• Journal of Information Technology Services
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• v.2 no.2
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• pp.87-95
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• 2003

With the increasing proliferation of World Wide Web, electronic mail systems have become very widely used communication tools. Researches on e-mail classification have been very important in that e-mail classification system is a major engine for e-mail response management systems which mine unstructured e-mail messages and automatically categorize them. in this research we develop e-mail classifiers for e-mail Response Management Systems (ERMS) using naive bayesian learning and centroid-based classification. We analyze which method performs better under which conditions, comparing classification accuracies which may depend on the structure, the size of training data set and number of classes, using the different data set of an on-line shopping mall and a credit card company. The developed e-mail classifiers have been successfully implemented in practice. The experimental results show that naive bayesian learning performs better, while centroid-based classification is more robust in terms of classification accuracy.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2423-2428
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• 2008

A turbo classifier having a rotating rotor of two stage classification region has been developed to have a cut size of 1 micro meter. Particle number concentrations were counted using Aerosol Particle Sizer (APS, TSI co., USA) at inlet and outlet of the classifier. Partial classification efficiency was obtained at various rotation speeds, total flow rates, and feed rates of powders, and classification characteristic depending on design parameters was discussed. Classification performance was enhanced as rotation speed of rotor increased and total flow rate decreased.

• Journal of the Semiconductor & Display Technology
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• v.10 no.3
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• ETRI Journal
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• v.13 no.2
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• pp.34-41
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• 1991

In this paper, a new distributed parallel algorithm for pattern classification based upon Self-Organizing Neural Network(SONN)[10-12] is developed. This system works without any information about the number of clusters or cluster centers. The SONN model showed good performance for finding classification information, cluster centers, the number of salient clusters and membership information. It took a considerable amount of time in the sequential version if the input data set size is very large. Therefore, design of parallel algorithm is desirous. A new distributed parallel algorithm is developed and experimental results are presented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.904-912
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• 1991

Automatic recognition of seal imprint has been required in the oriental countries. In this paper, a hierarchical approach for seal imprint verification is presented. Global features are used for seal imprint description in the first step. In the second step, conventional and several proposed local features are used to detect useful informations such as size, distribution and relative position of stroke length from seal imprint. In the last step, seal imprints are classified into one of three categories 'accept', 'ambiguous' and reject', based on the hierarchical classification. Experimental results show good performance on classification and recognition.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.698-701
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• 2005

This paper examines that is extracted certain information in forest areas within high resolution imagery based on wavelet transformation. First of all, study areas are selected one more species distributed spots refer to forest type map. Next, study area is cut 256 x 256 pixels size because of image processing problem in large volume data. Prior to wavelet transformation, five texture parameters (contrast, dissimilarity, entropy, homogeneity, Angular Second Moment (ASM≫ calculated by using Gray Level Co-occurrence Matrix (GLCM). Five texture images are set that shifting window size is 3x3, distance .is 1 pixel, and angle is 45 degrees used. Wavelet function is selected Daubechies 4 wavelet basis functions. Result is summarized 3 points; First, Wavelet transformation images derived from contrast, dissimilarity (texture parameters) have on effect on edge elements detection and will have probability used forest road detection. Second, Wavelet fusion images derived from texture parameters and original image can apply to forest area classification because of clustering in Homogeneous forest type structure. Third, for grading evaluation in forest fire damaged area, if data fusion of established classification method, GLCM texture extraction concept and wavelet transformation technique effectively applied forest areas (also other areas), will obtain high accuracy result.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.2C
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• pp.181-185
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• 2009

When we classify underwater transient signals with frame-by-frame decision, a database design method for reference feature vectors influences on the system performance such as size of database, computational burden and recognition rate. In this paper the LBG vector quantization algorithm is applied to reduction of the number of feature vectors for each reference signal for efficient classification of underwater transient signals. Experimental results have shown that drastic reduction of the database size can be achieved while maintaining the classification performance by using the LBG vector quantization.

• Fashion & Textile Research Journal
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• v.26 no.1
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• pp.35-43
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• 2024

This study aimed to objectively classify the lateral torso posture types and functions of older women. We used 3D body scan data of 119 women aged 70-85 years from the 6th SizeKorea project. First, we defined three torso axes to represent the lateral torso posture types: posterior waist-back, back-cervical, and whole torso axes. Next, we asked experts to select one of four lateral torso posture types-stooped, straight, leaning back, and swayback postures-by looking at the lateral photographic data of 119 older women. To identify the axis that best represented each lateral torso posture type, a discriminant analysis was conducted using the angle of each of the three torso axes as an independent variable and an expert's visual classification as a dependent variable. Based on the analysis, the whole torso and backcervical axis angles were selected as variables for judging lateral torso posture types. Subsequently, we developed a classification function to determine which of the four lateral torso posture types of a particular participant was applicable for a new individual. The method developed in this study is significant in that it enables the objective classification of the lateral torso postures types of older women.

• Journal of Digestive Cancer Research
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• v.9 no.2
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• pp.57-59
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• 2021

Carcinoid tumor is called as neuroendocrine tumor and is classified into neuroendocrine tumor Grade 1, neuroendocrine tumor Grade 2, and neuroendocrine carcinoma based on the differentiation of tumors. Recently, the incidence of rectal carcinoid tumor has been increasing probably due to the increased interest on screening colonoscopy and the advancement of endoscopic imaging technology. As the rectal carcinoid shows a wide range of clinical characteristics such as metastasis and long-term prognosis depending on the size and histologic features, it is a challenge to give a consistent diagnostic code in patients with the rectal carcinoid. If the rectal carcinoid tumor is less than 1 cm in size, it can be given as the code of definite malignancy or the code of uncertain malignant potential according to International Classification of Diseases for Oncology (ICD-O) by World Health Organization (WHO). Because patients get different amount of benefit from the insurance company based on different diagnostic codes, this inconsistent coding system has caused a significant confusion in the clinical practice. In 2019, WHO updated ICD-O and Statistics Korea subsequently changed Korean Standard Classification of Diseases (KCD) including the code of rectal carcinoid tumors. This review will summarize what has been changed in recent ICD-O and KCD system regarding the rectal carcinoid tumor and surmise its clinical implication.

• The Research Journal of the Costume Culture
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• v.6 no.2
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• pp.141-152
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• 1998

In the result of classification body types for 100 adult women by direct measurements and antropometric measurements, the mean of weight is bigger than he mean of weight of 97'. So modern 20's women is more than than 94'. In the result of factor analysis, 5 factors were extracted (horizontal sizes, vertical sizes, and degree of shoulder) from exponent sizes of the antropometric measurements item, and another 5 factors were extracted (thick of body, horizontal form of the torso, silhouette of the torso, and size of abdomen) from direct measurements item. The body types are classified into 4 types by cluster analysis in the result of direct measurement item, and another body types are classified into 5 types by antropometric measurement item, it was classified into the horizontal size and the shape and silhouette of torso, and by direct measurement item, it was only classified into the vertical and horizontal size. So for the patternmaking of clothing, it is more adoptable the classification of body by antropometric measurement item than direct measurement item.