• 한국응용곤충학회지
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• 제31권1호
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• pp.37-44
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• 1992

한국, 중국, 필리핀산 흰등멸구의 계량형태학적 차이를 분석하기를 위하여 총 89개 부분의 형태적 특징(촉각 34, 다리45, 주둥이10)을 조사하고 계량형태적 분석을 위하여 정준 판별분석법을 이용하였다. Scatter plot diagram 상에서 3개 집단의 중심점은 분리현상이 뚜렷하였고 그 정도는 단시형 암컷에서 크게 나타났다. Mahalanobis distance(MD)는 단시형 암컷의 경우 3개 집단 모두 5%에서 유의성이 있었고, 장시형 암컷의 경우 MD는 중국산 대 필리핀산, 한국산 대 필리핀산이 각각 0.1%, 1%에서 유의성이 있었으나, 중국산 대 한국산은 유의성이 없었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.105.1-105
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• 2001

In this paper, a technique is described for classifying normal cases and abnormal cases in automatic spinal deformity detection by computer based on moire topographic images of human backs. Displacement is evaluated statistically between the middle line extracted from the entire moire image and the middle line obtained from a small rectangle area defined on the moire image. The middle line is calculated employing a developed potential symmetry analysis technique. The displacement is calculated in several regions and the mean and the standard deviation of the displacement values are chosen as two features. A linear discriminant function (LDF) is defined on the 2-D feature space based on the Mahalanobis distance and the features are classified into two categories, i.e., normal and ...

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1998년도 가을 학술발표논문집 Vol.25 No.2 (2)
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• pp.458-460
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• 1998

본 논문은 주성분 분석으로 시점 기반 고유얼굴(view-based eigenface)을 생성하고, 그에 기반한 얼굴 인식을 수행하고자 한다. 주성분 분석을 통한 고유얼굴 생성은 얼굴 인식의 어려운 문제 중 하나인 특징 선택과 추출이라는 문제를 해결해 준다. 또한 얼굴 표정이나 방향의 변화에도 인식률이 저하되는 것을 방지할 수 있다. 얼굴 영상을 특징공간(고유공간)으로 변환할 때, 원 얼굴영상의 정보를 최대한으로 나타낼 수 있는 최적의 고유치 개수 선택은 얼굴 데이터베이스의 크기와 인식 속도에 영향을 끼친다. 따라서 본 논문에서는 고유치 개수를 고유치의 누적기여율을 이용해서 구한다. 이는 64$\times$64(=4096)차원의 원 얼굴 영상을 5~7차원으로 표현 가능하게 하였다. 그리고, 각 얼굴 방향에 따라 특징공간을 분리해서 생성함으로써 얼굴 방향의 변화에 따라 오인식률을 줄였다. 축소된 차원과 분리된 특징공간은 메모리 사용과 인식속도의 향상에 기여한다. 본 논문에서 얼굴의 인식은 Mahalanobis distance와 재구성 오차율을 고려해서 이루어졌다. 실험은 개인당 세가지 다른 방향을 가지는 얼굴 영상을 이용하여 이루어졌고, 실험결과, 약 93%의 인식률을 보여주었다.

• Communications for Statistical Applications and Methods
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• 제7권3호
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• pp.667-676
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• 2000

This paper focuses on the problem of detecting multiple leverage points and outliers in multivariate linear models. It is well known that he identification of these points is affected by masking and swamping effects. To identify them, Rousseeuw(1985) used robust estimators of MVE(Minimum Volume Ellipsoids), which have the breakdown point of 50% approximately. And Rousseeuw and van Zomeren(1990) suggested the robust distance based on MVE, however, of which the computation is extremely difficult when the number of observations n is large. In this study, e propose a new algorithm to reduce the computational difficulty of MVE. The proposed method is powerful in identifying multiple leverage points and outlies and also effective in reducing the computational difficulty of MVE.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.29-38
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• 2006

We proposed a procedure for detecting of multiple outliers in multivariate data. Rousseeuw and van Zomeren (1990) have suggested the robust distance $RD_i$ by using the Resampling Algorithm. But $RD_i$ are based on the assumption that X is in the general position.(X is said to be in the general position when every subsample of size p+1 has rank p) From the practical points of view, this is clearly unrealistic. In this paper, we proposed a computing method for approximating MVE, which is not subject to these problems. The procedure is easy to compute, and works well even if subsample is singular or nearly singular matrix.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.225-232
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• 2000

The linear and quadratic discrimination functions based on normal theory are widely used to classify an observation to one of predefined groups. But the discriminant functions are sensitive to outliers. A high breakdown procedure to estimate location and scatter of multivariate data is the minimum volume ellipsoid or MVE estimator To obtain high breakdown classifiers outliers in multivariate data are detected by using the robust Mahalanobis distance based on MVE estimators and the weighted estimators are inserted in the functions for classification. A samll-sample MOnte Carlo study shows that the high breakdown robust procedures perform better than the classical classifiers.

• 응용통계연구
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• 제26권1호
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• pp.201-208
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• 2013

이 논문에서는 공정변수를 갖는 혼합물 실험에 대하여 적절한 모형을 선택하는 한 방법으로 혼합물 성분의 공선성에 로버스트한 성질을 갖는 마할라노비스-다구찌 시스템을 활용한 전략을 소개한다. 여러 문헌에서 언급된 3개의 혼합물 성분과 2개의 공정변수를 갖는 제빵 실험 사례를 대상으로 이 전략적 방법을 적용하여 적절한 모형을 선택하였다.

• International Journal of Control, Automation, and Systems
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• 제2권3호
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• pp.354-361
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• 2004

To improve fault isolation performance of the Bayes isolator, this paper proposes the Fuzzy-Bayes isolator, which uses the Fuzzy-Bayes classifier as a fault isolator. The Fuzzy-Bayes classifier is composed of the Bayes classifier and weighting factor, which is determined by fuzzy inference logic. The Mahalanobis distance derivative is mapped to the weighting factor by fuzzy inference logic. The Fuzzy-Bayes fault isolator is designed for the BLDC motor fault diagnosis system. Fault isolation performance is evaluated by the experiments. The research results indicate that the Fuzzy-Bayes fault isolator improves fault isolation performance and that it can reduce the transition region chattering that is occurred when the fault is injected. In the experiment, chattering is reduced by about half that of the Bayes classifier's.

• 대한전자공학회논문지
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• 제24권5호
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• pp.880-886
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• 1987

This paper proposes a new pattern recognition system using Radon transform and analyzes the performances of the system for given input patterns. The proposed system uses many optical parts in order to utilize the high speed characteristics of light and processes a signal easily by transforming 2-D image into a 1-D signal to increase flexibility. The squared Mahalanobis distance obtained from means and standard deviations of the features for the given input patterns is used for discrimination. As a result, this system represents a better recognition rate than any other systems using the same input patterns.

• 반도체디스플레이기술학회지
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• 제12권3호
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• pp.53-57
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• 2013

Human emotions are expressed in various ways. It can be expressed through language, facial expression and gestures. In particular, the facial expression contains many information about human emotion. These vague human emotion appear not in single emotion, but in combination of various emotion. This paper proposes a emotional expression algorithm using Active Appearance Model(AAM) and Fuzz k- Nearest Neighbor which give facial expression in similar with vague human emotion. Applying Mahalanobis distance on the center class, determine inclusion level between center class and each class. Also following inclusion level, appear intensity of emotion. Our emotion recognition system can recognize a complex emotion using Fuzzy k-NN classifier.