• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.281-283
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• 2000

인터넷은 단순한 정보 교환의 수단이 아니라 기업들의 이윤 창출을 위한 새로운 공간이 되고 있으며 수많은 쇼핑몰들이 이를 설명해 주고 있다. 하지만 분류체계 측면에서 각 쇼핑몰들이 제공하고 있는 분류체계에는 크게 다음 두 가지의 문제점이 있다. 첫째로 각 쇼핑몰마다 서로 다른 자기만의 상품 분류체계를 가지고 있다는 점이다. 이로 인해 쇼핑몰을 이용하고자 하는 사용자는 각 쇼핑몰을 방문할 때마다 혼란스러울 수 밖에 없다. 두 번째는 각 쇼핑몰이 제공하고 있는 분류체계는 정적인 형태만을 띄고 있다는 점이다. 따라서 사용자는 이미 정해져 있는 상품에 대한 분류의 체계만을 좋건 싫건 간에 따라야 한다. 따라서 이러한 문제들을 해결하기 위하여 본 논문에서는 규칙이라는 추가 정보를 가지도록 모델링된 쇼핑몰의 분류체계들에 대하여 자동적인 통합의 기법을 제시한다. 제시된 기법에 의하여 쇼핑몰 사용자들에게 모든 쇼핑몰의 통합된 뷰의 제공, 사용자별 분류체계의 생성, 메타 쇼핑몰 간의 통일된 인터페이스 제공 등을 할 수 있다.

• Journal of the Korean Society for information Management
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• v.18 no.4
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• pp.201-215
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• 2001

The purpose of this study is to identify how to construct an internet shopping mall classification system used on the library classification theories. To aid in identifying classification system, this study focused on the Ranganathan’s classification canons; canons for characteristics, canons for terms. The study shows six priniciples for an internet shopping mall classification system construct: products’characteristics, inclusiveness, various access points, category sequence and term consistency, term currency and obviousness, no term duplication. For future research, product’s search patterns and relationship to interface are suggested.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.679-685
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• 2003

The cores and deltas of fingerprints designate the parts where the flow of the ridges change radically. Observations on the change of the orientation of the ridges around the cores and deltas enable us to guess the location of the cores and deltas. According]y clustering the orientation flowing to the same direction after doing research on the orientation of the ridges on the whole makes us see that the cores and deltas are shaping around the boundaries of the clustering area. It is also observed that The patterns of clustering of the orientation of the ridges classified as Arch, Tented Arch, Left loop, Right Loop and Whorl have its own characteristics respectively. In this paper the method of classifying the fingerprints effectively is proposed and proved its effectiveness by using the clustering of the orientation of the ridges, finding the cores of the fingerprints which don't secure the deltas.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.3
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• pp.91-101
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• 2005

Although the studies on the analysis and classification of source-level vulnerabilities in operating systems are not direct and positive solutions to the exploits with which the host systems are attacked, It is important in that those studies can give elementary technologies in the development of security mechanisms. But, whereas Linux systems are widely used in Internet and intra-net environments recently, the information on the basic and fundamental vulnerabilities inherent in Linux systems has not been studied enough. In this paper, we propose characteristic classification and correlational analyses on the source-level vulnerabilities in Linux kernel that are opened to the public and listed in the SecurityFocus site for 6 years from 1999 to 2004. This study may contribute to expect the types of attacks, analyze the characteristics of the attacks abusing vulnerabilities, and verify the modules of the kernel that have critical vulnerabilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.118-121
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• 2003

지문을 이용한 개인 인증 절차는 지문 형태 별로 구분하는 분류(classification) 과정과 본인임을 확인하는 정합(matching) 과정으로 구분할 수 있다. 지문의 분류와 정합을 위해서는 기존 연구들이 지문의 특징점 수와 방향성의 흐름 패턴에 의존한다. 본 논문에서는 방향성의 흐름 패턴을 이용한 중심점 추출에 초점이 맞춰져 있으며 추출된 중심점 정보는 현재 구현되어진 특징점 추출 정보와 연계해 정합을 위한 기준점으로 활용한다. 중심점 추출 방식은 입력된 지문 영상에 대해 3 $\times$ 3 Sobel 마스크를 적용한 후 8 $\times$ 8블록 영상을 분할하여 각 대표 방향 성분을 추출하며 추출되어진 방향 성분과 특이점 패턴을 비교하여 중심점을 탐색한다.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.547-559
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• 2010

