• 한국경영과학회지
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• 제28권3호
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• pp.31-47
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• 2003

Quality design in practice highly depends on human designer's intuition and past experiences due to lack of formal knowledge about the relationship among 10 variables. This paper represents an data mining approach for developing quality design support system that integrates Case Based Reasoning (CBR) and Artificial Neural Networks (ANN) to effectively support all the steps in quality design process. CBR stores design cases in a systematic way and retrieve them quickly and accurately. ANN predicts the resulting quality attributes of design alternatives that are generated from CBR's adaptation process. When the predicted attributes fail to meet the target values, quality design simulation starts to further adapt the alternatives to the customer's new orders. To implement the quality design simulation, this paper suggests (1) the data screening method based on ξ-$\delta$ Ball to obtain the robust ANN models from the large production data bases, (2) the procedure of quality design simulation using ANN and (3) model management system that helps users find the appropriate one from the ANN model base. The integration of CBR and ANN provides quality design engineers the way that produces consistent and reliable design solutions in the remarkably reduced time.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.1183-1188
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• 2009

Estimating project costs during the early stage of conceptual planning is very important when deciding whether to approve the project and allocate an appropriate budget. However, due to greater uncertainties involved in a project, it is challenging to estimate costs during this initial stage within a reasonable tolerance. This paper attempts to develop a cost-estimate model for public road projects under these circumstances and limitations. In the conceptual planning stage of a road project, there is only limited information for cost estimation, for example, such input data as total length of the route, origin and destination, number of lanes, general geographic characteristics of the route, and other basic attributes. This implies that the model should individuate suitable but restricted information without considering detailed features such as quantity of earthwork and a detailed route of a given condition. With these limited facts, this paper applies a case-based reasoning (CBR) method to solve a new problem by deriving similar past problems, which in turn is used to estimate the cost of a given project based on best-fitted previous cases. To develop a CBR cost-estimate model, the authors classified 8 representative variables, including project type, the number of lanes, total length, road design grades, etc. Then, we developed the CBR model, primarily by using 180 actual cases of public road projects, procured over the last decade. With the CBR model, it was found that the degree of error in estimation can be reasonably reduced, to below approximately 30% compared to the final costs estimated upon the completion of detailed design.

• 한국정보처리학회논문지
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• 제7권5S호
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• pp.1649-1656
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• 2000

현재 대다숭의 전자 상거래 시스쳄은 수요자 및 공급자에 많은 부하를 주고 있다. 본 논문은 이러한 문제점을 해결하기 위하여 사례 기반의 추론을 이용한 판매 지원 에이전트를 제안한다. 먼저, 본 논문은 상품 정보 수집 에이전트, 색인 구성 에이전트, 판매 지원 에이전트, 검색엔진 등으로 구성된 다중 에이전트 시스템의 설계 방안을 제안하고 판매 지원 에이전트가 사용자의 취향을 알아내고 이를 흥정과정에 적용할 수 있도록 사례 기반의 지능형 에이전트를 설계한다. 또한, 프로토타입을 구현하여 판매지원 에이전트가 사례기반 추론 방법의 학습을 통하여 고객의 나이, 직업, 학력, 드에 따라 고객의 취향에 맞는상품 정보만을 빠르게 검색하는 과정을 보인다.

• Asia pacific journal of information systems
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• 제16권4호
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• pp.217-238
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• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

• 지능정보연구
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• 제9권2호
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• pp.171-184
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• 2003

A project planning is one of the most important processes that determines success and failure of the project. A pre-project planning is also essential job for information system implementations at the early stage of project planning, especially for management information system like ERP. However, pre-project planning is very difficult, because lots of factors and their relationships should be considered. Pre-project planning of ERP implementation has been done by project manager's own knowledge and experiences. In this article, we propose a system that help project manager to make a pre-project plan of ERP project with case-based reasoning(CBR) framework. The proposed CBR system saves previous cases of ERP pre-project planning in the case base. Then, the system finds the most similar case with the current pre-project planning problem. Project manager can make a pre-project plan by adjusting the most similar case. From the interview with project managers, we collect some field cases of ERP implementation. We organized these cases by using XML(Extensible Markup Language), which is good for representing hierarchical information. XML gives us some flexibilities to correct and maintain cases. We make a prototype system, PPSS(Project Planning Support System) that help project manager to make a pre-project plan of ERP implementations. The object of the system is to support project manager to make a pre-project plan of ERP. We hope the result of the study can be applied to other information systems. Our research would be extended to cover other stages of project planning.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.171-177
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• 2003

