• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.163-168
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• 2002

The voting algorithm for action selection performs self-improvement by Reinforcement learning algorithm in the dynamic environment. The proposed voting algorithm improves the navigation of the robot by adapting the eligibility of the behaviors and determining the Command Set Generator (CGS). The Navigator that using a proposed voting algorithm corresponds to the CGS for giving the weight values and taking the reward values. It is necessary to decide which Command Set control the mobile robot at given time and to select among the candidate actions. The Command Set was learnt online by means as Q-learning. Action Selector compares Q-values of Navigator with Heterogeneous behaviors. Finally, real-world experimentation was carried out. Results show the good performance for the selection on command set as well as the convergence of Q-value.

• Plant Resources
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• 제6권3호
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• pp.170-174
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• 2003

The important component for medical effect in ginseng is ginsenoside. Korea Ginseng & Tobacco Research Institute contains approximately 200 lines produced by inbred selection. It is assumed that ginseng lines containing high level of ginsenoside should be included in those lines. Besides, new breeding methods such as cell line selection in vitro and hairy root were recently developed. Therefore, this study was carried out to detect genes related to ginsenoside, and to use it for selection marker to select and distribute lines containing high level of ginsenoside. DNA was extracted from both ginseng roots and hairy roots, and the difference between the line containing high ginsenoside(KG101) and normal ginsenoside(KG103) were analysed. As a result, 28 out of 36 primers showed bands, and many primers showed band difference between ginseng lines. It is considered that the bands should be analysed using DNA sequence comparison to check if those are related to ginsenoside. In case of hairy roots of ginseng, almost no differences were found between two lines.

• Journal of Animal Science and Technology
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• 제44권6호
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• pp.669-676
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• 2002

유전표지인자를 이용한 선발(marker assisted selection; MAS)과 전통적인 표현형기록에 근거한 반응의 비교분석을 위하여 고정효과모델(deterministic model)을 이용하여 시뮬레이션을 하였다. 자신의 단일 기록을 이용한 경우와 자신의 기록과 자신의 표지인자 정보를 이용할 경우 유전력이 높을수록 MAS의 효율성이 38%정도 높게 나타났다. 그러나 유전력이 높은 경우(50%) MAS의 효율성은 약 1%정도로서 효율성이 대단히 낮은 것으로 나타났다. 자신의 기록과 부모의 표현형 정보에 표지인자 정보를 추가할 경우 MAS의 효율성은 27% 정도였으며 마찬가지로 유전력이 높은 경우에는 효율성이 0에 가깝게 나타났다. MAS의 효율성은 유전력이 낮을수록 그리고 이용한 표지인자의 유전적 변이가 클수록 효율성이 높아지는 것으로 나타났다.

• 한국패류학회지
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• 제28권4호
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• pp.335-342
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• 2012

본 연구에서는 31개월령 북방전복의 성장형질에 대한 선발반응을 추정하기 위해 2008년에 생산된 2세대 육종전복의 모집단 6,858마리와 다음 세대 생산을 위해 최종 교배지침에 포함된 614마리의 자료를 이용하였다. 각 형질별 전체평균은 각 장, 각폭 및 중량이 각각 76.31 mm, 49.48 mm 및 56.90 g로 조사되었고, 중량의 변이계수가 28.70%로 나타나 다른 형질에 비해 자료의 변이가 가장 크게 나타났다. 각장, 각폭, 중량 및 비만도에 있어서 성과 성숙도의 효과에 대한 분산분석을 실시한 결과 비만도를 제외한 모든 형질에서 고도의 유의성이 인정되었으며 (p < 0.01), 성의 효과는 비만도를 제외한 모든 형질에 있어서 수컷보다 암컷이 유의적으로 높게 나타났다. 각 형질별 육종가와 표현형가의 상관관계는 각장, 각폭 및 중량에 있어서 순위상관계수와 단순상관계수의 범위가 0.63에서 0.71로 대체적으로 낮게 나타나 육종가와 표현형가의 상관관계를 더 높인다면 보다 효율적인 개량을 도모할 수 있을 것으로 사료되며, 선발반응 예측치 즉, 기대되는 유전적 개량량은 각장, 각폭 및 중량이 각각 9.6%, 9.5% 및 26.1%로 나타났다. 따라서 다음 세대에 있어서 보다 높은 선발반응을 기대하기 위해서는 선발의 정확도는 물론 최종교배를 위해 선발되는 개체의 비율과 선발강도를 적절히 고려하여야 할 것으로 사료된다.

• 한국융합학회논문지
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• 제11권9호
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• pp.163-168
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• 2020

이 연구의 목적은 스포츠클라이밍센터 이용객들이 인식하는 선택속성과 고객만족 및 재등록의사의 관계를 규명하는 것이다. 이를 위해 서울, 경기지역 소재 스포츠클라이밍센터 이용객들을 모집단으로 설정한 후 편의표본추출법을 이용하여 총 296부의 자료를 실제 분석에 사용하였다. 자료처리는 빈도분석, 탐색적 요인분석, 신뢰도 분석, 다중회귀분석을 실시하였다. 이를 통해 도출된 결론은 다음과 같다. 첫째, 선택속성 중 시설 요인은 고객만족에 정(+)의 영향을 미치며, 락커룸 요인은 부(-)의 영향을 미치는 것으로 나타났다. 둘째, 선택속성 중 지도자 관계 요인과 비용 요인은 재등록의사에 정(+)의 영향을 미치며, 직접적인 영향력은 비용 요인이 가장 큰 것으로 나타났다.

