• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.29-32
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• 2003

We introduce the subcategories IRe $l_{PR}$ (H), IRe $l_{PO}$ (H) and IRe $l_{E}$(H) of IRe $l_{R}$(H) and study their structures in a viewpoint of the topological universe introduced by L.D.Nel. In particular, the category IRe $l_{R}$(H)(resp. IRe $l_{P}$(H) and IRe $l_{E}$(H)) is a topological universe eve, Set. Moreover, we show that IRe $l_{E}$(H) has exponential objects.ial objects.

• Structural Engineering and Mechanics
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• v.62 no.4
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• pp.507-517
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• 2017

Due to the discontinuous nature of uncertainty quantification in conventional evidence theory(ET), the computational cost of reliability analysis based on ET model is very high. A novel ET model based on fuzzy distribution and the corresponding combination rule to synthesize the judgments of experts are put forward in this paper. The intersection and union of membership functions are defined as belief and plausible membership function respectively, and the Murfhy's average combination rule is adopted to combine the basic probability assignment for focal elements. Then the combined membership functions are transformed to the equivalent probability density function by a normalizing factor. Finally, a reliability analysis procedure for structures with the mixture of epistemic and aleatory uncertainties is presented, in which the equivalent normalization method is adopted to solve the upper and lower bound of reliability. The effectiveness of the procedure is demonstrated by a numerical example and an engineering example. The results also show that the reliability interval calculated by the suggested method is almost identical to that solved by conventional method. Moreover, the results indicate that the computational cost of the suggested procedure is much less than that of conventional method. The suggested ET model provides a new way to flexibly represent epistemic uncertainty, and provides an efficiency method to estimate the reliability of structures with the mixture of epistemic and aleatory uncertainties.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.341-346
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• 2013

In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

• Journal of Environmental Impact Assessment
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• v.4 no.2
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• pp.105-121
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• 1995

Nitrate contamination problems from groundwater supplies have been documented throughout many countries in the world, including Korea. Nitrate salts can induce methemoglobinemia and possibly human gastric cancer. In farmed areas. intensive agricultural activities have caused a major increase in nitrate loading to groundwater. To determine whether decision makers must take farm-management actions to control the increase of groundwater nitrate concentration and to decide the timing of such actions, it is important to predict groundwater Nitrate levels that would result over time from various farm-management practices. However, the input values such as soil, fertilizer and crop data) used to examine the effects of various farm-management practices on groundwater nitrate level are usually uncertain due to a lack of available information. In this paper. the ease of a community with a nitrate water quality problem is illustrated to examine the effects of various farm-management practices and to show bow to perform, with uncertain information. a time-series analysis on groundwater nitrate levels that would result. from each farm-management practice.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.437-443
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• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.119-124
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• 2004

Real data as web log file tend to be incomplete. But we have to find useful knowledge from these for optimal decision. In web log data, many useful things which are hyperlink information and web usages of connected users may be found. The size of web data is too huge to use for effective knowledge discovery. To make matters worse, they are very sparse. We overcome this sparse problem using Markov Chain Monte Carlo method as multiple imputations. This missing value imputation changes spare web data to complete. Our study may be a useful tool for discovering knowledge from data set with sparseness. The more sparseness of data in increased, the better performance of MCMC imputation is good. We verified our work by experiments using UCI machine learning repository data.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.242-244
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• 2000

This paper presents a method for real-time estimation of TCSC quantity in order to enhance the power system transient stability energy margin using fuzzy neural network in multi-machine system. This paper has two parts, the first part is to estimate the energy margin. To set critical energy, we use the potential energy boundary surface(PEBS) method which one of the transient energy function(TEF) method. And the second is to determine the TCSC quantify and the line to be injected. In order to make training data in this step, we use genetic algorithm. The proposed method is applied to 6-bus, 7-line, 4-machine model system to show its effectiveness.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.415-418
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• 1996

Etching Process, one of the most important process in semiconductor fabrication, has input control part of which components are pressure, gas flow, RF power and etc., and plasma gas which is complex and not exactly understood is used to etch wafer in etching chamber. So this process has not real-time feedback controller based on input-output relation, then it uses EPD(End Point Detection) signal to determine when to start or when to stop etching. Various type EPD controller control etching process using EPD signal obtained from optical intensity of etching chamber. In development EPD controller we concentrate on compensation of this signal intensity and setting the relative signal magnitude at first of etching. We compensate signal intensity using neural network learning method and set the relative signal magnitude using fuzzy inference method. Potential of this method which improves EPD system capability is proved by experiences.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.8-13
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• 1997

In order to utilize the information of well-known grinding data or grinding machine, a database needs to be designed by considering the delicate property of the machine tools for the high precision and quality of the demanding specification. Among the machine tools, machining conditions of the grinding are various and knowledge repeatance obtained form the grinding process are less credable.Therefore it is desirable for D/B, which is used to set the grinding conditions, to utilize the maximum machine tool capability. The present paper studied occurance limit of chatter vibration and burn considering the characteristics of machine tool. And also basic experiments were performed to establish optimum grinding canditions which can maximize the machining efficiency.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.403-410
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• 2008

This paper is cinderned with applying fussy measures and fuxxy integrals to analyze public attitude towards the use od manual control of traffic. To this end, a questionare on the use od manual control of traffic is set up and data are collected in expert, and layman. Factor analysis is performed to get the primary structure of public attitude. It is shown that the attitude of the responders to the questionare in each group is well explained with its hierarchical structure obtained by fuzzy measure analysis.