• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.6
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• pp.22-28
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• 2013

This study aims to propose the location selection method of car-sharing services in Daejeon Metropolitan city. In order to select locations for car-sharing, Daejoen area was divided in $500m{\times}500m$ cell size using GIS Arc/Info 10, and input factors which may affect car-sharing service were determined, and then each input factor was standardized for analysis. The weight for each input factor was determined through experts' survey and index of goodness of fit was estimated in each cell ($500m{\times}500m$ size) using AHP method. Also, This study proposed the method to select 30 service facility location using Location-allocation Model in Network Analysis module. The proposed method for the location selection of car-sharing service in this study can be used for preliminary data for initial car-sharing introduction. Henceforward, appropriate demand forecasting and economic evaluation for the location selection of car-sharing service are needed for the further study.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.814-825
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• 2009

This paper is to analyze the relative weight and the priority order of the selection factors for IPTV. To obtain the goal this paper, firstly, the selecting factors for IPTV were identified and conceptualized on reviewing previous literatures. And this paper proposed the hierarchy model using the factor analysis on IPTV users. and utilized AHP in analysis method. Secondly, AHP model constructed 4 higher factors, and 15 lower factors. The higher factors were 'Quality Acceptance', 'Social Effect', 'Using Motive' and 'Individual Psychology'. Results of this study show that relative weights among factors IPTV selection were founded as 'Quality Acceptance(33.0%)', 'Utilization Motive(31.8%)', 'Individual Psychology(21.3%)' and 'Social Influence(13.9%)'. Economic efficiency takes the top rank in sub-criteria included 15 factors and than convenience, amount of information, discrimination, self-efficacy, innovation, relative advantage in order listed. For the generalization of the research results, further researchs are needed to investigate intended use of IPTV on other method of analysis.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4102-4102
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• 2001

In multivariate analysis, absorbance spectrum is measured over a band of wavelengths. One does not often pay attention to the size of this wavelength band. However, it is desirable that spectrum is measured at only necessary wavelengths as long as the acceptable accuracy of prediction can be met. In this paper, the method of selecting an optimal band of wavelengths based on the loading vector analysis was proposed and applied for determining total protein in human serum using near-infrared transmission spectroscopy and PLSR. Loading vectors in the full spectrum PLSR were used as reference in selecting wavelengths, but only the first loading vector was used since it explains the spectrum best. Absorbance spectra of sera from 97 outpatients were measured at 1530∼1850 nm with an interval of 2 nm. Total protein concentrations of sera were ranged from 5.1 to 7.7 g/㎗. Spectra were measured by Cary 5E spectrophotometer (Varian, Australia). Serum in the 5 mm-pathlength cuvette was put in the sample beam and air in the reference beam. Full spectrum PLSR was applied to determine total protein from sera. Next, the wavelength region of 1672∼1754 nm was selected based on the first loading vector analysis. Standard Error of Cross Validation (SECV) of full spectrum (1530∼l850 nm) PLSR and selected wavelength PLSR (1672∼1754 nm) was respectively 0.28 and 0.27 g/㎗. The prediction accuracy between the two bands was equal. Wavelength selection based on loading vector in PLSR seemed to be simple and robust in comparison to other methods based on correlation plot, regression vector and genetic algorithm. As a reference of wavelength selection for PLSR, the loading vector has the advantage over the correlation plot since the former is based on multivariate model whereas the latter, on univariate model. Wavelength selection by the first loading vector analysis requires shorter computation time than that by genetic algorithm and needs not smoothing.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.29-36
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• 2006

In the process of location selection, it is assumed that transformer substation socially recognized as a dangerous facility require especially more rigorous and resonable process. This paper implements suitability analysis for optimum location selection of transformer substation in southern Gyeonggi province using AHP(Analytic Hierarchy Process) and spatial analysis of GIS in terms of safety, national land use, economical efficiency and environment preservation. To do this, necessary data from 1/5,000 digital map are extracted a s raster format for suitability analysis and a field investigation also was done. In the procedure of site selection, three candidate zones with 1.5km radius were selected for the whole research area and then through field survey low transformer sites were selected from candidate zones. In the last the appropriateness of selected sites was evaluated. The results of the suitability analysis showed that the first candidate site satisfied the location condition best and other candidate sites generally showed good location condition.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1996.06a
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• pp.111-131
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• 1996

In order to analyze stamping processes and to select optimum material properties of sheet steels for customer lines, 3-dimensional finite element analysis software were used. Commercial explicit finite element code, PAM-STAMP, was able to simulate 3-dimensional press formed parts with good accuracy and gave some useful results by orthogonal array experiments. Deformation of draw-bead were predicted by ABAQUS accurately, so that material selection for those parts by simulation were possible.

• Journal of Korea Technology Innovation Society
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• v.2 no.2
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• pp.266-274
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• 1999

The world market is being restructured into one global market. The globalization makes the competition m IT industry more vigorous. It is, therefore, the vital procedures that the selection of the promising items among IT equipment and the intensive investment on the selected items to gain the competitiveness in the area of IT global market. With these in mind, in this paper, we introduce a very systematic and objective method which appraises the promise of IT equipment. The method is based on the factor Analysis which is very popular and powerful statistical technique.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.151-156
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• 2003

Variable selection algorithm for principal component analysis using penalized likelihood method is proposed. We will adopt a probabilistic principal component idea to utilize likelihood function for the problem and use HARD penalty function to force coefficients of any irrelevant variables for each component to zero. Consistency and sparsity of coefficient estimates will be provided with results of small simulated and illustrative real examples.

• Journal of the Korean Home Economics Association
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• v.33 no.2
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• pp.19-27
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• 1995

This paper discusses the bias that results from using nonrandomly selectd samples of consumer research. A two stage system (maximum likelihood probit analysis and ordinary least square analysis) is a solution to sample selection bias. Empirical results show that correcting for sample selection bias improves the validity of consumer research results.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.605-616
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• 2004

In this paper we consider the proportional hazard models for survival analysis in the microarray data. For a given vector of response values and gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.175-178
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• 2006

본 논문에서는 주요성분분석과 상호정보 추정을 조합한 입력변수선택 기법을 제안하였다. 여기서 주요성분분석은 2차원 통계성을 이용하여 입력변수 간의 독립성을 찾기 위함이고, 상호정보의 추정은 적응적 분할을 이용하여 입력변수의 확률밀도함수를 계산함으로써 변수상호간의 종속성을 좀더 정확하게 측정하기 위함이다. 제안된 기법을 인위적으로 제시된 각 500개의 샘플을 가지는 6개의 독립신호와 1개의 종속신호를 대상으로 실험한 결과, 빠르고 정확한 변수의 선택이 이루어짐을 확인하였다.