• Journal of the Society of Naval Architects of Korea
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• v.32 no.3
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• pp.44-50
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• 1995

We describe here the research work concerning the development of the CAPP(computer aided process planning) system, named BLOCK. designed to support block division of ship. The system consists of the expert system part generating block division lines, and their evaluation and editing one. As a reasoning approach of expert system, the case-based reasoning is used. The division lines can be graphically edited and the satisfaction measure of block division can be checked up in the evaluation stage with separate window. The expert system is developed by using NEXPERT Object development tool in the workstation. Currently the target ship is VLCC.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.676-684
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• 2009

Decision making at the early stages of a construction project has a significant impact on the project, and various scenarios created based on the owner's requirements should be considered for the decision making. At the early stages of a construction project, the information regarding the project is usually limited and uncertain. As such, it is difficult to plan and manage the project (especially cost planning). Thus, in this study, a cost model that could be varied according to the owner's requirements was developed. The cost model that was developed in this study is based on the case-based reasoning (CBR) methodology. The model suggests cost estimation with the most similar historical case as a basis for the estimation. In this study, the optimization process was also conducted, using genetic algorithms that reflect the changes in the number of project characteristics and in the database in the model according to the owner's decision making. Two optimization parameters were established: (1) the minimum criteria for scoring attribute similarity (MCAS); and (2) the range of attribute weights (RAW). The cost model proposed in this study can help building owners and managers estimate the project budget at the business planning stage.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.185-190
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• 2003

Due to the rapid growth of Internet, means of communication with customers in a traditional customer support environment such as telephone calls are being replaced by mainly e-mail in a Web-based customer support system. Although such a Web-based support is efficient and promises potential benefits for firms, including reduced transaction costs, reduced time, and high quality of support, there are some difficulties associated with responding to many types of customer’s inbound e-mails appropriately .As many types of e-mail are received, considerable attention is being paid to methods for increasing the efficiency of managing and responding e-mails. This research proposes an intelligent system for managing customer’s inbound e-mails in organizations by applying case based reasoning technique for responding to various customers' inbound e-mails more effectively. In this approach, a case is represented as a frame-typed data structure corresponding to an inbound e-mail, keywords, and its reply e-mail. In the retrieval procedure, keywords and affinity set is developed to index a case, and then the case is represented as a vector, a case vector. Also, cosines value is calculated to measure the similarity between a new inbound e-mail and the cases in the case base. In the adaptation procedure, we provide several adaptation strategies to adapt and modify the retrieved case. The strategies guide to make an outbound e-mail using product databases, databases for customer support, etc. Additionally, the Web-based system architecture is proposed to implement our methodology. The proposed methodology and system will be helpful for developing more efficient Web-based customer support.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1693-1705
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• 2013

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5S
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• pp.1649-1656
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• 2000

In this paper, We describe a design of sales agent for support for negotiation during sales support on the Internet within Case-Based Reasoning (CBR) solutions. First, We propose an multi-agent system which can effectively search complex product on the WWW. And we represent databases and case-bases and propose a CBR cycle for sales agent. We then implement some of them in a prototype for a sales agent and a case study is shown where preliminary approaches are used to negotiate with a customer about his demands and available produucts in a CBR-based Electronic Commerce solution.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.117-137
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• 2002

A CBR(Case-Based Reasoning) system solves the new problems by adapting the solutions that were used to solve the old problems. Past cases are retained in the case base, each in a specific form that is determined by features. Features are selected for the purpose of representing the case in the best way. Similar cases are retrieved by comparing the feature values and calculating the similarity scores. Therefore, the performance of CBR depends on the selected feature subsets. In this research, we measured the Selection Effect and the Elimination Effect of each feature. The Selection Effect is measured by performing the CBR with only one feature, and the Elimination Effect is measured by performing the CBR without only one feature. Based on these measurements, the feature subsets are selected. The resulting CBR showed better performance in terms of accuracy and efficiency than the CBR with all features.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.131-146
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• 2011

It is becoming more essential than ever for office professionals to become competent in information collection/gathering and problem solving in today's global business society. In particular, office professionals do not only assist simple chores but are also forced to make decisions as quickly and efficiently as possible in problematic situations that can end in either profit or loss to their company. Since office professionals rely heavily on their tacit knowledge to solve problems that arise in everyday business situations, it is truly helpful and efficient to refer to similar business cases from the past and share or reuse such previous business knowledge for better performance results. Case-based reasoning(CBR) is a problem-solving method which utilizes previous similar cases to solve problems. Through CBR, the closest case to the current business situation can be searched and retrieved from the case or knowledge base and can be referred to for a new solution. This reduces the time and resources needed and increase success probability. The main purpose of this study is to design a system called COKRS(Case-based reasoning Office Knowledge Recommender System) and develop a prototype for it. COKRS manages cases and their meta data, accepts key words from the user and searches the casebase for the most similar past case to the input keyword, and communicates with users to collect information about the quality of the case provided and continuously apply the information to update values on the similarity table. Core concepts like system architecture, definition of a case, meta database, similarity table have been introduced, and also an algorithm to retrieve all similar cases from past work history has also been proposed. In this research, a case is best defined as a work experience in office administration. However, defining a case in office administration was not an easy task in reality. We surveyed 10 office professionals in order to get an idea of how to define a case in office administration and found out that in most cases any type of office work is to be recorded digitally and/or non-digitally. Therefore, we have defined a record or document case as for COKRS. Similarity table was composed of items of the result of job analysis for office professionals conducted in a previous research. Values between items of the similarity table were initially set to those from researchers' experiences and literature review. The results of this study could also be utilized in other areas of business for knowledge sharing wherever it is necessary and beneficial to share and learn from past experiences. We expect this research to be a reference for researchers and developers who are in this area or interested in office knowledge recommendation system based on CBR. Focus group interview(FGI) was conducted with ten administrative assistants carefully selected from various areas of business. They were given a chance to try out COKRS in an actual work setting and make some suggestions for future improvement. FGI has identified the user-interface for saving and searching cases for keywords as the most positive aspect of COKRS, and has identified the most urgently needed improvement as transforming tacit knowledge and knowhow into recorded documents more efficiently. Also, the focus group has mentioned that it is essential to secure enough support, encouragement, and reward from the company and promote positive attitude and atmosphere for knowledge sharing for everybody's benefit in the company.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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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.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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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.