• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.257-259
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• 2012

친환경 시대로 나아가는 현재, 능동소음제어는 저주파소음을 줄이기 위한 좋은 방법이다. 또한 수동소음제어만으로 선박소음을 제어하기에는 물리적인 무게가 심각히 가중되어 한계를 가지게 된다. ANC 시스템은 이러한 문제를 해결해 줌과 더불어 다양하게 변화하는 환경소음까지 줄여주는 특성을 가지고 있다. 우리는 본 논문에서 선박의 환경소음을 줄이기 위하여 LMS 알고리즘과 신경회로망 알고리즘을 기반으로하는 ANC 시스템을 제안한다. 먼저 선박과 유사한 유도전동기의 소음을 측정하고 다음으로 ANC 시스템을 위한 LMS 구조를 구축한다. 그리고 소음의 비정치와 불확실성 때문에 단층 퍼셉트론 모델로 디자인된 신경회로망 알고리즘을 추가하여 실시간으로 소음을 줄이도록 하였다. 이 하이브리드 ANC 시스템은 최급강하기법의 방법으로 파라미터 값들이 온라인으로 실시간 추정되며, 제안된 ANC 시스템은 컴퓨터 시뮬레이션을 이용하여 그 성능을 분석하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.1
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• pp.22-33
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• 2005

In this paper, an efficient signal interference control technique to improve the high convergence speed of LMS algorithms is introduced in the adaptive transversal filter of DS/SS. The convergence characteristics of the proposed algorithm, whose coefficients are multiply adapted in a symbol time period by recycling the received data, is analyzed to prove theoretically the improvement of high convergence speed. According as the step-size parameter ${\mu}$ is increased, the rate of convergence of the algorithm is controlled. Also, an increase in the stop-size parameter ${\mu}$ has the effect of reducing the variation in the experimentally computed learning curve. Increasing the eigenvalue spread has the effect of controlling which is downed the rate of convergence of the adaptive equalizer. Increasing the steady-state value of the average squared error, proposed algorithm also demonstrate the superiority of signal interference control to the filter algorithm increasing convergence speed by (B+1) times due to the data-recycling LMS technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1095-1106
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• 1994

This paper presents a method for determining optimal grayscale function processing(FP) morphological filters under the least square (LMS) error criterion. The optimal erosion and dilation filters with a grayscale structuring element(GSE) are determined by minimizing the mean square error (MSE) between the desired signal and the filter output. It is shown that convergence of the erosion and dilation filters can be achieved by a proper choice of the step size parameter of the LMS algorithm. In an attempt to determine optimal closing and opening filters, a matrix representation of both opening and closing with a basis matrix is proposed. With this representation, opening and closing are accomplished by a local matrix operation rather than cascade operations. The LMS and back-propagation algorithm are utilzed for obtaining the optimal basis matrix for closing and opening. Some results of optimal morphological filters applied to 2-D images are presented.

• The Journal of the Acoustical Society of Korea
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• v.22 no.6
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• pp.425-433
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• 2003

Acoustic Echo Canceller (AEC) is a signal processing system for removing unwanted echo signals in teleconference and hands-free communication. Least mean square (LMS) algorithm is one of the adaptive echo cancellation algorithms and it has been most attractive because of its simplicity and robustness. However, the convergence properties of the LMS algorithm degrade with highly correlated input signals such as speech. For this reason, transform-domain adaptive filtering algorithm was introduced to decorrelate the colored input samples by using the orthogonal transform matrix such as DCT, DFT and then LMS adaptive filtering process is applied. In this paper, we propose a MLT domain adaptive echo canceller base on the MLT (Modulated lapped Transform) orthogonal transform matrix. The proposed algorithm achieves high decorrelation efficiency and fast convergence speed via modulated lapped transform of size 2NXN instead of NXN unitary transform such as DCT, DFT, Hadamad and it is applied to the acoustical echo cancellation system. Form the computer simulation with both synthesis and real speech, the proposed MLT domain adaptive echo canceller shows approximately twice faster convergence speed and 20∼30 ㏈ ERLE improvements over the DCT frequency domain acoustic echo cancellation system.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.277-282
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• 2012

The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

• Journal of KSNVE
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• v.9 no.2
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• pp.265-272
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• 1999

A LMS algorithms has been widely used for an adaptive filter algorithm in active noise control systems. But this algorithm has poor convergence and it is very difficult to select optimal convergence parameters in this algorithm. In this paper, a fuzzy LMS algorithm where the convergence parameters are computed using a fuzzy logic controller was proposed. A proposed algorithm was applied to active noise control system in HVAC(central Heating Ventilation and Air Conditioning) ducts. The experimental ducts and experimental apparatus were designed and manufactured for experiments, and the modelling of the experimental ducts was also performed for computer simulations. Computer simulations and experiments were performed to show the effectiveness of a proposed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.241-250
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• 2023

In this paper, when using the LMS algorithm for adaptive ultrasonic beamforming system, the effect of initial value setting on the margin of step parameters was studied. To this end, quasi-ideal beams, rotational beams with arbitrarily set beam widths were used as examples. In the numerical simulations, an arbitrary initial value(the number of sound sources fixed to any number) was set in the ultrasonic beamforming system, and the margin of the step parameter and convergence characteristics thereof were compared.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.535-544
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• 2023

In this paper, we study the effect of gradient vector calculation method(analytical method, central finite difference method) on adaptive beamforming to control weight distribution during iterated calculation when LMS algorithm (repeating method) is used to realize desired beam pattern. To this end, a quasi-ideal beam having an arbitrarily set beam width, a rotating beam, and a multi-beam were reviewed as examples. Numerical experiments applied the step parameters of the appropriate values to the adaptive beamforming system through trial and error equally to the two calculations, and compared the convergence characteristics of objective functions that evaluate adaptability and error using two methods for calculating gradient vectors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3647-3653
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• 2012

In active noise control filter, IIR filter structure which used for control filter assures the stability property. The stability characteristics of IIR filter structure is mainly determined by pole location of control filter within unit disc, so stable selection of the value of control filter coefficient is very important. In this paper, we proposed novel adaptive stabilized Filtered_U LMS algorithms with IIR filter structure which has better convergence speed and less computational burden than conventional FIR structures, for multi-channel active noise control with vehicle enclosure signal case. For better convergence speed in adaptive algorithms, fuzzy LMS algorithms where convergence coefficient computed by a fuzzy PI type controller was proposed.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.224-227
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• 1993

In this paper, a new LMS algorithm with a variable step size (VVS LMS) is presented. The change of step size .mu. at each iteration, which increases or decreases according to the misadaptation degree, is computed by a proportional fuzzy logic controller. As a result the algorithm has very good convergence speed and low steady-state misadjustment. The norm of the cross correlation between the estimation error and input signal is used. As a measure of the misadaptation degree. Simulation results are presented to compare the performance of the VSS LMS algorithm with the normalized LMS algorithm.