• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.211-218
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• 2011

Although the disaster management of Korea such as mitigation of disasters, preparedness for them and recovery from them. It should be considered based on the failures of the disaster management and the past experimental knowledge, it is believed that the repetitive occurrence of similar disasters is caused by absence of learning of disaster management organizational. That is, non-learning of the management organs due to experimental errors indicates that the organization themselves are not able to adjust to environment and the same kinds of disasters may happen in the future. Therefore, this study identifies repetitive failures by analysing reasons of the failures in terms of organizational learning in order to prevent from repetition of similar failures, and presents suggestions on the policy of disaster management. For the purposes, it carries both bibliographical analysis and case analysis. this study targets Daegu Subway Fire in 2003.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1013-1021
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• 2014

For efficient investments of government budgets, It is important to analyze the similarities of R&D projects. So, existing studies have proposed a techniques for analyzing similarities using keywords or segments. However, the techniques have low accuracy. We propose a technique for similarities of projects using patent information. To achieve our goal, we suggest three metrics that are based some mathematic theories; set theory and probability theory. In order to validate our technique, we perform case studies that have 156 R&D projects and 160,218 patent informations.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2007.05a
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• pp.103-104
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• 2007

• 한국사회복지학회:학술대회논문집
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• 2006.04a
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• pp.381-385
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• 2006

• 전기의세계
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• v.40 no.4
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• pp.59-65
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• 1991

본 고에서는 퍼지 이론과 신경회로망 기법의 유사한 특성들을 살펴보고, 제어기 구성을 중심으로 한 응용 사례들을 소개하고자 한다. 그리고 앞으로 활발하게 연구가 이루어질 몇 분야들을 열거한다.

• 동물약계
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• no.3
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• pp.3-3
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• 1993

1. 국가검정 동물약품 검정증지 대체사용 2. 유사동물의약품에 대한 분류사례 3. 하계 양축농가 무료진료를 위한 약품 기증 4. 동물용 초음파 영상진단기 수입요령 5. 동물약품 가격 적정화 검토 6. 정기 이사회 개최 7. 제조업 소재지 변경

• Korean Journal of Construction Engineering and Management
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• v.12 no.1
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• pp.53-61
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• 2011

For construction projects, the importance of early cost estimates is highly recognized by the project team and sponsoring organization because early cost estimates are frequently a foundation of business decisions as well as a basis for identifying any changes as the project progresses from design to construction. However, it is difficult to accurately estimate construction cost in the early stage of a project due to various uncertainties in construction. To deal with these uncertainties, cost estimates should be made several times over the course of the project. In particular, early cost estimates are essential process for successful project management. For accurate construction cost estimates, it is necessary to compare cost estimates with actual costs based on historical project data. In this context, case-based reasoning (CBR), which is the process of solving new problems based on the solutions of similar past problems, can be considered as an effective method for cost estimating. To obtain this, it is also required to define the attribute similarities and the attribute weights. However, no existing method is capable of determining attribute weights of qualitative variables. Consequently, it has been a well-known barrier of accurate early cost estimates. Using Genetic Algorithms (GA), this research suggests the method of determining the attribute weight of qualitative variables. Based on building project case studies, the proposed methodology was validated.

• The Journal of Society for e-Business Studies
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• v.11 no.2
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• pp.31-48
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• 2006

Commercial internet auction systems have been successfully used recently. In those systems, because auction prices of auction items are given by sellers only, the success bid rate can be decreased due to the large difference between the reserve price and the normal price. In this paper, we propose an agent that generates auction prices to sellers based on past auction data and item prices gathered from the web Through performance experiments, we show that the successful bid rate increases by preventing sellers from making unreasonable reserve prices. Using the pricing agent, we design and implement an XML-based auction system on the web.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1189-1196
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• 2004

It is a major concern of e-shopping mall managers to satisfy a variety of customer's desire by recommending a proper product to the perspective purchaser. Customer information like customer's fondness, age, gender, etc. in shopping has not been used effectively for the customers or the suppliers. Conventionally, e-shopping mall managers have recommended specific items of products to their customers without considering thoroughly in a customer point of view. This study introduces the ways of a choosing and recommending of products using case-based reasoning and rule-based reasoning for customer themselves or others. A similarity measure between one member's idiosyncrasy and the other members' is developed based on the rule base and the case base. The case base is improved for the system intelligence by recognizing and learning the changes of customer's desire and shopping trend.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.