• 품질경영학회지
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• 제47권1호
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• pp.33-46
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• 2019

Purpose: This study proposed a DEA (Data Envelopment Analysis)-based stepwise benchmarking target selection for inefficient DMU (Decision Making Unit) to improve its efficiency gradually to reach most efficient frontier considering resource (DEA inputs and outputs) improvement preferences. Methods: The proposed method proceeded in two steps. First step evaluates efficiency of DMUs by using DEA, and an evaluated DMU selects benchmarking targets of HCU (Hypothesis Composit Unit) or RU (Real Unit) considering resource improvement preferences. Second step selects stepwise benchmarking targets of the inefficient DMU. To achieve this, this study developed a new DEA model, which can select a benchmarking target of an inefficient DMU in considering inputs or outputs improvement preference, and suggested an algorithm, which can select stepwise benchmarking targets of the inefficient DMU. Results: The proposed method was applied to 34 international ports for validation. In efficiency evaluation, five ports was evaluated as most efficient port, and the remaining 29 ports was evaluated as relative inefficient port. When port 34 was supposed as evaluated DMU, its can select its four stepwise benchmarking targets in assigning the preference weight to inputs (berth length, total area of pier, CFS, number of loading machine) as (0.82, 1.00, 0.41, 0.00). Conclusion: For the validation of the proposed method, it applied to the 34 major ports around the world and selected stepwise benchmarking targets for an inefficient port to improve its efficiency gradually. We can say that the proposed method enables for inefficient DMU to establish more effective and practical benchmarking strategy than the conventional DEA because it considers the resource (inputs or outputs) improvement preference in selecting benchmarking targets gradually.

• 수산해양기술연구
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• 제58권1호
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• pp.1-9
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• 2022

In this study, the selection action on the mesh in the net pot for whelk (Buccinum opisthoplectum) is experimentally considered, and the selectivity was compared by the SELECT model and the Nashimoto's method with the probability model according to the contact shape of the mesh and the whelk. The experiments of the mesh size selectivity was conducted for two mesh sizes: 70 mm (inner stretched size 65.4 mm) and 44 mm (inner stretched size 39.5 mm). Selectivity experiments were conducted three times in total for each mesh size used 264 whelks. In addition, Nashimoto's method analyzed the retention probability using probability model for whether the mesh passed or not based on the carapace width of the whelk. As a result of the selectivity analysis, the 50% selection carapace width for the mesh size of 70 mm was similar to 43.62 mm in the SELECT model and 42.64 mm in the Nashimoto's method. However, the 44 mm mesh with relatively small mesh size showed differences of 40.01 mm and 26.80 mm, respectively. As for the mesh size selectivity of whelk, it was found that the smaller the mesh size, the lower the selectivity. In addition, in the selectivity study on the mesh size of whelk, an evaluation method that closely considers the contact shape between the mesh and the target species is required.

• 한국도로학회논문집
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• 제9권4호
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• pp.227-236
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• 2007

선회다짐기를 이용하여 아스팔트 혼합물의 배합설계를 하는 경우 선회다짐기의 설계 다짐횟수가 필요하다. 본 연구는 이러한 선회다짐기의 설계 다짐횟수를 결정하는데 그 목적이 있다. 선회다짐횟수의 선정을 위하여 세 가지 방법을 이용하였다. 첫 번째 방법은 Marshall 다짐기로 75회 다짐된 혼합물의 밀도와 동일한 밀도를 주는 선회다짐횟수를 선정하는 것이며, 두 번째 방법은 Marshall 다짐기로 75회 다짐된 혼합물의 변형강도와 동일한 변형강도를 주는 선회다짐횟수를 선정하는 것이다. 세 번째 방법은 선회다짐횟수에 따른 공극률을 측정하여 공극률 4%에 해당하는 다짐횟수를 찾아 결정하는 방법이다. 실내 실험을 위해 총 10가지 종류의 아스팔트 혼합물(1가지 골재$\times$10가지 입도$\times$1가지 아스팔트 바인더)이 제작되었다. 세 가지 방법을 종합한 결과, 아스팔트 혼합물의 배합설계를 위한 설계 선회다짐횟수는 100회로 결정되었다. 이러한 결과는 외국에서 사용되고 있는 설계 선회다짐횟수와 유사한 경향을 보이는 결과로, 향후 국내에서 선회다짐기를 이용한 아스팔트 혼합물의 배합설계가 이루어질 경우 적용이 가능할 것으로 판단된다.

