• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 2004년도 유체기계 연구개발 발표회 논문집
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• pp.189-192
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• 2004

Utilizing pump selection softwares is becoming a new general trend in pump industries, substituting the old fashioned pump catalogs. One of the most powerful pump selection softwares is developed, which features pump performance viscosity correction function as well as pump selection based on the exact pump performance curves, NPSH warning, automatic determination of impeller diameter cutting to meet the customer's performance specification, performance simulation for the rpm and diameter variation, standard motor recommendation according to the motor standards and enclosure types and automatic pump datasheet generation for sales submission, automatic pump drawings and dimension generation for installation check and part preparation. This software provides pump distributors and customers with a quick, easy and exact pump selection, various performance curves review (system curves, performance curve of series or parallel operation) of the selected pumps.

• 대한가정학회지
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• 제39권11호
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• pp.43-62
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• 2001

This study investigated the relationships among Selection, Optimization, and Compensation(SOC) as strategies of life management in mid-life crisis respect to gender and age. The subjects of this study were 170 females and 182 males at the ages between 40 and 60 living in Seoul. Selection, Optimization, and Compensation(SOC) as strategies of life management were assessed by SOC-questionnaire while mid-life crisis was assessed by Mid-Life Crisis Scale. The data were analyzed using frequencies, percentiles, means, standard deviations, Cronbach's $\alpha$, two-way ANOVAS, and Pearson's correlations. Except compensation there was no significant difference in Selection and Optimization as strategies of life management as a function of gender and age. No signigicant difference was found in mid-life crisis as a function of gender and age. There were significant negative correlations among Selection, Optimization, and Compensation(SOC) as strategies of life management and mid-life crisis except the individuation.

• 응용통계연구
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• 제14권1호
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• pp.161-175
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• 2001

메타분석(Meta-analysis)은 서로 독립적으로 연구되어진 결과들을 전체적인 하나의 결과로 도출하기 위해 사용되어지는 통계적 방법이다. 이러한 통계적 방법을 설명할 모형으로는 선택모형(selection model)을 포함한 계층적 모형(hierarchical model)을 사용하며, 이러한 모형들은 베이지안 메타분석에 유용한 것으로 알려져 있다. 그러나, 메타분석의 자료들은 일반적으로 출판편의(publication bias)를 갖고 있으므로 이를 극복하고자 가중함수(weight function)를 이용하여 분포함수를 새롭게 정의하여 사용한다. 최근에 Silliman(1997)은 계층적 모형(hierarchical model)에 가중함수를 첨부한 계층적 선택모형(hierarchical selection model)을 정의하고 모수적 베이지안 방법을 제시하였다. 본 연구에서는 미관측된 연구효과에 디리슈레 과정 사전분포(Dirichlet process prior)를 적용한 준모수적 계층적 선택모형(semiparametric hierarchical selection models)을 소개한다. 여기서 제시된 준모수적 계층적 선택모형을 베이지안 방법으로 추정하기 위하여 마코프 연쇄 몬테칼로(Markov chain Monte Carlo)방법을 이용한다. 제시된 방법을 적용하기 위하여 실제 자료(Johnson, 1993)인 충치를 예방하기 위한 두 가지의 예방약의 효과에 대한 차이를 비교하기 위해 얻어진 12개의 연구를 이용하여 메타분석을 한다.

• Communications for Statistical Applications and Methods
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• 제14권1호
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• pp.215-227
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• 2007

Variable selection algorithm for Sliced Inverse Regression using penalty function is proposed. We noted SIR models can be expressed as generalized eigenvalue decompositions and incorporated penalty functions on them. We found from small simulation that the HARD penalty function seems to be the best in preserving original directions compared with other well-known penalty functions. Also it turned out to be effective in forcing coefficient estimates zero for irrelevant predictors in regression analysis. Results from illustrative examples of simulated and real data sets will be provided.

• Journal of the Korean Data and Information Science Society
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• 제5권1호
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• pp.11-20
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• 1994

In recent years, nonparametric kernel estimation of regresion function are abundant and widely applicable to many areas of statistics. Most of modern researches concerned with the fixed global bandwidth selection which can be used in the estimation of regression function with all the same value for all x. In this paper, we propose a method for selecting locally varing bandwidth based on bootstrap method in kernel estimation of fixed design regression. Performance of proposed bandwidth selection method for finite sample case is conducted via Monte Carlo simulation study.

• Journal of the Korean Data and Information Science Society
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• 제27권4호
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• pp.1059-1066
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• 2016

In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.201-209
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• 2013

For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.

• 대한전기학회논문지:전력기술부문A
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• 제53권11호
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• pp.630-635
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• 2004

Generation companies(Genco) submit the supply functions as a bidding function to a bid market in a competitive electricity market. The profits of Gencos vary in accordance with the bid functions, so the selection of a bidding function plays a key role in increasing their profits. In order to get a profitable bidding function which is usually linear, it is required to modify adequately the intersection and the slope of a linear supply function. This paper presents an analysis of the selection of the supply function from the viewpoint of Nash equilibrium(NE). Four types of bidding function parameters are used for analizing the electricity market. The competition of selecting bidding parameters is modeled as two level games in this research. One is a subgame where a certain type of parameters is given and the players compete to select values of the underlying parameters. The other is an overall game where the players compete to select a profitable type among the four types of parameters. The NEs in both games are computed by an using analytic method and a payoff matrix method. It is verified in case studies for the NE of overall game to satisfy the equilibrium condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.1961-1974
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• 2019

In this letter, a harmonic-mean-based dual-antenna selection scheme at relay node is proposed in two-way relaying networks (TWRNs). With well-designed distributed orthogonal concatenated Alamouti space-time block code (STBC), a dual-antenna selection problem based on the instantaneous achievable sum-rate criterion is formulated. We propose a low-complexity selection algorithm based on the harmonic-mean criterion with linearly complexity $O(N_R)$ rather than the directly exhaustive search with complexity $O(N^2_R)$. From the analysis of network outage performance, we show that the asymptotic diversity gain function of the proposed scheme achieves as $1/{\rho}{^{N_R-1}}$, which demonstrates one degree loss of diversity order compared with the full diversity. This slight performance gap is mainly caused by sacrificing some dual-antenna selection freedom to reduce the algorithm complexity. In addition, our proposed scheme can obtain an extra coding gain because of the combination of the well-designed orthogonal concatenated Alamouti STBC and the corresponding dual-antenna selection algorithm. Compared with the common-used selection algorithms in the state of the art, the proposed scheme can achieve the best performance, which is validated by numerical simulations.