• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.277-283
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• 2014

This paper proposes robot localization using Fuzzy-Extended Kalman Filter algorithm of the mobile robots equipped with least sensors. In order to improve the accuracy of the localization, we usually add the sensors or equipment. However, it increases the simulation time and expenses. This paper solves this problem using only the odometer and ultrasonic sensors to get the localization with the Fuzzy-Extended Kalman Filter algorithm method. By inputting the robot's angular velocity, sensor data variation, and residual errors into the fuzzy algorithm, we get the sensor weight factor to decide the sensor's importance. The performance of the designed method shows by the simulation and Pioneer 3-DX mobile robot test in the indoor environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2221-2234
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• 2011

The fuzzy vault scheme has emerged as a promising solution to user privacy and fingerprint template security problems. Recently, however, the fuzzy vault scheme has been shown to be susceptible to a correlation attack. This paper proposes a novel scheme for one-time templates for fingerprint authentication based on the fuzzy vault scheme. As in one-time passwords, the suggested method changes templates after each completion of authentication, and thus the compromised templates cannot be reused. Furthermore, a huge number of chaff minutiae can be added by expanding the size of the fingerprint image. Therefore, the proposed method can protect a user's fingerprint minutiae against the correlation attack. In our experiments, the proposed approach can improve the security level of a typical approach against brute-force attack by the factor of $10^{34}$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.50-54
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• 1997

In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge. So, some methods that can optimize the parameters for fuzzy logic controller automatically without expert knowledge was provided. Recently, tuning method for fuzzy logic controller using genetic algorithm(GA) were proposed in many papers. However, those are tuning methods for a part or some part of fuzzy logic controller. In this paper, we proposes auto tuning method for the whole part of tuzzy logic controller, such as parameters of membership functions for antecedence and consequence parts, rule base, scaling factor and the number of rule. Finally, second order dead time plant is provided to show the advantages of the proposed method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.184-189
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• 2006

This paper deals with a fractional model reduction for T-S fuzzy systems with time varying delayed states. A contractive coprime factorization of time delayed T-S fuzzy systems is defined and obtained by solving linear matrix inequalities. Using generalized controllability and observability gramians of the contractive coprime factor, a balanced state space realization of the system is derived. The reduced model will be obtained by truncating states in the balanced realization and an upper bound of model approximation error is also presented. In order to demonstrate efficacy of the suggested method, a numerical example is performed.

• Journal of Power System Engineering
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• v.20 no.6
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• pp.11-19
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• 2016

A variable speed refrigeration system(VSRS) has been received high attention for energy saving ability. This paper investigates effects of design factors such as membership function range and sampling time to control performances for systematical designing fuzzy logic controller of the VSRS. Some comparisons of control performance between the fuzzy and PI are conducted including comparative evaluation of robustness against noise by using computer simulations. The simulation results showed that the fuzzy is very useful design method for engineers in the industrial fields which have big noises system and deal with inherent nonlinear system like the VSRS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1187-1190
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• 1993

The problem of the optimization of fuzzy relational models for dealing with (non-fuzzy) numerical data is investigated. In this context, interfaces optimization assumes particular importance, becoming a determinant factor in what concerns the overall model performance. Considering this, several scenarios for building fuzzy relational models are presented. These are: (i) optimizing I/O interfaces in advance (independently from the linguistic part of the model); (ii) optimizing I/O interfaces in advance and allowing that their optimized parameters may change during the learning of the linguistic part of the model; (iii) build simultaneously both interfaces and the linguistic subsystem; and (iv) build simultaneously both linguistic subsystem and interfaces, now subject to semantic integrity constraints. As linguistic subsystems, both a basic type and an extended versions of fuzzy relation equations are exploited in each one of these scenarios. A comparative analysis of the differ nt approaches is summarized.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.474-483
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• 2003

A number of studies for corporate bond rating classification problems have demonstrated that artificial intelligence approaches such as Case-based reasoning (CBR) can be alternative methodologies to statistical techniques. CBR is a problem solving technique in that the case specific knowledge of past experience is utilized to find a most similar solution to the new problems. To build a successful CBR system to deal with human information processing, the representation of knowledge of each attribute is an important key factor We propose a hybrid approach of using fuzzy sets that describe the approximate phenomena of the real world because it handles inexact knowledge represented by common linguistic terms in a similar way as human reasoning compared to the other existing techniques. Integration of fuzzy sets with CBR is important to develop effective methods for dealing with vague and incomplete knowledge to statistical represent using membership value of fuzzy sets in CBR.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.225-235
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• 2005

In this Paper, it was designed the GA-fuzzy controller of a Thyristor Controlled Series Capacitor(TCSC) for enhancement of power system stability. The newly designed controller of TCSC was designed to overcome the nonlinearity such as operating point change of power system as well as to respond to disturbances as uncertainties of line parameters and line fault. So, fuzzy controller by intelligent control theory was used for it. And the fuzzy controller was optimized from a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller namely. scaling factor. membership function and control rules. Nonlinear simulation results show that the proposed control technique is superior to conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.608-611
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• 2003

Performance assessment was introduced to improvement of self-directed learning and method of assessment for differenced learning as the seventh educational curriculum is enforced. Performance assessment is overcoming limitation about problem solving ability and higher thinking abilities assessment that is problem of a written examination and get into the spotlight by way for quality of class and school normalization. But performance assessment has problems about possibilities of assessment fault by appraisal, fairness, reliability, and validity of grading, ambiguity of grading standard, difficulty about objectivity security etc. This study proposes fuzzy performance assessment system to solve problem of the conventional performance assessment. This paper presented an objective and reliable performance assessment method through fuzzy reasoning, design fuzzy membership function and define fuzzy rule analyzing factor that influence in each sacred ground of performance assessment to account principle subject. Also, performance assessment item divides by formation estimation and subject estimation and designed membership function in proposed performance assessment method. Performance assessment result that is worked through fuzzy performance assessment system can pare down burden about appraisal's fault and provide fair and reliable assessment result through grading that have correct standard and consistency to students.