• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.625-631
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• 2004

In general, the certainty factors of the fuzzy production rules and the certainty factors of fuzzy Propositions appearing in the rules are represented by real values between zero and one. If it can allow the certainty factors of the fuzzy production rules and the certainty factors of fuzzy propositions to be represented by interval-valued fuzzy sets, then it can allow the reasoning of rule-based systems to perform fuzzy reasoning in more flexible manner(15). This paper presents a fuzzy Petri nets and proposes an interval-valued fuzzy reasoning algorithm for rule-based systems based on fuzzy Petri nets. Fuzzy Petri nets model the fuzzy production rules in the knowledge base of a rule-based system, where the certainty factors of the fuzzy Propositions appearing in the furry production rules and the certainty factors of the rules are represented by interval-valued fuzzy sets. The proposed interval-valued fuzzy set reasoning algorithm can allow the rule-based systems to perform fuzzy reasoning in a more flexible manner.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1693-1705
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• 2013

To restore old aqueduct in Korea which is a irrigation bridge to supply water in paddy field area, it is needed to estimate approximate costs of restoration because the basic design for estimation of construction costs is often ruled out in current system. In this paper, estimating models of construction costs were developed on the basis of performance data for restoration of RC aqueduct bridges since 2003. The regression analysis (RA) model and case-based reasoning (CBR) model for the estimation of construction costs were developed respectively. Error rate of simple RA model was lower than that of multiple RA model. CBR model using genetic algorithm (GA) has been applied in the estimation of construction costs. In the model three factors like attribute weight, attribute deviation and rank of case similarity were optimized. Especially, error rate of estimated construction costs decreased since limit ranges of the attribute weights were applied. The results showed that error rates between RA model and CBR models were inconsiderable statistically. It is expected that the proposed estimating method of approximate costs of aqueduct restoration will be utilized to support quick decision making in phased rehabilitation project.

• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.187-193
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• 2005

In this paper, we proposes a man-machine interface design for fault diagnosis system with inter-node search method in a Petri net model. First, complicated fault cases are modeled as the Petri net graph expressions. Next, to find out causes of the faults on which we focus, a Petri net model is analyzed using the backward reasoning of transition-invariance in the Petri net. In this step, the inter-node search method algorithm is applied to the Petri net model for reducing the range of sources in faults. Finally, the proposed method is applied to a fault diagnosis of a refrigeration system to confirm the validity of the proposed method.

• International conference on construction engineering and project management
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• 2009.05a
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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.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• School Mathematics
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• v.6 no.3
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• pp.283-299
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• 2004

It is a very important objective of mathematical education to lead students to apply mathematics to the problem situations and to solve the problems. Assuming that mathematical modeling is appropriate for such mathematical education objectives, we must emphasize mathematical modeling learning. In this research, we focused what mathematical concepts are learned and what reasoning are applied and used through mathematical modeling. In the process of mathematical modeling, the students used several types of reasoning; deduction, induction and abduction. Although we cannot generalize a fact by a single case study, deduction has been used to confirm whether their model is correct to the real situation and to find solutions by leading mathematical conclusion and induction to experimentally verify whether their model is correct. And abduction has been used to abstract a mathematical model from a real model, to provide interpretation to existing a practical ground for mathematical results, and elicit new mathematical model by modifying a present model.

• Journal of The Korean Association For Science Education
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• v.33 no.2
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• pp.486-500
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• 2013

This study is conducted to examine how epistemic reasoning and argument structures of students vary according to data sources used in the process of argumentation implemented in the context of inquiry. To this end, three argument tasks using first-hand data and three argument tasks using second-hand data were developed and applied to the unit on 'Nutrition of Plants' for first year middle school students. According to the results of this study, epistemic reasoning of students manifested during the process of argumentation and varied according to data sources. While most students composed explanations with phenomenon-based or relation-based reasoning in argumentation using first-hand data, all the small groups composed explanations that included model-based reasoning in argumentation using second-hand data. In the case of arguments including phenomenon-based or relation-based reasoning, students described only observable characteristics, with warrants omitted from arguments in many cases. On the other hand, in the case of arguments that included model-based reasoning, explanations were composed by combining the results of observations with theoretical knowledge, with warrants more apparent in their arguments.

• (The) Korean Journal of Educational Psychology
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• v.22 no.2
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• pp.367-384
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• 2008

The purpose of this study was to develop picture testing instrument for measuring children's causal reasoning ability on events that can occur in daily life. The measurement instrument contains three domain of human development; haman behavior domain, human psychology domain, and natural/physical domain. Through this study, researchers designed a concept model based on theoretical framework and prior studies and investigated the reliability and validity of the measurement instrument which was developed in accordance with the concept model. For the empirical validation research, a pretest was conducted to 336 elementary school students in grade 2 to 4 in Seoul. Considering reliability and validity of the developed measurement instrument and factor loadings, researchers sorted out 18 questions. And then 18 question test and KICE Critical Thinking Ability Test was conducted to 509 elementary school students in grade 1 to 4 in Seoul. According to the result of the tests, the researchers sorted out final 12 questions. The Cronbach's alpha, reliability of the children's causal reasoning ability test consisted of the final 12 question, was significant as .72. Also, the result of exploratory factor analysis showed that factors of this test were haman behavior domain, human psychology domain, and natural/physical domain. Moreover, the correlation between the KEDI Reasoning Ability Test(2003) scores and the scores of the test developed in the current study was significant as .55. Finally, the result of the analysis about children's grade differences, the development by discrepancy of age was significant in total points and that of each domain. The children's causal reasoning ability test developed by this study can be useful not only for the evaluation of children's thinking ability but also for screening of the handicapped children in thinking ability development.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.223-225
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• 2006

유비쿼터스는 인간의 일상생활에 깊이 스며들어 삶을 풍요롭게 만들어 주는 기술이다. 즉. 여러 형태의 센서가 인지하는 상황정보를 바탕으로 인간을 위한 다양한 목적을 이루어 낼 수 있다. 각각의 유비쿼터스 시스템은 각자의 구조를 가지지만 상황인지(Context-aware), 학습(Learning), 추론(Reasoning) 의 요소는 대부분 필수적으로 갖추고 있다. 본 연구에서는 위 세가지 기본요소를 조합해서 구현할 수 있는 프레임워크를 제시하고 시나리오를 통해 그 적용 가능성을 살펴본다.