Professor Supervisor of Master's Candidates
Gender : Male
Alma Mater : China University of Geosciences
Education Level : Doctoral Degree in Education
Degree : Doctoral Degree in Engineering
Status : Employed
School/Department : School of Automation
Business Address : Building 702
Jie Hu received the B.S. degree in automation and the Ph.D. degree in control science and engineering from China University of Geosciences, Wuhan, China, in 2015 and 2020, respectively.
He is currently a Professor with the School of Automation, China University of Geosciences, Wuhan, China. He was a joint Ph.D. student with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada from 2018 to 2020. His current research interests include process control, intelligent control, and computational intelligence.
Dr. Hu was recipient of the Young Researcher Award of the 13th China-Japan International Workshop on Information Technology and Control Applications in 2020. He is a member of the Chinese Association of Automation.
Sep. 2015 - Pre:
PhD candidate in the School of Automation, China University of Geosciences, in Control Systems and Engineering. Courses taken: Intelligent System Principle and Application; Advanced Control Theory and Control Engineering; Linear System Theory; Professional English Writing and Communication.
Sep. 2011 - Jun. 2015:
Undergraduate student in the School of Automation, China University of Geosciences. Received B.Sc Degree, in Automation, in June 2015.
Process Modeling and Optimization of Complex Industrial; Information fusion.
National Natural Science Foundation of China:
(1) Optimization and Advanced Control for Improving Carbon Efficiency to Achieve Green Manufacturing for Sintering Process in the Iron and Steel Industry.
Main work: Modeling and Optimization of Carbon Efficiency.
Jie Hu, Min Wu, Luefeng Chen, et al., Real-time dynamic prediction model of carbon efficiency with working condition identification in sintering process, Journal of Process Control, 2022, 111: 97-105.
Jie Hu, Min Wu, Luefeng Chen, et al., Weighted kernel fuzzy c-means-based broad learning model for time-series prediction of carbon efficiency in iron ore sintering process[J], IEEE Transactions on Cybernetics, 2020, DOI: 10.1109/TCYB.2020.3035800.
Jie Hu, Min Wu, Luefeng Chen, et al., A novel modeling framework based on customized kernel-based fuzzy c-means clustering in iron ore sintering process[J], IEEE/ASME Transactions on Mechatronics, 2021, DOI: 10.1109/TMECH.2021.3076208
Jie Hu, Min Wu, Pan Zhang, et al., Prediction performance improvement via anomaly detection and correction of actual production data in iron ore sintering process[J], IEEE Transactions on Industrial Informatics, 2020, 16(12): 7602-7612.
Jie Hu, Min Wu, Xin Chen, et al., Multi-model ensemble prediction model for carbon efficiency with application to iron ore sintering process[J], Control Engineering Practice, 2019, 88: 141-151.
Jie Hu, Min Wu, Xin Chen, et al., Hybrid modeling and online optimization strategy for improving carbon efficiency in iron ore sintering process[J], Information Sciences, 2019, 483: 232-246.
Jie Hu, Min Wu, Xin Chen, et al., A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process[J], IEEE Transactions on Industrial Electronics, 2018, 65(11): 8778-8787.
Jie Hu, Min Wu, Xin Chen, et al., Hybrid prediction model of carbon efficiency for sintering process[J], IFAC PapersOnLine, 2017, 50(1): 10238-10243.
Jie Hu, Min Wu, et al., An intelligent optimization strategy based on prediction model for carbon efficiency in sintering process[C], Proceedings of the 37th Chinese Control Conference, Wuhan, China, July 25-27, 2018: 3454-3458.
Jie Hu, Min Wu, Xin Chen, Weihua Cao, Prediction model of comprehensive coke ratio based on principal component analysis for sintering process[C], Proceedings of the 35th Chinese Control Conference, Chengdu, China, July 27-29, 2016: 3612-3617.
Xin Chen, Jie Hu, Min Wu, Weihua Cao, T-S fuzzy logic based modeling and robust control for burning-through point in sintering process, IEEE Transactions on Industrial Electronics, 2017, 64(12): 9378-9388.