• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.16-22
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• 2010

Electrocardiogram(ECG) analysis and arrhythmia recognition are critical for diagnosis and treatment of ill patients. Cardiac arrhythmia is a condition in which heart beat may be irregular and presents a serious threat to the patient recovering from ventricular tachycardia (VT) and ventricular fibrillation (VF). Other arrhythmias like atrial premature contraction (APC), Premature ventricular contraction (PVC) and superventricular tachycardia (SVT) are important in diagnosing the heart diseases. This paper presented new method to classify various arrhythmias contrary to other techniques which are limited to only two or three arrhythmias. ECG is decomposed into Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD). Burg algorithm was performed on IMFs to obtain AR coefficients which can reduce the dimension of feature vector and utilized as Multi-class SVM inputs which is basically extended from binary SVM. We chose optimal parameters for SVM classifier, applied to arrhythmias classification and achieved the accuracies of detecting NSR, APC, PVC, SVT, VT and VP were 96.8% to 99.5%. The results showed that EMD was useful for the preprocessing and feature extraction and multi-class SVM for classification of cardiac arrhythmias, with high usefulness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.277-279
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• 2021

There is a problem that learning of a prediction model is not well performed depending on the characteristics of each particular matter concentration. To solve this problem, it is necessary to design a prediction model for low concentration and high concentration separately. Therefore, a classification model is needed to classify the concentration of particular matter into low and high concentrations. This paper proposes a classification model to classify low and high concentrations based on the concentration of particular matter. DNN was used as the classification model algorithm, and the classification model was designed by applying the optimal parameters after searching for hyper parameters. As for the result of evaluating the performance of the model, 97.54% of the low concentration classification was measured. And in the case of high concentration classification, 85.51% was measured.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.55-66
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• 2024

Gender classification techniques have received a lot of attention from researchers because they can be used in various fields such as forensics, surveillance systems, and demographic studies. As previous studies have shown that there are distinctive features between male and female gait, various techniques have been proposed to classify gender from three dimensional(3-D) gait data. However, some of the gait features extracted from 3-D gait data using existing techniques are similar or redundant to each other or do not help in gender classification. In this study, we propose a method to select features that are useful for gender classification using a correlation-based feature selection technique. To demonstrate the effectiveness of the proposed feature selection technique, we compare the performance of gender classification models before and after applying the proposed feature selection technique using a 3-D gait dataset available on the Internet. Eight machine learning algorithms applicable to binary classification problems were utilized in the experiments. The experimental results show that the proposed feature selection technique can reduce the number of features by 22, from 82 to 60, while maintaining the gender classification performance.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.25-30
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• 2005

In this paper, we analyzed and compared the performances of modular FFMLP(feedforward multilayer perceptron) and SVUT(Support Vector Machine) for the large-set classification problems. Overall, SVM dominated modular FFMLP in the correct recognition rate and other aspects Additionally, the recognition rate of SVM degraded more slowly than neural network as the number of classes increases. The trend of the recognition rates depending on the rejection rate has been analyzed. The parameter set of SVM(kernel functions and related variables) has been identified for the large-set classification problems.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.43-49
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• 2004

The classification time required by conventional multi-class SVMs(Support Vector Machines) greatly increases as the number of pattern classes increases. This is due to the fact that the needed set of binary class SVMs gets quite large. In this paper, we propose a method to reduce the number of classes by using nearest neighbor rule (NNR) in the principle component analysis and linear discriminant analysis (PCA+LDA) feature subspace. The proposed method reduces the number of face classes by selecting a few classes closest to the test data projected in the PCA+LDA feature subspace. Results of experiment show that our proposed method has a lower error rate than nearest neighbor classification (NNC) method. Though our error rate is comparable to the conventional multi-class SVMs, the classification process of our method is much faster.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.2
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• pp.53-57
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• 2009

