• 한국건설관리학회논문집
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• 제11권4호
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• pp.22-31
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• 2010

프로젝트 초기단계에서 산정된 공사비는 발주자의 중요한 의사결정에 영향을 미치므로 그 중요성이 강조되고 있지만, 정보의 부족으로 인하여 주로 견적전문가의 경험과 지식에 의존하여 진행된다. 이것은 현재 문제와 가장 유사한 과거 사례를 선택하여 사용하는 사례기반추론으로 발전되었다. 사례기반추론 모델의 예측 성능은 속성 가중치의 산정 결과에 많은 영향을 받으므로, 정확한 속성 가중치의 산정이 요구된다. 기존의 연구는 수학적 방법 또는 전문가의 주관적 판단을 이용하는 방법을 사용한다. 본 연구는 기존 연구의 문제점을 보완하기 위해 유전자 알고리즘을 이용한 사례기반추론 공사비 예측 모델을 제안한다. 공사비 예측 모델은 최근이웃 조회 방법의 과정에 의해 추출한 사례의 공사비 정보를 이용하여 예측 대상의 공사비를 산정한다. 검증 결과 AACE에서 정의한 견적시기별 예측 정확도와 표준화 회귀계수 동일가중치를 사용한 방법보다 높은 오차율을 나타내었다. 따라서 본 연구는 유전자 알고리즘을 도입하여 예측 성능을 향상시키고, 사례기반추론 방법을 사용하여 사용자가 이해하기 용이한 해결책 도출과정을 제시하였다는데 그 의미가 있다.

• 한국전자거래학회지
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• 제8권1호
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• pp.55-70
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• 2003

With the gradual growth of the electronic commerce various forms of shopping malls are constructed, and their searching methods and function are studied many ways. However, the recent outcome is still inadequate to search for goods for the tastes and demands of customers. To construct the shopping mall on the electronic commerce and help customers with purchasing goods, the efficient interface for the customers to contact the shopping malls should be founded and the customers should be able to search the goods they want. Therefore, in this paper, we designed the Intelligent Integration Agent System (IIAS) using the multi-agent formed by the integration agent which integrates the case based reasoning(CBR) and the rule based reasoning(RBR) and the user agent which manages users' profiles. IIAS performs the rule based reasoning on the subject issue first, then provides the unsatisfying search results from the rule-base reasoning to the customers through the user agent, which enables the search of the goods most similar to the ones that meet the tastes and demands of the customers. That is, the accuracy and the speed has been improved by reasoning with the similarity adjustable integration agent which can pick out the goods of customers wants by modifying the weights of properties according to those of the customers.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.203-211
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• 2009

Korean public owners who order public multi-family housing construction projects have yet to gain access to a model for predicting construction cost. For this reason, their construction cost prediction is mainly dependent upon historic data and experience. In this paper, a cost-prediction model based on Case-Based Reasoning (CBR) in the design phase of public multi-family housing construction projects was developed. The developed model can determine the total construction cost by estimating the different Building, Civil, Mechanical, Electronic and Telecommunication, and Landscaping work costs. Model validation showed an accuracy of 97.56%, confirming the model's excellent viability. The developed model can thus be used to predict the construction cost to be shouldered by public owners before the design is completed. Moreover, any change orders during the design phase can be immediately applied to the model, and various construction costs by design alternative can be verified using this model. Therefore, it is expected that public owners can exercise effective design management by using the developed cost prediction model. The use of such an effective cost prediction model can enable the owners to accurately determine in advance the construction cost and prevent increase or decrease in cost arising from the design changes in the design phase, such as change order. The model can also prevent the untoward increase in the duration of the design phase as it can effectively control unnecessary change orders.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.475-478
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• 2013

To carry out a one-off construction project successfully, effective and accurate early cost estimation is crucial, especially during the conceptual stage where very limited minimum information of construction project is given. As the level of accuracy of the early cost estimation has huge impacts on precise budgeting and cost management of a project, in other words, reducing the risk of a project, cost must be managed with special awareness. In an effort to improve the estimate accuracy of cost during the conceptual stage, this research introduces a Parameter Impact (PI) which can quantify weights of parameters and rank them; and PI development derived from the principle of impulse in physics is explicated. For a case study, 76 public apartment building cases in Korea are analyzed. To examine the validity of the proposed PI, a validation in terms of CBR applicability test and estimate accuracy comparisons using 10-nearest neighbor cases are carried out. The validation results support that the suggested PI can be applied in quantifying the weights of the parameters and CBR method for early cost estimation.

