• 디지털산업정보학회논문지
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• 제10권3호
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• pp.197-205
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• 2014

In applications of adaptive noise control or active noise control, the presence of a transfer function in the secondary path following the adaptive controller and the error path, been shown to generally degrade the performance of the Least Mean Square (LMS) algorithm. Thus, the convergence rate is lowered, the residual power is increased, and the algorithm can become unstable. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used. But these algorithms have slow convergence speed and weakness in the environment that the secondary path and error path are varied. Therefore, I present the new algorithm called the "Bi-directional Filtered-x (BFX) LMS" algorithm with nearly equal computation complexity. Through experimental study, the proposed BFX-LMS algorithm has better convergence speed and better performance than the conventional FX-LMS algorithm, especially when the secondary path or error path is varied and the impulsive disturbance is flow in.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.269-273
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• 2000

This paper describes Active Noise Control (ANC) using Discrete Wavelet Transform (DWT) Domain Least Mean Square (LMS) Method. DWT-LMS is one of the transform domain input decorrelation LMS and improves the convergence speed of adaptive filter especially when the input signal is highly correlated. Conventional transform domain LMS's use Discrete Cosine Transform (DCT) because it offers linear band signal decomposition and fast transform algorithm. Wavelet transform can project the input signal into the several octave band subspace and offers more efficient sliding fast transform algorithm. In this paper, we propose Wavelet transform domain LMS algorithm and shows its performance is similar to DCT LMS in some cases using ANC simulation.

• 전기학회논문지
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• 제56권2호
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• pp.311-316
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• 2007

This paper proposes the algorithm which estimates moment of the inertia and friction coefficient of friction for high performance speed control of electric motor. The proposed algorithm finds the moment of inertia and friction coefficient of friction by observing the speed error signal generated by the speed observer and using Recursive Least-Mean-Square method(RLS). By feedbacking the estimated inertia and estimated coefficient of friction to speed controller and full order speed observer, then the errors of the inertia and coefficient of friction and speed due to the inaccurate initial value are decreased. Inertia and coefficient of friction converge to the actual value within several times of speed changing. Simulation and actual experiment results are given to demonstrate the effectiveness of the proposed parameter estimator.

• 한국음향학회지
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• 제35권4호
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• pp.272-279
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• 2016

본 논문은 보청기의 피드백 제거 알고리즘을 실시간으로 실현한 내용을 다루었다. 기존의 시간 영역에서의 최소 평균 자승 기법을 주파수 영역으로 변환하여 처리함으로써 계산상의 부하를 최소화하였다. 적응 필터 알고리즘의 확인은 Matlab(Matrix laboratory) 기반으로 수행하였고, 이를 CSR 8675 블루투스 DSP IC(Digital Signal Processor Integrated Circuit) 칩 펌웨어로 실현하고 검증해보였다. 스마트폰으로의 원격 무선 제어 기능이 포함된 스마트 보청기는 사용자 접근 편의성이 강화된다.

• 대한의용생체공학회:의공학회지
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• 제9권1호
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• pp.101-108
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• 1988

In this paper the power spectrum of background EEG is estimated by the LMS PHD based on least mean square. At the power spectrum estimatiom, the stocastic process of background EEG is assumed to consist of the nonharmonic sinusoid and the white noise. In the LMS PHD the model parameters are obtained by the least mean square at optimal order which is obtained from the fact that the eigenvalue's fluctuation of autocorrelation matrix of the normal back-ground EEG is smaller at some order than at other order when the power spectrum of background EEG is esitmated by PHD. The optimal order of this model is the 6-th order when the eigenvalue's fluctuation of autocorrelation matrix of background EEG is considered. The estimation results are with compared the results from the Maximum Entropy Spectral Estimation and Pisarenko Harmonic Decomposition. From the comparison results. The LMS PHD is possible to estimate the power spectrum of background EEG.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권12호
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• pp.3149-3165
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• 2013

Content based image retrieval is increasingly gaining popularity among image repository systems as images are a big source of digital communication and information sharing. Identification of image content is done through feature extraction which is the key operation for a successful content based image retrieval system. In this paper content based image retrieval system has been developed by adopting a strategy of combining multiple features of shape, color and relevance feedback. Shape is served as a primary operation to identify images whereas color and relevance feedback have been used as supporting features to make the system more efficient and accurate. Shape features are estimated through second derivative, least square polynomial and shapes coding methods. Color is estimated through max-min mean of neighborhood intensities. A new technique has been introduced for relevance feedback without bothering the user.

• 동력기계공학회지
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• 제14권5호
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• pp.56-62
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• 2010

In this paper, a recurrent cerebellar modulation articulation control (RCMAC) has been developed for improvement of noise attenuation performance in active noise control system. For the narrow band noise, a filter-x least mean square (FXLMS) method has bee frequently employed as an algorithm for active noise control (ANC) and has a partial satisfactory noise attenuation performance. However, noise attenuation performance of an ANC system with FXLMS method is poor for broad band noise and nonlinear path since it has linear filtering structure. Thus, an ANC system using RCMAC is proposed to improve this problem. Some simulations in duct system using harmonic motor noise and KTX cabin noise as a noise source were executed. It is shown that satisfactory noise attenuation performance can be obtained.

• 한국전자통신학회논문지
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• 제5권2호
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• pp.145-151
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• 2010

본 논문에서는 두 개의 음으로부터 전달되어온 음장의 상태를 구하여 역 필터를 구성하는 적응필터 및 신경회로망을 사용한 음장의 역 필터링 시스템을 제안한다. 본 논문에서는 최소 2승 평균법을 사용하여 FIR 필터의 계수를 계산하여 이를 갱신함으로써 역 필터링을 구축하는 방법을 사용한다. 본 논문에서 제안한 신경회로망 및 적응필터의 기법에 의하여 비선형 왜곡이 있는 간단한 파형이 학습 가능한 것을 실험 결과로부터 확인할 수 있었다.

• 제어로봇시스템학회논문지
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• 제13권6호
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• pp.547-554
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• 2007

This thesis presents a kind of active noise control(ANC) algorithm for reducing noise due to engine inside a car. The proposed control algorithm is, by using WP(Wavelet Packet), a one improving the instability due to delay of noise transmission and the lack of response ability for the rapid change of noise, which are defects of the existing FXLMS(Filtered-X Least Mean Square) algorithm. The chief character of this system is a thing that faster operation than the FXLMS is implemented by inserting WP in the secondary path. In other words, WP implements parallel operation. Then, the weights of filter in the adaptive algorithm will be updated faster. In addition, because WP have so excellent a resolution, they can process very minute noise. The efficiency of this control algorithm will be demonstrated in the matlab simulation and in the actual experiments by using a Labview program and a car.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2007년도 하계종합학술대회 논문집
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• pp.119-120
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• 2007

The operation of Interference cancellation algorithm for wireless repeater cancellation depends on either existing correlation properties between desired signal and reference signal or not At the time, due to the correlation properties at the ICS system, adaptive algorithms without considering system delay do not function properly. Thus, this system should be oscillated. In this paper, to solve these problems, we use the delayed least mean square algorithm. For the best performance of ICS, the system delays must be estimated. To efficiently estimate the delay of ICS, we use relations between bandwidth and correlation properties of the received signal.