Yanfeng Han
Papers
3
Total Citations
52
H-Index
2
About
Yanfeng Han is a leading researcher at the intersection of intelligent manufacturing, robotics, and industrial automation. His work focuses on advancing machine perception and adaptive control systems for complex industrial environments. Han’s major contributions include pioneering a method for the recognition and sorting of coal and gangue using image processing and multilayer perceptrons, a breakthrough that significantly enhances production efficiency in mining operations—a paper that has garnered 30 citations. He has also developed Barrier Lyapunov function-based adaptive prescribed performance control for permanent magnet synchronous motors (PMSMs) in robots, addressing critical challenges of safety and reliability under dynamic conditions, with 21 citations. Most recently, Han has innovated in predictive maintenance by proposing a lightweight multiscale attention deep network for remaining useful life prediction of harmonic reducers using in-situ current signals, a work that is already gaining attention. His research not only pushes the boundaries of intelligent control and deep learning but also delivers practical solutions for cost reduction and operational safety in robotics and heavy industries.
Research Focus
Key Achievements
Top Papers
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