• 한국경영과학회지
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• 제28권3호
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• pp.81-101
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• 2003

The financial statements purpose to provide useful information to decision-making process of business managers. The value-relevant information, however, embedded in the financial statement has been often overlooked in Korea. In fact, the financial statements in Korea have been utilized for nothing but account reports to Security Supervision Boards (SSB). The objective of this study is to develop earnings forecasting models through financial statement analysis using artificial intelligence (AI). AI methods are employed in forecasting earnings: artificial neural networks (ANN) for manufacturing industry and case~based reasoning (CBR) for banking industry. The experimental results using such AI methods are as follows. Using ANN for manufacturing industry records 63.2% of hit ratio for out-of-sample, which outperforms the logistic regression by around 4%. The experiment through CBR for banking industry shows 65.0% of hit ratio that beats the statistical method by 13.2% in holdout sample. Finally, the prediction results for manufacturing industry are validated through monitoring the shift in cumulative returns of portfolios based on the earning prediction. The portfolio with the firms whose earnings are predicted to increase is designated as best portfolio and the portfolio with the earnings-decreasing firms as worst portfolio. The difference between two portfolios is about 3% of cumulative abnormal return on average. Consequently, this result showed that the financial statements in Korea contain the value-relevant information that is not reflected in stock prices.

• Journal of Information Technology Applications and Management
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• 제12권3호
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• 한국건설관리학회논문집
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• 제4권4호
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• pp.114-122
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• 2003

Dynamic and fragmented characteristics are two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics, and do not provide considerable support This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, the choice of the numbers of referring projects was investigated. It is concluded that selecting similar projects at $5{\~}6{\%}$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• 정보학연구
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• 제7권3호
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• pp.127-137
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• 2004

본 연구에서는 프로젝트의 계획단계에서 프로젝트 관리자가 수행해야 할 프로젝트에 대하여 규모, 범위, 기간, 성격 등의 측면에서 가장 유사한 과거의 사례를 찾아주고 이를 참조하여 WBS를 설계할 수 있도록 규칙과 사례기반 추론에 근거한 프로젝트 계획수립 지원모듈(PPSM: Project Planning Support Module)개발 방법을 제안한다.

• 한국전자거래학회지
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• 제18권4호
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• pp.195-214
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• 2013

사례기반추론은 과거의 사례들로부터 주어진 문제와 가장 유사한 사례를 가져와 이를 현재의 상황에 맞게 변형함으로써 보다 빠르고 효과적으로 문제를 해결하기 위한 방법론이다. 사례기반추론의 가장 중요한 성능의 지표는 사례의 수라고 할 수 있는데, 따라서 사례가 풍부하지 않은 분야에서는 적용하기 어려운 방법이다. 본 논문에서는 이를 극복하기 위해 건설분야를 대상으로 시맨틱 웹을 기반으로 하여 사례를 교환할 수 있는 방안을 제안하였다. 특히 사례를 여러 개의 세부 사례로 분리함으로써 적절한 전체 사례가 없더라도 적절한 세부 사례들을 조합하여 새로운 사례를 만들어낼 수 있도록 하였다. 이를 위하여 온톨로지를 이용하여 사례와 세부 사례의 연결, 세부 사례 단위의 유사도 규칙, 그리고 세부 사례의 조합을 위한 규칙을 표현하였으며 이를 이용하여 웹에서 세부 사례를 요청하고 조합할 수 있는 시스템을 설계 및 구현하였다. 본 연구에서 제안된 시스템은 건설분야를 대상으로 하였으므로 세부 사례로의 분리 및 조합이 건설분야에 제한된다는 점이 있으나, 향후 지속적인 연구를 통해 다른 분야에도 적용될 수 있을 것으로 기대된다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1997년도 추계학술대회발표논문집; 홍익대학교, 서울; 1 Nov. 1997
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• pp.181-184
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• 1997

Practical applications of learning systems usually involve complex domains exhibiting nonlinear behavior and dilution by noise. Consequently, an intelligent system must be able to adapt to nonlinear processes as well as probabilistic phenomena. An important class of application for a knowledge based systems in prediction: forecasting the future trajectory of a process as well as the consequences of any decision made by e system. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes in the form of chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a Henon process in the presence of various patterns of noise.