• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.11 no.9
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• pp.414-421
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• 2001

If a machine has some faults, we can detect them using vibration or noise signals. However some maintenance engineers who don\`t have export knowledge, need the help of vibration experts for diagnosing the machine. In this paper a case based reasoning (CBR) system is developed which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve the information form previous cases which are most similar to new problem s that they can solve new problem using solutions form the previous cases. In this paper, a new case retrieval method of CBR system using Petri net is proposed and also applied to diagnosis for electric motors as a practical problem.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.774-779
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• 2001

If rotating machinery have a fault, we can detect it using vibration or noise signals. However some maintenance engineers who doesn't have expert knowledge, needs the help of vibration experts for diagnosing rotating machinery. But qualified experts are rare, therefore we have been developed the case based reasoning (CBR) system which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve too information from previous cases which are most similar to new problem and they can solve new problem using solutions from the previous cases. In this paper, we propose a new method which is the case retrieval of CBR system using Petri net and we also applied it to diagnosis for electric motors as a practical problem.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.119-126
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• 2006

This paper addresses the methodology development far the automated machining setup planning system using case-based reasoning(CBR). The case-based reasoning is used to develop a setup planning system. which consists of part input and representation module, case retrieval module, and case adaptation module. We present new approaches in the part input and representation module and the case retrieval module focusing on the similarity index determination. An illustrative example is included to demonstrate the proposed method.

• Management & Information Systems Review
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• v.3
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• pp.179-199
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. In case-based approach the problem solving experience of the domain expert is encoded in the form of cases. CBR has successfully been applied to many kinds of problems such as design, planning, diagnosis and forecasting. In this paper, CBR was applied for forecasting default risk. The applied result was successful in spite of the small casebase. Generally, CBR requires large casebase. So, if the number of data was large, the result was better. But in this paper, what financial variable was more forecastable was not tested. Next, this should be tested.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.178-184
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• 2003

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the similarity function or optimization of case features and their weights. However, according to some of prior researches, finding the optimal k parameter for k-nearest neighbor (k-NN) is also crucial to improve the performance of CBR system. Nonetheless, there have been few attempts which have tried to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the new model to the real-world case provided by an online shopping mall in Korea. Experimental results show that a GA-optimized k-NN approach outperforms other AI techniques for purchasing behavior forecasting.

• Journal of the Korea Institute of Building Construction
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• v.12 no.2
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• pp.133-141
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• 2012

As we move from the industrial age to the information age, domestic industries are changing rapidly, and rural society is also laying the foundation to make use of information technologies. Through this kind of modernization, the size of agricultural facilities has been increasing on a significant scale. But, in reality, there are many difficulties in the maintenance of agricultural facilities in proportion to their growing number. Accordingly, this research aims to solve the fundamental problems that occur with agricultural facilities in the maintenance stage. In addition, it aims to provide information on how to maintain and manage facilities for farmers. The presentation of the maintenance information was conducted using a case-based reasoning method that solves current problems based on past cases. The tool of case-based reasoning was applied to define the establishment of the base for cases, characteristic variables and maintenance measures. The effectiveness of a CBR model was examined through the case study. The use of the case-based reasoning method is judged to be effective as a tool to support the decisions of farmers regarding maintenance. When the maintenance measures derived through the CBR model are offered to farmers, the fundamental problems of maintaining agricultural facilities will be solved, and the damage to such facilities minimized.

• The Journal of Information Systems
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• v.18 no.3
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• pp.231-249
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• 2009

Recently, plant construction industries are enjoying a favorable business climate centering around developing countries and oil producing countries rich in oil money. This paper proposes a methodology of implementing case-based reasoning(CBR) system for managing knowledge like lessons learned and various documents accumulated in performing power plant construction projects which are receiving a lot of order from foreign countries such as the Middle East, etc. Our methodology is consisted of 10 steps : user requirement gathering, information modeling, case modeling, case base design, similarity function design, user interface design, case base building, CBR module development, user interface implementation, integration test. Also, to illustrate the effectiveness of proposed methodology, the real CBR system is implemented for the plant business division of 'H' company which has international competitiveness in the field of plant construction industry. At present, the implemented CBR system is successfully utilizing as storing, sharing, and reusing knowledge which is accumulated in performing power plant construction projects in the target enterprise.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.51
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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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1207-1213
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• 2006

This paper shows that case-based reasoning (CBR), an artificial intelligence technique, is a quite efficient tool in monitoring financial market against its possible collapse. For this purpose, daily financial condition indicator (DFCI) monitoring financial market is built on CBR and its performance is compared to DFCI on neural network. This study is empirically done for the Korean financial market.

• Journal of Information Technology Applications and Management
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• v.14 no.2
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.