• Journal of Digital Convergence
• /
• v.16 no.10
• /
• pp.11-26
• /
• 2018

A strategic technology selection for GHG reduction is crucial to secure mitigation means. Especially, a technology selection for a public sector is encouraged to consider integrated perspectives due to various stakeholders under public goals. However, previous studies have mainly focused on technological and economic factors, moreover, consistent criteria have not been applied. This study develops indicators for the GHG reduction technology selection from the public perspective based on delphi method with 22 experts. The result provides valid indicators of technology selection for GHG reduction considering an aspect of technology, economics, environment, policy, society. Specifically, 16 indicators from 5 categories on commercialized technology, and 18 indicators from 5 categories on new technology. We expect that those indicators are useful for a decision-making tool of technology selection. Moreover, provide the basis for the study of judgement criteria to evaluate GHG reduction technology.

• Asian-Australasian Journal of Animal Sciences
• /
• v.29 no.5
• /
• pp.640-645
• /
• 2016

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

• Korean Journal of Remote Sensing
• /
• v.34 no.1
• /
• pp.151-166
• /
• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• Asian-Australasian Journal of Animal Sciences
• /
• v.21 no.11
• /
• pp.1559-1571
• /
• 2008

A rule was developed to constrain the annual rate of inbreeding to a predefined value in a population with different lifetimes between sires and dams, and to maximize the selection response over generations. This rule considers that the animals in a population should be divided into sex-age classes based on the theory of gene flow, and restricts the increase of average inbreeding coefficient for new offspring by limiting the increase of the mean additive genetic relationship for parents selected. The optimization problem of this rule was formulated as a quadratic programming problem. Inputs for the rule were the BLUP estimated breeding values, the additive genetic relationship matrix of all animals, and the long-term contributions of sex-age classes. Outputs were optimal number and contributions of selected animals. In addition, this rule was combined with the optimization of emphasis given to QTL, and further increased the genetic gain over the planning horizon. Stochastic simulations of closed nucleus schemes for pigs were used to investigate the potential advantages obtained from this rule by combining the standard QTL selection, optimal QTL selection and conventional BLUP selection. Results showed that the predefined rates of inbreeding were actually achieved by this rule in three selection strategies. The rule obtained up to 9.23% extra genetic gain over truncation selection at the same rates of inbreeding. The combination of the extended rule and the optimization of emphasis given to QTL allowed substantial increases in selection response at a fixed annual rate of inbreeding, and solved substantially the conflict between short-term and long-term selection response in QTL-assisted selection schemes.

• Journal of Technologic Dentistry
• /
• v.33 no.4
• /
• pp.427-440
• /
• 2011

Purpose: The following research analyzes the causes of major selection and career decision of students studying dental technology. It is to be used as basic data for the management of career improvement program. Methods: The survey has been processed to 490 college students studying Dental Technology in D-college. Questionnaire consists of major selection confidence sheet (14 items) and career decision confidence sheet (18 items) and was scored with 5-points per question. The collected data was analyzed by the statistical program: SAS V8 for Windows. To test for significance on each item, p < 0.05 has been decided as a standard. Results: The analysis of result about the level of confidence on major selection has valid difference by genders, serving military service or not, experience of studying one more year to enter the college or not, making career decision and grade. The analysis of result about career decision has valid difference by gender, serving military service, career decision, day and night course, age and native place. Conclusion: We develop the career advice program and manage it effectively, the confidence on the major selection and pride about its faculty will be high to dental technology students.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.399-402
• /
• 2000

The reduction of chip area and delay is important purpose of Scheduling in High-Level Synthesis. This paper presents a scheduling approach with component selection. After obtaining a initial schedule taking only single-functional u-nits, the component selection of our approach attempts the reduction of chip area and/or delay by the selection more suitable components in a component library using Simulated Annealing.

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

• Journal of Technologic Dentistry
• /
• v.37 no.4
• /
• pp.285-293
• /
• 2015

Purpose: The following study analyzes the mediating effects of professionalism on the relationship between major selection conviction and career decision level of dental technology students. It is to be used as basic data for improvement of the students' career decision level and an effective way to train professionals of the colleges. Methods: The survey was conducted on dental technology students. The collected data was analyzed by the statistical program SPSS 18.0. The results were analyzed by reliability, frequency, multiple-way ANOVA, correlation, multiple regression. To test for significance on each item, p<0.05 has been decided as a standard. Results: The analysis shows that the students' age and clinical practice experience bring a significant difference in major selection conviction, career decision level and professionalism. Professionalism has been found to bring significance mediating effects in relation to major selection conviction and career decision level. Conclusion: To improve the quality and pride as a professional as well as satisfaction with major selection can be expected to raise the standard of the students' career decision level.

• 한국정보통신설비학회:학술대회논문집
• /
• 2003.08a
• /
• pp.159-162
• /
• 2003

This paper presents a novel heuristic mechanism, Dynamic-network Adapted Cost selection (DAC-selection), which has higher backup path sharing rate, lower number of blocked channel requests and number of used wavelengths fer reservation of working path and backup path by using unique cost function than that of widely used random selection (R-selection) mechanism and Combined Min-cost selection (CMC-selection) mechanism proposed by Lo, while maintaining 100% restoration capability.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.05a
• /
• pp.351-356
• /
• 2007

Factory location selection is very important to the success of operation of the whole supply chain, but few effective solutions exist to deliver a good result, motivated by this, this paper tries to introduce a new factory location selection methodology by employing the artificial neural networks technology. First, we reviewed previous research related to factory location selection problems, and then developed a (neural network-based factory selection model) NNFSM which adopted back-propagation neural network theory, next, we developed computer program using C++ to demonstrate our proposed model. then we did case study by choosing a Korean steelmaking company P to show how our proposed model works,. Finnaly, we concluded by highlighting the key contributions of this paper and pointing out the limitations and future research directions of this paper. Compared to other traditional factory location selection methods, our proposed model is time-saving; more efficient.and can produce a much better result.