Yo Ping Huang

National Taipei University of Technology

Papers

Total Citations

H-Index

About

Yo Ping Huang is a prominent researcher in fuzzy logic systems, human-robot interaction, and intelligent control, with a focus on enhancing machine perception and decision-making in noisy environments. His major contribution lies in developing robust clustering algorithms that mitigate the influence of outliers—a critical advancement for real-world applications like hand gesture recognition. His 2013 paper, "A Novel Fuzzy Clustering Method with No Outliers Influence," introduced an improved Fuzzy C-Means (FCM) algorithm that corrects the tendency of standard FCM to pull cluster centers toward noise, thereby enabling more accurate classification in human-robot interaction tasks. Although this work has garnered 5 citations, its conceptual impact is significant, as it addresses a fundamental limitation in fuzzy clustering. Huang’s research bridges theoretical innovation and practical deployment, advancing fields such as assistive robotics and smart environments. His work is particularly notable for its emphasis on robustness, making fuzzy systems more reliable in uncontrolled, real-world settings. For students and researchers, Huang’s contributions offer a compelling example of how refining foundational algorithms can unlock new possibilities in interactive and adaptive technologies.