S
Shih-Hsien Tseng
Researcher at Chung Yuan Christian University
Publications - 11
Citations - 61
Shih-Hsien Tseng is an academic researcher from Chung Yuan Christian University. The author has contributed to research in topics: Computer science & Key (lock). The author has an hindex of 3, co-authored 7 publications receiving 52 citations. Previous affiliations of Shih-Hsien Tseng include Kainan University & Ohio State University.
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Improving the Hospital Discharge Process with Six Sigma Methods
TL;DR: A case study describes how a community hospital applied a Six Sigma define, measure, analyze, and control approach to improve patient discharge as discussed by the authors, using statistical process control charting, process mapping, Pareto charting and cause and effect analysis.
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Variance plus bias optimal response surface designs with qualitative factors applied to stem choice modeling
TL;DR: New designs are proposed specifically to address bias and compared with five types of alternatives ranging from types of composite to D-optimal designs using four criteria including D-efficiency and measured accuracy on test problems.
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Design of experiments for steady-state system identification with applications in genetic and business network modelinG
TL;DR: It is found that the optimal experimental planning can likely improve the sensitivity, specificity, and efficiency of the process of deriving genetic networks.
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Key Success Factors of Sustainable Organization for Traditional Manufacturing Industries: A Case Study in Taiwan
TL;DR: In this article , the authors explored the key success factors for sustainable development in traditional industries through expert knowledge and proposed an effective solution based on Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based Analytic Network Process (ANP) to determine the correlation and causality of these factors based on the decision laboratory method for multi-criteria decision-making.
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Sequential forward-inverse design for genetic network modeling
TL;DR: This paper proposes methods for forward and inverse system modeling using Bayesian and least squares regression based on both space-filling design criteria for multiple response problems and linear optimality criteria focusing on D-optimality.