• Korean Journal of Plant Resources
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• v.27 no.1
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• pp.89-94
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• 2014

Varietal distances were measured by Mahalanobis's $D^2$ statistics in 190 possible comparisons among twenty varieties of Prunus mume with twelve characters such as seed weight, length, width, and diameter, fruit weight, and number of sepals, petals, pistils, and stigmas, and leaf length and width. A complete linkage cluster analysis based on the Mahalanobis's distance ($D^2$) was attempt. Twenty varieties of Prunus mume were largely classified into five subgroups. Group I, II, III, IV and V included two, four, five, five and four varieties, respectively. Most of the varietal groups were not associated with their geographical origins. Number of stigmas, and leaf length and width among the twelve characters were the largest contributors to the $D^2$ in both intra-and inter groups.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• Journal of Korean Society of Disaster and Security
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• v.10 no.2
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• pp.1-6
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• 2017

In the previous research, the research on the development of subway disaster detection system that discovers the disaster early warning of the subway station disaster and the evacuation to the passengers based on the Internet of things. This paper as a follow-up study analyzes the sensor data installed in the station in real time to quickly detect the disaster. In particular, we developed a statistical methodology based on the Mahalanobis distance in consideration of the environment that varies depending on the installation location of the sensor during initial system construction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.6
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• pp.625-629
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• 2015

In this study, we develop a prognostic method of assessing the stator windings of power generators against water absorption through statistical data analysis and degradation modeling. The 42 windings of the generator are divided into two groups: the absorption and normal groups. A degradation model of a winding is constructed using Fick's second law to predict the level of absorption. By analyzing data from the normal group, we can determine the distribution of the data of normal windings. The health index of a winding is estimated using the directional Mahalanobis distance (DMD) method. Finally, the probability distributions of the failure time of the windings are determined.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.4
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• pp.375-380
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• 1985

Single linkage dendrograms by Mahalanobis's D$^2$, Q correlation, and distance from Principal Component Analysis, respectively, were made to eight rice breeding lines in the none and high fertilizer levels. The dendrograms in the two fertilizer levels were similar in shape. The shape of dendrograms by D$^2$ and Q correlation were identical and they were very similar in shape to that by PCA in the both fertilizer levels.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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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 ...

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.35 no.2
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• pp.126-131
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• 1990

Sixty two Job's Tear cultivars were largely classified into ten varietal groups and every varieties. except for Group V forming the Group by one variety. uniformly formed the Groups. From Group I to Group Ⅹ respectively contained three(5%), eighteen(29%), five(8%), thirteen(21%), one(2%), five(8%), seven(11%), four(7%), two(3%) and four(7%) varieties. Group II and Group IV showed considerably large variation whereas Group Ⅷ, IV and Ⅹ showed low variation and inferiority in vigorosity and yield components. Most of the varietal Group were not associated with their geographical origin. Days to flowering and plant height among the nine characters were the largest contributors to the D$^2$ in both inter- and inter groups.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.568-571
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• 1997

Recently, the number of techniques for analyzing medical images has been increasing in computer vision, employing X-ray CT images, ultrasound images, MR images, moire topographic images, etc. Spinal deformity is a serious problem especially for teenagers and medical doctors inspect moire topographic images of their backs visually for the primary screening. If a subject is normal, the moire image is almost symmetric with respect to the middle line of the subject's back, otherwise it shows asymmetric shape. In this paper, an image analysis technique is described for discriminating suspicious cases from normal in human spinal deformity by recognizing asymmetric moire images of human backs. The principal axes which are sensitive to asymmetry of the moire image are extracted at two parts on a subject's back and their angles are evaluated with respect to the detected middle line of the back. The two angles compose a 2-D feature space and inspected cases are divided into two clusters in the space by a linear discriminant function based on the Mahalanobis distance. Given 120 cases, 60 normal and 60 abnormal, the leave-out method was applied for the recognition and 75% recognition rate was achieved.