• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2006년도 춘계학술대회
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• pp.122-129
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• 2006

The context-awareness has become the one of core technologies and the indispensable function. for application services in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a music recommendation system. Our proposed system consists of such components as Intention Module, Mood Module and Recommendation Module. Among these modules, the Intention Module infers whether a user wants to listen to the music or not from the environmental context information. We built the Intention Module using data mining techniques such as decision tree, support vector machine and case-based reasoning. The results showed that the case-based reasoning model outperformed the other models and its accuracy was 84.1%.

• 아동학회지
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• 제34권6호
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• pp.1-11
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• 2013

The purpose of this study was to investigate the process of counterfactual reasoning which children undergo, based on mental model theory and dual process theory. The subjects were 120 four-year-olds and 120 five-year-olds from Ulsan. Counterfactual reasoning task conditions were created, including task type and content, which were type 1-specific, type 1-general, type 2-specific, type 2-general. There were two stories used for each task condition. Children's counterfactual reasoning score range was 0 to 8. Data were analyzed using SPSS by mean, standard deviation, one sample t-test, repeated measures of Anova. The results of this study were as follows. First, children's counterfactual reasoning was above chance level regardless of the task condition. Second, children's counterfactual reasoning was lowest when type 1-specific or type 2-specific tasks were given, slightly higher when type1-general tasks were given, and the highest when type 2-general tasks were given. There was no significant difference between 4-year-old and 5-year-old children's counterfactual reasoning.

• 한국정보시스템학회지:정보시스템연구
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• 제14권1호
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• 대한수학교육학회지:수학교육학연구
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• 제24권2호
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• pp.103-124
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• 2014

기존의 문제해결 유추(Problem Solving Analogies)의 사고과정은 표상, 접근, 사상, 적용, 학습의 5단계로 요약된다. 본 연구의 목적은 일반적인 문제해결 유추의 사고과정을 토대로 수학교육이라는 특수성이 반영된 '유추 사고과정 모델'을 개발하여 궁극적으로 학생들이 더 많이 유추를 사용할 수 있도록 도움을 주는데 있다. 모델의 개발과정은 먼저 Euler가 유추를 사용해 수학적 발견을 시도한 역사적인 사례를 분석하여 가설적 유추 사고과정 모델(초안)을 설계한 후, 연구자가 고안한 유추과제 즉, 피타고라스 정리의 증명을 유추적으로 연결시켜 코사인법칙을 증명하는 과제를 수학영재들로 하여금 해결하도록 하고, 그 해결과정에서 나타나는 사고과정의 특성을 반영하여 모델을 2차에 걸쳐 수정 보완하였으며, 교육적인 시사점을 도출하였다.

• 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.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.182-186
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• 2011

Early cost estimates are important to decision-making for a construction project. Moreover, the possibility of reducing the project cost is getting less as the project is progressed. Case-based reasoning (CBR), which can be viewed as an effective method for early cost estimating, is widely utilized recently. Early cost estimates using CBR have advantages over the traditional ones as they produce reasonable outputs and self-studying is possible by simply adding new cases. Case-based reasoning is composed of a cycle of retrieve, reuse, revise, and retain process. However, in the majority of research cases, they are focused on how to retrieve the similar cases, instead of revising the cases which is expected to increase accuracy results of cost estimation. This research suggests a method of revising retrieved similar cases in a GA-CBR cost model which is widely studied and utilized for early cost estimating recently. To validate the proposed method, case study is conducted based on Korean public apartment projects.

• 한국과학교육학회지
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• 제37권1호
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• pp.25-37
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• 2017

이 연구의 목적은 초등학교 6학년 1학기 4. 여러 가지 기체 단원에서 입자 개념을 학습할 때 효과적인 방안을 제시하여 현장의 교사와 학생들에게 도움을 주고자 하는 것이다. 연구자는 입자 개념을 도입하는 효과적인 방법으로 귀추적 추론법에 주목하였다. 실험집단(N=26)은 귀추적 추론 모형(Kim, 2003)을 바탕으로 재구성된 총 열두 차시 수업을 실시하였으며 비교집단((N=26)은 일반적인 교과서의 순서와 내용대로 수업을 실시하였다. 입자 개념을 살펴보기 위하여 기체 개념 이해도 검사를, 입자라는 추상적 실체를 다루는 단원이므로 인지 수준을 알기 위하여 GALT 검사를 실시하였다. 연구 결과 첫째, 처치 후 두 집단의 효과 차이를 알아본 독립표본 t 검정에서 21점 만점에 비교집단의 기체 개념 이해도 평균은 10.76, 처치집단의 기체 개념 이해도 평균은 14.65이며 t 통계값 2.890, 유의확률은 0.006으로 유의수준 .05에서 유의한 차이가 있는 것으로 나타났다. 또한 기체 개념 이해도 검사지에 입자로 표현한 횟수를 살펴보았는데 일반 집단 53회, 실험집단 114회로 두 배 이상 차이가 나타났다. 둘째, 학생들의 인지 수준과 처치 유무의 상호작용 효과에 대한 이원분산 교차설계 검증을 하였는데 상호작용 효과는 없었고, 실험집단이 인지 수준에 관계없이 비교집단보다 점수가 모두 높게 나타났다. 연구 결과를 통하여 귀추적 추론 모형을 적용하여 입자 개념을 다룬 수업의 효과를 확인할 수 있었다.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2000년도 추계공동학술대회논문집
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• Journal of the Korean Data and Information Science Society
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• 제14권3호
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• 한국지능시스템학회논문지
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• 제2권2호
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• pp.3-33
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• 1992

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown ill this paper that the performance of fuzzy control systems call be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate all optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.