• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.153-156
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• 2006

We propose some properties of fuzzy p-value and fuzzy significance level to the test statistics for the fuzzy hypotheses testing. Appling the principle of agreement index, we suggest two method for fuzzy hypothesis testing by fuzzy rejection region and fuzzy p-value with fuzzy hypothesis to separately ${\alpha}-level$.

• Fire Science and Engineering
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• v.11 no.2
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• pp.45-53
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• 1997

In this article, the Fuzzy Logic as the principle of the multcriteria fire detector is used to determine whether the fire takes out or not. The main contents of this method as follow; most of all, the degree of the fire is represented as the type of the Fuzzy, and then it is possible to examine whether the fire takes out or not by the principle of the Fuzzy Logic. The input fators of the Fuzzy fire detector are temperature sensor, smoke sensor, light sensor applied to digital type. On the result of this study, the first, the number of the case of the nonfire alarm which is represented in the existing fire detector is reduced, and the second, the applicability of the fuzzy detector is demonstrated by the test.

• Proceedings of the KIPE Conference
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• 2000.07a
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• pp.460-463
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• 2000

Fuzzy logic control(FLC) has been studied extensively and has been applied in various applications. The most popular control strategy takes the Fuzzy Proportional-Integral(FPI) form while systematic methods have been developed to derive the fuzzy rules and membership functions the choice of the scaling factors remains an open problem, In this paper an analytical FPI scaling factor determining method is derived based on the functional equivalence of the PI and FPI controllers. Simulation have been carried out with a brushless DC motor drive system as test-bed the obtained results drive system as test-bed the obtained results have verified that the derived method is applicable to both the initial choice and further tuning of the FPI scaling factors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.953-955
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• 1993

In this paper we propose a new stability theorem and a robust stability condition for linguistic fuzzy model systems in state space. First we define a stability in linear sense. After representing the fuzzy model by a system with disturbances, A necessary and sufficient condition for the stability is derived. This condition is proved to be a sufficient condition of the fuzzy model. The Q in the Lyapunov equation is iteratively adjusted by an gradient-based algorithm to improve its stability test. Finally, stability robustness bounds of a system having modeling error is derived. An example is also included to show that the stability test is powerful.

• The Journal of the Acoustical Society of Korea
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• v.10 no.4
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• pp.38-47
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• 1991

The frequency variation among speakers is one of problems in the speech recognition. This paper applies fuzzy theory to solve the variation problem of frequency features. Reference patterns are expressed by fuzzified patterns which are produced by the peak frequency and the peak energy extracted from codebooks which are generated from training words uttered by several speakers, as they should include common features of speech signals. Words are recognized by fuzzy inference which uses the certainty factor between the reference patterns and the test fuzzified patterns which are produced by the peak frequency and the peak energy extracted from the power spectrum of input speech signals. Practically, in computing the certainty factor, to reduce memory capacity and computation requirements we propose a new equation which calculates the improved certainty factor using only the difference between two fuzzy values. As a result of experiments to test this word recognition method by fuzzy interence with Korean digits, it is shown that this word recognition method using the new equation presented in this paper, can solve the variation problem of frequency features and that the memory capacity and computation requirements are reduced.

• Journal of the Korea Computer Industry Society
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• v.2 no.8
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• pp.1069-1074
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• 2001

ASP(Active Server Page) is adopted in searching optimal solution for VAR planning algorithm by FUZZY mathemathical programming. FUZZY theory is powerful tool dealing with the fuzziness of satisfaction levels of the constraints and the goal of objective funnctions. The effectivness of the proposed algorithm has been verifyed by the test on the IEEE-30 bus system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1114-1117
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• 1993

The paper presents an effective method for finding the propagation structure of the real origin of a system malfunction. It uses a combined system model consisting of Structural Model (SM) in the form of Fuzzy Directed Graph and Behavior Model (BM) as a set of Fuzzy Relational Equations $A\;{\circ}\;R\;=\;B$. Here a specially proposed fuzzy inference technique is checked and investigated. Finally a test example for fault diagnosis of an industrial system is given and analyzed.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.43-46
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• 1990

Modern society is prevailed a lot of allergies. So, the allergy test is very important. There are many kinds of allergy test. A doctor usually uses skin allergy test among many allergy tests. However, little standadization and objectivity of grading-standard has been established in the skin allergy test. A measurement of the reaction area has been a major objective to perform skin allergy test. Recently, a doctor's method is to measure the reaction area after drawing a line that represents the reaction area on the skin. But this method differs slightly from the real reaction area and individual doctor's measurement is different, because the edge of the reaction area is obscure. In this paper, we propose a algorithm which is able to detect vague edges using the fuzzy set. The algorithm that detects the line and curve is proposed first. Here, the maximum value is calculated by comparing the membership function of the line and curve seperately. We also encode the direction of the line and curve by using 8-direction code. Then, we calculate the reaction area by measuring the pixels which are inside the reaction area. And finally the Allergy grade is decided by grading-standard, and we accomplish faster, the 80re accurate and objective allergy grade decision.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.473-475
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• 1992

This paper compares performance of neural and fuzzy truck backer-upper control systems. Conventional controllers require a mathematical model of how outputs depend on inputs. Neural and fuzzy control systems offer a key advantage over conventional control systems. They are model-free controllers. Neural networks learn a control process by examples (training samples). Fuzzy systems directly encode designer's experience as IF-THEN rules. For robustness test, we gradually removed training samples for the neural controller, and fuzzy rules for the fuzzy system. The errors increased laster in the neural controller than in the fuzzy system.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.584-592
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• 2003

This paper presents a fuzzy-GA method for the allocation and operation of dispersed generator systems(DGs) based on load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The problem formulation considers an objective to reduce power loss of distribution systems and the constraints such as the number or total capacity of DGs and the deviation of the bus voltage. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 12 bus and 33 bus test systems to demonstrate its effectiveness. .