• Journal of the Korean Society of Safety
• /
• v.20 no.3 s.71
• /
• pp.19-26
• /
• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1354-1357
• /
• 1993

This paper proposes that the best reasoning(i.e. rule evaluation) method which should be used in a fuzzy system significantly depends on the reasoning environment. It is shown that allowing for dynamic switching of reasoning methods leads to better performance, even when only two different reasoning methods are considered. This paper discusses DSFS (Dynamic Switching Fuzzy System) which dynamically switches and finds the best reasoning method (from among 80 different possible reasoning methods) to use depending on the reasoning situation. To overcome the reasoning speed and memory problem of DSFS due to its computational requirements, the DSFS Switching Reasoning Table method is proposed and its higher performance as compared to a conventional fuzzy system is shown. Finally, efforts to obtain general relationships between the characteristics of different reasoning methods and the actual control surface/environment is discussed.

• The Mathematical Education
• /
• v.41 no.3
• /
• pp.273-289
• /
• 2002

It tends to be emphasized that mathematics is the important discipline to develop students' mathematical reasoning abilities such as deduction, induction, analogy, and visual reasoning. This study is aimed for investigating the present state about mathematical reasoning in secondary school. We survey teachers' opinions and analyze the results. The results are analyzed by frequency analysis including percentile, t-test, and MANOVA. Results are the following: 1. Teachers recognized mathematics as knowledge constructed by deduction, induction, analogy and visual reasoning, and evaluated their reasoning abilities high. 2. Teachers indicated the importances of reasoning in curriculum, the necessities and the representations, but there are significant difference in practices comparing to the former importances. 3. To evaluate mathematical reasoning, teachers stated that they needed items and rubric for assessment of reasoning. And at present, they are lacked. 4. The hindrances in teaching mathematical reasoning are the lack of method for appliance to mathematics instruction, the unpreparedness of proposals for evaluation method, and the lack of whole teachers' recognition for the importance of mathematical reasoning

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.543-545
• /
• 1999

In this paper, the composition method of global and local fuzzy reasoning concepts is researched for reducing the number of rules, not losing the performance for fuzzy controller. A new method is proposed in details that controls the interaction between global reasoning and local reasoning. In order to automatically acquire and optimize the method, the DNA coding algorithm is introduced to the local fuzzy reasoning of the proposed composition fuzzy reasoning method. The method is applied to the real liquid level control system for the purpose of evaluating the Performance. The simulation results show that the proposed technique can produce the fuzzy rules with higher accuracy and feasibility than the conventional methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.945-948
• /
• 1993

• School Mathematics
• /
• v.14 no.4
• /
• pp.445-468
• /
• 2012

This study is tried in order to link informal arithmetic reasoning to formal algebraic reasoning. In this study, we investigated elementary school student's non-formal algebraic reasoning used in algebraic problem solving. The result of we investigated algebraic reasoning of 839 students from grade 1 to 6 in two schools, Korea, we could recognize that they used various arithmetic reasoning and pre-formal algebraic reasoning which is the other than that is proposed in the text book in word problem solving related to the linear systems of equation. Reasoning strategies were diverse depending on structure of meaning and operational of problems. And we analyzed the cause of failure of reasoning in algebraic problem solving. Especially, 'quantitative reasoning', 'proportional reasoning' are turned into 'non-formal method of substitution' and 'non-formal method of addition and subtraction'. We discussed possibilities that we are able to connect these pre-formal algebraic reasoning to formal algebraic reasoning.

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

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.04a
• /
• pp.531-535
• /
• 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.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.3
• /
• pp.49-58
• /
• 2019

Efficient evidential reasoning is an important issue in the development of advanced knowledge based systems. Efficiency is closely related to the design of problems solving methods adopted in the system. The explicit modeling of problem-solving structures is suggested for efficient and effective reasoning. It is pointed out that the problem-solving method framework is often too coarse-grained and too abstract to specify the detailed design and implementation of a reasoning system. Therefore, as a key step in developing a new reasoning scheme based on properties of the problem, the problem-solving method framework is expanded by introducing finer grained problem-solving primitives and defining an overall control structure in terms of these primitives. Once the individual components of the control structure are defined in terms of problem solving primitives, the overall control algorithm for the reasoning system can be represented in terms of a finite state diagram.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.1145-1150
• /
• 1993

In this paper, two methods of fuzzy modeling are prsented to describe the input-output relationship effectively based on relation characteristics utilizing simplified reasoning and neuro-fuzzy reasoning. The methods of modeling by the simplified reasoning and the neuro-fuzzy reasoning are used when the input-output relation of a system is 'crisp' and 'fuzzy', respectively. The structure and the parameter identification in the modeling method by the simplified reasoning are carried out by means of FCM clustering and the proposed GA hybrid scheme, respectively. The structure and the parameter identification in the modeling method by the neuro-fuzzy reasoning are carried out by means of GA and BP algorithm, respectively. The feasibility of the proposed methods are evaluated through simulation.