• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.253-255
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• 2016

본 논문에서는 다중 지역 이진 패턴(Multi-scale Bock LBP, MB-LBP) 특징과 랜덤 포레스트에 기반한 새로운 기법의 머리 방향 분류 기법을 제안한다. 제안 기법에서는 occlusion 과 조명의 변화에 강인한 분류 정확도를 얻기 위해서 랜덤화된 트리를 학습하는 것을 목표로 한다. 우선, 얼굴 이미지로부터 많은 MB-LBP 특징을 추출하고, 얼굴 영상들을 랜덤하게 입력하고 MB-LBP 크기 파라미터와 같은 랜덤 특징과 블록 좌표들을 사용하여 트리를 생성한다. 게다가 각 노드에서 정보 이득을 최대화 하는 트리의 내부 노드를 생성하기 위해서 uniform LBP 의 특성을 고려한 분할 함수를 개발한다. 랜덤화된 트리는 랜덤 포레스트에 포함되어 있으며 마지막 결정단계에서 Maximum-A-Posteriori criterion 으로 최종 결정을 한다. 실험 결과는 제안 기법이 다양한 조명, 자세, 표현, occlusion 상황에서 기존의 방법보다 개선된 성능으로 머리 방향을 분류 할 수 있음을 보여준다.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.855-863
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• 2016

In this study, a simple diagnostic statistic for determining the size of random forest is proposed. This method is based on MV (margin of victory), a scaled difference in the votes at the infinite forest between the first and second most popular categories of the current random forest. We can note that if MV is negative then there is discrepancy between the current and infinite forests. More precisely, our method is based on the proportion of cases that -MV is greater than a fixed small positive number (say, 0.03). We derive an appropriate diagnostic statistic for our method and then calculate the distribution of the statistic. A simulation study is performed to compare our method with a recently proposed diagnostic statistic.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.515-524
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• 2017

In this study, a measure of discrepancy based on MV (margin of victory) has been suggested that might be useful in determining the size of random forest for classification. Here MV is a scaled difference in the votes, at infinite random forest, of two most popular classes of current random forest. More specifically, max(-MV,0) is proposed as a reasonable measure of discrepancy by noting that negative MV values mean a discrepancy in two most popular classes between the current and infinite random forests. We propose an appropriate diagnostic statistic based on this measure that might be useful for the determination of random forest size, and then we derive its asymptotic distribution. Finally, a simulation study has been conducted to compare the performances, in finite samples, between this proposed statistic and other recently proposed diagnostic statistics.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.139-148
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• 2020

The most important factor affecting the berthing energy generated when a ship berths is the berthing velocity. Thus, an accident may occur if the berthing velocity is extremely high. Several ship features influence the determination of the berthing velocity. However, previous studies have mostly focused on the size of the vessel. Therefore, the aim of this study is to analyze various features that influence berthing velocity and determine their respective importance. The data used in the analysis was based on the berthing velocity of a ship on a jetty in Korea. Using the collected data, machine learning classification algorithms were compared and analyzed, such as decision tree, random forest, logistic regression, and perceptron. As an algorithm evaluation method, indexes according to the confusion matrix were used. Consequently, perceptron demonstrated the best performance, and the feature importance was in the following order: DWT, jetty number, and state. Hence, when berthing a ship, the berthing velocity should be determined in consideration of various features, such as the size of the ship, position of the jetty, and loading condition of the cargo.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.407-419
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• 2022

The semiconductor fabrication process is complex and time-consuming. There are sometimes errors in the process, which results in defective die on the wafer bin map (WBM). We can detect the faulty WBM by finding some patterns caused by dies. When one manually seeks the failure on WBM, it takes a long time due to the enormous number of WBMs. We suggest a two-step approach to discover the probable pattern on the WBMs in this paper. The first step is to separate the normal WBMs from the defective WBMs. We adapt a hierarchical clustering for de-noising, which nicely performs this work by wisely tuning the number of minimum points and the cutting height. Once declared as a faulty WBM, then it moves to the next step. In the second step, we classify the patterns among the defective WBMs. For this purpose, we extract features from the WBM. Then machine learning algorithm classifies the pattern. We use a real WBM data set (WM-811K) released by Taiwan semiconductor manufacturing company.