• 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 IEEK Conference
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• 2000.06d
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• pp.147-150
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• 2000

Hausdorff distance(HD) commonly used measures for object matching, and calculates the distance between two point set of pixels in two-dimentional binary images without establishing correspondence. And it is realized as the image filter applying the fuzzy. In this paper, the fuzzy hardware realizes in order to construct the image filter applying HD, also, propose as the method for the noise removal using it in the image. MIN-MAX circuit designs the circuit using MAX-PLUS, and the fuzzy HD hardware results are obtained to the simulation. And then, the previous computer simulation is confirmed to the result by using MATLAB.

• Communications of the Korean Mathematical Society
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• v.17 no.2
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• pp.207-213
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• 2002

We discuss the filter generated by an arbitrary set in fuzzy implication algebra, and consider the uniformity of a fuzzy implication algebra by using filters.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.174-178
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• 2005

This paper presents a novel fuzzy logic based control method for shunt active filters. Since the fuzzy sets are based on linguistic description, therefore they don't need to the mathematical model of the investigated systems. The proposed method is very suitable to nonlinear and time variant loads. The controller is robust, reliable and it has a smooth response. Also transient response of method is much better than the other classical methods. The simulation results confirm the suitable performance of the filter using this control method.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.84-90
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• 2011

In active noise control filter, LMS algorithms which used for control filter, assure the convergence property, and computational burden of these algorithms are proportionate to the filter taps. The convergence speed of LMS algorithms is mainly determined by value of the convergence coefficient, so optimal selection of the value of convergence coefficient is very important. In this paper, We proposed novel adaptive fuzzy logic LMS algorithms with FIR filter structure which has better convergence speed and less computational burden than conventional LMS algorithms, for single channel active noise control with ill conditioned signal case. Computer simulations were performed to show the effectiveness of a proposed algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.563-566
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• 2004

An intelligent Kalman filter (IKF) is proposed for tracking an incoming anti-ship missile. In the proposed IKF, the unknown target acceleration is regarded as an additive process noise. When the target maneuver is occurred, the residual of the Kalman filter increases in proportion to its magnitude. From this fact, the overall process noise variance can be approximated from the filter residual and its variation at every sampling time. A fuzzy system is utilized to approximate this valiance, and the genetic algorithm (GA) is applied to optimize the fuzzy system. In computer simulations, the tracking performance of the proposed IKF is compared with those of conventional maneuvering target tracking methods.

• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.264-269
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• 2015

The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.214-221
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• 2016

An attitude estimation algorithm which integrates gyroscope and vision measurements using an adaptive complementary filter is proposed in this paper. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, fuzzy interpolator is applied. For recognizing the dynamic condition of the system and vision measurement fault, the cut-off frequency of the complementary filter is determined adaptively by using the fuzzy logic with designed membership functions. The performance of the proposed algorithm is evaluated by experiments and it is confirmed that proposed algorithm works well in the static or dynamic condition.

• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.9-19
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• 2013

This paper deals with fuzzy $H_{\infty}$ filtering problem of discrete-time nonlinear Markovian jump systems with state and output time delays. The purpose is to design fuzzy $H_{\infty}$ filter such that the corresponding estimation error system with time delays and initial state uncertainties is stochastically stable and satisfies an $H_{\infty}$ performance level. A sufficient condition for the existence of fuzzy $H_{\infty}$ filter is given in terms of matrix inequalities. In order to relax conservatism, a stochastic mode dependent fuzzy Lyapunov function is employed. The Lyapunov function not only is dependent on the operation modes of system, but also includes the fuzzy membership functions. An illustrative example is finally given to show the applicability and effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.349-354
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• 2015

This paper presents the stability conditions of the Takagi-Sugeno (T-S) fuzzy sampled-data filter. The error system between the T-S fuzzy system and fuzzy filter is presented. In the sense of the Lyapunov stability analysis, the stability conditions are given, which can be represented in terms of linear matrix inequalities (LMIs). The proposed stability conditions utilize the different approach from the conventional methods, and have better performance than that of the conventional ones. The simulation example is given to show the effectiveness of the proposed method.