The dividing flow in an open channel has a number of distinctive characteristics. One of these is that the separation zone interacts with a secondary motion along the inner wall of a branch channel, generating sediment accumulation. To investigate this phenomenon, a two-dimensional numerical model based on the shallow-water equations, RMA2, which calculates water surface elevations and horizontal-velocity components, was used to analyze the dividing flow. The obtained numerical results fully coincide with the laboratory measurements reported by Hsu et al.(2002). For the analysis of the numerical results, a separation zone-discharge rate relationship was proposed. To reduce the size of a separation zone, the topographies of diagonal and curved edges were proposed, smoothly connecting the upstream corner to branch channel.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.1
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• pp.43-51
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• 1995

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classifi¬cation accuracy but also for being able to classify objects whose attribute values do not have clear bound¬aries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. 'The transformed input is processed in the postprocessing module. The experimental results indi-cate the superiority of fuzzy input signal representation scheme in comparison to binary input signal rep¬resentation scheme and decimal input signal representation scheme.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.563-568
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• 2016

The word sense disambiguation problem is to find the correct sense of an ambiguous word having multiple senses in a dictionary in a sentence. We regard this problem as a multi-class classification problem and classify the ambiguous word by using Support Vector Machines. Context words of the ambiguous word, which are extracted from Sejong sense tagged corpus, are represented to two kinds of vector space. One vector space is composed of context words vectors having binary weights. The other vector space has vectors where the context words are mapped by word embedding model. After experiments, we acquired accuracy of 87.0% with context word vectors and 86.0% with word embedding model.

• Korean Journal of Plant Taxonomy
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• v.36 no.4
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• pp.335-360
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• 2006

The distribution on 88 taxa of wild orchids in the Korean Peninsula was investigated by the examinations of specimens and the distribution maps were presented. The species numbers distributed at each eight floral regions in the Korean Peninsula are as the followings; Gapsan Province 27 taxa, Gwanbuk 21, Gwanseo 13, Middle 37, South 39, Southern-coast 33, Jeju 64 and Ulleung 19. Most species (72.7%) of Korean wild orchids are found in the Jeju Island. Eighteen taxa of them are restricted to the island in the Korean Peninsula. Among Korean species, 30 taxa grow at evergreen broad-leaved forests, and 16 are northern elements distributed at high mountains or northern part. In terms of distribution, the Korean wild orchid species are classified into IUCN Red List Categories by a modified criterion for Korean plants. No orchid species included EX or EW categories is found in the Korean Peninsula. Ten species are designated to be in CR category; Cymbidium kanran, C. lancifolium, C. ensifolium, Cypripedium japonicum, Cyrtosia septentrionalis, Dendrobium moniliforme, Habenaria chejuensis, H. radiata, Neofinetia falcata and Sedirea japonica, of which C. ensifolium and H. chejuensis are regarded as CR species for the first time. On the other hand, 22 taxa are classified into EN category, and the following nine taxa are newly proposed to be EN species; Gastrochilus japonicum, G. fuscopunctatus, Gastrodia verrucosa, Habenaria flagellifera, Herminium lanceum var. longicrure, Chamaegastrodia sikokiana, Lecanorchis kiusiana, Neottia hypocastanoptica and Tipularia japonica.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1347-1359
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• 2005

We propose an efficient block classification of the document images using the second-order statistical texture features computed from spatial gray level dependence matrix (SGLDM). We studied on the techniques that will improve the block speed of the segmentation and feature extraction speed and the accuracy of the detailed classification. In order to speedup the block segmentation, we binarize the gray level image and then segmented by applying smoothing method instead of using texture features of gray level images. We extracted seven texture features from the SGLDM of the gray image blocks and we applied these normalized features to the BP (backpropagation) neural network, and classified the segmented blocks into the six detailed block categories of small font, medium font, large font, graphic, table, and photo blocks. Unlike the conventional texture classification of the gray level image in aerial terrain photos, we improve the classification speed by a single application of the texture discrimination mask, the size of which Is the same as that of each block already segmented in obtaining the SGLDM.