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Shu-Chuan Chu
Researcher at Shandong University of Science and Technology
Publications - 303
Citations - 5475
Shu-Chuan Chu is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 28, co-authored 231 publications receiving 3652 citations. Previous affiliations of Shu-Chuan Chu include University of South Australia & Sewanee: The University of the South.
Papers
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Journal ArticleDOI
Compact Sine Cosine Algorithm applied in vehicle routing problem with time window
TL;DR: In this article, a compact Sine Cosine Algorithm (cSCA) is proposed to solve the vehicle routing problem with time window in transportation and the quality of the solution is further improved by introducing the relocate operator.
Book ChapterDOI
Improving K-Means with Harris Hawks Optimization Algorithm
TL;DR: In this paper , a Harris Hawks Optimization (HHO) algorithm was used to optimize the central data set, where the data inside the same segment are closely related, and the ones between two different segments are not.
Journal ArticleDOI
Improved Black Hole Algorithm for Intelligent Traffic Navigation
TL;DR: In this article, the authors proposed a novel algorithm for path searching based on an improved black hole (BH) method to enhance the real-time navigation efficiency, where parallel evolution, and information exchange strategy inspired by the quasi-affine transformation evolution (QUATRE) algorithm, allow agents with effective information to search the solution space quickly and effectively that can prompt the convergence speed and expand the diversity of solutions.
Book ChapterDOI
Statistical Learning-Based Face Recognition
TL;DR: A comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning are implemented.
Proceedings ArticleDOI
Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition
TL;DR: Experimental results on ORL, YALE and UMIST face databases show that NKDA outperforms NDA on recognition, which demonstrates that it is feasible to improve NDA with kernel trick for feature extraction.