• Journal of Biomedical Engineering Research
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• v.37 no.4
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• pp.127-133
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• 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:Computing Practices and Letters
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• v.9 no.3
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• pp.301-310
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• 2003

In this paper, we design and Implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the m()st effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 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
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• v.27 no.8
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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.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.296-306
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• 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
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• v.63 no.11
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• pp.1551-1558
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• 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.

• Journal of Crop Science and Biotechnology
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• v.10 no.2
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• pp.86-91
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• 2007

A representative group of 38 improved basmati lines including maintainers of sterile lines were studied for genetic diversity utilizing Mahalanobis $D^2$ statistics. A wide diversity was observed having ten clusters with high intra- and inter-cluster distance. Heterosis was estimated utilizing the cytoplasmic male sterile lines from the clusters having high intra- and inter-cluster distance. Highly heterotic hybrids were obtained from the hybridization programme. Cross combinations IR68281A/Pusa 1235-95-73-1-1, IR68281A/RP 3644-41-9-5, Pusa 3A/UPR 2268-4-1, IR 68281A/Pusa Basmati-1, IR68281A/BTCE 10-98, and IR58025A/HKR 97-401 were found to be highly heterotic for grain yield/plant with other agronomic and quality traits. Additionally, a positive association of intra-cluster distance with heterosis was observed, which could be utilized as a guideline for predicting heterosis in basmati hybrid rice breeding program. Also, a positive association between inter-cluster distance and heterosis was observed.

• Smart Structures and Systems
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• v.5 no.1
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• pp.95-117
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• 2009

In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF's from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.609-615
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• 2021

Chemical accidents can happen anywhere in the world. To prevent chemical accidents, Korea introduced the Chemicals Control Act. However, Small and medium-sized businesses do not meet these regulations. Accordingly, the Ministry of Environment is providing a chemical safety management support project for Small and medium-sized businesses. However, there are many small and medium-sized businesses, and businesses that need support need priority. In this study, the risk of the plants was classified into hig h, medium, and low risk based on four methods. As a result, out of 90 plants subject to the study, high risk was 30% and medium risk was 70%. The industries with the high risk were chemical products manufacturing and paint manufacturing. The plating and printing industries showed relatively medium risk. This risk classification has the advantage that it can obtain intuitive and quick results. These studies can be used as basic data for chemical safety management of local governments and Ministry of Environment.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.695-705
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• 2019

The level of psychological damage caused by verbal abuse among cyberbully victims is very serious. It is going to introduce a system that determines the level of sanctions against chatting in real time using the automatic prohibited words filtering based on artificial neural network. In this paper, we propose a keyword filtering method that detects the modified prohibited words and determines whether the corresponding chat should be sanctioned in real time, and a real-time chatting screening system using it. The accuracy of filtering through machine learning was improved by processing data in advance through coding techniques that express consonants and vowels of similar pronunciation at close distances. After comparing and analyzing Mahalanobis-based clustering algorithms and artificial neural network-based algorithms, algorithms that utilize artificial neural networks showed high performance. If it is applied to Internet chatting, comments or online games, it is expected that it will be able to filter more effectively than the existing filtering method and that this will ease communication inconvenience due to existing indiscriminate filtering methods.