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.147-157
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• 2015

We consider a sparse high-dimensional linear regression model. Penalized methods using LASSO or non-convex penalties have been widely used for variable selection and estimation in high-dimensional regression models. In penalized regression, the selection and prediction performances depend on which penalty function is used. For example, it is known that LASSO has a good prediction performance but tends to select more variables than necessary. In this paper, we propose an additive sparse penalty for variable selection using a combination of LASSO and minimax concave penalties (MCP). The proposed penalty is designed for good properties of both LASSO and MCP.We develop an efficient algorithm to compute the proposed estimator by combining a concave convex procedure and coordinate descent algorithm. Numerical studies show that the proposed method has better selection and prediction performances compared to other penalized methods.

• Journal of the Korean Data and Information Science Society
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• 제27권2호
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• pp.499-510
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• 2016

생존분석 회귀모형에서 적절한 변수를 선택하는 것은 매우 중요하다. 본 논문에서는 "frailtyHL" R 패키지 (Ha 등, 2012)를 기반으로 하여 다수준 프레일티 모형 (multi-level frailty models)에서 벌점화 변수선택 방법 (penalized variable-selection method)의 절차를 소개한다. 여기서 모형 추정은 벌점화 다단계 가능도에 기초하며, 세 가지 벌점 함수 (LASSO, SCAD 및 HL)가 고려된다. 개발된 방법의 예증을 위해 벨기에 EORTC (European Organization for Research and Treatment of Cancer; 유럽 암 치료기구)에서 수행된 다국가/다기관 임상시험 자료를 이용하여 세 가지 변수 선택 방법의 결과를 비교하고, 그 결과들의 상대적 장 단점에 대해 토론한다. 특히, 자료 분석 결과에 의하면 SCAD와 HL방법이 LASSO보다 중요한 변수를 잘 선택하는 것으로 나타났다.

• Genomics & Informatics
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• 제10권2호
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• pp.81-87
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• 2012

Large-scale copy number variants (CNVs) in the human provide the raw material for delineating population differences, as natural selection may have affected at least some of the CNVs thus far discovered. Although the examination of relatively large numbers of specific ethnic groups has recently started in regard to inter-ethnic group differences in CNVs, identifying and understanding particular instances of natural selection have not been performed. The traditional $F_{ST}$ measure, obtained from differences in allele frequencies between populations, has been used to identify CNVs loci subject to geographically varying selection. Here, we review advances and the application of multinomial-Dirichlet likelihood methods of inference for identifying genome regions that have been subject to natural selection with the $F_{ST}$ estimates. The contents of presentation are not new; however, this review clarifies how the application of the methods to CNV data, which remains largely unexplored, is possible. A hierarchical Bayesian method, which is implemented via Markov Chain Monte Carlo, estimates locus-specific $F_{ST}$ and can identify outlying CNVs loci with large values of FST. By applying this Bayesian method to the publicly available CNV data, we identified the CNV loci that show signals of natural selection, which may elucidate the genetic basis of human disease and diversity.

• 대한안전경영과학회지
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• 제14권3호
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• pp.207-220
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• 2012

Nowadays, logistics parks are no longer simply storage areas but rather the center of corporate supply activities. As the importance of logistics increases, so does the value of logistics related facilities. Unlike the past, in which the sole purpose of logistics services was the spatial movement of materials through transportation, modern logistics are required to carry out functions such as timely adjustment and swift adaptation to changing patterns, and this leads to the emphasis on forming logistics parks. Logistics parks make profits using the efficiency of time and space. Such logistics parks play an important role in a corporation creating operating profits as well as acting as a method of alternative investment for individuals. Logistics parks no longer simply store materials, but have become a place that plays an important role in various areas of corporate and individual activities, and thus the analysis of the selection of the location of logistics parks and the related characteristics is extremely important. There are many existing studies on the selection of locations of logistics parks but work on the factors related to location selection by industry seem lacking. As such, in the course of this study we have used preceeding studies to draft a questionnaire on which selection factors affect the selection of logistics park location in different industries and conducted empirical analysis of the questionnaire results to uncover the factors that affect the selection of the locations of logistics parks in different industries.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2006년도 춘계공동학술대회 논문집
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• pp.1754-1757
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• 2006

Recently many firms operate a cross-docking center in addition to run a distribution center to reduce logistics costs and maintain or enhance logistics service. However, it is true that many firms just operate their cross-docking centers as they are without any change, in spite of that the location of the cross-docking center should be changed and operated when the location of distribution center is changed and moved. This study presents the method that re-selects the location of the cross-docking center when the existing distribution center is changed. Describing the operation environment to apply the cross-docking system and the selection criteria of the cross-docking center under the environment of changeable logistics network, we define the simulation model which can analyze and select the location of the cross-docking center applied to a logistics field. The simulation model presents experiential algorithm selecting the location with the data of the demand point such as volume, transportation costs, and delivery distance.