• Journal of applied mathematics & informatics
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• v.39 no.3_4
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• pp.569-585
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• 2021

In this paper, we introduce the concept of relative co-fuzzy annihilator filters in distributive lattices. We give a set of equivalent conditions for a co-fuzzy annihilator to be a fuzzy filter and we characterize distributive lattices with the help of co-fuzzy annihilator filters. Furthermore, using the concept of relative co-fuzzy annihilators, we prove that the class of fuzzy filters of distributive lattices forms a Heyting algebra. We also study co-fuzzy annihilator filters. It is proved that the set of all co-fuzzy annihilator filters forms a complete Boolean algebra.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.311-314
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.748-753
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• 2005

When the maneuver occurs, the performance of the standard Kalman filter has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, the unknown acceleration is determined by using the fuzzy logic based on genetic algorithm(GA) method. This algorithm is the method to estimate the increment of acceleration by a fuzzy system using th relation between maneuver filler residual and non-maneuvering one. To optimize this system, a GA is utilized. And then, the modified filter is corrected by the new update equation method which is a fuzzy system using the relation between the filter residual and its variation. To shows the feasibility of the suggested method with only one filter, the computer simulations system are provided, this method is compared with multiple model method.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.499-501
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• 2005

When the target maneuver occurs, the estimate of the standard Kalman filter is biased and its performance may be seriously degraded. To solve this problem, this paper proposes a new intelligent estimation algorithm for a maneuvering target. This algorithm is to estimate the unknown target maneuver by a fuzzy system using the relation between the filter residual and its variation. The detected acceleration input is regarded as an additive process noise. To optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. And then, the modified filter is corrected by the new update equation method using the fuzzy system. The tracking performance of the proposed method is compared with those of an interacting multiple model (IMM).

• Journal of Mechanical Science and Technology
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• v.17 no.2
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• pp.221-229
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• 2003

In the virtual environment, reality can be enhanced by offering the motion based on a motion simulator in harmony with visual and auditory modalities. In this research the Stewart-Gough-platform-based motion simulator has been developed. Implementation of vehicle dynamics is necessary in the motion simulator for realistic sense of motion, so bicycle dynamics is adopted in this research. In order to compensate for the limited range of the motion simulator compared with the real vehicle motion, washout algorithm composed of high-pass filter, low-pass filter and tilt coordination is usually employed. Generally, the washout algorithm is used with fixed parameters. In this research a new approach is proposed to tune the filter parameters based on fuzzy logic in real-time. The cutoff frequencies of the filters are adjusted according to the workspace margins and driving conditions. It is shown that the washout filter with the fuzzy-based parameters presents better performance than that with the fixed ones.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.217-218
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• 2006

We propose a method for improving the performance of the Amplitude Shift Keying (ASK) system using a fuzzy logic system for automatically tuning the bandwidth of low pass filter. Instead of a fixed bandwidth of a low pass filter of receiver, the fuzzy logic system is used to automatically adjust the bandwidth. The inputs to the fuzzy logic system are the error and change of error, and output is a bandwidth. Simulation results showed that the proposed system improves considerably on the performance of the fixed bandwidth.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.154-157
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• 2000

In this paper, we propose a method for vehicle tracking systems using fuzzy logic. The standard ${\alpha}$-${\beta}$ filter estimates the future target positions using fixed ${\alpha}$,${\beta}$ coefficients. We utilize the if-then fuzzy logic to make ${\alpha}$ and ${\beta}$ coefficients vary with the position. Comparisons are made in tracking vehicles using three different schemes: the standard ${\alpha}$-${\beta}$ filter, ${\alpha}$-${\beta}$ filter using fuzzy logic, and the Kalman filter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.938-946
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• 1992

A novel 2-dimensional matched filter of the parallel-jaw type using fuzzy logic is proposed for bin picking. Specifically, the averaged pixel intensity of the windowed region for the filtering is considered to be fuzzy. Also membership functions for darkness and brightness are designed by employing the intensity histogram of the image. Then a rule is given to know how much a windowed region can be a possible holdsite. Furthermore eight rules are made to determine the part orientation, where Mamdani's reasoning method is applied. The proposed technique shows better performances than that of the conventional matched filtering technique in the following senses` 1) most of holdsites determined by the proposed technique are not concentrated at the locations nearly the end of part and 2) our filter is rather insensitive to noises than the conventional method. To show the validities of our proposed technique, some experimental results are illustrated and compared with the results by conventional matched filter technique.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1577-1586
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• 2017

The Proportional-Integral-Derivative (PID) controller is still the most widespread control strategy in the industry. PID controllers have gained popularity due to their simplicity, better control performance and excellent robustness to uncertainties. This paper presents the optimal tuning of a PID controller for domestic induction heating systems with a series resonant inverter for controlling the induction heating power. The objective is to design a stable and superior control system by tuning the PID controller with a derivative filter (PIDF) through Fuzzy logic. The paper also compares the performance of the Fuzzy PIDF controller with that of a Ziegler-Nichols PID controller and a fine-tuned PID controller with a derivative filter. The system modeling and controllers are simulated in MATLAB/SIMULINK. The results obtained show the effectiveness and superiority of the proposed Fuzzy PID controller with a derivative filter.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.