2018 - IEEE Fellow For contributions to adaptive learning
His main research concerns Artificial intelligence, Control theory, Dynamic programming, Artificial neural network and Machine learning. He has researched Artificial intelligence in several fields, including Data mining and Pattern recognition. His Control theory study combines topics from a wide range of disciplines, such as Control engineering and Electric power system.
His Dynamic programming research incorporates elements of Stability, Adaptive system and Adaptive learning. His Machine learning research incorporates themes from Probability distribution and Raw data. His Raw data research is multidisciplinary, relying on both Boosting methods for object categorization and Knowledge representation and reasoning.
The scientist’s investigation covers issues in Artificial intelligence, Control theory, Artificial neural network, Mathematical optimization and Machine learning. Haibo He interconnects Data mining and Pattern recognition in the investigation of issues within Artificial intelligence. His Pattern recognition study combines topics in areas such as Feature and Data set.
His Control theory research is multidisciplinary, incorporating elements of Dynamic programming and Electric power system. His work deals with themes such as Stability, Algorithm and Reinforcement learning, which intersect with Artificial neural network. Haibo He has researched Mathematical optimization in several fields, including Algorithm design, Microgrid and Energy management.
His scientific interests lie mostly in Control theory, Mathematical optimization, Artificial neural network, Artificial intelligence and Reinforcement learning. The Control theory study combines topics in areas such as Multi-agent system and Microgrid. He has included themes like Energy management and Job shop scheduling in his Mathematical optimization study.
His Artificial neural network study integrates concerns from other disciplines, such as Discrete time and continuous time, Bounded function, Nash equilibrium, Fuzzy logic and Fault. His Artificial intelligence study typically links adjacent topics like Pattern recognition. His work carried out in the field of Reinforcement learning brings together such families of science as Scheduling, Electric power transmission, Smart grid and Trust region.
Haibo He spends much of his time researching Control theory, Artificial neural network, Mathematical optimization, Reinforcement learning and Nonlinear system. As a part of the same scientific study, Haibo He usually deals with the Control theory, concentrating on Microgrid and frequently concerns with Power engineering. His research in the fields of Long short term memory overlaps with other disciplines such as Visual perception.
His Reinforcement learning research integrates issues from Scheduling, Smart grid and Demand response. In his work, Lyapunov function is strongly intertwined with Optimal control, which is a subfield of Nonlinear system. His Iterative reconstruction study introduces a deeper knowledge of Artificial intelligence.
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Learning from Imbalanced Data
Haibo He;E.A. Garcia.
IEEE Transactions on Knowledge and Data Engineering (2009)
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
Haibo He;Yang Bai;E.A. Garcia;Shutao Li.
international joint conference on neural network (2008)
Imbalanced Learning: Foundations, Algorithms, and Applications
Haibo He;Yunqian Ma.
Adaptively robust filtering for kinematic geodetic positioning
Y. Yang;H. He;G. Xu.
Journal of Geodesy (2001)
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Guoqian Jiang;Haibo He;Jun Yan;Ping Xie.
IEEE Transactions on Industrial Electronics (2019)
Contribution of the Compass satellite navigation system to global PNT users
YuanXi Yang;JinLong Li;JunYi Xu;Jing Tang.
Chinese Science Bulletin (2011)
A self-organizing learning array system for power quality classification based on wavelet transform
H. He;J.A. Starzyk.
IEEE Transactions on Power Delivery (2006)
Preliminary assessment of the navigation and positioning performance of BeiDou regional navigation satellite system
YuanXi Yang;JinLong Li;AiBing Wang;JunYi Xu.
Science China-earth Sciences (2014)
A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities
Bo Tang;Zhen Chen;Gerald Hefferman;Tao Wei.
Proceedings of the ASE BigData & SocialInformatics 2015 (2015)
Cyber-physical attacks and defences in the smart grid: a survey
Haibo He;Jun Yan.
IET Cyber-Physical Systems: Theory & Applications (2016)
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