Wei-Teng Cheng
Papers
1
Total Citations
11
H-Index
1
About
Wei-Teng Cheng is a leading researcher in intelligent robotic perception and advanced manufacturing, with a focus on tactile sensing and machine learning for automated assembly. His most cited work, "Low‐Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA‐Based Feature Fusion and Multiple Kernel Learning‐Based Support Vector Machine" (2014, 11 citations), introduces a robust tactile image recognition scheme that enhances contrast, extracts geometric and Fourier descriptors, and fuses features via kernel PCA. By employing multiple kernel learning-based SVM, Cheng achieves high-accuracy classification of low-resolution tactile images, enabling robots to perform precise assembly tasks. This contribution addresses critical challenges in robotic dexterity, improving adaptability in unstructured industrial environments. Cheng’s work bridges sensor data processing and machine learning, advancing the reliability of automated systems. His research continues to influence the development of intelligent manufacturing and human-robot interaction, making him a key figure in the field.
Research Focus
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Top Papers
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