• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.8C
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• pp.782-787
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• 2007

This paper presents the D(Diagonal)-QR-RLS algorithm which normalizes the fast algorithm minimizes the MSE by using Givens rotated inputs and analyzes its characteristic. This proposed one has computational complexity of O(N) and the merit that it obtains the coefficients of TDL filter directly. Although this proposed normalized algorithm has the similar form to NLMS algorithm, we can see that D-QR-RLS has superior convergence characteristic to NLMS by computer simulation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.1001-1004
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• 2010

Acoustic feedback and background noise variation can degrade the performance of an active noise control (ANC) system. In this paper, nonlinear ANC using a non-parametric VSS-NLMS (or NPVSS-NLMS) algorithm and online feedback path modeling is proposed, whereby the conventional linear ANC with online acoustic feedback-path modeling is further extended to nonlinear Volterra ANC with a linear acoustic feedback path. In particular, the step-size of the NPVSS-NLMS algorithm is controlled to reduce the effect of background noise variation in the ANC system. Simulation results demonstrate that the proposed approach yields better nonlinear ANC performance compared with the conventional nonlinear ANC method.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1183-1186
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• 1998

Adaptive nois canceller can extract the noiseremoved spech in noisy speech signal by adapting the filter-coefficients to the background noise environment. A kind of LMS algorithm is one of the most popular adaptive algorithm for noise cancellation due to low complexity, good numerical property and the merit of easy implementation. However there is the matter of increasing misadjustment at voiced speech signal. Therefore the demanded speech signal may be extracted. In this paper, we propose a fast and noise robust wavelet packet adaptive noise canceller with NLMS-SUM method LMS combined algorithm. That is, we decompose the frequency of noisy speech signal at the base of the proposed analysis tree structure. NLMS algorithm in low frequency band can efficiently dliminate the effect of the low frequency noise and SUM method LMS algorithm at each high frequency band can remove the high frequency nosie. The proposed wavelet packet adaptive noise canceller is enhanced the more in SNR and according to Itakura-Satio(IS) distance, it is closer to the clean speech signal than any other previous adaptive noise canceller.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1475-1478
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• 2002

This paper presents a performance analysis of an improved adaptive algorithm proposed by the authors recently. It is based on the normalized least mean square (NLMS) algorithm, which Is one of the major techniques to adapt the cofficients of a transversal filter. Generally, the performance of an adaptive algorithm is often discussed by investigating the mis-adjustment. In this paper, unlike these approaches, a novel analytical method is considered. letting the parameters so that the residual mean square error (MSE) after the convergence of the algorithm is equal to that of the NLMS algorithm, the MSE level is compared. It is shown that the theoretical analysis is agreed with the simulation results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.7C
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• pp.562-568
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• 2008

This paper proposes the scheme which obtain the coefficients of TDL filter and two normalization algorithms among methods which get solution of equivalent Wiener-Hopf Equation in Gram-Schmidt algorithm. Compared to the conventional NLMS algorithm, normalizes with sum of power of inputs, the presented algorithms normalize using sums of eigenvalues. Using computer simulation, we perform an system identification in an unstable environment where two poles are located in near position outside unit circle. Consequently, the proposed algorithms get the coefficients of TDL filter in Gram-Schmidt algorithm recursively and show better convergence performance than conventional NLMS algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.4 s.95
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• pp.389-396
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• 2005

In this paper, we propose a pre-distortion method using the NLMS(Normalized Least Mean Square) algorithm to cope with hish PAPR(Peak to Average Power Ratio) problem in OFDM communication system. This proposed scheme estimates the distortion characteristics of HPA, and changes the characteristic against the distortion. Therefore, it can be shown that the adaptive characteristic of the NLMS pre-distorter is good to track the various nonlinear characteristic of HPA, even though HPA characteristic is changed by temperature variation or aging. From the performance analysis, SNR efficiency of NLMS pre-distorter is about $0.5\;\cal{dB}$ less than that of common numerical non-adaptive pre-distorter, when IBO(Input Back Off) is $0\;\cal{dB}$. However, the NLMS pre-distorter is better than the common numerical pre-distorter, because these two pre-distorters have similar performance in higher than $3\;\cal{dB}$ IBO, and the NLMS pre-distorter maintains the constant performance even though characteristic of HPA is changed.

