• Journal of Information Science Theory and Practice
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• 제3권1호
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• pp.24-39
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• 2015

Pre-retrieval query formulation is an important step for identifying local text reuse. Local reuse with high obfuscation, paraphrasing, and translation poses a challenge of finding the reused text in a document. In this paper, three pre-retrieval query formulation strategies for heuristic retrieval in case of low obfuscated, high obfuscated, and translated text are studied. The strategies used are (a) Query formulation using proper nouns; (b) Query formulation using unique words (Hapax); and (c) Query formulation using most frequent words. Whereas in case of low and high obfuscation and simulated paraphrasing, keywords with Hapax proved to be slightly more efficient, initial results indicate that the simple strategy of query formulation using proper nouns gives promising results and may prove better in reducing the size of the corpus for post processing, for identifying local text reuse in case of obfuscated and translated text reuse.

• 토지주택연구
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• 제1권1호
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• pp.1-7
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• 2010

Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.

• 한국공간구조학회논문집
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• 제7권6호
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• pp.75-82
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• 2007

본 연구에서는 국내 초고층 건물 사례들을 바탕으로 사례 데이터베이스를 구축하여 구조시스템 대안 생성 및 선정 업무를 효율적으로 처리 할 수 있는 설계지원시스템을 개발하였으며, 사례 데이터베이스의 정보를 이용한 사례기반추론기법을 제안하였다. 국내 47개 초고층 건물에 대한 사례 데이터베이스의 설계정보를 분석하여 초기설계 단계에서 구조시스템 선정을 위한 귀납적 조회 모듈 및 유사사례선정을 위한 최근린 조회기법도 제시하였다.

• 산업경영시스템학회지
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• 제22권51호
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• pp.129-138
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• 1999

The aim of this research is the development of an automated fixture planning system for prismatic parts using the case-based reasoning (CBR). CBR is the problem solving paradigm that uses the similarity between a new problem and old cases to solve the new problem. This research uses CBR for the fixture planning. A case is composed with the information of the part, the components of fixture and the method of fixing for the part. The basic procedure is the retrieval and adaptation for the case, and this research presents the method of retrieval that selects most similar case to the new situation. The retrieval-step is divided into an index matching and an aggregated matching. The adaptation is accomplished by the modification, which transforms the selected case to the solution of the situation of the input part by the specified CBR algorithm. The components of fixture and the method of fixing are determined for a new part by the procedure.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.283-286
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• 2007

This paper proposes a genetic algorithm (GA) approach to instance selection in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in complex problem solving. Nonetheless, compared to other machine learning techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for instance selection in CBR.

• 한국콘텐츠학회논문지
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• 제6권3호
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• pp.20-27
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• 2006

본 연구는 객체 지향 소스 코드의 검색과 재사용을 효율적으로 수행할 수 있는 의미망을 구축하였다. 이를 위하여 각 노드 간 객체지향 상속의 개념을 표현할 수 있도록 의미망의 초기 관련값을 시소러스로 구축하였다. 또한, 의미망의 노드와 간선을 활성화시키고 활성값을 전파시키기 위해 사용되는 스프레딩 엑티베이션 방법의 단점을 보완하여 스프레딩 엑티베이션의 성능은 최대한 유지하면서 검색 속도를 향상 시킬 수 있는 방법을 제안하였다.

• 한국건설관리학회논문집
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• 제12권2호
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• pp.53-63
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• 2011

유사 재해의 반복적 발생은 건설재해의 가장 큰 특징 중 하나이다. 안전사고 예방을 위한 유사 재해사례는 수행 예정인 작업의 위험을 발견하고 안전 대책을 수립하는데 직접적인 정보를 제공한다. 과거 재해 사례들을 검색하여 안전관리 분야에 사용하기 위해 많은 검색시스템들이 개발되었다. 하지만 현장의 다양한 조건과 반영한 결과를 얻기 위해서는 수많은 검색을 수행하거나, 실제 현장의 안전관리 계획 수립 단계별 특성을 반영하지 못하는 단점이 존재한다. 본 연구에서는 기존 안전관리 검색시스템들의 한계점을 개선하기 위해 정보 검색 방법과 BIM(building information modeling)을 이용한 안전관리 단계별 적용이 가능하며 현장의 상황과 유사한 재해사례를 검색하는 시스템을 개발하였다. 검색 시스템은 BIM의 객체를 추출하여 이를 현장 정보 DB와 결합하여 질의를 구성한다. 그리고 질의를 과거 재해사례 DB와 비교하여 가장 유사한 과거 재해 사례를 찾고, 안전 관리자에게 정보를 제공한다. 본 연구의 결과를 바탕으로 안전 관리자는 검색 결과를 얻기 위한 과도한 질의 생성을 줄일 수 있다. 또한 BIM 객체를 통해 과거 유사한 재해가 발생한 현장의 좌표를 얻을 수 있어 위험을 쉽게 인지할 수 있을 것이다.

• 한국정보시스템학회지:정보시스템연구
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• 제8권1호
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• pp.27-44
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. CBR has several research issues which can be divided into two categories : (1) static issues and (2) dynamic issues. The static issues are related to case representation scheme and case data model, that is, focus on casebase which is a repository of cases. The dynamic issues, on the other hand, are related to case retrieval procedure and problem solving process, i.e. case adaptation phase. This research is forcused on retrieval procedure Traditional query processing accepts precisely specified queries and only provides exact answers, thus requiring users to fully understand the problem domain and the casebase schema, but returning limited or even null information if the exact answer is not available. To remedy such a restriction, extending the classical notion of query answering to approximate query answering(AQA) has been explored. AQA can be achieved by neighborhood query answering or associative query answering. In this paper, neighborhood query answering technique is used for AQA. To reinforce the CBR process, a new retrieval procedure(cooperative CBR) using neighborhood query answering is proposed. An neighborhood query answering relaxes a query scope to enlarge the search range, or relaxes an answer scope to include additional information. Computer Aided Process Planning(CAPP) is selected as cooperative CBR application domain for test. CAPP is an essential key for achieving CIM. It is the bridge between CAD and CAM and translates the design information into manufacturing instructions. As a result of the test, it is approved that the problem solving ability of cooperative CBR is improved by relaxation technique.

• Asia pacific journal of information systems
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• 제25권4호
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• pp.626-641
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• 2015

Conventional case-based reasoning (CBR) does not perform efficiently for high-volume datasets because of case retrieval time. To overcome this problem, previous research suggested clustering a case base into several small groups and retrieving neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performance than the conventional CBR. This paper proposes a new case-based reasoning method called the clustering-merging CBR (CM-CBR). The CM-CBR method dynamically indexes a search pool to retrieve neighbors considering the distance between a target case and the centroid of a corresponding cluster. This method is applied to three real-life medical datasets. Results show that the proposed CM-CBR method produces similar or better predictive performance than the conventional CBR and clustering-CBR methods in numerous cases with significantly less computational cost.

• 제어로봇시스템학회논문지
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• 제8권6호
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• pp.455-461
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• 2002

Object-based image retrieval method is addressed. A new image segmentation algorithm and image comparing method between segmented objects are proposed. For image segmentation, color and texture features are extracted from each pixel in the image. These features we used as inputs into VQ (Vector Quantization) clustering method, which yields homogeneous objects in terns of color and texture. In this procedure, colors are quantized into a few dominant colors for simple representation and efficient retrieval. In retrieval case, two comparing schemes are proposed. Comparing between one query object and multi objects of a database image and comparing between multi query objects and multi objects of a database image are proposed. For fast retrieval, dominant object colors are key-indexed into database.