• ETRI Journal
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• 제36권3호
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• pp.333-342
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• 2014

Most hyper-ellipsoidal clustering (HEC) approaches use the Mahalanobis distance as a distance metric. It has been proven that HEC, under this condition, cannot be realized since the cost function of partitional clustering is a constant. We demonstrate that HEC with a modified Gaussian kernel metric can be interpreted as a problem of finding condensed ellipsoidal clusters (with respect to the volumes and densities of the clusters) and propose a practical HEC algorithm that is able to efficiently handle clusters that are ellipsoidal in shape and that are of different size and density. We then try to refine the HEC algorithm by utilizing ellipsoids defined on the kernel feature space to deal with more complex-shaped clusters. The proposed methods lead to a significant improvement in the clustering results over K-means algorithm, fuzzy C-means algorithm, GMM-EM algorithm, and HEC algorithm based on minimum-volume ellipsoids using Mahalanobis distance.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 추계학술대회 논문집
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• pp.346-353
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• 2013

Industrial processes need to be monitored in real-time based on the input-output data observed during their operation. Abnormalities in an induction motor should be detected early in order to avoid costly breakdowns. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results shows that the proposed approach yields higher classification accuracies than the state-of-the-art approach for both noiseless and noisy environments for identifying the induction motor faults.

• 대한원격탐사학회지
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• 제32권5호
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• pp.471-482
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• 2016

본 논문에서는 수계 영역의 감독 분류 성능을 향상시키기 위하여 블록 기반의 영상 분할과 수계 경계의 확장을 이용하는 수계 검출 방법을 제안한다. 초기 수계 영역을 추출하기 위하여 수계 훈련 지역의 Normalized Difference Water Index (NDWI) 및 Near Infrared (NIR) 밴드 영상의 분광 정보를 이용하여 Mahalanobis 거리 영상을 생성한다. Mahalanobis 거리 영상에 포함된 잡음 성분의 영향을 감소시키기 위해서 인접한 화소의 연결 강도에 의해 확산 계수가 제어되는 평균 곡률 확산을 적용한 후에 초기 수계 영역을 추출한다. 추출된 수계 영상을 같은 크기의 블록으로 분할한 후에 수계 경계에 속하는 수계 영역의 정보를 이용하여 수계 영역을 갱신한다. 수계 경계에 속하는 수계 영역과 수계 훈련 지역 사이의 통계적인 거리가 임계값 이하이면, 수계 영역 갱신을 반복적으로 수행한다. 제안한 알고리즘을 KOMPSAT-2 영상에 적용한 결과 블록 크기가 $11{\times}11$에서 $19{\times}19$사이인 경우에 overall accuracy는 99.47%에서 99.53%, Kappa coefficient는 95.07%에서 95.80%의 분류 정확도를 보였다.

• 한국소음진동공학회논문집
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• 제18권4호
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• pp.393-399
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• 2008

The reduction of the Vehicle interior noise has been the main interest of NVH engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. And, optimal characteristic values influenced on the result of the SQ evaluation were derived by signal to noise ratio(SN ratio) of the Taguchi method. Finally, the new method to evaluate SQ is constructed using Mahalanobis-Taguchi system(MTS). Furthermore, the MTS method for SQ evaluation was compared by the result of SQ grade table at the previous study and their virtues and faults introduced.

• 지구물리와물리탐사
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• 제17권3호
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• pp.137-146
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• 2014

