• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.227-230
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• 1997

In this paper, robot navigation experiments with a new navigation algorithm are carried out in real environments. The authors already proposed a reactive navigation algorithm for mobile robots using optimal via-point selection method. At each sampling time, a number of via-point candidates is constructed with various candidates of heading angles and velocities. The robot detects surrounding obstacles, and the proposed algorithm utilizes fuzzy multi-attribute decision making in selecting the optimal via-point the robot would proceed at next step. Fuzzy decision making allows the robot to choose the most qualified via-point even when the two navigation goals-obstacle avoidance and target point reaching-conflict each other. The experimental result shows the successful navigation can be achieved with the proposed navigation algorithm for real environments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.225-228
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• 1992

The purpose of this project is the presentation of new method for selection of a scalar control of linear time-periodic system. The approach has been proposed by Radziszewski and Zaleski [4] and utilizes the quadratic form of Lyapunov function. The system under consideration is assigned either in closed-loop state or in modal variables as in Calico, Wiesel [1]. The case of scalar control is considered, the gain matrix being assumed to be at worst periodic with the system period T, each element being represented by a Fourier series. As the optimal gain matrix we consider the matrix ensuring the minimum value of the larger real part of the two Poincare exponents of the system. The method, based on two-step optimization procedure, allows to find the approximate optimal gain matrix. At present state of art determination of the gain matrix for this case has been done by systematic numerical search procedure, at each step of which the Floquet solution must be found.

• 한국소음진동공학회논문집
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• 제14권9호
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• pp.819-826
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• 2004

A multi-step optimum strategy for the selection of the locations and directions of strain gauges is proposed in this paper to capture at best the modal response of blade in a series of modes on fan blades. It is consist of three steps including two pass reduction step, genetic algorithm and fine optimization to find the locations-directions of strain gauges. The optimization is based upon the maximum signal-to-noise ratio(SNR) of measured strain values with respect to the inherent system measurement noise, the mispositioning of the gauge in location and gauge failure. Optimal gauge positions for a fan blade is analyzed to prove the effectiveness of the multi-step optimum methodology and to investigate the effects of the considering parameters such as the mispositioning level, the probability of gauge failure, and the number of gauges on the optimal strain gauge position.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2000년도 춘계 학술대회 논문집 통권 3호 Proceedings of the 2000 KSRS Spring Meeting
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• pp.9-9
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• 2000

For the planning of future land use for economic activities, an essential component is the identification of the vulnerable areas for natural hazard and environmental impacts from the activities. Also, exploration for mineral and energy resources is carried out by a step by step approach. At each step, a selection of the target area for the next exploration strategy is made based on all the data harnessed from the previous steps. The uncertainty of the selected target area containing undiscovered resources is a critical factor for estimating the exploration risk. We have developed not only spatial prediction models based on adapted artificial intelligence techniques to predict target and vulnerable areas but also validation techniques to estimate the uncertainties associated with the predictions. The prediction models will assist the scientists and decision-makers to make two critical decisions: (i) of the selections of the target or vulnerable areas, and (ii) of estimating the risks associated with the selections.

• 지능정보연구
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• 제12권1호
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• pp.75-93
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• 2006

화주의 선박 선정과정은 선박과 화물의 일정에 따른 1차 선정과 화물을 재선적하여 하나의 단위로 선복을 집중시키는 2차 선정으로 구분된다. 지금까지 3자 물류업체는 이러한 선적업무가 수작업으로 진행됨으로써 비효율성을 초래하였다. 그러므로 본 연구에서는 에이전트 협상을 통해 전체 물류비를 감소시킬 수 있는 방안을 제안하고자 한다. 화물의 집중과 배분을 통해 얻을 수 있는 물류비 절감을 최대화시키기 위해 재고비와 운송비간의 상관관계에 서 최적점을 찾아야 하며 이를 화주간 협상으로 해결할 수 있다. 실험에서는 현업에서 이루어지는 화물 선적방법인 EPDS(Earliest Possible Departure-Date Scheduling)와 LPDS(Latest Possible Departure-Date Scheduling)에 본 협상방법론을 접목하여 SBF(Scheduling Bundle Factor, 선적동시 처리량)에 따른 재고비, 운송비 그리고 물류비등을 도출하고 실험결과를 분석하였다. 분석결과, 에이전트 기반의 협상방법론이 EPDS와 사용될 경우 전체 물류비를 최소화시킬 수 있었다.

• Asian-Australasian Journal of Animal Sciences
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• 제33권12호
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• pp.1912-1921
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• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• 농촌계획
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• 제13권2호
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• pp.17-26
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• 2007

This study aims to develop a rational evaluation system which consists of the selection of evaluation items and the estimation of its weight for the Enhancement Program of the Quality of Life for Farmers and Fishermen and Rural Development. This system has two hierarchical steps. The first step shows the evaluation goals which are relevance, efficiency and effectiveness of the program. The second step stands for the evaluation items which have 11 sub-items such as necessity and externalization for the program, rationality of procedures, feedback and monitoring system, budgetary allocations, information activities, impacts on the program, achievements of the goals and so on. A tentative evaluation system was proposed by brainstorming and Delphi method of expert-group. Weighting values of evaluation items were calculated through pair-comparison works of expert group using stepwise matrix sheets by AHP(Analytic Hierarchy Process).

• Communications for Statistical Applications and Methods
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• 제24권2호
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• 한국지리정보학회지
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• 제4권4호
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• pp.8-20
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• 2001

본 연구는 공공시설의 적지선정을 위해 기존의 방법과는 다른 방법론을 제시하였다. 본 연구의 적지선정모형은 다양한 입지인자를 고려하여 입지인자의 상대적인 중요도에 따라 다단계로 가중치를 부여하여 후보지를 평가하는 방법이다. 여기에 GIS의 도면중첩방법(지도대수)을 적용하여 여러 다양한 입지요인을 만족시키는 후보지를 찾아내는 방법을 제시하였다. GIS를 이용한 적지분석은 간단하면서도 효과적이고, 많은 자료를 시각화할 수 있는 장점이 있다. 이 방법을 사례지구인 대구시 달성군 군청사의 입지선정에 적용하여 후보지를 평가하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2162-2179
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• 2019

The performance of text classification is highly related to the feature selection methods. Usually, two tasks are performed when a feature selection method is applied to construct a feature set; 1) assign score to each feature and 2) select the top-N features. The selection of top-N features in the existing filter-based feature selection methods is biased by their discriminative power and the empirical process which is followed to determine the value of N. In order to improve the text classification performance by presenting a more illustrative feature set, we present an approach via a potent representation learning technique, namely DBN (Deep Belief Network). This algorithm learns via the semantic illustration of documents and uses feature vectors for their formulation. The nodes, iteration, and a number of hidden layers are the main parameters of DBN, which can tune to improve the classifier's performance. The results of experiments indicate the effectiveness of the proposed method to increase the classification performance and aid developers to make effective decisions in certain domains.