Delaunay triangulation is suitable for fingerprint matching because of its robustness to rotation and translation. However, missing and spurious minutiae degrade the performance and computational efficiency. In this paper, we propose a method of combining local quality assessment and 4-category minutiae classification to improve accuracy and decrease computational complexity in matching process. Experimental results suggest that removing low quality areas from matching candidate areas and classifying minutiae improve computational efficiency without degrading performance. The results proved that the proposed algorithm outperforms the matching algorithm (BOZORTH3) provided by NIST.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.395-407
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• 2020

Most classification accuracy measures for optimal threshold are divided into two types: one is expressed with cumulative distribution functions and probability density functions, the other is based on ROC curve and AUC. Unal (2017) proposed the index of union (IU) as an accuracy measure that considers two types to get them. In this study, ten kinds of accuracy measures (including IU) are divided into six categories, and the advantages of the IU are studied by comparing the measures belonging to each category. The optimal thresholds of these measures are obtained by setting various normal mixture distributions; subsequently, the first and second type of errors as well as the error sums corresponding to each threshold are calculated. The properties and characteristics of the IU statistic are explored by comparing the discriminative power of other accuracy measures based on error values.The values of the first type error and error sum of IU statistic converge to those of the best accuracy measures of the second category as the mean difference between the two distributions increases. Therefore, IU could be an accuracy measure to evaluate the discriminant power of a model.

• Communications for Statistical Applications and Methods
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• v.17 no.6
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• pp.755-765
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• 2010

For classification problems on mixture distribution, a threshold based on cost functions is optimal from the viewpoint of a minimum expected cost. Assuming that there is no cost information, we propose cost ratios in the expected cost corresponding to thresholds where the total accuracy and the true rate are maximized to explain the relation of these cost ratios minimizing the expected cost. Other cost ratios are also proposed by comparing the normalized expected costs when classification accuracy is maximized. The values of these cost ratios are located between two cost ratios for the expected costs based on classification accuracies, and converge to that of the minimum expected cost. This work suggests two cost ratios: one is minimized by the expected cost and the normalized expected cost, and the other in the expected cost and the normalized expected cost functions that are maximized classification accuracies. We discuss their compatibility based on the relation of these cost ratios.

• 한국공간정보시스템학회:학술대회논문집
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• 2007.06a
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• pp.156-161
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• 2007

도시화에 따른 도시문제발생이라는 결과로 미루어 볼 때, 지속적인 도시 성장을 위한 도시 성장 관리는 필수적이며, 이것을 위해서 도시지역을 추출하는 것은 도시의 성장 추이를 파악할 수 있게 한다는 점에서 매우 의미 있는 일이다. 본 연구에서는 도시 성장 모니터링에 있어서 정규식생지수(NDVI)와 정규시가지화지수(NDBI)를 결합한 방법의 활용성을 규명하는데 목적을 두었다. 이를 위해 토지피복분류에 일반적으로 사용되는 감독 분류기법과 도시지역추출에 이용되는 NDVI와 NDBI를 결합한 방법(식생지수결합법)으로 1988년과 2000년 두 시기의 Landsat TM 영상을 이용하여 도시지역을 추출하고 일치도를 분석하였다. 분석 결과, 1988년 식생지수결합법과 감독분류기법으로 추출한 도시지역의 일치도는 98%, 식생지수결합법 비도시지역으로 추출된 지역이 감독분류기법으로는 도시지역으로 추출될 확률은 37.35%로 나타났고, 같은 경우 2000년은 각각 99.3%와 7.7%로 나타났다. 이를 통해 식생지수결합법을 사용한 도시지역 추출 결과와 감독분류기법을 사용한 도시지역 추출 결과의 일치도가 비교적 높게 나타남을 알 수 있었다. 또, 각 기법을 통한 도시지역 추출 결과와 실제 도시 검사점과의 일치도의 분석을 통해서도 도시지역 추출 결과의 일치도가 비교적 높게 나타났다. 따라서 분류를 통한 도시지역 추출 방법에 비해 식생지수결합법을 이용한 도시지역 추출이 절차상 수월한 점을 감안하면 도시지역 추출에 있어서 식생지수결합법의 효율성을 입증할 수 있었다.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.85-89
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• 2010

Most images are composed as union of the various objects which can describe meaning respectively. Unlike human perception, The general computer systems used for image processing analyze images based on low level features like color, texture and shape. The semantic gap between low level image features and the richness of user semantic knowledges can bring about unsatisfactory classification results from user expectation. In order to deal with this problem, we propose a semantic cue based image classification method using salient points from object of interest. Salient points are used to extract low level features from images and to link high level semantic concepts, and they represent distinct semantic information. The proposed algorithm can reduce semantic gap using salient points modeling which are used for image classification like human perception. and also it can improve classification accuracy of natural images according to their semantic concept relative to certain object information by using salient points. The experimental result shows both a high efficiency of the proposed methods and a good performance.