• Journal of Korean Institute of Industrial Engineers
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• v.43 no.1
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• pp.49-61
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• 2017

This paper proposes an R&D project selection methodology for green technology centered on developing country-oriented technology commercialization. Eight selection criteria are derived from the R&BD logic model : technology needs of developing countries, effectiveness of green technology, technological potentials, domestic technological capability, commercialization feasibility, economic benefits, business feasibility, and spillover effects of developing countries. 21 qualitative and quantitative indicators are then defined for each criterion. The analytic hierarchy process is conducted to produce relative importance of evaluation indicators and to set final priority scores of R&D project candidates. The working of the proposed methodology is provided with the help of a case study example of Green Technology Center. The proposed methodology is expected to be effectively utilized for policy practices of R&D project selection in the field of green technology.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.279-285
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• 2004

In the disease association study, the tagSNP selection problem is important at the view of time and cost. We developed the new tagSNP selection system that has also facilities for the haplotype reconstruction and missing data processing. In our system, we improved biological meanings using LD coefficients as well as dynamic programming method. And our system has capability of processing large -scale dataset, such as the total SNPs on a chromosome. We have tested our system with various dataset from daly et al., patil et al., HapMap Project, artificial dataset, and so on.

• Proceedings of the KIEE Conference
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• 2002.11b
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• pp.283-285
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• 2002

The paper describes a study of selection of existing a.c. Transmission facilities converted fur use with d.c. system. In order to increasing the power transfer capability of existing a.c. transmission facilities, d.c. is one of good alternatives. Some advantages and disadvantages of this method are discussed, and the selection method of existing transmission facilities for use with d.c. considering economics and stability is presented in this paper.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2815-2817
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• 2002

Classification method of cancers using cDNA microarrays data was developed using genetic algorithms and neural networks. For gene selection, 2308 genes were ranked using genetic algorithms, and selected by frequency number of selection from 1000 of genetic iterative runs. To calculate fitness values, artificial neural networks are used as classifier. The small, round blue cell tumors (SRBCTs) which is difficult to distinguish via pathological single test was used as test diseases for classification, and the test results showed the 96% of exact classification capability for 25 test samples.

• Proceedings of the KIEE Conference
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• 1994.07b
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• pp.1505-1508
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• 1994

Lately in KEPCO, the power plant capacity has increasingly become larger than before and it has become difficult to get R.O.W for T/L. Therefore KEPCO decided to increase its system voltage level from 345kV to 765kV. By doing this, KEPCO would like to expand its transmission capability by less T/L route. In 765kV system, we should consider various kinds of environmental impacts that can be neglected in lower voltage level. These environmental impacts are very important factor in T/L design. That can be changed greatly according to the selected conductor. And also conductor selection has relation with the economy of T/L construction directly. This paper deals with some general factors to be considered and basic principles about the conductor and ground wire selection for 765KV T/L with referring to the experiences of foreign utilities.

• Journal of KIISE:Software and Applications
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• v.33 no.6
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• pp.574-579
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• 2006

The feature selection algorithms should broadly and efficiently explore the huge problem spaces to find a good solution. This paper attempts to gain insights on the fitness landscape of the spaces and to improve search capability of the algorithms. We investigate the solution spaces in terms of statistics on local maxima and minima. We also analyze behaviors of the existing algorithms and improve their solutions.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.1
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• pp.185-195
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• 2020

This study is intended to determine that there is a casual relationship between technological innovation capability and new product development performance in small and mid-sized venture companies, and that the introduction of collaboration as a means to step up technological innovation capability will improve new product development performance. To achieve this, a survey was carried out to employees who are engaged in R&D work for small and mid-sized venture companies based in Korea, and the results were analyzed by regression analysis. The findings showed that technology strategy, technology learning and open innovation belonging to technological innovation capability in small and mid-sized venture companies had an effect on new product development performance. In other words, the selection of collaboration as a wider array of core strategies on new product development performance showed that collaboration was a strategy affecting new product development performance. In addition, the moderating role of technological innovation capability in boosting new product development performance through the introduction of collaboration showed that common collaboration had a positive effect on stepping up technology strategy, and collaboration as a core strategy had a positive effect on the size of new product development performance by strengthening technology strategy and open innovation.

• Korean Journal of Community Nutrition
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• v.21 no.4
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• pp.332-343
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• 2016

Objectives: Study purpose was to investigate perception on diet, diet problems and related factors among young adult women using focus group interviews (FGI) based on the Social Cognitive Theory (SCT). Methods: Eight groups of FGI were conducted with 47 female undergraduate or graduate students. Guide for FGI included questions regarding perception on optimal diet, diet problems and cognitive, behavioral, and environmental factors of SCT. FGI were video, audio-taped, transcribed and analyzed by themes and sub-themes. Results: Subjects showed irregular eating habits (skipping breakfast, irregular meal time) and selection of unhealthy foods as the main diet problems. Regarding cognitive factors related to optimal diet, subjects mentioned positive outcome expectations (e.g., health promotion, skin health, improvement in eating habits, etc.) and negative outcome expectations (e.g., annoying, hungry, expensive, taste). Factors that promoted optimal diet were mainly received from information from mobile or internet and access to menu or recipes. Factors that prevented optimal diet included influence from friends, lack of time and cooking skills. Behavioral factors for optimal diet included behavioral capability regarding snacks, healthy eating and smart food selection. Subjects mentioned mass media (mobile, internet, TV) as the influential physical environment, and significant others (parents, friends, grandparents) as the influential social environment in optimal diet. For education topics, subjects wanted to learn about healthy meals, basic nutrition, disease and nutrition, and weight control. They wanted to learn those aspects by using mobile or internet, lectures (cooking classes), campaign and events. Conclusions: Study results might be used for planning education regarding optimal diet for young adult women. Education programs need to focus on increasing positive outcome expectations (e.g., health) and behavioral capability for healthy eating and food selection, reducing negative outcome expectations (e.g., cost, taste) and barriers, making supportive environments for optimal diet, and incorporating topics and methods found in this study.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.135-140
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• 2007

Feature selection is a preprocessing technique commonly used on high dimensional data. Feature selection studies how to select a subset or list of attributes that are used to construct models describing data. Feature selection methods attempt to explore data's intrinsic properties by employing statistics or information theory. The recent developments have involved approaches like correlation method, dimensionality reduction and mutual information technique. This feature selection have become the focus of much research in areas of applications with massive and complex data sets. In this paper, we provide a feature selection method considering data characteristics and generalization capability. It provides a computational approach for feature selection based on fuzzy cluster analysis of its attribute values and its performance measures. And we apply it to the system for classifying computer virus and compared with heuristic method using the contrast concept. Experimental result shows the proposed approach can give a feature ranking, select the features, and improve the system performance.