• 대한인간공학회지
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• 제23권1호
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• pp.1-12
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• 2004

The purpose of this study was to develop an automatic expert system for the evaluation of human sensibility, where human sensibility can be inferred from objective physiological signals. The study aim was also to develop an algorithm in which human arousal and pleasant level can be judged by using measured physiological signals. Fuzzy theory was applied for mathematical handling of the ambiguity related to evaluation of human sensibility. and the degree of belonging to a certain sensibility dimension was quantified by membership function through which the sensibility evaluation was able to be done. Determining membership function was achieved using results from a physiological signal database of arousal/relaxation and pleasant/unpleasant that was generated from imagination. To induce one final result (arousal and pleasant level) based on measuring the results of more than 2 physiological signals and the membership function of each physiological signal. Dempster-Shafer's rule of combination in evidence was applied, through which the final arousal and pleasant level was inferred.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.14-18
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• 1997

In this paper, we suggested and made a classifier or qualitative and quantitative analysis in urine analysis system. Input variable number and fuzzy membership function was made from determination of standard sample, and the fuzzy rules were determined by the analysis of spectroscopic properties of pads in strip. Fuzzy classifier used in urine analysis system was evaluated or the standard samples in each items and degrees. Negative and positive response of urine test was classified in good property, but detail classification or quantitative analysis had 8% maximum error in each items. If fuzzy membership unction and generation of rule are supplemented, suggested fuzzy classifier can be applied to the clinical test.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권3호
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• pp.135-144
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• 2004

In this paper, we propose competitive fuzzy polynomial neurons-based advanced Self-Organizing Neural Networks(SONN) architecture for optimal model identification and discuss a comprehensive design methodology supporting its development. The proposed SONN dwells on the ideas of fuzzy rule-based computing and neural networks. And it consists of layers with activation nodes based on fuzzy inference rules and regression polynomial. Each activation node is presented as Fuzzy Polynomial Neuron(FPN) which includes either the simplified or regression polynomial fuzzy inference rules. As the form of the conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as linear, quadratic, and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership (unction are studied and the number of the premise input variables used in the rules depends on that of the inputs of its node in each layer. We introduce two kinds of SONN architectures, that is, the basic and modified one with both the generic and the advanced type. Here the basic and modified architecture depend on the number of input variables and the order of polynomial in each layer. The number of the layers and the nodes in each layer of the SONN are not predetermined, unlike in the case of the popular multi-layer perceptron structure, but these are generated in a dynamic way. The superiority and effectiveness of the Proposed SONN architecture is demonstrated through two representative numerical examples.