• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.4
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• pp.239-246
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• 2003

Application of the filtered-x Least Mean Square(LMS) adaptive filter to active noise control requires to estimate the transfer characteristics between the output and the error signal of the adaptive controller. In this paper, we derive the filtered-x adaptive noise control algorithm and analyze its convergence behavior when the acoustic noise consists of multiple sinusoids. The results of the convergence analysis of the filtered-x LMS algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Simulation results are presented to support the theoretical convergence analysis.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.265-276
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• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.12
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• pp.793-798
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• 2004

In this paper, the problem of adaptively identifying time-varying nonlinear systems is considered. For that purpose, the discrete time-varying Volterra series is employed as a system model, and the filtered weighted least squares (FWLS) algorithm, developed for adaptive identification of linear time-varying systems, is utilized for the adaptive identification of time-varying quadratic Volterra systems. To demonstrate the performance of the proposed approach, some simulation results are provided. Note that the FWLS algorithm, decomposing the conventional weighted basis function (WBF) algorithm into a cascade of two (i.e., estimation and filtering) procedures, leads to fast parameter tracking with low computational burden, and the proposed approach can be easily extended to the adaptive identification of time-varying higher-order Volterra systems.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.795-803
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• 2015

Autoreclosure provides a means of improving power transmitting ability and system stability. Conventional reclosure adopts the fixed dead time interval strategy, where the reclosure is activated after a time delay to restore the system to normal as quickly as possible without regard to the system conditions. However, these simple techniques cannot provide optimal operating performance. This paper presents an adaptive autoreclosure algorithm including variable dead time, optimal reclosure, phase-by-phase reclosure and emergency extended equal-area criterion (EEEAC) algorithm in order to improve system stability. The reclosure algorithm performs the operations that are attuned to the power system conditions. The proposed adaptive reclosure algorithm is verified and tested using ATP/EMTP MODELS, and the simulation results show that the system oscillations are reduced and the transient stability is enhanced by employing the proposed adaptive reclosure algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.443-448
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• 2000

In this paper, we propose a novel adaptive feedforward controller for periodic disturbance and noise cancellation, with a frequency tracking capability. It can be added to an existing feedback control system without altering the original closed-loop characteristics, which is based on adaptive algorithm. We introduce novel algorithm "Constrained AFC(adaptive feedforward controller) algorithm" that increase the convergence region regardless of the delay in the closed loop system. In the algorithms, coefficients of the controller are adapted using the residuals of constrained structure which are defined in such a way that the coefficients become time invariant. The proposed algorithm not only estimate the magnitude and phase of the tonal disturbance and noise but also track the frequency of the tone, which changes in quasi-static manner. The frequency tracking algorithm uses the instantaneous frequency approach based on Hilbert transform. A number of computer simulations have been carried out in order to demonstrate the effectiveness of proposed method under various conditions.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.88-94
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• 2019

A leakage canceller and adaptive algorithm for FMCW Radar is presented. Because a strong leakage signal causes various problems in the transceiver and digital processor, specific FMCW radars are in need of a leakage canceller. The leakage canceller has an adaptive structure and the algorithm calculates the prediction vector and learns the adaptive coefficient simultaneously. The proposed algorithm an improvement of 10 dB in the cancellation performance.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.4
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• pp.1-18
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• 2007

In this paper, we develop an adaptive genetic algorithm (aGA). The aGA has an adaptive scheme which can automatically determine the use of local search technique and adaptively regulate the rates of crossover and mutation operations during its search process. For the adaptive scheme, the ratio of degree of dispersion resulting from the various fitness values of the populations at continuous two generations is considered. For the local search technique, an improved iterative hill climbing method is used and incorporated into genetic algorithm (GA) loop. In order to demonstrate the efficiency of the aGA, i) a canonical GA without any adaptive scheme and ii) several conventional aGAs with various adaptive schemes are also presented. These algorithms, including the aGA, are tested and analyzed each other using various test problems. Numerical results by various measures of performance show that the proposed aGA outperforms the conventional algorithms.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2007.11a
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• pp.146-146
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• 2007

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.5
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• pp.12-19
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• 1982

In general the design methods of adaptive filter are divided into two categories, one is based upon the local parameter optimization theory and the other is based upon stability theory. Among the various design techniques, the LMS algorithm by steepest-descent method which is based upon local parameter optimization theory is used widely. In designing the adaptive filter, the most important factor is the convergence rate of the algorithm. In this paper a new algorithm is proposed to improve the convergence rate of adaptive firter compared with the commonly used LMS algorithm. The faster convergence rate is obtained by adjusting the adaptation gain of LMS algorithm. And various aspects of improvement of the adaptive filter characteristics are discussed in detail.