• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.106-115
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• 2007

농촌진흥청에서 제공하고 있는 수치토양도는 수문 수자원 분야에서 SCS-CN법을 이용한 유효우량 및 유출곡선 산정에 가장 많이 활용되고 있다. 토양조사 국책사업 결과 전산화된 토양도 및 토양검정 데이터베이스에 기초한 토양정보 웹 시스템은 전국의 토양 전자지도와 토양 통계 자료를 주제별로 검색하거나 필지별 토양분석 성적에 따른 토양관리처방서를 조회하는데 사용되는 농업분야 이외의 사용자 그룹을 위해 필요한 정보를 제공하도록 활용 및 유통 요구도가 높아지고 있다. 수치토양도가 수문학적 토양유형 정보를 포함하도록 제공하는 것이 먼저 필요할 것으로 생각되고 다음으로는 수문 수자원 분야 활용 측면에서 수치토양도가 제공하는 속성, 축척, 제공형태, 좌표체계, 서비스 방식 등에 대하여 활용정책을 마련하여 이에 따라 자료가 유통될 수 있도록 하여야 할 것이다. 앞으로 활용이 가능하게 될 수치세부정밀토양도와 토양유형을 이용하여 충북 괴산군 소수면의 소유역에 대해 SCS 삼각법에 따른 단위도 작성, 유효우량 산출 및 유출곡선을 작성한 결과 농업과학기술원의 정 등(2006)이 분류한 토양유형을 이용한 결과 정 등(1995)에 따른 토양유형을 이용한 결과에 비해 CN값과 유효우량이 더 높게 나타났고 삼각단위도로부터 유도한 정점의 유출량과 시간별 유량 관측값에 더 가까운 것으로 나타났다.

• Journal of the Korean Society for Library and Information Science
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• v.43 no.1
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• pp.215-234
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• 2009

This study aims to perform an evaluation of directory services provided by major Korean search portals: Naver, Daum, Yahoo-Korea, and Empas. A directory service of Open Directory is also compared. These directory services are evaluated in terms of the coverage, category creation criteria, site selection criteria, breadth and depth of hierarchy, clarity and currency of category names, order of category listing, and overall classification system. The results of this study can be implemented for the development and improvement of portal's directory services. Users can refer to the results of this study in choosing directory services from search portals.

• Korean Journal of Cognitive Science
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• v.20 no.4
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• pp.535-554
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• 2009

Information collected on mobile devices might be utilized to support user's memory, but it is difficult to effectively retrieve them because of the enormous amount of information. In order to organize information as an episodic approach that mimics human memory for the effective search, it is required to detect important event like landmarks. For providing new services with users, in this paper, we propose the prediction model to find landmarks automatically from various context log information based on attributed Bayesian networks. The data are divided into daily and weekly ones, and are categorized into attributes according to the source, to learn the Bayesian networks for the improvement of landmark prediction. The experiments on the Nokia log data showed that the Bayesian method outperforms SVMs, and the proposed attributed Bayesian networks are superior to the Bayesian networks modelled daily and weekly.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.139-163
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• 2024

This study aims to analyze classification systems used in the public sector, collected based on legislation, and to improve the classification system for public records. From the Korean Law Information Center, 375 legislative clauses were searched, revealing about 80 classification systems. These systems were initially divided into lists, tables, and hierarchical classifications. Six types of classification system uses were proposed after combining three management types and two system functions. Among these models, classification systems used for core operations in public agencies often had the same entity as both developer and user. While systems adopted from other institutions were often modified as needed, they were predominantly used for reference tasks rather than core operations. However, in records management, crucial tasks such as record classification and disposal commonly use unmodified classification system items developed and managed by other agencies. Consequently, this study proposes that structural improvements are necessary for the record classification system. It suggests developing dedicated classification systems to support core functions or modifying existing systems and also applying records management disposal standards and guidelines to other relevant legislative provisions.

• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.177-202
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• 2015

This research was conducted to evaluate whether knowledge and information resources produced in four educational institutions function as a knowledge management system in the institutions, and to suggest a proposed plan for the foundation of the national knowledge management system. Knowledge and information resources available on the institutes' websites were classified as 'knowledge and information resources of result', 'knowledge and information resources of process', and 'knowledge and information resources of communication'. The application of metadata and the configuration of the search system were examined by content analysis method. From the result, 8 ways to improve the management of knowledge and information resources have been found; the development of standardized metadata and classification scheme, high recognition that the website is a counter for external distribution of knowledge and information resources, various searching skills and the provision of knowledge maps available, provision of linked information for original information acquisition, awareness of the agency managers and homepage managers to the homepages as knowledge management systems, segmentation of appropriate data size unit in a mobile environment work, an organic linkage function of the internal electronic approval system and the homepage have been proposed.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.57-66
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• 2003

