• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.27-31
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• 2003

This paper proposes a robust on-line signature verification system based on a new segmentation method and fusion scheme. The proposed segmentation method resolves the problem of segment-to-segment comparison where the variation between reference signature and input signature causes the errors in the location and the number of segments. In addition, the fusion scheme is adopted, which discriminates genuineness by calculating each feature vector's fuzzy membership degree yielded from the proposed segmentation method. Experimental results show that the proposed signature verification system has lower False Reject Rate(FRR) for genuine signature and False Accept Rate(FAR) for forgery signature.

• Nuclear Engineering and Technology
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• v.53 no.5
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• pp.1612-1618
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• 2021

The fusion-barrier distribution is very sensitive to the structure of the colliding nuclei such as nuclear quadrupole and hexadecapole deformation parameters and their signs. If the nuclei that enter the fusion reaction are deformed, the barrier problem becomes complicated. Therefore the deformation parameters are taken into account in the calculations. In this study, Neuro-Fuzzy approach, ANFIS, method has been used for the estimation of ground-state quadrupole (𝜀₂) and hexadecapole (𝜀₄) deformation parameters for the nuclei. According to the results, the method is suitable for this task and one can confidently use it to obtain the data that is not available in the literature.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1597-1600
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• 1991

In this paper, we describe a notion of sensor modeling method in multisensor data fusion using fuzzy set theory. Each sensor module is characterized by its fuzzy constraints to specific features of environment. These sensor fuzzy constraints can be imposed on multisensory data to verify their degree of truth and compatibility toward the final decision making. In comparison with other sensor modeling methods, such as probabilistic models or rule-based models, the proposed method is very simple and can be easily implemented in intelligent robot systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.403-406
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• 2003

This paper presents a robust algorithm for edge detection based on fuzzy fusion, using a novel local edge information measure based on Renyi's a-order entropy. The calculation of the proposed measure is carried out using a parametric classification scheme based on local statistics. By suitably tuning its parameters, the local edge information measure is capable of extracting different types of edges, while exhibiting high immunity to noise. The notions of fuzzy measures and the Choquet fuzzy integral are applied to combine the different sources of information obtained using the local edge information measure with different sets of parameters. The effectiveness and the robustness of the new method are demonstrated by applying our algorithm to various synthetic computer-generated and real-world images.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.73-82
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• 1995

The paper demonstrates that an adaptive fuzzy controller can be used effectively for the control of the temperature and density of the Tokarnak fusion recator which is nonlinear and has dynamic uncertainties. The dynamic uncertainties are non-parametric but state dependent. Thus the conventional adaptive nonlinear control methods have difficulties to cope with the problem. The proposed adaptive fuzzy controller can be used as a solution and performs well in a predetermined local space. Simulation result verifies the effectiveness of the scheme.

• Korean Journal of Logic
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• v.12 no.1
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• pp.89-110
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• 2009

This paper investigates generalizations of weakening-free uninorm logics not assuming associativity of intensional conjunction (so called fusion) &, as non-associative fuzzy-relevance logics. First, the strong t-associative monoidal uninorm logic StAMUL and its schematic extensions are introduced as non-associative propositional fuzzy-relevance logics. (Non-associativity here means that, differently from classical logic, & is no longer associative.) Then the algebraic structures corresponding to the systems are defined, and algebraic completeness results for them are provided. Next, predicate calculi corresponding to the propositional systems introduced here are considered.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1017-1020
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• 1993

This paper describes a neo fuzzy neuron which was produced by a fusion of fuzzy logic and neuroscience. Some learning algorithms are presented. The guarantee for the global minimum on the error-weight space is proved by a reduction to absurdity. Enhanced is that the learning speed of the neo fuzzy neuron exceeds 100,000 times of that of conventional multi-layer neural networks.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.146-150
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• 2008

In this paper, we consider concepts of subsethood measure introduced by Fan et al. [2]. Based on this, we give various subsethood measure defined by Choquet integral with respect to a fuzzy measure on fuzzy sets which is often used in information fusion and data mining as a nonlinear aggregation tool and discuss some properties of them. Furthermore, we introduce simple examples.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.15-15
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• 2000

In the past couple of years, there has been increasing interest in the fusion of neural networks and fuzzy logic. Most of the existing fused models have been proposed to implement different types of fuzzy reasoning mechanisms and inevitably they suffer from the dimensionality problem when dealing with complex real-world problem. To overcome the problem, we propose the self-organizing networks with activation nodes based on fuzzy inference and polynomial function. The proposed model consists of two parts, one is fuzzy nodes which each node is operated as a small fuzzy system with fuzzy implication rules, and its fuzzy system operates with Gaussian or triangular MF in Premise part and constant or regression polynomials in consequence part. the other is polynomial nodes which several types of high-order polynomials such as linear, quadratic, and cubic form are used and are connected as various kinds of multi-variable inputs. To demonstrate the effectiveness of the proposed method, time series data for gas furnace process has been applied.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.