• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.189-196
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• 2001

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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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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%.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.11a
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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.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.91-100
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• 2020

Unlike the existing Visual Question Answering(VQA) problems, the new Visual Commonsense Reasoning(VCR) problems require deep common sense reasoning for answering questions: recognizing specific relationship between two objects in the image, presenting the rationale of the answer. In this paper, we propose a novel deep neural network model, KG_VCR, for VCR problems. In addition to make use of visual relations and contextual information between objects extracted from input data (images, natural language questions, and response lists), the KG_VCR also utilizes commonsense knowledge embedding extracted from an external knowledge base called ConceptNet. Specifically the proposed model employs a Graph Convolutional Neural Network(GCN) module to obtain commonsense knowledge embedding from the retrieved ConceptNet knowledge graph. By conducting a series of experiments with the VCR benchmark dataset, we show that the proposed KG_VCR model outperforms both the state of the art(SOTA) VQA model and the R2C VCR model.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.25-37
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• 2017

The purpose of this study is to analyze the effects on particulate concept formation based on abductive reasoning model for elementary science class. For this study, an author selected two groups in the sixth grade. One group is an ordinary textbook-based control group (N=26) and the other group is an abductive reasoning model-based treatment group (N=26). After twelve lessons, the scores of Concepts Test for Gas were analyzed by t-test and two-way ANOVA. The result of t-test showed both the control and treatment groups have higher score than before they take the lesson. But after the lesson, an author found out that the treatment group had higher score than that of the control group. And compared to the number of particles expressed, the number of the treatment group were higher than that of the control class. The two-way ANOVA result revealed that the interaction effect between their cognitive level and treatment was not significant. And regardless of the level of cognition, the scores of treatment group are higher than those of control group. Therefore, abductive reasoning model-based elementary science class were found to be more effective for particulate concept formation. Based on the results, an author concluded that abductive reasoning model is very effective in teaching particulate concepts to elementary students.

• Land and Housing Review
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• v.1 no.1
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• pp.1-7
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• 2010

Case-based reasoning (CBR) method can make estimators understand the estimation process more clearly. Thus, CBR is widely used as a methodology for cost estimation. In CBR, the quality of case retrieval affects the relevance of retrieved cases and hence the overall quality of the reminding capability of CBR system. Thus, it is essential to retrieve relevant past cases for establishing a robust CBR system. Case retrieval needs the following tasks to obtain appropriate case(s); indexing, search, and matching (Aamodt and Plaza 1994). However, the previous CBR researches mostly deal with matching process that has limits such as accuracy and efficiency of case retrieval. In order to address this issue, this research presents a CBR cost model for building projects that has two-step retrieval process: decision tree and nearest neighbor methods. Specifically, the proposed cost model has indexing, search and matching modules. Features in the model are divided into shape-based and scale-based attributes. Based on these, decision tree is established for facilitating the search task and nearest neighbor method was utilized for matching task. In regard to applying nearest neighbor method, attribute weights are assigned using GA optimization and similarity is calculated using the principle of distance measuring. Thereafter, the proposed CBR cost model is developed using 174 cases and validated using 12 test cases.

• Korean Journal of Adult Nursing
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• v.27 no.5
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• pp.604-611
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• 2015

Purpose: Knowledge, clinical reasoning, and self-confidence are the basis for undergraduate education, and determine students' level of competence. The purpose of this study was to assess the effects of the addition of a one-time simulation experience to the didactic curriculum on nursing students' knowledge acquisition, clinical reasoning skill, and self-confidence. Methods: Using a quasi-experimental crossover design consisted of intervention and wait-list control groups. Participants were non-randomly assigned to the first intervention group (Group A, n=48) or the wait-list control group (Group B, n=46). Knowledge level was assessed through a multiple choice written test, and clinical reasoning skill was measured using a nursing process model-based rubric. Self-confidence was measured using a self-reported questionnaire. Results: Results indicated that students in the simulation group scored significantly higher on clinical reasoning skill and related knowledge than those in the didactic lecture group; no difference was found for self-confidence. Conclusion: Findings suggest that undergraduate nursing education requires a simulation-based curriculum for clinical reasoning development and knowledge acquisition.

• The Mathematical Education
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• v.49 no.2
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• pp.223-246
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• 2010

Goos(2004) introduced educational researchers' demand for change on the way that mathematics is taught in schools and the series of curriculum documents produced by the National council of Teachers of Mathematics. The documents have placed emphasis on the processes of problem solving, reasoning, and communication. In Korea, the national curriculum documents have also placed increased emphasis on mathematical activities such as reasoning and communication(1997, 2007).The purpose of this study is to analyze the impact of inquiry-oriented instruction with guided reinvention on students' mathematical activities containing communication and reasoning for science high school students. In this paper, we introduce an inquiry-oriented instruction containing Polya's plausible reasoning, Freudenthal's guided reinvention, Forman's sociocultural approach of learning, and Vygotsky's zone of proximal development. We analyze the impact of mathematical findings from inquiry-oriented instruction on students' mathematical activities containing communication and reasoning.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.2
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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.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.753-772
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• 2014

The paper presents functional modelling and its application for diagnosis in nuclear power plants. Functional modelling is defined and its relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed and it is demonstrated that the levels of abstraction in models for diagnosis must reflect plant knowledge about goals and functions which is represented in functional modelling. Multilevel flow modelling (MFM), which is a method for functional modelling, is introduced briefly and illustrated with a cooling system example. The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool, the MFMSuite. MFM applications in nuclear power systems are described by two examples: a PWR; and an FBR reactor. The PWR example show how MFM can be used to model and reason about operating modes. The FBR example illustrates how the modelling development effort can be managed by proper strategies including decomposition and reuse.