• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.395-398
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• 2003

Two component analysis methods based on the fuzzy relational calculus are proposed in the setting of the ordered structure. First component analysis is based on a decomposition of fuzzy relation into fuzzy bases, using gradient method. Second one is a component analysis based on the eigen fuzzy sets of fuzzy relation. Through experiments using the test images extracted from SIDBA and View Sphere Database, the effectiveness of the proposed component analysis methods is confirmed. Furthermore, improvements of the image compression/reconstruction and image retrieval based on ordered structure are also indicated.

• 한국융합학회논문지
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• 제4권2호
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• pp.35-41
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• 2013

Data management with fuzzy entropy and similarity measure were discussed and verified by applying reliable data selection problem. Calculation of certainty or uncertainty for data, fuzzy entropy and similarity measure are designed and proved. Proposed fuzzy entropy and similarity are considered as dissimilarity measure and similarity measure, and the relation between two measures are explained through graphical illustration.Obtained measures are useful to the application of decision theory and mutual information analysis problem. Extension of data quantification results based on the proposed measures are applicable to the decision making and fuzzy game theory.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.268-271
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• 1998

this paper presents the potential application of fuzzy logic to the automatic incident detection system. While the conventional incident detection algorithms are based on a binary decision process, the algorithm using fuzzy logic can incorporate ambiguity which occurs in determining incidents. Since collecting good amount of data to construct data base for incidents is pretty expensive, a traffic simulator called FRESIM is used to simulate traffic condition in a freeway. Incident data are obtained by changing input parameters of the simulator and the fuzzy algorithm generates fuzzy rule for determining normal and incident traffic conditions. In this paper, various steps are described to test the algorithm and its results are summarized.

• 한국지능시스템학회논문지
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• 제7권1호
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• pp.34-44
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• 1997

The Belgian Nuclear Research Centre(SCK${\cdot}$CEN) has been a pioneer of the peaceful uses of nuclear energy after over forty years of existence. Recently, SCK${\cdot}$CEN's financial support of doctoral and postdoctoral research in close collaboration with universities has been a vital ingredient for securing a quality profile committed to the pursuit of execllence. FLINS, Fuzzy Logic and Intelligent technologies in Nuclear Science, was initially built within one of the postdoctoral research project at SCK${\cdot}$CEN. Among SCK${\cdot}$CEN's activities which will have an important impact on its scientific future, the application of fuzzy logic and intelligent technologies in nuclear science and engineering opens new domains in radiation protection, safety assessment, human reliability, nuclear reactor control, waste and disposal, etc. In this paper, we review the available literature on fuzzy logic in nuclear applications. We then present the initiative of R&D on fuzzy logic applications at SCK${\cdot}$CEN, namely, (1) safety control for a nuclear reactor, and (2) a safety evaluation model for nuclear transmission lines. By these two examples of nuclear applications, we illustrate the potential use of fuzzy logic in nuclear safety issues.

• 한국안전학회지
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• 제14권4호
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• pp.108-113
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• 1999

This paper presents a study on fuzzy speed and flux controller used in a vector control of a CRPWM(Current Ragulated PWM) induction motor drive. In this paper, an approach for an easier design of the fuzzy controller is presented in order to obtain the desired value for the response time with minimal overshoot and to improve the steady state performance for speed step commands. The fuzzy controller is constructed only upon the knowledge of the motor behaviour and the desired speed response, and provides fast and robust control by reducing the effects of nonlinearities, parameter changes and load disturbance. The results of applying the fuzzy logic controller to an IM drive system are compared with those obtained by application of a conventional PI controller. The fuzzy controller provided a better response than the PI controller.

• 소음진동
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• 제6권5호
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• pp.547-554
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• 1996

Diagnosis is one of the dominant applications of expert systems technology today. Most diagnosis system is apply to if-then rule, and it is called production systems which consist of linguistic data. A new diagnosis method is suggested in this paper, in which the fuzzy reasoning theory is used to diagnosis the rotating machinery. Diagnosis algorithm is made fuzzy reasoned by using linguistic data of fuzziness. Linguistic data for fuzziness was described in fuzzy scale and fuzzy membership function. Then, those lingnistic data have been synthesized and defuzzificated according to every item observed. This system is successfully used for linguistic data in fuzziness of rotating machinery. The results indicate that the realistic application can be built in precision diagnosis system.

• Journal of Mechanical Science and Technology
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• 제14권11호
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• pp.1206-1215
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• 2000

This paper introduces the application of evolutionary fuzzy modeling (EFM) in constructing global function approximations to subsequent use in non-gradient based optimizations strategies. The fuzzy logic is employed for express the relationship between input training pattern in form of linguistic fuzzy rules. EFM is used to determine the optimal values of membership function parameters by adapting fuzzy rules available. In the study, genetic algorithms (GA's) treat a set of membership function parameters as design variables and evolve them until the mean square error between defuzzified outputs and actual target values are minimized. We also discuss the enhanced accuracy of function approximations, comparing with traditional response surface methods by using polynomial interpolation and back propagation neural networks in its ability to handle the typical benchmark problems.

• Computers and Concrete
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• 제12권2호
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• pp.109-129
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• 2013

This work presents an approach to model concrete shrinkage. The goal is to permit the concrete industry's experts to develop independent prediction models based on a reduced number of experimental data. The proposed approach combines fuzzy logic and genetic algorithm to optimize the fuzzy decision-making, thereby reducing data collection time. Such an approach was implemented for an experimental data set related to self-compacting concrete. The obtained prediction model was compared against published experimental data (not used in model development) and well-known shrinkage prediction models. The predicted results were verified by statistical analysis, which confirmed the reliability of the developed model. Although the range of application of the developed model is limited, the genetic-fuzzy approach introduced in this work proved suitable for adjusting the prediction model once additional training data are provided. This can be highly inviting for the concrete industry's experts, since they would be able to fine-tune their models depending on the boundary conditions of their production processes.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2007년도 춘계종합학술대회
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• pp.732-735
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• 2007

Recently the control of induction motor for position control has been extensively studied. The representative method is PIDA controller proposed by Jung&Dorf. By designed PIDA controller' parameter had large value. Moreover, this method is very analyze, so that, not adapted controller parameter in disturbance. Besides using generalize fuzzy controller. Because input and output membership function is linguistic type, therefore system response is very slow. So, in this paper we used optimized fuzzy controller. Optimized fuzzy controller is output membership function is unity value. The controller performance was estimated applied to induction motor' position control.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 2000년도 춘계학술대회
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• pp.111-123
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• 2000

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a novel approach to the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy clustering algorithm and Fuzzy-ART neural network are applied to describe the states of disposal product with the membership functions and to make recycling cell formation. This approach leads to recycling and reuse of the materials, components, and subassemblies and can evaluate the value at each cell of disposal products. Application examples are illustrated by disposal refrigerators, compared fuzzy clustering with Fuzzy-ART neural network performance in cell formation.