Chin‐Yi Lin

National Taiwan University of Science and Technology, National Cheng Kung University

Papers

Total Citations

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About

Chin-Yi Lin is a researcher at the intersection of autonomous robotics and advanced manufacturing, whose work spans deep-learning based object tracking and yield-enhancement algorithms for Industry 4.0. In autonomous systems, Lin’s most-cited paper (2022, 15 citations) tackles the critical challenge of robust object tracking for unmanned surface vehicles, proposing a deep-learning surveillance method that overcomes environmental uncertainties like illumination changes, occlusion, and seasonal variation—directly improving real-world deployment reliability. In semiconductor manufacturing, Lin has pioneered systematic search schemes for yield loss root-cause identification. The "Blind-Stage Search Algorithm" (2017, 4 citations) and the "Golden Path Search Algorithm" (2021, 4 citations) for the Key-Variable Search Scheme represent methodical approaches to achieving Zero-Defect manufacturing, a core Industry 4.0 goal. These contributions demonstrate a rare dual expertise: advancing perception in autonomous navigation while simultaneously developing data-driven quality control for high-precision production. Lin’s work bridges the gap between intelligent robotics and industrial efficiency, offering practical algorithms that reduce costs and enhance system reliability in both domains.