation Frequency Detection Using Real-world PMU Data. In 2022 IEEE Texas Power and Energy Conference (TPEC) (pp. 1-6). IEEE.
  • [C05] Niazazari, I., Livani, H., Ghasemkhani, A., Liu, Y., & Yang, L. (2021, April). Event cause analysis in distribution networks using synchro waveform measurements. In 2020 52nd North American Power Symposium (NAPS) (pp. 1-5). IEEE.
  • [C04] Ghasemkhani, A., Liu, Y., & Yang, L. (2021, February). Low-rank Tensor Completion for PMU Data Recovery. In 2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-5). IEEE.
  • [C03] Niazazari, I., Livani, H., Ghasemkhani, A., Liu, Y., & Yang, L. (2021, April). Event cause analysis in distribution networks using synchro waveform measurements. In 2020 52nd North American Power Symposium (NAPS) (pp. 1-5). IEEE.
  • [C02] Liu, Y., Ghasemkhani, A., Yang, L., Zhao, J., Zhang, J., & Vittal, V. (2020, November). Seasonal self-evolving neural networks based short-term wind farm generation forecast.
    In 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 1-6). IEEE.
  • [C01] Liu, Y., Huo, Y., and Niu, H. (2015, December). A method for reducing the sidelobes in superoscillation imaging. In MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis (Vol. 9811, pp. 76-81). SPIE.