We propose a technique to extract a relevant passage from text collection based on field-associated terms since they tries to concentrate relevant text to users query. Documents are supposed to be managed as a whole without any segmentation into small pieces, but the method presented is independent upon any text-embedded auxiliary information, and is based on topic continuity and transition. For users needs-relative sentences or passages, we present a passage retrieval techniques by using occurrence frequency of a field-associated term to delimit text, that is likely to be relevant to a particular topic, considering continuity and transition within topic flowing in text. We evaluate 50 Japanese documents and verify the usefulness with 82% for average precision and 63% for recall.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.328-333
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• 2003

NGE (Nested Generalized Exemplars) proposed by Salzberg improved the storage requirement and classification rate of the Memory Based Reasoning. It constructs hyperrectangles during training and performs classification tasks. It worked not bad in many area, however, the major drawback of NGE is constructing hyperrectangles because its hyperrectangle is extended so as to cover the error data and the way of maintaining the feature weight vector. We proposed the OH (Optimizing Hyperrectangle) algorithm which use the feature weight vectors and the ED(Exemplar Densimeter) to optimize resulting Hyperrectangles. The proposed algorithm, as well as the EACH, required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the EACH. Also, by reducing the number of stored patterns, it showed excellent results in terms of classification when we compare it to the k-NN and the EACH.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.139-152
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• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.345-352
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• 2002

무선 인터넷을 이용하는 사용자는 정보의 양의 따른 시간적 통신비용의 증가 문제로 개인화 에이전트가 사용자의 관심에 따라 서비스를 제공하는 기능과 맞춤화된 정보를 제공하는 기능, 지식 기반 방식으로 정보를 예측하는 기능을 가지기를 바라고 있다. 본 논문에서는 이와 같이 무선 인터넷을 사용하는 사용자를 위한 PDA 개인화 에이전트 시스템을 구축하고자 한다. PDA 개인화 에이전트 시스템 구축을 위해 프로파일 기반의 에이전트 엔진과 사용자 프로파일을 이용한 지식기반 방식을 사용한다. 사용자가 웹페이지에서 행하는 행위들을 모니터링하여 사용자가 관심 가지는 문서를 파악하고 정보 검색을 통해 얻어진 문서를 분석하여 사용자 각각의 관심 문서로 나누어 서비스하게 된다. 모니터링 되어진 문서를 효과적으로 분석하기 위해 unsupervised clustering 기계학습 방식인 Cobweb을 이용한다. unsupervised 기계 학습은 conceptual 방식을 이용하여 검색되어진 정보를 사용자의 관심 분야별로 clustering한다. 클러스터링을 통해 얻어진 결과를 다시 기계학습을 통해 사용자 관심문서에 대한 프로파일을 생성하게 된다. 이렇게 만들어진 프로파일을 룰(Rule)로 만들어 이를 기반으로 사용자에게 서비스하게 된다. 이러한 룰은 사용자의 모니터링 결과로 얻어지기 때문에 주기적으로 업데이트하게 된다. 제안하는 시스템은 인터넷신문이나 웹진 등에서 사용자들에게 뉴스를 전달하기 위한 목적으로 생성하는 뉴스문서를 특정 대상으로 선정하였고 사용자 정보를 이용한 검색을 실시하고 결과로 얻어진 정보를 정보 분류를 통해 PDA나 휴대폰을 통해 사용자에게 제공한다. 상품을 검색하기 위한 검색노력을 줄이고, 검색된 대안들로부터 구매자와 시스템이 웹상에서 서로 상호작용(interactivity) 하여 해를 찾고, 제약조건과 규칙들에 의해 적합한 해를 찾아가는 방법을 제시한다. 본 논문은 구성기반 예로서 컴퓨터 부품조립을 사용해서 Template-based reasoning 예를 보인다 본 방법론은 검색노력을 줄이고, 검색에 있어 Feasibility와 Admissibility를 보장한다.매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without complexity of compu

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.51-56
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• 2009

In a language, noun and keyword extraction is a key element in information processing. When it comes to processing Korean language information, however, there are still a lot of problems with noun and keyword extraction. This paper proposes an effective noun extraction method that considers noun emergence features. The proposed method can be effectively used in areas like information retrieval where large volumes of documents and data need to be processed in a fast manner. In this paper, a category-based keyword construction method is also presented that uses an unsupervised learning technique to ensure high volumes of queries are automatically classified. Our experimental results show that the proposed method outperformed both the supervised learning-based X2 method known to excel in keyword extraction and the DF method, in terms o classification precision.