• 터널과지하공간
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• 제14권4호
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• pp.275-286
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• 2004

본 논문에서는 신규 터널노선과 교차하는 폐터널의 보강방식을 결정하기 위하여 의사결정기법을 검토하였고, 여러 가지 의사결정기법들 중에서 설문조사의 과정을 최소화 하고, 조사항목 별 정성적ㆍ정량적 특성을 모두 반영할 수 있도록 쌍대비교와 퍼지근사추론을 이용하여 폐터널의 보강방식에 대한 적정성을 평가하여 보았다. 페터널 보강방식을 선정하기 위하여 4개의 주 요인들 즉, 시공성, 경제성, 안전성, 유지관리성을 평가의 수단으로 사용하였고, 간단한 설문조사와 쌍대비교행렬을 이용하여 4가지 주 요인들의 가중치를 결정하였다. 퍼지근사추론은 4개의 주 요인별 평가점수를 산정 하는데 사용되어졌고, 이 결과들에 가중치를 반영하여 최종적인 폐터널의 보강방식을 선정할 수 있었다.

• 융합신호처리학회논문지
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• 제19권1호
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• pp.1-6
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• 2018

본 논문에서는 다중 스케일 영상 공간에서 특징점 검출을 위해 수행되는 반복적인 과정을 제거하는 방법을 제안한다. 제안한 방법은 원 영상으로부터 특징점을 검출하고, 클러스터 필터를 이용하여 유효한 특징점을 선별하고, 특징점 클러스터를 생성한다. 그리고 특징점 클러스터의 방향 각도를 이용하여 참조 객체를 선별하고, 분산 거리 비율을 이용하여 원 영상의 스케일을 예측한다. 예측한 스케일에 따라 참조 영상의 스케일을 변환하고, 변환된 참조 영상에 대해 특징점 검출을 적용한다. 실험 결과 제안한 방법은 다중 스케일 영상을 사용하는 SIFT 방법 및 Scaled ORB 방법에 비해 특징점 검출 시간이 각각 75% 및 71% 향상됨을 알 수 있었다.

• Journal of the Optical Society of Korea
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• 제13권1호
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• pp.116-122
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• 2009

In this paper, a robust method is developed to locate the irises of both eyes. The method doesn't put any restrictions on the background. The method is based on the AdaBoost algorithm for face and eye candidate points detection. Candidate points are tuned such that two candidate points are exactly in the centers of the irises. Mean crossing function and convolution template are proposed to filter out candidate points and select the iris pair. The advantage of using this kind of hybrid method is that AdaBoost is robust to different illumination conditions and backgrounds. The tuning step improves the precision of iris localization while the convolution filter and mean crossing function reliably filter out candidate points and select the iris pair. The proposed structure is evaluated on three public databases, Bern, Yale and BioID. Extensive experimental results verified the robustness and accuracy of the proposed method. Using the Bern database, the performance of the proposed algorithm is also compared with some of the existing methods.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.41-45
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• 2023

Currently, the method of loading items in the warehouse, the worker directly decides the loading location, and the most used method is to load the product at the location closest to the entrance. This can be effective when there is no difference in the required amount for goods, but when there is a difference in the required amount for goods, it is inefficient because items with a small required amount are loaded near the entrance and occupy the corresponding space for a long time. Therefore, in order to minimize the release time of goods, it is essential to select an appropriate location when loading goods. In this study, a method for determining the loading location by predicting the required amount of goods was studied to select the optimal loading location. Deep learning based bidirectional long-term memory networks (Bi-LSTM) was used to predict the required amount for goods. This study compares and analyzes the release time of goods in the conventional method of loading close to the entrance and in the loading method using the required amount for goods using the Bi-LSTM model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1589-1592
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• 1997

Among the prerequisite abilities (perception of environment, path planning and position estimation) of an autonomous mobile robot, position estimation has been seldom studied by mobile robot researchers. In most cases, conventional positioin estimation has been performed by placing landmarks or giving the entrire environmental information in advance. Unlikely to the conventional ones, the study addresses a new method that the robot itself can select distinctive features in the environment and save them as landmarks without any a priori knowledge, which can maximize the autonomous behavior of the robot. First, an orjentaion probaility model is applied to construct a lcoal map of robot's surrounding. The feature of the object in the map is then extracted and the map is saved as landmark. Also, presented is the position estimation method that utilizes the correspondence between landmarks and current local map. In dong this, the uncertainty of the robot's current positioin is estimated in order to select the corresponding landmark stored in the previous steps. The usefulness of all these approaches are illustrated with the results porduced by a real robot equipped with ultrasonic sensors.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• 전기학회논문지
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• 제60권10호
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• pp.1951-1958
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• 2011

The objective of this study is to analyze the performance of digital low-pass differentiators(LPD) and then to provide a method to select effective LPD filter, for detecting spikes of surface motor unit action potentials(MUAP). The successful spike detection of MUAPs is a first important step for EMG signal decomposition. The performances of simple and weighted LPD(SLPD and WLPD) filters are analyzed based on different filter lengths and varying MUAPs from simulated surface EMG signals. The SNR improving coefficient and effective MUAP duration range from the analysis results can be used to select proper LPD filters under the varying conditions of surface EMG.