• Journal of Korea Technology Innovation Society
• /
• v.2 no.2
• /
• pp.266-274
• /
• 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 Society of Grassland Science Conference
• /
• 2005.06a
• /
• pp.220-221
• /
• 2005

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.7
• /
• pp.321-330
• /
• 2020

The defense industry has recently been focused on boosting exports of weapon systems. Investigation and selection of promising export items for SMEs in the defense industry is essential to establish a defense promotion policy. This study presents a model for selecting promising export items applicable to the defense industry through case studies, such as criteria for selecting promising items from other organizations. The evaluation index is largely composed of three categories, competitiveness of the item itself, capabilities of the exporter, and ripple effect of the export, and consists of eight detailed evaluation indicators. The relative weight between categories was calculated through the AHP method. In the selection model, if a certain score is exceeded, it is then possible to adopt a promising item or verify validity. In particular, promising items were selected by applying this methodology to those involved in the defense industry. Using the model presented in this study, it is expected that domestic small and medium-sized enterprises with relatively high export competitiveness and excellent quality items will be given priority, and more effective and intensive export support will be possible.

• Journal of the Korean Statistical Society
• /
• v.28 no.2
• /
• pp.167-182
• /
• 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
• /
• v.29 no.4
• /
• pp.407-422
• /
• 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.

• Knowledge Management Research
• /
• v.13 no.3
• /
• pp.55-77
• /
• 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.

• Journal of Technology Innovation
• /
• v.13 no.1
• /
• pp.25-54
• /
• 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
• /
• 2003.07a
• /
• pp.374-377
• /
• 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
• /
• v.29 no.6
• /
• pp.629-640
• /
• 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.

• The Journal of the Korea Contents Association
• /
• v.10 no.5
• /
• pp.1-9
• /
• 2010

This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.