H
Hung-Ming Chen
Researcher at National Chiao Tung University
Publications - 178
Citations - 1566
Hung-Ming Chen is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Routing (electronic design automation) & Floorplan. The author has an hindex of 18, co-authored 161 publications receiving 1415 citations. Previous affiliations of Hung-Ming Chen include Synopsys & University of Texas at Austin.
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Journal ArticleDOI
SODOCK: swarm optimization for highly flexible protein-ligand docking.
TL;DR: Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands.
Journal ArticleDOI
Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space
TL;DR: The performance comparison and statistical analysis of experimental results show that the IGA-based method without heuristics is efficient in designing accurate and compact fuzzy classifiers using 11 well-known data sets with numerical attribute values.
Proceedings ArticleDOI
Integrated floorplanning and interconnect planning
TL;DR: This work proposes a method to combine interconnect planning with floorplanning based on the Wong-Liu (1986) floorplaning algorithm, which uses a multi-stage simulated annealing approach in which different interConnect planning methods are used in different ranges of temperature to reduce running time.
Journal ArticleDOI
Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis.
TL;DR: The proposed interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis has better performance than the existing fuzzy rule-based classifier, and is more accurate than some existing non-rule- based classifiers.
Journal ArticleDOI
Accurate prediction of enzyme subfamily class using an adaptive fuzzy k-nearest neighbor method
TL;DR: This study proposes an efficient non-parametric classifier for predicting enzyme subfamily class using an adaptive fuzzy r-nearest neighbor (AFK-NN) method, where k and a fuzzy strength parameter m are adaptively specified.