• 한국수산과학회지
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• 제50권2호
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• pp.195-206
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• 2017

Joint time-frequency images of the broadband echo signals of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution in controlled environments. Affine transformation and principal component analysis were used to obtain eigenimages that provided species-specific acoustic features for each of the six fish species. The echo images of an unknown fish species, acquired in real time and in a fully automated fashion, were identified by finding the smallest Euclidean or Mahalanobis distance between each combination of weight matrices of the test image of the fish species to be identified and of the eigenimage classes of each of six fish species in the training set. The experimental results showed that the Mahalanobis classifier performed better than the Euclidean classifier in identifying both single- and mixed-species groups of all species assessed.

• International Journal of Quality Innovation
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• 제3권2호
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• The Journal of the Acoustical Society of Korea
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• 제29권1E호
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• pp.45-53
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• 2010

In this paper, we present the use of the Features Weighted Mahalanobis Distance (FWMD) in improving the performance of Likelihood Maximizing Beamforming (Limabeam) algorithm in speech recognition for microphone array. The proposed approach is based on the replacement of the traditional distance measure in a Gaussian classifier with adding weight for different features in the Mahalanobis distance according to their distances after the variance normalization. By using Features Weighted Mahalanobis Distance for Limabeam algorithm (FWMD-Limabeam), we obtained correct word recognition rate of 90.26% for calibrate Limabeam and 87.23% for unsupervised Limabeam, resulting in a higher rate of 3% and 6% respectively than those produced by the original Limabearn. By implementing a HM-Net speech recognition strategy alternatively, we could save memory and reduce computation complexity.

• 한국멀티미디어학회논문지
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• 제14권6호
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• pp.711-718
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• 2011

얼굴인식과 같이 클래스의 수가 변하는 분류 문제에는 학습이 필요하지 않은 k-최근접이웃 분류기가 적합하다. 최근 학습 데이터의 분포를 반영하여 거리 척도를 학습하는 방법은 k 최근접이웃 분류기의 획기적 성능향상을 보고하였다. 거리척도학습 방법은 적용 분야에 따라 성능 개선 정도가 다르다. 본 논문에서는 얼굴인식에 대하여 주요 거리척도학습 방법의 성능을 비교한다. 공개 얼굴 데이터베이스에 대한 실험 결과는 성능 및 계산시간 측면에서 주성분 분석 기반의 마하라노비스 거리척도가 얼굴인식 문제에서는 여전히 좋은 선택이 될 수 있음을 보여준다.

• 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.

• 전자공학회논문지CI
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• 제43권1호
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• pp.9-16
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• 2006

RDA(Relational Discriminant Analysis)는 패턴의 특징벡터 대신에 학습 패턴을 대표하는 프로토타입들과의 비유사도 벡터에 기반하여 식별기를 설계하는 방법이다. 따라서 RDA 식별기의 성능은 프로토타입을 선택하는 방법과 비유사도를 측정하는 방법에 따라 결정된다. 본 논문에서는 PRS(Prototype Reduction Schemes)를 이용하여 프로토타입을 추출한 다음, 샘플 벡터들간의 마할라노비스 거리에 의한 상관행렬로 RDA의 식별성능을 향상시키는 방법을 제안한다. 인공 데이터 및 실-생활 데이터를 대상으로 실험한 결과, 제안한 방법의 식별성능이 기존의 방법에 비하여 개선되었음을 확인하였다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1993년도 Proceedings of International Conference for Agricultural Machinery and Process Engineering
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• pp.403-412
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• 1993

This paper presents the progress of the development of a nondestructive technique for the classification of normal, septicemic , and cadaver poultry carcasses by the Instrumentation and Sensing Laboratory at Beltsville, Maryland, U.S.A. The Sensing technique is based on the diffuse reflectance spectroscopy of poultry carcasses.

• 대한전자공학회논문지
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• 제27권8호
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• pp.1239-1249
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• 1990

In this paper, using the neural network design techniques, an adaptive Mahalanobis distance classifier(AMDC) is designed. This classifier has three layers: input layer, internal layer and output layer. The connection from input layer to internal layer is fully connected, and that from internal to output layer has partial connection that might be thought as an Oring. If two ormore clusters of patterns of one class are laid apart in the feature space, the network adaptively generate the internal nodes, whhch are corresponding to the subclusters of that class. The number of the output nodes in just same as the number of the classes to classify, on the other hand, the number of the internal nodes is defined by the number of the subclusters, and can be optimized by itself. Using the method of making the subclasses, the different patterns that are of the same class can easily be distinguished from other classes. If additional training is needed after the completion of the traning, the AMDC does not have to repeat the trainging that has already done. To test the performance of the AMDC, the experiments of classifying 500 Hangeuls were done. In experiment, 20 print font sets of Hangeul characters(10,000 cahracters) were used for training, and with 3 sets(1,500 characters), the AMDC was tested for various initial variance \ulcornerand threshold \ulcorner and compared with other statistical or neural classifiers.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제15권6호
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• pp.464-468
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• 2009

본 연구는 Apoptosis세포들의 형상을 검출하기 위하여 전통적인 세포측정법과는 다른 영상기반 접근법을 제안한다. 이 방법은 세포측정 법의 단점을 극복하고 Apoptosis 세포들을 정확하게 인식할 수 있다. 본 연구에서 K-means 군집화 방법이 Apoptosis 세포의 거시적인 분할을 행하는 데 사용되었으며, '스네이코'라고 불리는 액티브 윤곽선 모델이 정밀한 경계선 검출을 위해 사용되었다. 그리고 Apoptosis세포들의 물리적 특징, 형태적 특징 그리고 무늬특징들을 포함하는 몇가지 특징들이 추출되었다. 마지막으로 Mahalanobis 거리 분류기가 Apoptosis세포와 비Apoptosis 세포로서 분할영상들을 분류한다.

• 한국지능시스템학회논문지
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• 제20권5호
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• pp.677-682
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• 2010

본 논문은 AdaBoost 알고리즘을 통한 얼굴 검출 기술에서 학습된 하-웨이블렛의 개별값을 비교하여 대략적인 자세를 추정하는 방법과 이를 이용한 얼굴인식 속도 향상에 대하여 기술한다. 학습된 약한 분류기는 얼굴 검출 과정 중 각각 계수값을 비교하여 각 자세의 특징에 강인한 하-웨이블렛을 선별한다. 하-웨이블렛 선별과정에는 각 항목의 유사도를 나타내는 마할라노비스 거리를 사용하였다. 선별된 하-웨이블렛을 사용하여 임의의 얼굴 이미지를 검출하였을 때 각각의 자세를 구별하는 결과를 전체 실험결과를 통해 평가한다.