• Journal of the Earthquake Engineering Society of Korea
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• v.21 no.4
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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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• v.29 no.4
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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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• v.23 no.4
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• pp.627-641
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• 2012

We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.

• Journal of Korean Society for Quality Management
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• v.40 no.4
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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.

• Journal of the Earthquake Engineering Society of Korea
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• v.21 no.4
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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

• Knowledge Management Research
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• v.19 no.2
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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.

• Korean Journal of Poultry Science
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• v.9 no.1
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• pp.35-42
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• 1982

Data pertaining to the first generation of a Synthetic White Leghorn flock were used to estimate the heritabilities of and genetic correlation between partial egg production(egg number or percentage) or diversely segmented part records and other traits such as age at sexual maturity, residual and annual egg production, and to compare the expected genetic gain from selection on partial egg number or partial percent production with correlated response in other traits. The estimated heritabilites for six measures of egg Production were ranged from 29 to 35, while heritability for age at sexual maturity (SM) was intermediate (48). Genetic correlations between partial egg number (P) and annual egg number. (A), and partial percent production (P') and annual percent production (A') were 51 and 72, respectively. Genetic correlation between P and SM was estimated largely negative (-.64), while correlation bettween P' and SM was positively intermediate(34). In comparing direct response from selection on partial production (P or P') with another response in correlated trait, selection on P would be 25% more efficient than selection of P' for improving A, while selection of P' would be 94% more efficient than selection P for improving A' For shortening SM selection of P would be 98% more efficient than selection on P'.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.3
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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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• v.25 no.3
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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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• v.28 no.2
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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.