Papers
5
Total Citations
135
H-Index
5
About
Lie Yang is a prolific researcher specializing in intelligent fault diagnosis, machine learning-based signal processing, and human-machine interface systems. His work has made significant contributions to the reliability and performance of industrial robotics, particularly through advanced diagnostic methods for harmonic drives — a critical yet fault-prone component in robotic systems. Yang's landmark 2021 study on fault diagnosis using Generative Adversarial Networks (GANs) addressed the challenging real-world problem of imbalanced fault data, garnering 47 citations and establishing him as a leading voice in data-driven diagnostics. His complementary 2020 paper employing multiscale convolutional neural networks for fault detection further demonstrated his expertise in deep learning applications, accumulating 38 citations. Beyond industrial robotics, Yang has broadened his impact into biomedical engineering, developing a practical EEG-based brain-computer interface to control upper-limb assist robots for rehabilitation — a study cited 29 times that underscores his versatility. His work on EMG signal purification using GANs further bridges human physiology and intelligent systems. Collectively, Yang's research reflects a consistent commitment to applying cutting-edge machine learning to solve complex, practical engineering challenges.
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