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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Journal Article
Hierarchical Gradient Diffusion Algorithm for Wireless Sensor Networks
TL;DR: In this article, a hierarchical gradient diffusion algorithm is proposed to solve the transmission problem and the sensor node's loading problem by adding several relay nodes and arranging the sensor nodes routing path.
Book ChapterDOI
Improved Whale Optimization Algorithm and Its Application to UCAV Path Planning Problem.
TL;DR: The improved whale optimization algorithm (WOA), termed improved WOA, is proposed by proposing a new judgment criterion for selecting the process of encircling prey or searching for prey in the WOA that is based on the quality of agent’s fitness instead of a random value used in the original WOA.
Book ChapterDOI
Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization
TL;DR: An altering strategy for dynamic diversity Flower pollination algorithm (FPA) is proposed for solving the multimodal optimization problems and shows the better performance in comparison with others methods.
Book ChapterDOI
Novel Reversible Data Hiding Scheme for AMBTC-Compressed Images by Reference Matrix
TL;DR: A novel reversible data hidng scheme in images compressed by absolute moment block truncation coding (AMBTC) where secret data is embedded into the quantization levels of each AMBTC-compressed image block based on a reference matrix.
Proceedings ArticleDOI
A Compact Flower Pollination Algorithm Optimization
TL;DR: A novel compact flower pollination algorithm for addressing the class of optimization problems in the restricted hardware condition by employing a novel probabilistic representation on the population based on the single competition.