• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.79-89
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• 2004

Since the amount of E-mail messages has increased , we need a new technique for efficient e-mail classification. E-mail classifications are grouped into two classes: binary classification, multi-classification. The current binary classification methods are mostly spm mail classification methods which are based on rule driven, bayesian, SVM, etc. The current multi- classification methods are based on clustering which groups e-mails by similarity. In this paper, we propose a novel method for e-mail classification. It combines the automatic category generation method based on the vector model and the dynamic category hierarchy construction method. This method can multi-classify e-mail automatically and manage a large amount of e-mail efficiently. In addition, this method increases the search accuracy by dynamic reclassification of e-mails.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.407-416
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• 2013

This paper proposes an approach to analyzing the relationship between technology and services, and to identifying promising service areas for technology-based firms with the analysis of business model (BM) patents. First, BM patents and technology patents are collected and classified into their relevant categories, respectively. Second, patent citation analysis is conducted to analyze the linkage and impacts between each technology and service field at macro level. Third, as a micro level analysis, patent co-classification analysis is employed to identify the interrelationships among specific technology and service areas. Finally, the promising service areas for technology-based firms seeking service areas for diversification is investigated with portfolio analysis. The working of the proposed approach is provided with the help of a case study of IT and mobile services. The proposed approach could guide and help managers of technology-based firms to discover the opportunity of the diversification to new areas in emerging service fields.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05b
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• pp.100-103
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• 2004

In this paper, BP learning algorithm is studied to apply as a PD source classification in GIS specimens. For occurred partial discharge, three defected models are made; floating particle, surface discharge of spacer, needle to plane. And PD data for discrimination were acquired from PD detector. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for three PD sources.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.58-64
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• 2022

Breast ultrasonic reading is critical as a primary screening test for the early diagnosis of breast cancer. However, breast ultrasound examinations show significant differences in diagnosis based on the difference in image quality according to the ultrasonic equipment, experience, and proficiency of the examiner. Accordingly, studies are being actively conducted to analyze the texture characteristics of normal breast tissue, positive tumors, and malignant tumors using breast ultrasonography and to use them for computer-assisted diagnosis. In this study, breast ultrasonography was conducted to select 247 ultrasound images of 71 normal breast tissues, 87 fibroadenomas among benign tumors, and 89 malignant tumors. The selected images were calculated using a statistical method with 21 feature parameters extracted using the gray level co-occurrence matrix algorithm, and classified as normal breast tissue, benign tumor, and malignancy. In addition, we proposed five feature parameters that are available for computer-aided diagnosis of breast cancer classification. The average classification rate for normal breast tissue, benign tumors, and malignant tumors, using this feature parameter, was 82.8%.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.407-410
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• 2001

최근 영상 검색(retrieval)과 분류(classification)에서 질감 특징(texture feature)을 이용한 연구들이 활발하게 진행되고 있다. 본 논문에서는 효율적인 질감 특징 추출을 위해 명암도 상호발생 행렬법(gray level co-occurrence matrix)과 웨이블릿 변환(wavelet transform)을 이용하여 질감의 특징을 추출한 후 특징의 중요도에 따라서 가중치를 부여하는 방법을 제안한다. 이렇게 추출된 가중치 대표 벡터들을 기반으로 베이시안 분류기(Bayesian classifier)를 통해 임의의 질감을 인식하였다.

• Journal of the Korean Professional Engineers Association
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• v.24 no.3
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• pp.43-50
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• 1991

On the ground foundation works for Bldg site, Rock classification test can be obtained as follows due to the International Society for Rock Mechanics. 1. In-take test ; Compression strength, Point load test. 2. In-situ test : Schmidt hammer test. Burden test finaly the convinient co-relation table between strength and S.H. test were carried out for site-engineer. This project is one of contineous works regarding to Burden test from Jack leg drill( ø 36mm) to Crawler drill( ø 75mm) use.

• Explosives and Blasting
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• v.9 no.3
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• pp.10-16
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• 1991

On the ground foundation works for Bldg site, Rock classification test can be obtained as follows due to the International Society for Rock Mechanics. 1. In-situ test : Compressive strength, Point load test. 2. In-situ test Schmidt hammer test. Burden test finaly the convinient co-relation table between strength and 5. H, test were carried out for site-engineer, This project is one of contineous works regarding to Burden test from Jack leg drill($\phi{\;}75mm$) use.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.627-635
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• 2003

In this short communication we discuss the bias problem of C4.5 in split variable selection and suggest a method to reduce the variable selection bias among categorical predictor variables. A penalty proportional to the number of categories is applied to the splitting criterion gain of C4.5. The results of empirical comparisons show that the proposed modification of C4.5 reduces the size of classification trees.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.200-203
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• 2008

The objective of this study is to produce the $CO_2$ (carbon dioxide) absorption map using KOMPSAT-2 imagery. For estimating the amount of $CO_2$ absorption, the stand biomass of forest was estimated with the total weight, which was the sum of individual tree weight. Individual tree volumes could be estimated by the crown width extracted from KOMPSAT-2 imagery. In particular, the carbon conversion index and the ratio of the $CO_2$ molecular weight to the C atomic weight, reported in the IPCC (Intergovernmental Panel on Climate Change) guideline, was used to convert the stand biomass into the amount of $CO_2$ absorption. Thereafter, the KOMPSAT-2 imagery was classified with the SBC (segment based classification) method in order to quantify $CO_2$ absorption by tree species. As a result, the map of $CO_2$ absorption was produced and the amount of $CO_2$ absorption was estimated by tree species.

• The Journal of the Korean life insurance medical association
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• v.9
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• pp.23-38
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• 1990