• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.257-260
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• 2007

유비쿼터스 컴퓨팅의 목표 중 하나는 사용자의 직접적이거나 은연중에 내포된 요청에 따라 적절한 서비스를 제공하는 것이다. 최근에는 사용자의 다양한 요청에 보다 유연하게 대응할 수 있는 연구가 이루어지고 있으며 그 중 단일서비스의 조합을 통해 복합서비스를 제공할 수 있는 서비스 컴포지션(Service Composition)이 주목을 받고 있다. 하지만 기존 연구들은 늦은 처리속도로 인해 빠른 응답이 필요한 실시간 상황인식 서비스에는 부적합 하다. 또한 사례기반 추론은 사례 기저에 쌓인 사례의 수가 늘어감에 따라 속도가 저하되는 단점이었다. 이러한 단점을 최소화 하기 위하여 클러스터링 기법이 사용되고 있다. 본 논문은 사례기반 추론을 이용한다. 또한 사례 기저의 수를 유지하면서 사례 기저의 수치화 및 트리구조 판리를 이용하여 기존방법보다 빠른 서비스 컴포지션을 구현하는 방법을 제안한다. 그리고 기존의 서비스 컴포지션 기법과 비교 분석을 통하여 제안하는 기법의 유효함을 확인하였다.

• Korean Journal of Construction Engineering and Management
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• v.17 no.1
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• pp.3-9
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• 2016

This study proposes a approximate cost estimation system based on Excel with VBA using weighted CBR(Case Based Reasoning). One characteristic of this system is that it generates the sheet automatically as many as the number of similar case and new estimation when it performs a case learning and a new estimate and cell formula is automatically entered into each sheet. User can be free to compose a combination of attribute factors because they can select up to ten attribute factors. This paper presents an applicability of estimation model for estimating the soil remediation cost when it use a landfarming method. When compared to a estimation model by using average unit cost and optimum multiple regression, this model shows a better result. This study was aimed at landfarming method, but it is expected that a cost estimation model using CBR will be more likely to apply in soil remediation technologies which various remediation technologies and pollutant species exist.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.42-50
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• 2010

It is necessary to implementation of system contain intelligent decision making algorithm considering marine feature because existing vessel USN system is simply monitoring obtained data from vessel USN. In this paper, we designed inference system using case based reasoning method and implemented knowledge base that case for fire and demage of digital marine vessel. We used K-Nearest Neighbor algorithm for recommend best similar case and input 3.000 EA by case for fire and demage context case base. As a result, we obtained about 82.5% average accuracy for fire case and about 80.1% average accuracy for demage case. We implemented digital marine vessel monitoring system using inference result.

• Proceedings of the Korea Association of Information Systems Conference
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• 1997.10a
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• pp.149-164
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• 1997

사례기반형 추론(CBR)은 과거의 경험을 이용해서 문제를 해결하려는 방법으로서 규칙기반형 추론(RBR)과 달리 문제해결경험이 풍부한 도메인에 적합한 방법이다. CBR은 정적인 측면에서 사례의 표현과 구조화문제가 중요시되며, 동적인 측면에서는 사례의 검색 절차와 수정이라는 해결안 생산과정이 중요시된다. 본 논문은 정적 측면에서 효과적인 CAPP 지원을 위해 사례베이스(CB)를 계층적으로 구조화하였다. 또한 CB의 구조화시 시스 템의 문제해결 능력을 향상시켜주기 위하여 CB를 응용도메인 종속적 CB(DDCB)와 독립적 CB(DICB)로 분리하여 과거의 문제해결 경험에 관한 지식은 DDCB에 나타내었으며, 도메인 전문가가 가지는 일반적인 문제해결 지식은 DICB에 나타내었다.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.349-359
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• 2010

The purpose of this study is to design the On-line Insurance Sales Support System using Case-Based Reasoning(CBR). In on-line insurance subscription process, this system provides the personalized insurance payment cases and insurance statistics for customers to entice an insurance subscription. By measuring, specifically, similarities between the user profile and insurance payment cases, it suggests the best insurance payment case which has the highest similarity and reflects the latest in the insurance payment cases. In addition, it serves the insurance statistical information that matches with the attributes of the finally-selected case. These functions can be useful in on-line insurance sales.

• Journal of Korean Association for Spatial Structures
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• v.7 no.6
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• pp.75-82
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• 2007

In this study, a design-supporting system, which is intended to assist engineers in the schematic phase of the structural design, is developed using a case database that contains design information of tall buildings in Korea. A case-based reasoning method utilizing the case database is proposed. The inductive retrieval module for selecting structural system, in the initial stage, from the design information of case database for 47 tall buildings is presented. Also, the nearest-neighbor retrieval method for selecting similar design cases is introduced.

• Korean Journal of Construction Engineering and Management
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• v.3 no.3 s.11
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• pp.49-57
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• 2002

This paper describes the second stage of the work aiming at proposing a reasonable risk management response system for construction safety. By means of questionnaires/interviews and two sample t-tests, significant risk factors are identified for three different conditions. Then a Case-Based Reasoning System is built for use at construction sites to simulate possible accidents. This Construction Management Reasoning System(CMRS) nay be used by safety managers at sites every day (or education and training of workers to prevent accidents. The case base built so far is limited to the construction of expressway bridges. There is much need for further research since the simulation of possible accidents is to be a good means to enhance safety awareness of construction workers.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.282-287
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• 2007

Information society is dramatically developing in the various areas of finance, trade, medical service, energy, and education using information system. Evaluation for risk analysis should be done before security management for information system and security risk analysis is the best method to safely prevent it from occurrence, solving weaknesses of information security service. In this paper, Modeling it did the evaluation-base CBD function it will be able to establish the evaluation plan of optimum. Evaluation-based CBD(case-based reasoning) functions manages a security risk analysis evaluation at project unit. it evaluate the evaluation instance for beginning of history degree of existing. It seeks the evaluation instance which is similar and Result security risk analysis evaluation of optimum about under using planning.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.107-121
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• 2005

Recently, many researches on recommendation systems and collaborative filtering have been proceeding in both research and practice. However, although product items may have multi-valued attributes, previous studies did not reflect the multi-valued attributes. To overcome this limitation, this paper proposes new methodology for recommendation system. The proposed methodology uses multi-valued attributes based on clustering technique for items and applies the collaborative filtering to provide accurate recommendations. In the proposed methodology, both user clustering-based CBR and item attribute clustering-based CBR technique have been applied to the collaborative filtering to consider correlation of item to item as well as correlation of user to user. By using multi-valued attribute-based clustering technique for items, characteristics of items are identified clearly. Extensive experiments have been performed with MovieLens data to validate the proposed methodology. The results of the experiment show that the proposed methodology outperforms the benchmarked methodologies: Case Based Reasoning Collaborative Filtering (CBR_CF) and User Clustering Case Based Reasoning Collaborative Filtering (UC_CBR_CF).