• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.295-300
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• 2007

The similarity analysis for fuzzy set pair or crisp set pair are carried out. The similarity measure that is based on distance measure is derived and proved. The proposed similarity measure is considered with the help of analysis for uncertainty or certainty part of the membership functions. The usefulness of proposed similarity is verified through the computation of similarity between fuzzy set and crisp set or fuzzy set and fuzzy set. Our results are also compared with those of previous similarity measure which is based on fuzzy number.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.252-255
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• 1997

In this paper, we will propose the methodology of data analysis using the fuzzy property set model. In real world, the data can be represented with the object. $\theta$. and the property, $\pi$, and its has-property relation, P. Then, the conceptual space can be defined with the chosen properties. Each object has a unique location in the conceptual space. In Fuzzy mode, the fuzzy property, and fuzzy conceptual space can be redefined. To analyze data using the fuzzy property set model, the rough set need to be defined in the fuzzy conceptual space.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.1
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• pp.39-48
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• 2000

In this paper we define T-fuzzy integrals of set-valued mappings, which are extensions of fuzzy integrals of the single-valued functions defined by Sugeno. And we discuss their properties.

• Journal of the Korean Geotechnical Society
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• v.23 no.11
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• pp.109-117
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• 2007

Uncertainty is pervasive in rock slope stability analysis due to various reasons and subsequently it may cause serious rock slope failures. Therefore, the importance of uncertainty has been recognized and subsequently the probability theory has been used to quantify the uncertainty since 1980's. However, some uncertainties, due to incomplete information, cannot be handled satisfactorily in the probability theory and the fuzzy set theory is more appropriate for those uncertainties. In this study the random variable is considered as fuzzy number and the fuzzy set theory is employed in rock slope stability analysis. However, the previous fuzzy analysis employed the approximate method, which is first order second moment method and point estimate method. Since previous studies used only the representative values from membership function to evaluate the stability of rock slope, the approximated analysis results have been obtained in previous studies. Therefore, the Monte Carlo simulation technique is utilized to evaluate the probability of failure for rock slope in the current study. This overcomes the shortcomings of previous studies, which are employed vertex method. With Monte Carlo simulation technique, more complete analysis results can be secured in the proposed method. The proposed method has been applied to the practical example. According to the analysis results, the probabilities of failure obtained from the fuzzy Monte Carlo simulation coincide with the probabilities of failure from the probabilistic analysis.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.182-185
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• 2003

This paper presents a new design algorithm for the combination with the fuzzy classifier and the Bayesian classifier. Only few attempts have so far been made at providing an effective design algorithm combining the advantages and removing the disadvantages of two classifiers. Specifically, the suggested algorithms are composed of three steps: the combining, the fuzzy-set-based pruning, and the fuzzy set tuning. In the combining, the multi-inputs and multi-outputs (MIMO) fuzzy model is used to combine two classifiers. In the fuzzy-set-based pruning, to effectively decrease the complexity of the fuzzy-Bayesian classifier and the risk of the overfitting, the analysis method of the fuzzy set and the recursive pruning method are proposesd. In the fuzzy set tuning for the misclassified feature vectors, the premise parameters are adjusted by using the gradient decent algorithm. Finally, to show the feasibility and the validity of the proposed algorithm, a computer simulation is provided.

• KIEE International Transactions on Power Engineering
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• v.2A no.3
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• pp.121-128
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• 2002

This study proposes a new method for transmission system expansion planning using fuzzy integer programming. It presents stepwise cost characteristics analysis which is a practical condition of an actual system. A branch and bound method which includes the network flow method and the maximum flow - minimum cut set theorem has been used in order to carry out the stepwise cost characteristics analysis. Uncertainties of the permissibility of the construction cost and the lenient reserve rate and load forecasting of expansion planning have been included and also processed using the fuzzy set theory in this study. In order to carry out the latter analysis, the solving procedure is illustrated in detail by the branch and bound method which includes the network flow method and maximum flow-minimum cut set theorem. Finally, case studies on the 21- bus test system show that the algorithm proposed is efficiently applicable to the practical expansion planning of transmission systems in the future.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1529-1532
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• 2007

Failure Mode, Effects, and Criticality Analysis (FMECA) is an extension of FMEA which includes a criticality analysis. The criticality analysis is used to chart the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. However, there are several limitations. Measuring severity of failure consequences is subjective and linguistic. Since The result of FMECA only gives qualitative and quantitative informations, it should be re-analysed to prioritize critical units. Fuzzy set theory has been introduced by Lotfi A. Zadeh (1965). It has extended the classical set theory dramatically. Based on fuzzy set theory, fuzzy logic has been developed employing human reasoning process. IF-THEN fuzzy rule based assessment approach can model the expert's decision logic appropriately. Fault tree analysis (FTA) is one of most common fault modeling techniques. It is widely used in many fields practically. In this paper, a simple fault tree analysis is proposed to measure the severity of components. Fuzzy rule based assessment method interprets linguistic variables for determination of critical unit priorities. An rail-way transforming system is analysed to describe the proposed method.

• Journal of Technology Innovation
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• v.16 no.1
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• pp.1-21
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• 2008

We attempt to make regional innovative capability indices for overall understanding of regional innovation. We'll analyze various indicators on it using fuzzy set theory and compare regional innovative capabilities of 16 regions in Korea. The fuzzy set theory can reflect more normally the uncertainty of the stakeholder's responses than other decision making analysis methods. The overall results suggest that experts on regional innovation rank GRDP most important and Daejeon is the most innovative region. Building up regional innovative capabilities should be made for more balanced national land development.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.337-348
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• 2007

A fuzzy neural network model is presented to predict residual stress for dissimilar metal welding under various welding conditions. The fuzzy neural network model, which consists of a fuzzy inference system and a neuronal training system, is optimized by a hybrid learning method that combines a genetic algorithm to optimize the membership function parameters and a least squares method to solve the consequent parameters. The data of finite element analysis are divided into four data groups, which are split according to two end-section constraints and two prediction paths. Four fuzzy neural network models were therefore applied to the numerical data obtained from the finite element analysis for the two end-section constraints and the two prediction paths. The fuzzy neural network models were trained with the aid of a data set prepared for training (training data), optimized by means of an optimization data set and verified by means of a test data set that was different (independent) from the training data and the optimization data. The accuracy of fuzzy neural network models is known to be sufficiently accurate for use in an integrity evaluation by predicting the residual stress of dissimilar metal welding zones.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.71-80
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• 1994

The purpose of this paper is to explore the use of fuzzy set theory for image processing and analysis. As an application example, the fuzzy method of edge detection is proposed to extract the edges of raised characters on tires.In general, Sobel, Prewitt, Robert and LoG filters are used to detect the edge, but it is difficult to detect the edge because of ambiguity of representations, noise and general problems in the interpretation of tire image. Therefore, in his paper, the fuzzy property plane has been extracted from the spatial domain using the ramp-mapping function. And then the ideas of fuzzy MIN and MAX are applied in removing noise and enhancement of the image simultaneously. From the result of MIN and MAX procedure a new fuzzy singleton is generated by extracting the difference between adjacent membership function values. And the edges are extracted by applying fuzzy $\alpha$-cut set to the fuzzy singletion, Finally, these ideas are applied to the tire images.