• 기술혁신학회지
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• 제2권2호
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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.

• 한국초지학회:학술대회논문집
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• 한국초지조사료학회 2005년도 학술심포지엄, 제43회 학술발표회
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• pp.220-221
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• 2005

• 한국산학기술학회논문지
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• 제21권7호
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• pp.321-330
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• 2020

방위산업은 최근 방산수출과 연계하여 국가의 주력사업으로 대두되었고 방산분야 중소벤처기업 육성은 국정과제로 진행 중이다. 따라서 방산분야 유망수출품목을 조사하고 합리적인 선정기준을 정하는 것은 방산 중소기업 육성정책을 수립하고 지원하는데 필수적이다. 본 연구에서는 타 유사기관 유망품목 선정기준 등 사례연구를 통해 방산 중소기업에 적용 가능한 유망수출품목 선정모형을 제시하였다. 평가지표는 크게 품목 자체 경쟁력, 수출업체 역량, 수출파급효과 등 3가지 평가분야 및 하위 8가지 세부평가항목으로 구성되며, 항목 간 상대적 가중치는 AHP 설문을 통해 산정하였다. 평가 결과 일정 점수 이상시 유망품목 채택 혹은 타당성 검증을 통한 동의 후 채택 등 단계를 두어 유망품목 후보군을 좀 더 면밀히 검토하고 발굴할 수 있는 기준을 제시하였다. 특히, 실제 유망수출품목 발굴업무 시 방산분야 관계자 및 중소기업 종사자들을 대상으로 본 방법론을 적용하여 업무에 적용 중이다. 본 논문에서 제시한 모형은 중소기업 생산품목 중 상대적으로 수출경쟁력이 높고 방산수출에 적합한 우수품목을 신속하게 선별하여 지원하기 위한 효과적 수단으로 활용 가능하다.

• Journal of the Korean Statistical Society
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• 제28권2호
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• pp.167-182
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• 1999

This article is concerned with the selection of subsets of predictor variables to be included in building the binary response probit regression model. It is based on a Bayesian approach, intended to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure reformulates the probit regression setup in a hierarchical normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. The appropriate posterior probability of each subset of predictor variables is obtained through the Gibbs sampler, which samples indirectly from the multinomial posterior distribution on the set of possible subset choices. Thus, in this procedure, the most promising subset of predictors can be identified as the one with highest posterior probability. To highlight the merit of this procedure a couple of illustrative numerical examples are given.

• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• 지식경영연구
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• 제13권3호
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• pp.55-77
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• 2012

Selecting promising technology is becoming more and more difficult due to the increased number and complexity. In this study, we propose hybrid AHP/DEA-AR method and hybrid AHP/DEA-AR-G method to evaluate efficiency of technology alternatives based on ordinal rating data collected through survey to technology experts in a certain field and select efficient technology alternative as promising technology. The proposed method normalizes rating data and uses AHP to derive weights to improve the credibility of analysis, then in order to avoid basic DEA models' problems, use DEA-AR and DEA-AR-G to evaluate efficiency of technology alternatives. In this study, we applied the proposed methods to clean technology and compared with the basic DEA models. According to the result of the comparison, we can find that the both proposed methods are excellent in confirming most efficient technology, and hybrid AHP/DEA-AR method is much easier to use in the process of technology selection.

• 기술혁신연구
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• 제13권1호
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• pp.25-54
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• 2005

In this study, priority setting model of new promising IT industries which will be the growth engines for the Korean IT industry, was established. Based on the AHP model, priority setting of IT new promising IT industries was conducted. Firstly, the selection cases of the new promising IT industries and major priority setting methodologies including the AHP methodology, were analyzed. The AHP model was selected as the most feasible methodology for priority setting of the new IT industries, among the various priority setting methodologies. Secondly, in setting up the AHP model for prioritization of the new promising If industries, a 'goal' was established to be priority setting of the new promising IT industries, and an 'alternatives' to be 18 new promising IT industries. Then a logical and a systematic assessment criteria including 5 main criteria('Technological Innovation', 'Market Ability', 'SPin-off Effect', 'Public Benefit', 'Strategic Importance') and 14 sub-criteria, were developed for priority setting of the 18 new promising industries. Finally, with the AHP model, the substantial analysis was made to set up priority of the 18 new promising IT industries. The substantial analysis showed the following priority setting results and implications for the 18 new promising IT industries.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2003년도 International Meeting on Information Display
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• pp.374-377
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• 2003

We reported researches of possibility of the usage of known electrooptical effects (EOE) for diffractive displays (DLCD). We found different EOEs provide the possibility of broad selection of steepness of volt-contrast characteristics at rather large steep of modulation without the usage polarizes. The data are represented much promising for broad development DLCDs.

• Communications for Statistical Applications and Methods
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• 제29권6호
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• pp.629-640
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• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• 한국콘텐츠학회논문지
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• 제10권5호
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• pp.1-9
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• 2010

이 논문은 특징 선택에 사용되는 개미 군락 최적화의 수렴 특성을 개선하기 위해 선택적 평가라는 새로운 기법을 제시한다. 이 방법은 불필요하거나 가능성이 덜한 후보 해를 배제함으로써 계산량을 줄인다. 이 방법은, 그런 해를 찾아내는데 사용할 수 있는 페로몬 정보 때문에 구현이 가능하다. 문제 크기에 따른 알고리즘의 적용가능성을 판단할 목적으로, 특징 선택에 사용되는 세 가지 알고리즘인 탐욕 알고리즘, 유전 알고리즘, 그리고 개미 군락 최적화의 계산 시간을 분석한다. 엄밀한 분석을 위해 원자 연산이라는 개념을 사용한다. 실험 결과는 선택적 평가를 채택한 개미 군락 최적화가 계산 시간과 인식 성능 모두에서 우수함을 보여준다.