Jae‐Yoon Jung
Papers
5
Total Citations
197
H-Index
4
About
Jae-Yoon Jung is a leading researcher in intelligent fault detection and biomedical data analysis, with a focus on edge computing and sensor-based monitoring. His most significant contribution is the development of LiReD, a light-weight real-time fault detection system for edge computing that leverages LSTM recurrent neural networks, which has garnered 167 citations for its innovative approach to reducing computational demands in smart factory environments. Jung has also advanced one-class classification techniques, particularly the Mahalanobis–Taguchi system, for fault detection in industrial applications like aircraft engines and wind turbines. In biomedical engineering, he pioneered the use of robotic assessment and committee-based classifiers to objectively evaluate sensory-motor impairment in stroke patients, addressing the subjectivity of traditional clinical methods. His work includes feature selection and classification for chronic stroke impairment, as well as the "Trial Map" visualization tool for verifying stroke assessment databases. With a career spanning from real-time industrial monitoring to rehabilitation technology, Jung's research bridges engineering and medicine, offering practical solutions for predictive maintenance and patient care.
Research Focus
Key Achievements
Top Papers
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