• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.696-702
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• 2014

Recently, researches on analyzing relationship between the state of emotion and musical stimuli using EEG are increasing. A selection of feature vectors is very important for the performance of EEG pattern classifiers. This paper proposes a comparison of EEG feature vectors for emotion classification according to music listening. For this, we extract some feature vectors like DAMV, IAV, LPC, LPCC from EEG signals in each class related to music listening and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification according to music listening.

• Journal of the Korean Society of Safety
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• v.20 no.3 s.71
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• pp.112-119
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• 2005

In road tunnel, in order to prevents an accident and minimize the damage of an accident in the case of fire, safety facilities and equipments are integral parts. The type and amount of safety facilities are based on tunnel type and length, traffic flow rate, etc. Therefore many countries use a tunnel classification system that categories tunnel into groups, and specifies the necessary emergency equipment for each group. In this study, for the purpose of classifying tunnel based on tunnel ist investigated the domestic and foreign standards and regulations for safety of road tunnel. As a results, we suggest the method of classification of tunnel by traffic performance, tunnel grade, the volume of traffic, fraction of HGV, rules or regulations for transports of dangerous good through tunnel.

• Spatial Information Research
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• v.19 no.6
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• pp.43-54
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• 2011

This study proposes a rule-based image classification method for the time-series analysis of changes in the land surface of the Seongnam-Yongin area using satellite-image data from 2000 to 2009. In order to identify the change patterns during each period, 11 classes were employed in accordance with statistical/mathematic rules. A generalized algorithm was used so that the rules could be applied to the unsupervised-classification method that does not establish any training sites. The results showed that the urban area of the object increased by 145% due to housing-site development. The image data from 2009 had a classification accuracy of 98%. For method verification, the results were compared to land-cover changes through Post-classification comparison. The maximum utilization of the available data within multiple images and the optimized classification allowed for an improvement in the classification accuracy. The proposed rule-based image-classification method is expected to be widely employed for the time-series analysis of images to produce a thematic map for urban development and to monitor urban development and environmental change.

• Korean Journal of Remote Sensing
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• v.15 no.3
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• pp.195-208
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• 1999

In the classification of satellite images, the representative of training of classes is very important factor that affects the classification accuracy. Hence, in order to improve the classification accuracy, it is required to optimize pre-classification stage which determines classification parameters rather than to develop classifiers alone. In this study, the normality of training are calculated at the preclassification stage using SPOT XS and LANDSAT TM. A correlation coefficient of multivariate Q-Q plot with 5% significance level and a variance of initial training are considered as an object function of genetic algorithm in the training normalization process. As a result of normalization of training using the genetic algorithm, it was proved that, for the study area, the mean and variance of each class shifted to the population, and the result showed the possibility of prediction of the distribution of each class.

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.568-573
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• 2019

In the diagnosis of pulmonary diseases, auscultation technique is simpler than the other methods, and lung sounds can be used for predicting the types of pulmonary diseases as well as identifying patients with pulmonary diseases. Therefore, in this paper, we identify patients with pulmonary diseases and classify lung sounds according to their sound characteristics using various convolutional neural networks, and compare the classification performance of each neural network method. First, lung sounds over affected areas of the chest with pulmonary diseases are collected by using a single-channel lung sound recording device, and spectral features are extracted from the collected sounds in time domain and applied to each neural network. As classification methods, we use general, parallel, and residual convolutional neural network, and compare lung sound classification performance of each neural network through experiments.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• Journal of the Korean Society for information Management
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• v.22 no.2 s.56
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• pp.57-85
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• 2005

Bliss stated that one of the purposes of the classification was to demonstrate that a coherent and comprehensive system, based on the logical principles of classification and consistent with the systems of science and education, may be available to services in libraries, to aid revision of long established classifications and to provide an adaptable, efficient and economical classification, notation and index. A fundamental principle is the idea of subordination each specific subject is subordinated to the appropriate general one. The full scheme followed the publication of two massive theoretical works on the organization of knowledge. Its main feature was the carefully designed maul class order. reflecting Comte principle of gradation in speciality.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.215-224
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• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Fashion & Textile Research Journal
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• v.6 no.3
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• pp.285-296
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• 2004

Fashion business must reflect the seasonable fashion trend because fashion has change always, and therefore fashion business has a big risk at the attribute. Careful consideration should be given to the selection of a particular color code to meet the purpose of marketing research in various color products. It must be designed to grasp systematically and comprehensively the current trend of colors. The most suitable color code for meeting this proposition would be one based on the designation by color ranges. The ISCC-NBS method of designating colors, published in 1955, was established by dividing the color solid into 267 color name blocks. The detailed classification like the ISCC-NBS system is very appropriate to serve the purpose of giving all color names according to color ranges. But it is somewhat too complicated to answer the purpose of surveying the trend of colors and of comparing and evaluating the ups and downs in the popularity of the range of each individual color. I have worked out the most convenient method of designating colors in accordance with the type of investigation needed. It is the classification which involves four classification system in itself, fundamental, gross, medium, and minute. The fundamental classification system classifies hues and neutrals into 16ranges. The gross classification system divides the above 16 ranges into 30. The medium classification divides the above 30 ranges into 103 in terms of tones. The minute classification divides the above 103 ranges into 207 in terms of specipic hues.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1889-1893
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• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.