Sang‐Kwon Lee
Papers
2
Total Citations
43
H-Index
2
About
Sang-Kwon Lee is a distinguished researcher whose work bridges mechanical engineering and artificial intelligence, with a primary focus on structural health monitoring and sound quality engineering. His most impactful contribution lies in developing innovative diagnostic methods for industrial machinery, exemplified by his highly cited 2012 study on health monitoring of glass transfer robots in liquid crystal display production lines. This work, which has garnered 37 citations, pioneered the use of wavelet packet transform combined with artificial neural networks to analyze abnormal operating sounds—a technique that significantly improved early fault detection in mass manufacturing environments. Lee’s research extends into human-centric engineering, as demonstrated by his 2015 development of a comprehensive index for evaluating both the sound and haptic quality of seat belts, a study that has earned 6 citations for its novel approach to integrating sensory feedback in automotive safety systems. His contributions are particularly notable for their practical industrial applications, offering cost-effective, non-invasive monitoring solutions that enhance production efficiency and product quality. Through these achievements, Lee has established himself as a key figure in applying signal processing and machine learning to real-world engineering challenges, with his work continuing to influence both academic research and industrial practice.
Research Focus
Key Achievements
Top Papers
- 1
- 2Development of an index for the sound and haptic quality of a seat belt6 citations · 2015