• The Pure and Applied Mathematics
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• v.13 no.3 s.33
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• pp.167-187
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• 2006

We introduce the concepts of intuitionistic fuzzy submodules and intuitionistic fuzzy weak congruences on an R-module (Near-ring module). And we obtain the correspondence between intuitionistic fuzzy weak congruences and intuitionistic fuzzy submodules of an R-module. Also, we define intuitionistic fuzzy quotient R-module of an R-module over an intuitionistic fuzzy submodule and obtain the correspondence between intuitionistic fuzzy weak congruences on an R-module and intuitionistic fuzzy weak congruences on intuitionistic fuzzy quotient R-module over an intuitionistic fuzzy submodule of an R-module.

• East Asian mathematical journal
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• v.27 no.1
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• pp.75-82
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• 2011

In this paper, we will introduce the concept of fuzzy mulitplication module. We will define a new operation called a product on th family of all fuzzy submodules of a fuzzy mulitplication module. We will define a fuzzy subset of the idealization ring R+M and find some relations with the product of fuzzy submodules and product of fuzzy ideals of the idealization ring R+M. Some properties of weakly fuzzy prime submoduels and fuzzy prime submodules which are de ned by T.K.Mukherjee M.K.Sen and D.Roy will be introduced. We will investigate some properties of fuzzy prime submodules of a fuzzy multiplication module.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1998.10a
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• pp.3-8
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• 1998

The present work proposes the potential fuzzy framework, based on fuzzy set theory, for supporting decision-making problems, especially, selection problems of a best design in the area of nuclear energy system. The framework proposed is composed of the hierarchical structure module, the assignment module, the fuzzification module, and the defuzzification module. In the structure module, the relationship among decision objectives, decision criteria, decision sub-criteria, and decision alternatives is hierarchically structured. In the assignment module, linguistic or rank scoring approach can be used to assign subjective and/or vague values to the decision analyst's judgment on decision variables. In the fuzzification module, fuzzy numbers are assigned to these values of decision variables. Using fuzzy arithmetic operations, for each alternative, fuzzy preference index as a fuzzy synthesis measure is obtained. In the defuzzification module, using one of methods ranking fuzzy numbers, these indices are defuzzified to overall utility values as a cardinality measure determining final scores. According these values, alternatives of interest are ranked and an optimal alternative is chosen. To illustrate the applicability of the framework proposed to selection problem, as a case example, the best option choice of four design options under five decision criteria for primary containment wall thickening around large penetrations in an advanced nuclear energy system is studied.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.53-66
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• 1994

In this paper, a fuzzy expert system is developed for fault diagnoisis of a drum-type boiler system in fossil power plants. The develped fuzzy espert system is composed of knowledge base, fuzzification module, knowledge base process module, knowledge base management module, inference module, and linguistic approximation module. The main objective of the fuzzy expert system is to check the states of the system including the drum level and detect faults such as the feedwater flow sensor fault, feedwater flow control valve fault, and water wall bube rupture. The fuzzy expert system diagnoses faults using process values, manipulated values, and knowledge base which is built via interviews and questionaries with the experts on the plant operations. Finally, the validity of the developed fuzzy expert system is shown via experiments using the digital simulator for boiler system is Seoul Power Plant Unit 4.

• Journal of applied mathematics & informatics
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• v.11 no.1_2
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• pp.265-271
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• 2003

The purpose of this paper is to present certain results arising from product between fuzzy $H_{v}$-submodules. In particular, we consider the fundamental relation $\varepsilon$* defined on an $H_{v}$-module and give a property of the fundamental relations and fundamental modules with respect to the fuzzy product of $H_{v}$-modules.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.247-256
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• 2016

This paper proposes Context-Awareness Healthcare for Disease Reasoning based on Fuzzy Logic. It consists of a Fuzzy-based Context-Awareness Module (FCAM) and a Fuzzy-based Disease Reasoning Module (FDRM). The FCAM computes a Correlation coefficient and Support between a Condition attribute and a Decision attribute and generates Fuzzy rules by using just the Condition attribute whose Correlation coefficient and Support are high. According to the result of accuracy experiment using a SIPINA mining tool, those generated by Fuzzy Rule based on Correlation coefficient and Support (FRCS) and Improved C4.5 are 0.84 and 0.81 each average. That is, compared to the Improved C4.5, the FRCS reduces the number of generated rules, and improves the accuracy of rules. In addition, the FDRM can not only reason a patient’s disease accurately by using the generated Fuzzy Rules and the patient disease information but also prevent a patient’s disease beforehand.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.116-119
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• 1998

Abstract-In paper, an operation support software for combustion control of refuse incineration plant is developed using fuzzy model and genetic algorithm. It has two major modules which are simulation module and optimal operation module. In simulation module modelling is performed to obtain fuzzy model of the refuse incineration plant and obtained fuzzy model predicts outputs of the plant when inputs are given. This module can be used to obtain control strategy, and train and enhance operators' skill by simulating the plant. And in optimal operation module, genetic algorithm searches and finds out optimal control inputs over all possible solutions in respect to desired outputs. In order to testify proposed operation support software, computer simulation was carried out.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.71-76
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• 2023

In this paper, we propose a fuzzy U-Net, a fuzzy deep learning model that applies fuzzy logic to improve performance in image segmentation using deep learning. Fuzzy modules using fuzzy logic were combined with U-Net, a deep learning model that showed excellent performance in image segmentation, and various types of fuzzy modules were simulated. The fuzzy module of the proposed deep learning model learns intrinsic and complex rules between feature maps of images and corresponding segmentation results. To this end, the superiority of the proposed method was demonstrated by applying it to dental CBCT data. As a result of the simulation, it can be seen that the performance of the ADD-RELU fuzzy module structure of the model using the addition skip connection in the proposed fuzzy U-Net is 0.7928 for the test dataset and the best.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.03b
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• pp.101-108
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• 2000

In this study, an expert system was developed to predict the safety of tunnel and choose proper tunnel reinforcement system using fuzzy quantification theory and fuzzy inference rule based on tunnel information database. The expert system developed in this study have two main parts named pre-module and post-module. Pre-module decides tunnel information imput items based on the tunnel face mapping information which can be easily obtained in-situ site. Then, using fuzzy quantification theory II, fuzzy membership function is composed and tunnel safety level is inferred through this membership function. The comparison result between the predicted reinforcement system level and measured ones was very similar. In-situ data were obtained in three tunnel sites including subway tunnel under Han river, This system will be very helpful to make the most of in-situ data and suggest proper applicability of tunnel reinforcement system developing more resonable tunnel support method from dependance of some experienced experts for the absent of guide.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.1
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• pp.35-42
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• 1998

This paper presents the introduction of Pseudo-2 axes robot module and its GA-based fuzzy control implementation. Pseudo-2 axes robot module which use a single motor and controller for driving 2 joints of a robot mechanism, is devised towards a lower priced robot with its degree of freedom maintained GA-based Fuzzy controller is considered for the better control implementation of the developed system than the conventional PID controller. Here. the scaling factors of the membership function with high fitness values are selected using a genetic algorithm for a pulse-type input trajectory. The obtained controller also shows better trajectory tracking performance than a PID controller.