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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Book ChapterDOI
A Reduce Identical Event Transmission Algorithm for Wireless Sensor Networks
TL;DR: In the simulation, the RIET algorithm can enhance sensor nodes’ life time about 12.9 times and saving power consumption about 52.43 % than tradition algorithms.
Journal ArticleDOI
Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm
Jeng-Shyang Pan,Jeng-Shyang Pan,Jeng-Shyang Pan,Jie Shan,Shi-Guang Zheng,Shu-Chuan Chu,Shu-Chuan Chu,Cheng-Kuo Chang +7 more
TL;DR: A adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented and it is shown that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.
Journal ArticleDOI
A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability
TL;DR: In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density.
Proceedings ArticleDOI
Deep convolutional neural networks-based age and gender classification with facial images
TL;DR: The results indicate that the accuracy of the classification network can be improved by pre-training and the multi-GPUs training platform can improve the training speed during the recognition.
Proceedings ArticleDOI
Efficient search approaches for k-medoids-based algorithms
TL;DR: The hybrid search approach combines the previous medoid index, the utilization of memory, the criterion of triangular inequality elimination and the partial distance search for nearest neighbor search and is applied to the k-medoids-based algorithms.