• Journal of the Korea Society for Simulation
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• v.3 no.2
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1058-1062
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• 1993

A fuzzy reasoning method is proposed for the implementation of control systems based on non-fuzzy microprocessors. The essence of the proposed method is to search the local active miles instead of the global rule base. Thus the reasoning is conveniently performed on a master cell as a fuzzy accelerating kernel, which is transformed from an active fuzzy cell. The interpolative reasoning is simplified via adopting the algebraic product of fulfillment for the conditional connective AND and the weighted average for the rule sentence connective ALSO.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.84-94
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• 1996

In this work, a fuzzy petri net model for modeling a general form of fuzzy production system which consists of chaining fuzzy production rules and so requires multistage reasoning process is presented. For the obtained fuzzy petri net model, the net will be transformed into some matrices, and also be systematically led to an algebraic form of a state equation. Since it is fond that the approximate reasoning process in fuzzy systems corresponds to the dynamic behavior of the fuzzy petri net, it is further shown that the multistage reasoning process can be carried out by executing the state equation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.138-144
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• 1994

An inference network is proposed as a tool for bidirectional approximate reasoning. The inference network can be designed directly from the given fuzzy data(knowledge). If a fuzzy input is given for the inference netwok, then the network renders a reasonable fuzzy output after performing approximate reasoning based on an equality measure. Conversely, due to the bidirectional structure, the network can yield its corresponding reasonable fuzzy input for a given fuzzy output. This property makes it possible to perform forward and backward reasoning in the knowledge base system.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.75-77
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• 1998

Perhaps one of the most versatile approaches to learning in practical domains lies in case based reasoning. To date, however, most case based reasoning systems have tended to focus on relatively simple domains. The current study involves the development of a decision support system for a complex production process with a limited database. This paper presents a set of critical issues underlying CBR, then explores their consequences for a complex domain. Finally, the performance of the system is examined for resolving various types of quality control problems.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.873-874
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• 2009

$L^*$ 기반의 Assume-Guarantee Reasoning[1]은 시스템 검증 시의 상태 폭발(state explosion)을 줄이는 데 크게 기여하였다. 그러나 시스템 검증에 소모되는 시간은 일반적인 모델 체킹 도구를 사용할 때 보다 크게 증가시킨다. 본 논문에서는 시스템의 검증에 소모되는 시간을 줄이기 위하여 $L^*$ 기반의 Assume-Guarantee Reasoning의 개선안을 제안하였다.

• Journal of The Korean Association For Science Education
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• v.24 no.3
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• pp.417-428
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• 2004

Conservation reasoning makes operational thought possible as a functional tool and it is the essential concept not only in the area of science and mathematics but also in several aspects of daily life. The abilities to solve mathematical problems and that of scientific reasoning and abstract way of thinking depend on whether thereis conservation reasoning or not and they are critical concepts that enables us to confirm the steps of cognitive development. Therefor in the study, we emphasized the issue that is the ways to speed up the scientific era by analyzing the correlation between the formation of conservation reasoning and neuro-cognitive variables. About 50% of 1-3 grade students did not had conservation reasoning skills. The formation of conservations was not linear. Scientific reasoning ability, planing and inhibiting ability were significantly different in levels of conservation, And, conservation reasonings were significantly correlated with cognitive variables. Scientific reasoning and planning ability significantly explained about 20% of the conservation reasoning ability of 1-3 grades.

• Journal of Elementary Mathematics Education in Korea
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• v.19 no.4
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• pp.457-484
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• 2015

This study aims to investigate an approach to teach proportional reasoning in elementary mathematics class by analyzing the proportional strategies the students use to solve the proportional reasoning tasks and their percentages of correct answers. For this research 174 sixth graders are examined. The instrument test consists of various questions types in reference to the previous study; the proportional reasoning tasks are divided into algebraic-geometric, quantitative-qualitative and missing value-comparisons tasks. Comparing the percentages of correct answers according to the task types, the algebraic tasks are higher than the geometric tasks, quantitative tasks are higher than the qualitative tasks, and missing value tasks are higher than the comparisons tasks. As to the strategies that students employed, the percentage of using the informal strategy such as factor strategy and unit rate strategy is relatively higher than that of using the formal strategy, even after learning the cross product strategy. As an insightful approach for teaching proportional reasoning, based on the study results, it is suggested to teach the informal strategy explicitly instead of the informal strategy, reinforce the qualitative reasoning while combining the qualitative with the quantitative reasoning, and balance the various task types in the mathematics classroom.

• School Mathematics
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• v.5 no.4
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• pp.421-440
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• 2003

It is difficult for the learner to understand completely the ratio concept which forms a basis of proportional reasoning. And proportional reasoning is, on the one hand, the capstone of children's elementary school arithmetic and, the other hand, it is the cornerstone of all that is to follow. But school mathematics has centered on the teachings of algorithm without dealing with its essence and meaning. The purpose of this study is to analyze the essence of ratio concept from multidimensional viewpoint. In addition, this study will show the direction for improvement of ratio concept. For this purpose, I tried to analyze the historical development of ratio concept. Most mathematicians today consider ratio as fraction and, in effect, identify ratios with what mathematicians called the denominations of ratios. But Euclid did not. In line with Euclid's theory, ratio should not have been represented in the same way as fraction, and proportion should not have been represented as equation, but in line with the other's theory they might be. The two theories of ratios were running alongside each other, but the differences between them were not always clearly stated. Ratio can be interpreted as a function of an ordered pair of numbers or magnitude values. A ratio is a numerical expression of how much there is of one quantity in relation to another quantity. So ratio can be interpreted as a binary vector which differentiates between the absolute aspect of a vector -its size- and the comparative aspect-its slope. Analysis on ratio concept shows that its basic structure implies 'proportionality' and it is formalized through transmission from the understanding of the invariance of internal ratio to the understanding of constancy of external ratio. In the study, a fittingness(or comparison) and a covariation were examined as the intuitive origins of proportion and proportional reasoning. These form the basis of the protoquantitative knowledge. The development of sequences of proportional reasoning was examined. The first attempts at quantifying the relationships are usually additive reasoning. Additive reasoning appears as a precursor to proportional reasoning. Preproportions are followed by logical proportions which refer to the understanding of the logical relationships between the four terms of a proportion. Even though developmental psychologists often speak of proportional reasoning as though it were a global ability, other psychologists insist that the evolution of proportional reasoning is characterized by a gradual increase in local competence.