• Asia pacific journal of information systems
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• 제16권2호
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• pp.69-84
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• 2006

Importance of Today's diffusion of integrated hospital information systems is that various and huge amount of data is being accumulated in their database systems. Many researchers have studied utilizing such hospital data. While most researches were conducted mainly for medical diagnosis, there have been insufficient studies to develop medical care cost prediction model, especially using machine learning techniques. In this research, therefore, we built a medical care cost prediction model for cancer patients using CBR (Case-Based Reasoning), one of the machine learning techniques. Its performance was compared with those of Neural Networks and Decision Tree models. As a result of the experiment, the CBR prediction model was shown to be the best in general with respect to error rate and linearity between real values and predicted values. It is believed that the medical care cost prediction model can be utilized for the effective management of limited resources in hospitals.

• Computers and Concrete
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• 제20권6호
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• pp.645-654
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• 2017

Continuing from previous studies of sustainable concrete containing environmentally friendly materials and existing modeling approach to predicting concrete properties, this study developed an estimation methodology to predicting the strength of sustainable concrete using an advanced case-based reasoning approach. It was conducted in two steps: (i) establishment of a case database and (ii) development of an advanced case-based reasoning model. Through the experimental studies, a total of 144 observations for concrete compressive strength and tensile strength were established to develop the estimation model. As a result, the prediction accuracy of the A-CBR model (i.e., 95.214% for compressive strength and 92.448% for tensile strength) performed superior to other conventional methodologies (e.g., basic case-based reasoning and artificial neural network models). The developed methodology provides an alternative approach in predicting concrete properties and could be further extended to the future research area in durability of sustainable concrete.

• 한국시뮬레이션학회논문지
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• 제3권2호
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• pp.1-13
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• 1994

Deep reasoning procedures are model-based, inferring single or multiple causes and/or timing relations from the knowledge of behavior of component models and their causal structure. The overall goal of this paper is to develop an automated deep reasoning methodology that exploits deep knowledge of structure and behavior of a system. We have proceeded by building a software environment that uses such knowledge to reason from advanced symbolic simulation techniques introduced by Chi and Zeigler. Such reasoning system has been implemented and tested on several examples in the domain of performance evaluation, and event-based control.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.531-535
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• 1996

Computer aided conceptual solution of engineering problems can be effectively implemented by qualitative reasoning based on a physical model. Qualitative reasoning needs modeling paradigm which provides intellignet control of modeling assumptions and robust inferences without quantitative information about the system. We developed reasoning method using new algebra of qualitative mathematics. The method is applied to a conceptual design scheme of anadaptive control system of cutting process. The method identifies differences between proportional and proportional-integral control scheme of cutting process. It is shown that unfeasible investment could be prevented in the early conceptual stage by the qualitative reasoning procedures proposed in this paper.

• 지식경영연구
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• 제10권2호
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• pp.35-48
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• 2009

The paper proposes a quantitative causal ordering map (QCOM) to combine qualitative and quantitative methods in a framework. The procedures for developing QCOM consist of three phases. The first phase is to collect partially known causal dependencies from experts and to convert them into relations and causal nodes of a model graph. The second phase is to find the global causal structure by tracing causality among relation and causal nodes and to represent it in causal ordering graph with signed coefficient. Causal ordering graph is converted into QCOM by assigning regression coefficient estimated from path analysis in the third phase. Experiments with the prediction model of Korea stock price show results as following; First, the QCOM can support the design of qualitative and quantitative model by finding the global causal structure from partially known causal dependencies. Second, the QCOM can be used as an integration tool of qualitative and quantitative model to offerhigher explanatory capability and quantitative measurability. The QCOM with static and dynamic analysis is applied to investigate the changes in factors involved in the model at present as well discrete times in the future.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.355-361
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• 1999

An application of case-based reasoning is proposed to build an influence diagram for identifying successful new ventures. The decision to invest in new ventures in characterized by incomplete information and uncertainty, where some measures of firm performance are quantitative, while some others are substituted by qualitative indicators. Influence diagrams are used as a model for representing investment decision problems based on incomplete and uncertain information from a variety of sources. The building of influence diagrams needs much time and efforts and the resulting model such as a decision model is applicable to only one specific problem. However, some prior knowledge from the experience to build decision model can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem solving experience to solve a new decision. In this paper, we suggest a case-based reasoning approach to build an influence diagram for the class of investment decision problems. This is composed of a retrieval procedure and an adaptation procedure. The retrieval procedure use two suggested measures, the fitting ratio and the garbage ratio. An adaptation procedure is based on a decision-analytic knowledge and decision participants knowledge. Each step of procedure is explained step by step, and it is applied to the investment decision problem in new ventures.

• 한국안전학회지
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• 제18권1호
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• pp.50-55
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• 2003

This paper presents an intelligent computer system, which can easily diagnose electrical fire causes, without the help of human experts of electrical fires diagnosis. For this system, a database is built with facts and rules driven from real electrical fires, and an intellectual database system which even a beginner can diagnose fire causes has been developed, named as an Electrical Fire Causes Diagnosis System : EFCDS. The database system has adopted, as an inference engine, a mixed reasoning approach which is constituted with the rule-based reasoning and the case-based reasoning. The system for a reasoning model was implemented using Delphi 3, one of program development tools, and Paradox is used as a database building tool. To verify effectiveness and performance of this newly developed diagnosis system, several simulated fire examples were tested and the causes of fire examples were detected effectively by this system. Additional researches will be needed to decide the minimal significant level of the solution and the weighting level of important factors.

• 한국게임학회 논문지
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• 제9권6호
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• pp.117-125
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• 2009

본 논문은 포석 바둑을 위해, 패턴 지식을 근간으로 바둑 용어 지식을 수행할 수 있는 뉴로-퍼지 추론에 대한 실험 결과를 설명하였다. 즉, 포석 시 최선의 착점을 결정하기 위한 뉴로-퍼지 추론 시스템의 구현을 논하였다. 또한 추론 시스템의 성능을 시험하기 위하여 시차 학습(TD($\lambda$) learning) 시스템과의 대결을 벌였다. 대결 결과에 의하면 단순한 뉴로-퍼지 추론 시스템조차 시차 학습 모델과 충분히 대결할 만하며, 뉴로-퍼지 추론 시스템이 실제 바둑 게임에도 적용될 수 있는 잠재력을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권4호
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• 수산해양기술연구
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• 제29권4호
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• pp.279-285
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• 1993

Robot inverse kinematics solution is a complex nonlinear equation and very time-consuming task. This paper propose to use TSK fuzzy reasoning for solving robot inverse kinematics. A fuzzy model of inverse kinematics is identified by using input-output data and the model is used to solve the inverse kinematics. To show that, when used in robot inverse kinematics, fuzzy model is simple and generates a fairly accurate solution, a fuzzy model of inverse kinematics for PUMA robot is constructed.