A project planning is one of the most important processes that determines success and failure of the project. A pre-project planning is also essential job for information system implementations at the early stage of project planning, especially for management information system like ERP. However, pre-project planning is very difficult, because lots of factors and their relationships should be considered. Pre-project planning of ERP implementation has been done by project manager's own knowledge and experiences. In this article, we propose a system that help project manager to make a pre-project plan of ERP project with case-based reasoning(CBR) framework. The proposed CBR system saves previous cases of ERP pre-project planning in the case base. Then, the system finds the best similar case with the current pre-project planning problem. Project manager can make a pre-project plan by adjusting the most similar case. From the interview with project managers, we collect some field cases of ERP implementation. We organized these cases by using XML(Extensible Markup Language), which is good for representing hierarchical information. XML gives us some flexibilities to correct and maintain cases. We make a prototype system, PPSS(Project Planning Support System) that help project manager to make a pre-project plan of ERP implementations. The object of the system is to support project manager to make a pre-project plan of ERP. We hope the result of the study can be applied to other information systems. Our research should be extended to cover other stages of project planning.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 춘계학술대회 논문집
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• pp.370-374
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• 1993

This paper describes a study on the expert system based process planning of the block division process in shipbuilding. The prototype system developed deterines the block division line of the midship of crude-oil tanker. Case-based reasoning (CBR) approach relying on previous similar cases to solve the problem is applied instead of rule-based reasoning (RBR). Similar cases are retrieved from case base according to the similarity metrics between input problem and cases. The retrieved case with the highest priority is then adapted to fit to the input problem buy adaptation rules. The adapted solution is proposed as the division line for the input problem.

• 한국정보과학회논문지:시스템및이론
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• 제34권7호
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• pp.282-287
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• 2007

정보시스템을 이용하는 금융, 무역, 의료, 에너지, 교육 등 사회 각 분야에서 정보화가 급속하게 진전되고 있다. 정보시스템에 대한 보안관리는 위험분석평가가 선행 되어야하며, 보안위험분석은 요구되는 정보보호서비스의 취약점을 해결하고 위협으로부터 시스템을 안전하게 관리할 수 있는 최선의 방법이다. 본 논문에서는 최적의 평가계획을 수립한 수 있는 평가사례기반추론 기능을 모델링하였다. 평가 사례기반추론(case-based reasoning) 기능은 보안위험분석평가를 프로젝트단위로 관리하며, 기존의 평가사례 간유사도를 평가하고, 유사한 평가 사례를 바탕으로 최적의 보안위험분석평가 계획을 수립할 수 있다.

• Asia pacific journal of information systems
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• 제9권2호
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• pp.19-37
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• 1999

Computer aided process planning(CAPP) is a key for implementing CIM. It is bridge between CAD and CAM and translates the design information into manufacturing instructions. Generally, manufacturing is an area where intelligent systems will not be able to rely on methods requiring formalized knowledge. Manufacturing lacks a body of knowledge that is specific, formalized, and rigorous, and which can be coded as rules or procedures. Thus expertise in manufacturing is developed over a period of many years. Case-based reasoning(CBR) offers a new approach for developing intelligent system. In the case-based approach the problem solving experience of the experts is encoded in the form of cases. CBR's retrieval process can be divided to two step. The first step is matching step, and the second step is selection step. For selecting base case, new preference heuristics were introduced using similarity concept. Similarity concept has three has three dimensions, i.e. entity similarity, structural similarity, and goal similarity. In this paper, bolt's process planning was selected an application domain. Following the test result, the new preference heuristics were approved as a useful procedure in CAPP.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 추계학술대회
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.