• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.2059-2064
• /
• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.4
• /
• pp.231-240
• /
• 2017

This paper aims to study a method to estimate precise carbon absorption by quantification of forest information that uses accurate LiDAR data, hyperspectral image. To estimate precise carbon absorption value by using spatial data, a problem was found out of carbon absorption value estimation method with statistical method, which is already existed method, and then offered optimized carbon absorption estimation method with spatial information by analyzing with methods of compare digital aerial photogrammetry and LiDAR data. It turned out possible Precise classification and quantification in case of using LiDAR and hyperspectral image. Various classification of tree species was possible with use of LiDAR and hyperspectral image. Classification of hyperspectral image was matched in general with field survey and Mahalanobis distance classification method. Precise forest resources could be extracted using high density LiDAR data. Compared with existing method, 19.7% in forest area, 19.2% in total carbon absorption, 0.9% in absorption per unit area of difference created, and improvement was found out to be estimated precisely in international code.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.10 no.5
• /
• pp.617-628
• /
• 2018

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK(R) model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model.

• Korean journal of applied entomology
• /
• v.32 no.1
• /
• pp.68-75
• /
• 1993

Morphometric investigations of the leg characters of both sexes of brachypterous Korean N. lugens biotypes were made. Simple and multivariate statistical analyses revealed that the three N. lugens biotypes differed from one another. The amount of variation and segregation between and among the three biotype populations were approximated by the scatter plot diagrams based on the computed discriminant scores. The variables of leg characters provided the most significant segregations of three biotype populations, thus, categorizing the three biotypes as distinct intraspecific populations of N. lugens.

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

• Journal of Biomedical Engineering Research
• /
• v.37 no.4
• /
• pp.127-133
• /
• 2016

This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.

• Journal of KIISE:Software and Applications
• /
• v.36 no.12
• /
• pp.1009-1015
• /
• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.8
• /
• pp.1239-1249
• /
• 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.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2004.11a
• /
• pp.296-306
• /
• 2004

LC/MS를 이용하여 펩타이드 혹은 단백질 같은 물질을 분석하는 실험이 급격히 늘어남에 따라 LC/MS 데이터를 자동으로 처리하는 기술에 대한 요구가 커지고 있다. 이러한 LC/MS 데이터의 자동 분석 기술에 대한 연구는 현재 활발히 진행되어 왔고, 이를 직접 구현한 여러 상용 소프트웨어들이 개발되어 있는 상태이다. LC/MS 데이터는 noise 제거, background 데이터 제거, deconvolution 알고리즘을 적용한 분자량(molecular weight) 할당 등의 작업을 거쳐 분석하게 된다. 이러한 과정을 거쳐 얻어진 분자량에 대한 데이터가 올바른 값인지 검증하는 작업이 필요하다. 본 논문에서는 이러한 검증 작업과 관련하여 Peak Clustering and Fitting(이하 PC&F)에 대한 알고리즘을 제안한다. PC&F은 peak 데이터들이 지니고 있는 속성에 대한 Mahalanobis distance를 이용하여 peak 데이터를 각 retention time에 따라 clustering 분석을 하는 작업이다. 본 논문에서 제안하는 PC&F 알고리즘을 Microsoft Visual C++ 6.0 MFC 환경에서 직접 개발한 소프트웨어(PeakClusterFitLCMS)로 실험하였다. 실험결과 PC&F 작업을 통해 동일한 구성물질로부터 발생한 peak 데이터를 모아서 보다 신뢰할 수 있는 분자량을 구할 수 있었고, 구성물질에 의해 발생되지 않은 noise peak 데이터를 찾아 제거시킬 수 있음을 확인할 수 있었다.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1551-1558
• /
• 2014

We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.