J
Jeng-Shyang Pan
Researcher at Shandong University of Science and Technology
Publications - 889
Citations - 14887
Jeng-Shyang Pan is an academic researcher from Shandong University of Science and Technology. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 50, co-authored 789 publications receiving 11645 citations. Previous affiliations of Jeng-Shyang Pan include National Kaohsiung Normal University & Technical University of Ostrava.
Papers
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Proceedings ArticleDOI
Two classifiers based on nearest feature plane for recognition
TL;DR: Two improved methods based on nearest feature plane, called as center-based nearest features plane (CNFP) and line-based near feature plane (LNFP), are proposed for recognition, taking lower computational complexity and achieve better recognition rate than the other improved classifiers.
Journal Article
Security Analysis and Improvement on an Image Encryption Algorithm Using Chebyshev Generator
TL;DR: A recent two-stage image encryption algorithm proposed by Wang et al., is insecure against chosen plaintext attack and an subtle but efficient improvement is presented over Wang etal.
Journal ArticleDOI
Optimal path planning for motion robots based on bees pollen optimization algorithm
TL;DR: The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.
Book ChapterDOI
Study on Hazardous Scenario Analysis of High-Tech Facilities and Emergency Response Mechanism of Science and Technology Parks Based on IoT
Kuo-Chi Chang,Kai-Chun Chu,Yuh-Chung Lin,Trong-The Nguyen,Tien-Wen Sung,Yu-Wen Zhou,Jeng-Shyang Pan +6 more
TL;DR: In this article, a high-tech plant hazard types and possible situational analysis including fire, explosion, and toxic gas leakage for analysis is presented. But, this study uses the risk assessment method and simulates for disaster prevention and prevention operations.
Proceedings ArticleDOI
Solving Constrained Optimization Problems by an Improved Particle Swarm Optimization
TL;DR: The average velocity of the swarm and the best history position in the particle's neighborhood are introduced as two turbulence factors, which are considered to influence the fly directions of particles, into the IPSO algorithm so as not to converge prematurely.