• The Journal of the Acoustical Society of Korea
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• v.12 no.5
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• pp.47-53
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• 1993

능동소음제어를 위하여 수렴속도가 빠르면서 정상상태오차를 최소화하는 오차보상 NLMS 알고리듬인 CNLMS 알고리듬을 제안하였다. 이 CNLMS 알고리듬에서는 수렴속도를 빠르게 하기 위하여 입력소음과 오차소음의 전력에 따라 수렴인자를 가변시킨 NLMS 알고리듬을 사용하였다. 도한 정상상태오차를 최소화하기 위하여 소음발생기를 사용하지 않고 오차경로에서 발생하는 소음오차를 더욱 작아지도록 보상하는 보조시스템을 사용하여 안정된 능동소음제어가 되도록 하였다. 이와 같은 시스템의 성능을 기존의 적응디지틀필터인 LMS 및 NLMS 알고리듬을 이용한 필터의 그 성능과 비교하였다. 각 알고리듬에 대한 시뮬레이션을 행한 결과, 제안한 CNLMS 알고리듬의 소음레벨은 LMS 및 NLMS 알고리듬의 것보다 각각 평균 14dB 및 6dB 정도 더 큰 감쇄효과가 있음을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.179-180
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• 2014

The main goal of this paper is to present an adaptive filter system using NLMS(Normalized Least mean square) adaptive algorithm for noise cancellation. The proposed algorithm has less computational complexity and better convergence property than the former algorithms like spectral subtraction algorithm, etc. We use TIMIT criterion voice and Noisex-92 for the experiment. The experimental result shows the feasibility of our algorithm for filtering noise from voice effectively.

• Smart Structures and Systems
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• v.32 no.6
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• pp.393-402
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• 2023

Recently, hybrid and electric vehicles have been actively developed to replace internal combustion engine (ICE) vehicles. However, their vibrations and noise with complex spectra cause discomfort to drivers. To reduce the vibrations transmitted through primary excitation sources such as powertrains, structural changes have been introduced. However, the interference among different parts is a limitation. Thus, active mounting systems based on smart materials have been actively investigated to overcome these limitations. This study focuses on diminishing the source movement when a structure with two active mounting systems is excited to a single sinusoidal and a multi-frequency signal, which were investigated for source movement reduction. The overall structure was modeled based on the lumped parameter method. Active vibration control was implemented based on the modeled structure, and a multi-normalization least mean square (NLMS) algorithm was used to obtain the control input for the active mounting system. Furthermore, the performance of the NLMS algorithm was compared with that of the quantification method to demonstrate the performance of active vibration control. The results demonstrate that the vibration attenuation performance of the source component was improved.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2995-2997
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• 2005

통신망에서 최적 적응 반향제거기(Echo Canceller; EC)는 반향성분이 길게 존재하는 환경에서도 실시간으로 동작할 수 있도록 알고리즘이 간결하여야 하며, 시간에 따라 빠르게 변하는 동특성의 반향경로에서도 동작을 보장할 수 있도록 빠른 수렴특성을 갖아야 한다. 또한, 전화망에서 수십 [ms] 이상의 지연이 발생 할 경우에도 반향제거 성능이 우수해야 한다. 본 논문에서는 이러한 조건을 만족시키기 위해 오차의 크기에 따라 수렴속도를 가변시키는 오차적응 NLMS(Error-Adaptive NLMS) 알고리즘을 제안하였으며, 시뮬레이션을 통해 일반적으로 사용되는 LMS(Least Mean Square) 알고리즘과 이를 개선한 NLMS(Normalized LMS) 알고리즘과 성능을 비교하였다.