• 한국안전학회지
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• 제20권3호
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• pp.19-26
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• 2005

There are three reasoning method in fault diagnosis process. The shallow reasoning is based on the experiential knowledge and deep reasoning is based on physical model. Hybrid reasoning is mixing two type reasoning. This study describes about fault train embodiment of screw type air compressor that is used widely in industrial facilities by using various experimental method and shallow reasoning. We investigate macroscopic failure cause of air compressor through naked eye observation and then microscopic failure cause by various experimental method. We composed fault train with fault knowledge based on empirical data and scientific data that is acquired through several experiments. It is possible to analysis system reliability and failure rate with these fault train.

• 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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1326-1329
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• 1993

This work presents a net-based structure to model approximate reasoning using fuzzy production rules, the Fuzzy Petri Net model. The Fuzzy Petri Net model is formally defined as a n-uple of elements. It allows for the representation of simple and complex forms of rules such as rules with conjunction in the antecedent and qualified rules. Parallel rules and conflicting rules can be modeled as well. We also developed an analysis method based on state equations and two fuzzy reasoning algorithms. Finally, the proposed method is applied to an example.

• 한국통신학회논문지
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• 제32권12B호
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• pp.767-778
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• 2007

본 논문에서는 스마트 홈 환경에서 데이터 마이닝 기법을 이용하여 사용자에게 상황에 적합한 서비스를 추론하는 모델을 제안한다. 의사결정트리 알고리즘들 중에 하나인 C4.5 알고리즘을 기반으로 서비스 추론에 쓰이는 서비스 트리를 생성하고, 정량적 특성 규칙과 정량적 판별 규칙을 이용하는 정량적 가중치 산정 알고리즘을 통해 사용자에게 제공될 서비스를 추론한다. 또한 시뮬레이션을 통해 그 성능을 검증하였다.

• 한국초등과학교육학회지:초등과학교육
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• 제42권1호
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• pp.109-126
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• 2023

이 연구는 초등 예비교사가 모의수업 시연에서 구성한 과학적 추론을 증거-설명의 연속선 관점에서 해석하여 그들의 과학적 추론이 갖는 인식론적 의미를 조사하였다. 연구를 위해 계절 변화에 관한 모의 수업을 시연한 예비교사 2명, 고기압과 저기압 및 바람에 관한 모의수업을 시연한 예비교사 2명이 연구 참여자로 선정되었다. 예비교사의 교수발화 중에서 귀납적, 연역적(가설-연역적) 추론, 또는 귀추적 추론의 사례가 드러난 에피소드에서 각 추론이 증거-설명의 연속선의 단계에서 어떤 역할을 하는지 비교하여 예비교사의 과학적 추론이 가진 인식론적 의미를 분석하였다. 계절 변화의 원인에 관한 모의수업을 시연했던 두 예비교사는 학생들이 수집한 데이터를 비교하여 증거를 인식하였고, 증거와 가설을 비교하여 가설을 검증하는 가설-연역적 추론을 활용하여 설명을 구성하였다. 고기압과 저기압 및 바람의 방향을 주제로 모의수업을 시연했던 두 예비교사는 모둠별 데이터를 종합하여 증거로 인식하는 귀납적 추론과 선형적 논리 구조를 가진 연역적 추론을 설명구성 전략으로 선택하여 최종 설명을 제시하였다. 연구에 참여한 예비교사들은 유사한 주제의 모의수업 시연에서 대체로 비슷한 흐름의 과학적 추론을 활용하여 과학지식을 구성하였으나, 증거-설명의 연속선에서 데이터, 증거, 모델, 설명으로 전개되는 인식론적 의미 측면에서 조금씩 다른 양상을 보였다. 또한, 일부 사례를 제외하면, 공통적으로 증거에서 모델을 탐색하는 과학적 추론은 부족하였으며, 가설이나 설명모델을 추리하기 위한 귀추적 추론이 부재하였다. 이 연구에서 분석틀로 적용했던 증거-설명의 연속선 접근은 과학적 추론의 인식론적 의미를 파악할 수 있게 하며 대안적인 과학적 추론 함양 지도 방법으로 사용될 수 있음을 논의하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• 지능정보연구
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• 제3권2호
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• pp.33-55
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• 1997

A data model is a diagram that describes the properties of different categories of data and the associations among them within a business or information system. In spite of its importance and usefulness, data modeling activity requires not only a lot of time and effort but also extensive experience and expertise. The data models for similar business areas are analogous to one another. Therefore, it is reasonable to reuse the already-developed data models if the target business area is similar to what we have already analyzed before. In this research, we develop a case-based reasoning system for data model reuse, which we shall call CB-DM Reuser (Case-Based Data Model Reuser). CB-DM Reuse consists of four subsystems : the graphic user interface to interact with end user, the data model management system to build new data model, the case base to store the past data models, and the knowledge base to store data modeling and data model reusing knowledge. We present the functionality of CB-DM Reuser and show how it works on real-life a, pp.ication.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.97-101
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• 1997

In recent years, there have been a number of applications of fuzzy logic in fuzzy reasoning system. The main objective of these applications is to approximate a decision making using the fuzzy reasoning system. This paper designs a fuzzy reasoning model for the decision making of registration fee at a private school, implements it applying for linguistic variables and fuzzy rules, and evaluates the practical availability of the model. The system accepts fuzzy rules, the type of membership functions, the domain of fuzzy sets and hedge, and fuzzifies the linguistic variables to generates fuzzy sets. The fuzzy sets generated are combined to constructs a solution fuzzy set. Finally, the system defuzzifies the solution fuzzy set to calculate a scalar value which is used for decision making.

• Journal of Information Technology Applications and Management
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• 제14권2호
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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.

• 동의생리병리학회지
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• 제23권4호
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• pp.745-750
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• 2009

The goal of this study is to examine on reasoning and search for construction of diagnosis ontology as a knowledge base of diagnosis expert system in oriental medicine. Expert system is a field of artificial intelligence. It is a system to acquire information with diverse reasoning methods after putting expert's knowledge in computer systematically. A typical model of expert system consists of knowledge base and reasoning & explanatory structure offering conclusion with the knowledge. To apply ontology as knowledge base to expert system practically, consideration on reasoning and search should be together. Therefore, this study compared and examined reasoning, search with diagnosis process in oriental medicine. Reasoning is divided into Rule-based reasoning and Case-based reasoning. The former is divided into Forward chaining and Backward chaining. Because of characteristics of diagnosis, sometimes Forward chaining or backward chaining are required. Therefore, there are a lot of cases that Hybrid chaining is effective. Case-based reasoning is a method to settle a problem in the present by comparing with the past cases. Therefore, it is suitable to diagnosis fields with abundant cases. Search is sorted into Breadth-first search, Depth-first search and Best-first search, which have respectively merits and demerits. To construct diagnosis ontology to be applied to practical expert system, reasoning and search to reflect diagnosis process and characteristics should be considered.