• Journal of Korean Library and Information Science Society
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• v.39 no.3
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• pp.345-368
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• 2008

This study investigated the general problems concerning the subject Computer Science in the KDC(Korean Decimal Classification) 4th edition based on the comparative analysis with DDC, NDC, Disciplinary Classification System of Korean Research Foundation and National Standard Science and Technology Classification and Science and Technology Classification of Korea Science and Engineering Foundation, and suggested some ideas for the improvements of them. The subject of Computer science in the KDC 4th edition will be helpful to be improved to integrate in classes 004-005 now separated into two main classes of 000(004-005) and 500(566) in KDC4, to systematize subdivisions, to add new subjects, to delete and relocate some inappropriate subjects and to add notes.

• Journal of Information Management
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• v.37 no.3
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• pp.139-163
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• 2006

This article analyze a constructing process of photographic digital archive based on photographs of korean democratization movements. After photographic digital archive defines as integrated systems in which photographic digital objects collect, classify, describe, preserve and access, this article explains metadata elements and classification schema reflecting a special quality of photograph. Especially, this article presents dynamic classification structure using subject keywords. After all, this method provides integrity and interrelationship with photographes which promote usability of photographic digital objects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1998.11a
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• pp.190-193
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• 1998

최근 들어 컴퓨터와 네트워크의 발전에 기인하여 일상업무의 대부분을 컴퓨터를 이용하여 할 수 있으므로 신원 확인은 중요한 분야로 부상되었으며, 지문은 편리한 입력과 종생불변하고 만인부동한 특성으로 생체 측정 분야 중 가장 각광받고 있는 분야가 되었다. 지문은 입력 방법에 따라 중심점과 삼각주를 전부 취득하는 회전 지문과 손가락을 회전하지 않고 취득한 평면 지문으로 나뉜다. 지문 인식 기술은 특징점 추출과 분류 그리고 매칭으로 나뉘는 AFIS에 이용되는 기술과 분류기술은 생략할 수 있는 검증 기술이 있는 데 본 논문에서는 AFIE에 관련된 전반적인 기술에 관하여 기술한다.

• Journal of the KSME
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• v.24 no.1
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• pp.19-27
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• 1984

GT란 부품들을 그룹별로 분류하고 각 그룹에 동일한 기술을 사용하여 부품을 생산하는 방법으로 이러한 생산방식에서 얻을 수 있는 이점은 대량생산에서와 같은 높은 생산성을 다품종 소량생산에서도 얻을 수 있다는데 있다. GT도입을 위해서는 부품의 분류기준인 부품분류시스템이 필요하며 한국기계연구원에서는 공작기계 제조업체를 대상으로 한 부품분류시스템을 개발하였다.

• Review of KIISC
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• v.5 no.2
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• pp.49-63
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• 1995

본 논문은 정보시스템 보안관리에 대한 개념적 모형을 INFORSEC 및 한국전산원이 제시한 모형을 중심으로 기술했으며 보안관리의 표준적인 분류체계를 근거로 BDSS의 위협. 자산, 취약성의 분류체계를 비교함으로써 위협, 자산, 취약성의 관련성을 탐색 하고자 했다. 특히 위협의 원천과 가해자 측면에서 BDSS와 CRAMM의 경우를 비교함으로써 분류체계의 장단점을 파악하고자 했다.

• The Journal of Society for e-Business Studies
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• v.9 no.1
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• pp.179-195
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• 2004

We have experienced a revolutionary development in e-business related technologies during the last decade. This research is an effort to figure out and predict the direction of technological development in the area of electronic business. Current e-business related technology typology has been revised with a thorough literature review and expert focus groups. An instrument was developed to measure experts' perception on seven dimensions of e-business related technology. Based on this survey results, e-business technology roadmaps were developed and presented. Policy implications were discussed at the end.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.2
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• pp.517-524
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• 2010

Recently, the size of online texts and textual information is increasing explosively, and the automated classification has a great potential for handling data such as news materials and images. Text classification system is based on supervised learning which needs laborous work by human expert. The main goal of this paper is to reduce the manual intervention, required for the task. The other goal is to increase accuracy to be high. Most of the documents have high complexity in contents and the high similarities in their described style. So, the classification results are not satisfactory. This paper shows the implementation of classification system based on ETOM+RPost algorithm and classification progress using SPAM data. In experiments, we verified our system with right-training documents and wrong-training documents. The experimental results show that our system has high accuracy and stability in all situation as 16% improvement in accuracy.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.202-204
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• 2004

앙상블은 다수의 분류기를 효과적으로 결합하여 분류의 성능을 향상시키는 대표적인 기술이다. 효과적인 앙상블을 위해서는 다양한 특성을 지닌 분류기를 확보하여야 한다. 기존의 앙상블은 개별 분류기의 결과를 바탕으로 분류기 사이의 의존성이나 유사성을 평가하여 분류기 결합을 시도하였다. 따라서 분류기 사이의 유사도의 정확한 측정에 한계를 지니고 있다. 본 연구에서는 이를 극복하기 위해서 다수의 산술연산자 기반 분류규칙을 유전자 프로그래밍을 이용하여 획득하고, 실제 표현형의 유사성을 측정한 후 이를 바탕으로 분류기를 결합한다. 생물정보학에서 많이 사용되는 유전자 데이터 중 하나인 림포마 암 데이터에 제안하는 방법을 적용하여 97% 수준의 높은 분류 성능과 해석 가능한 분류규칙을 획득하였다.