• 한국지진공학회논문집
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• 제21권4호
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• pp.171-179
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• 2017

For estimating the seismic demand of buildings, most seismic design provisions permit conducting linear and nonlinear response history analysis. In order to obtain reliable results from response history analyses, a proper selection of input ground motions is required. In this study, an accurate algorithm for selecting and scaling ground motions is proposed, which satisfies the ASCE 7-10 criteria. In the proposed algorithm, a desired number of ground motions are sequentially scaled and selected from a ground motion library without iterations.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제23권4호
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• pp.627-641
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• 2012

본 연구는 다차원 분할표 형태로 정리된 범주형 자료가 결측치나 무응답을 가지고 있을 때 주어진 자료를 가장 잘 설명하고 예측의 정확도를 높일 수 있는 모형의 추정과 모형의 선택 문제를 다루었다. 무시할 수 없는 무응답 (non-ignorable non-response)체계하에서 최대우도 추정에서 발생할 수 있는 변방값 문제를 해결하기 위하여 계층적 베이지안 모형을 고려하였다. 또한 모형 적도를 높이기 위한 변수 조합을 찾는 모형 선택의 문제를 함께 다루었다. 베이지안 접근하에서 모형 선택의 문제를 다루기 위하여 베이즈 인자 (Bayes factor)를 모형 선택의 기준으로 이용하였다. 제시된 방법은 2004년 실시된 우리나라 국회의원 선거를 앞두고 수행된 여론조사 데이터를 이용하여 실증분석을 수행하였다. 분석결과 무시할 수 없는 무응답 체계하에서 설명변수로 투표참여여부를 이용하는 것이 가장 적합한 모형으로 판명되었다.

• 품질경영학회지
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• 제40권4호
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• pp.481-496
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• 2012

Purpose: This paper aims at improving inefficiency of an existing posterior preference articulation method proposed for dual response surface optimization. The method generates a set of non-dominated solutions and then allows a decision maker (DM) to select the best solution among them through an interval selection strategy. Methods: This paper proposes an iterative posterior preference articulation method, which repeatedly generates the predetermined number of non-dominated solutions in an interval which becomes gradually narrower over rounds. Results: The existing method generates a good number of non-dominated solutions not used in the DM's selection process, while the proposed method generates the minimal number of non-dominated solutions necessitated in the selection process. Conclusion: The proposed method enables a satisfactory compromise solution to be achieved with minimal cognitive burden of the DM as well as with light computation load in generating non-dominated solutions.

• 한국지진공학회논문집
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• 제21권4호
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• pp.181-188
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• 2017

Current seismic design provisions such as ASCE 7-10 provide criteria for selecting ground motions for conducting response history analysis. This study is the sequel of a companion paper (I - Ground Motion Selection) for assessment of the ASCE 7-10 criteria. To assess of the ASCE 7-10 criteria, nonlinear response history analyses of twelve single degree of freedom (SDF) systems and one multi-degree of freedom (MDF) system are conducted in this study. The results show that the target seismic demands for SDF can be predicted using the mean seismic demands over seven and ten ground motions selected according to the proposed method within an error of 30% and 20%, respectively

• 지식경영연구
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• 제19권2호
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• 한국가금학회지
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• 제9권1호
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• pp.35-42
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• 1982

본 연구는 산업계의 산란능력을 개량하는데 있어서 부분검정의 효율성을 구명하기 위하여 White Leghorn 합성종계통 선발 1세대의 478수에서 측정된 산란기록을 분석자료로 이용하였다. 조사된 형질은 초산일령, 40주령까지의 산란수(P) 및 산란율(P'). 41주령에서 64주령까지의 산란수(R) 및 산란율(R'), 그리고 전체산란수(A) 및 산란율(A') 이었으며, 초산일령부터 40주령까지의 단기검정성적을 다시 세분하여 초산일령에서 22주, 24주, ㆍㆍㆍㆍ, 40주까지 2주씩 더해 나간 기간동안의 산란수(E$_{t}$) 및 산란율(E'$_{t}$), 그 반대로 40주령까지의 성적을 최초의 초산일령 19주령 부터, 2주씩 제외시키고 얻은 나머지 기간의 산란수(S$_{t}$) 및 산란율(S'$_{t}$)을 각각 구하여 상대적 효율을 비교하였다.을 비교하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.84-91
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• 2021

The purpose of this study is to investigate the structural relationship between resort selection attributes, emotional responses, and empirical values of scuba diving participants. The general population who enjoys scuba diving in Korea was selected as the population. Using the convenience sampling method, 553 of the 600 questionnaire samples were extracted as the final valid sample. For data processing, frequency analysis, exploratory factor analysis, and Cronbach's α test were performed using SPSS 23, and confirmatory factor analysis and structural equation model analysis were performed with AMOS 18. The results are as follows: First, among the sub-factors of selection attributes, equipment, facility environment, and diving point showed a positive effect on emotional response, but staff service did not have any significant effect. Second, the emotional response positively affected by the selection attribute showed a positive effect on all factors of service excellence, consumer utility, fun value, and aesthetic value of empirical value. Therefore, scuba diving resort managers must recognize the importance of equipment, facility environment, and diving point among these selection attributes of customers. And to satisfy the customer needs the resort must accurately identify the needs for diving equipment, facility environment and diving point. Various methods for this should be explored through the needs of the identified customers, and efforts should be made to provide safe equipment, comfortable facilities, and various diving points.

• Asian-Australasian Journal of Animal Sciences
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• 제25권3호
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• pp.299-303
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• 2012

Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.

• 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.