Browsing by Author "Hsieh, CH"

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Determination of optical parameters of a twisted-nematic liquid crystal by phase-sensitive optical heterodyne interferometric ellipsometry
(2005-12-10)
Tsai, CC
;
Chou, C
;
Han, CY
;
Hsieh, CH
;
Liao, KY
What is believed to be a novel phase-sensitive optical heterodyne interferometric ellipsometer is set up to characterize a twisted-nematic liquid crystal (TN-LC) by the elliptical parameters of the output polarization state. This ellipsometer presents the advantages of both polarized optical heterodyne interferometry and optical photometry, which introduce a polarization modulation that is capable of performing with high-sensitivity on phase detection in real time. The twist angle Phi and the untwisted phase retardation Gamma of TN-LC are measured precisely. The experimental results verify that a TN-LC can be treated as identical to an elliptical retarder. (c) 2005 Optical Society of America.
Evolutionary divide-and-conquer approach to inferring S-system models of genetic networks
(2005)
Ho, SY
;
Hsieh, CH
;
Yu, FC
;
Huang, HL
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生物科技學系
This paper proposes an efficient evolutionary divide-and-conquer approach (EDACA) to inferring S-system models of genetic networks from time-series data of gene expression. Inference of an S-system model has 2N(N+l) parameters to be optimized where N is the number of genes in a genetic network. To cope with higher dimensionality, the proposed approach consists of two stages where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on orthogonal experimental design (OED). The intelligent crossover divides two parents into n pairs of parameter groups, economically identifies the potentially better one of two groups of each pair, and systematically obtains a potentially good approximation to the best one of all 2(n) combinations using at most 2n function evaluations. At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm (OSA) for handling noisy gene expression data. The effectiveness of EDACA is evaluated using simulated expression patterns with/without noise running on a single-CPU PC. It is shown that: 1) IGA is efficient enough to solve subproblems; 2) IGA is significantly superior to. the existing method of using GA with simplex crossover; and 3) EDACA performs well in inferring S-system models of genetic networks from small-noise gene expression data.
Personal background and reasons for choosing a career in policing - An empirical study of police students in Taiwan
(2001-01-01)
Tarng, MY
;
Hsieh, CH
;
Deng, TJ
;
交大名義發表
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National Chiao Tung University
Policing has long been thought of as a job with high danger and pressure. Why and what type of people join the police force is becoming one of the more important issues in criminal justice research. This article examines career choice behavior of police students in Taiwan to help police departments develop recruitment strategies. Findings were obtained using survey research questionnaire with nineteen suggested reasons given to the freshmen of the Central Police University in Taiwan. The study found that most of the students are males from a lower- or middle-level socioeconomic family status. The major reasons that the students gave for selecting a policing career were for "good salary and fringe benefits" and "influence from parents." Some statistical differences exist between the reasons given by the male and female students, but less difference exists when the three levels of socioeconomic backgrounds are considered. In addition to the analysis of the responses to the questionnaire, a brief discussion of the above findings and the implications for police recruitment is presented. (C) 2001 Elsevier Science Ltd. All rights reserved.
Scoring method for tumor prediction from microarray data using an evolutionary fuzzy classifier
(2006)
Ho, SY
;
Hsieh, CH
;
Chen, KW
;
Huang, HL
;
Chen, HM
In this paper, we propose a novel scoring method for tumor prediction using an evolutionary fuzzy classifier which can provide accurate and interpretable information. The merits of the proposed method are threefold. 1) The score ranged in [0, 100] can further illustrate the degree of tumor status in contrast to the conventional tumor classifier. 2) The derived score system can be used as a tumor classifier using a system-suggested or human-specified threshold value. 3) The derived classifier with a compact fuzzy rule base can. generate an interpretable and accurate prediction result. The effectiveness of the proposed method is evaluated and compared using two well-known datasets from microarray data and an existing tumor classifier. It is shown by computer simulation that the proposed scoring method is effective using ROC curves of classification.
The development of capability for tackling R&D issues in small companies: A case of a CNC-controller manufacturer in Taiwan
(1997)
Hsieh, CH
;
KoWang, SP
;
交大名義發表
;
管理科學系
;
National Chiao Tung University
This study attempts to clarify the question of how we should observe the growth of R&D capabilities in Taiwan's small companies. It examines the local characteristics of SMEs' (Small and Medium-sized Enterprises) R&D activities and argues the inadequacy of applying western theories directly in the unique environment of Taiwan. A new premise of how to conduct the observation is proposed. A case study is administrated in a small firm of CNC control boxes manufacturer to illustrate the adequacy of this premise. The implications of this case and its theoretical generalization for observation of SMEs' R&D capabilities are discussed.