원주 KSRS 지진관측망을 이용하여 한반도 및 주변지역의 인공지진원을 식별하기 위한 연구를 수행하였다. 신호대 잡음비가 높은 자연지진 150개와 인공지진 56개를 선별하여 표본 집단을 형성하였다. 2차원 Pg/Lg 스펙트럼 진폭 비의 격자를 구성하여 지진원 식별이 용이한 최적의 주파수 영역으로 Pg(4-6 Hz)/Lg(5-7 Hz)을 도출하였다. 스펙트럼으로 부터 진폭에 대한 지진 규모와 진원 거리의 영향을 보정함으로써 식별 능력을 향상시키고자 하였다. 지진모멘트에 대한 모서리주파수의 역비례 관계로 인한 규모 의존 효과를 보정하기 위하여, Brune의 진원 모델을 가정한 이론 스펙트럼을 관측 스펙트럼에서 제거하였다. 진원 거리에 따른 감쇠효과를 제거하기 위하여, 최적의 감쇠상수를 계산하여 스펙트럼을 보정하였다. 지진파 전파 경로에 대한 보정으로 지역에 따라 스펙트럼 진폭 비가 달라지는 효과를 제거하였다. 자연지진과 인공지진 표본 집단 사이의 식별 정도를 각 보정 단계에서 Mahalanobis 거리 계산을 통하여 비교하였다. 규모와 거리 및 경로 보정 전후 두 표본 집단 사이의 Mahalanobis 거리가 1.98에서 3.01로 증가하여 식별 결과에 뚜렷한 향상이 있었다.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.529-536
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• 2006

Investigators use either Euclidean distance or volume of a simplex defined composed of data points as agreement index to measure chance-corrected agreement among observers for multivariate interval data. The agreement coefficient proposed by Um(2004) is based on a volume of a simplex and does not depend on the variables' measuring units. We consider a comparison of Um(2004)'s agreement coefficient with others based on two unit-free distance measures, Pearson distance and Mahalanobis distance. Comparison among them is made using hypothetical data set.

• 전자공학회논문지
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• 제52권12호
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• pp.174-182
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• 2015

본 연구에서는 저가형 컬러센서를 이용하여 원거리 인식이 가능한 소형 RGB 센서를 개발하였다. 이 센서의 수광부에는 원거리 인식을 위해 카메라 렌즈를 사용하였으며, 고출력 백색 LED와 반사경이 장착된 렌즈를 조명부에 사용하여 조명의 강도를 높였다. RGB 색상 인식 알고리즘은 학습과정과 실시간 인식과정으로 구성되어 있다. 학습과정에서는 기준색으로 도색된 시편을 이용하여 RGB 색상에 대한 정규화된 기준 데이터를 취득하고, 인식과정에서는 마할라노비스 거리를 이용하여 3색을 분류한다. 개발한 RGB 색상 인식 센서를 부품 분류 시제품에 적용하여 성능을 검증하였다.

• The Journal of the Acoustical Society of Korea
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• 제26권2E호
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• pp.39-43
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• 2007

Music summarization refers to a technique which automatically extracts the most important and representative segments in music content. In this paper, we propose and evaluate a technique which provides the repeated part in music content as music summary. For extracting a repeated segment in music content, the proposed algorithm uses the weighted sum of similarity measures based on multi-level vector quantization for fixed-length summary or optimal-length summary. For similarity measures, count-based similarity measure and distance-based similarity measure are proposed. The number of the same codeword and the Mahalanobis distance of features which have same codeword at the same position in segments are used for count-based and distance-based similarity measure, respectively. Fixed-length music summary is evaluated by measuring the overlapping ratio between hand-made repeated parts and automatically generated ones. Optimal-length music summary is evaluated by calculating how much automatically generated music summary includes repeated parts of the music content. From experiments we observed that optimal-length summary could capture the repeated parts in music content more effectively in terms of summary length than fixed-length summary.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.597-598
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• 2015

In order to improve the reliability of cost estimation results using CBR, there has been a continuous issue on similarity measurement to accurately compute the distance among attributes and cases to retrieve the most similar singular or plural cases. However, these existing similarity measures have limitations in taking the covariance among attributes into consideration and reflecting the effects of covariance in computation of distances among attributes. To deal with this challenging issue, this research examines the weighted Mahalanobis distance based similarity measure applied to CBR cost estimation and carries out the comparative study on the existing distance measurement methods of CBR. To validate the suggest CBR cost model, leave-one-out cross validation (LOOCV) using two different sets of simulation data are carried out. Consequently, this research is expected to provide an analysis of covariance effects in similarity measurement and a basis for further research on the fundamentals of case retrieval.