• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.861-876
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• 2008

This paper concerns a new method called Fuzzy Supervised Method for error matrix, the method has developed based on Adoptive Neuro- Fuzzy Inference Systems(ANFIS). For the performance point of view initially the new method tested with trial data and then this paper applies the proposed method with real world problems. So that this paper generated 1000 random error matrices in programming language [R] and then it tests the new proposed method for the error matrices. The results of Fuzzy Supervised Method given in terms of Kappa Index and Congalton Accuracy Indexes, and performance of Fuzzy Supervised Method has evaluated by using Pearson's test.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1545-1551
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought. Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally under a real-time operating system environment, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.57-62
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• 1999

A disassembility assessment has mostly depend on the subjective decision making from the qualitative element. It is not useful in the practical assessement because it is not specified. The purpose of this paper is an assessmet based on Fuzzy-study-rule. This rule is definitely modified fromnon-fuzzy language of qualitative element. The new assessment method of design for disassembility assessment(DFDA) is practical to introduce the fuzzy number as the conversion of quantitative element from qualitative. It is appled to air-cleaner of passenger-vehicle for the usefulness.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.1-6
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• 2010

Ying[M.S. Ying, Linguistic quantifiers modeled by Sugeno integrals, Artificial Intelligence 170(2006) 581-606] studied a framework for modeling quantifiers in natural languages in which each linguistic quantifier is represented by a family of fuzzy measures and the truth value of a quantified proposition is evaluated by using Sugeno integral. In this paper, we consider interval-valued fuzzy measures and interval quantifiers which are the generalized concepts of fuzzy measures and quantifiers, respectively. We also investigate logical properties of a first order language with interval quantifiers modeled by the Sugeno integral with respect to an interval-valued fuzzy measures.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.134-149
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• 2004

This paper is aimed at proposing a fuzzy inference mechanism to enhancing the quality of cognitive map-based inference. Its main virtue lies in the two mechanisms： (1) a mechanism for avoiding a synchronization problem which is often observed during inference process with traditional cognitive map, and (2) a mechanism for fuzzifying decision maker's subjective judgment. Our proposed fuzzy inference mechanism (FIM) is basically based on the cognitive map stratification algorithm which can stratify a cognitive map into number of strata and then overcome the synchronization problem successfully. Besides, the proposed FIM depends on fuzzy membership function which is administered by decision maker. With an illustrative B2B negotiation problem, we applied the proposed FIM, deducing theoretical and practical implications. Implementation was conducted by Matlab language.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.995-998
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• 1995

As the modern industrial processes become more complex, it is getting more difficult to model and control the processes. Naturally, an advanced type of DCS(Distributed Control System) with higher level functions is being sought Advanced DCS is a DCS with advanced functions such as fault diagnosis, GPC(Generalized Predictive Control), NN(Neural Network), and Fuzzy Control. In this thesis, we have studied a fuzzy control algorithm for realizing an advanced DCS. Its algorithm is implemented in a form of function code which is a process control language, being used by the industrial engineers. To verify the realized function code of the fuzzy control, the function code is applied to a continuous casting process of the Pohang Iron & Steel Works in Kwangyang. The rules of the fuzzy control were collected via interviews of the field operators and their operation documents. Finally, usability of the function code of the fuzzy control is shown via simulation for the continuous casting process model.

• Genomics & Informatics
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• v.16 no.4
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• pp.37.1-37.3
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• 2018

Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP ($c^{{+}{+}}$ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1082-1085
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• 1993

In this paper, we notice the fact that a human learning process is characterized by a process under a natural language environment, and discuss an approach of learning based on indirect linguistic instructions. An instruction is interpreted through some meaning elements and each trend. Fuzzy evaluation rule are constructed for the searched meaning elements of the given instruction, and the performance of a system to be learned is improved by the evaluation rules. In this paper, we propose a framework of learning based on indirect linguistic instruction based learning using fuzzy theory: FULLINS(FUzzy-Learning based on Linguistic IN-Struction). The validity of FULLINS is shown by applying it to helicopter flight control.

• Proceedings of the KIEE Conference
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• 2003.07e
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• pp.38-41
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• 2003

An Autonomous Mobile Robot(AMR) performs duty by sensing a recognized situation and controlling suitably. The existing algorithm has some advantages that it is possible to express the obstacle exactly and the robot is sensitive to the change of environment. However, this algorithm needs to control repeatedly according to the modelling and working environment that requires a great quantity of calculations. In this paper, We supplement shortcoming and designed direction algorithm of AMR using fuzzy controller. Fuzzy controller does not derive special quality spinning expression for system, and uses rules by value expressed by language. It is used extensively to non-linear, plant which mathematical modelling is difficult etc... Fuzzy control algorithm of AMR that is used by this research applies obstacle position, distance of obstacle, Progress direction of robot, speed of robot, Perception area of sensor, etc... by fuzzy control and decide steering angle of robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.246-251
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• 2001

In this paper, we proposed the intelligent navigation agent model for successive electronic commerce system management. Fuzzy theory is very useful method where keywords have vague conditions and system must process that conditions. So, using fuzzy theory, we proposed the model that can process the vague keywords effectively. Through the this, we verified that we can get the more appropriate navigation result than any other crisp retrieval keywords condition.