• Genomics & Informatics
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• 제14권4호
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• 산업경영시스템학회지
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• 제33권2호
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• pp.1-8
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• 2010

A vendor selection problem consists of two different kinds of decision making. First one is to choose the best suppliers among all possible suppliers and the next is to allocate the optimal quantities of orders among the selected vendors. In this study, an integration of the technique for order preference by similarity to ideal solution (TOPSIS) and a multi-objective mixed integer programming (MOMIP) is developed to account for all qualitative and quantitative factors which are used to evaluate and choose the best group of vendors and to decide the optimal order quantity for each vendor. A solution methodology for the vendor selection model of multiple-vendor, multiple-item with multiple decision criteria and in respect to finite vendor capacity is presented.

• 한국기계가공학회지
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• 제7권3호
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• pp.87-95
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• 2008

The purpose of this study is to provide a decision support to select an appropriate rapid prototyping(RP) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model for molding, material property, build time and part cost that greatly affect the performance of RP machines. However, the selection of a RP is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate RP machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify RP machines that the users consider. After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of RP machines.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.535-546
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• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.

• 한국정보통신학회논문지
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• 제26권9호
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• pp.1340-1346
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• 2022

본 논문에서는 다중 송신 안테나를 이용하는 하향링크 비직교 다중 접속 시스템을 고려한다. 여기서, 시스템 복잡도를 줄이기 위하여 송신 안테나 선택 기법을 이용한다. 따라서, 본 논문에서는 송신 안테나 선택을 이용하는 하향링크 비직교 다중 접속 시스템의 아웃티지 확률 성능을 개선하기 위한 무선 자원 할당 및 수신기 선택 기법을 제안한다. 특히, 수신기 선택 기법은 아웃티지 성능 개선을 위한 수신기들의 그룹핑 방법을 의미하고, 무선 자원 할당 기법은 아웃티지 성능 개선을 위한 각각의 그룹에 대한 자원 할당 방법을 의미한다. 또한, 레일레이 페이딩 채널을 가정한 컴퓨터 시뮬레이션을 통하여 제안 기법이 총 데이터 전송률의 손실로부터 아웃티지 확률 성능을 개선할 수 있음을 보여준다.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.47-53
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• 2009

We define product line (or mix) selection problem as selecting a subset of potential product variants that can simultaneously minimize product proliferation and maintain market coverage. Selecting the most efficient product mix is a complex problem, which requires analyses of multiple criteria. This paper proposes a method based on Data Envelopment Analysis (DEA) for product line selection. Data Envelopment Analysis (DEA) is a linear programming based technique commonly used for measuring the relative performance of a group of decision making units with multiple inputs and outputs. Although DEA has been proved to be an effective evaluation tool in many fields, it has not been applied to solve the product line selection problem. In this study, we construct a five-step method that systematically adopts DEA to solve a product line selection problem. We then apply the proposed method to an existing line of staplers to provide quantitative evidence for managers to generate desirable decisions to maximize the company profits while also fulfilling market demands.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2007년도 추계학술대회 논문집
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• pp.37-38
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• 2007

• 산업경영시스템학회지
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• 제19권38호
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• pp.131-138
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• 1996

In real world, the organization has multiple objects. Inorder to solve the multiple objects, we present the goal programming for solving project selection problem we also developed expert system which is focused on function of analysis. User which doesn't have knowledge of goal programming can solve the project selection problem and get a result of analysis.

• 한국산업정보학회논문지
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• 제11권3호
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• pp.69-81
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• 2006

In this paper, an adaptive algorithm is proposed in streaming MPEG-4 contents with fluctuating resource amount such as throughput of network conditions. In the area of adaptive streaming issue, a lot of researches have been made on how to represent encoded media(such as video) bitstream in scalable way. By contrast, MPEG-4 supports object-based multimedia content which is composed of various types of media streams such as audio, video, image and other graphical elements. Thus, it can be more effective to provide individual media streams in scalable way for streaming object-based content to heterogeneous environment. The proposed method provides the multiple media streams corresponding to an object with different qualities and bit rate in order to support object based scalability to the MPEG-4 content. In addition, an optimal selection of the multiple streams for each object to meet a given constraint is proposed. The selection process is adopted a multiple choice knapsack problem with multi-step selection for the MPEG-4 objects with different scalability levels. The proposed algorithm enforces the optimal selection process to maintain the perceptual qualities of more important objects at the best effort. The experimental results show that the set of selected media stream for presenting objects meets a current transmission condition with more high perceptual quality.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.871-875
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• 2008

본 논문은 개체 변환 유전자 알고리즘을 (GAVaPS) 이용하여 k-nearest neighbor (k-NN) 분류기에서 사용되는 특징들을 선정하는 방법을 제시한다. 우리는 다수의 k-NN 분류기들을 사용하기 때문에 사용되는 특징들을 선정하는 문제는 매우 탐색 영역이 크고 해결하기 어려운 문제이다. 따라서 우리는 효과적인 특징득의 선정을 위해 일반적인 유전자 알고리즘 (GA) 보다 효율적이라고 알려진 개체군 변환 유전자 알고리즘을 사용한다. 또한 다수 k-NN 분류기를 개체군 변환 유전자 알고리즘으로 효과적으로 결합하는 방법을 제시한다. 제안하는 알고리즘의 우수성을 여러 실험을 통해 보여준다.