• Journal of Digital Convergence
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• v.11 no.7
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• pp.135-140
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• 2013

Noise estimation and detection algorithm to adapt quickly to changing noise environment using the LMS Filter. However, the LMS Filter for noise estimation for a certain period of time and need time to adapt. If the signal changes occur, have the disadvantage of being more adaptive time-consuming. Therefore, noise removal method is proposed to a frame basis AELMS Filter to compensate. In this paper, we split the input signal on a frame basis in noisy environments. Remove the LMS Filter by configuring noise predictions using the mean and variance. Noise, even if the environment changes fast adaptation time to remove the noise. Remove noise and environmental noise and speech input signal is mixed to maintain the unique characteristics of the voice is a way to reduce the damage of voice information. Noise removal method using a frame basis AELMS Filter To evaluate the performance of the noise removal. Experimental results, the attenuation obtained by removing the noise of the changing environment was improved by an average of 6.8dB.

• The Journal of the Acoustical Society of Korea
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• v.4 no.2
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• pp.13-25
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• 1985

광대역 혹은 협대역 잡음이 섞인 음성의 음질을 향상시키기 위하여 빠른 수렴속도를 갖고 잇는 UFBLMS 알고리즘을 음성처리에 응용한다. 광대역 잡음이 섞인 음성인 경우에는, 입력음성의 SNR 이 0 dB에서 10 dB 사이일 때, UFBLMS 알고리즘의 성능이 spectral subtraction 방법이나 Wiener filtering 방법보다도 FWSNR\sub SEG\ 척도로 약 3 dB 더 좋음을 알 수 있다. 협대역 잡음이 섞인 음 성인 경우에는 UFBLMS 알고리즘의 spectral subtraction 방법보다 FWSNR\sub SEG\ 척도로 약 2 dB 정도 성능이 더 좋다. 여러 음질 향상 알고리즘의 계산상의 복잡도와 음질 향상도 및 인식도를 고려해 보면 frequency weighting 기능을 갖고 있는 UFBLMS 알고리즘을 사용하는 것이 바람직함을 알 수 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12A
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• pp.1781-1786
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• 2000

본 논문에서는, 적응 간섭 제거기(AIC : adaptive interference canceller)에 사용되는 적응 알고리즘 중 계산량이 적고, 하드웨어적 복잡성이 낮은 최소 평균 자승(LMS)알고리즘의 적응화 상수(constant step size)를 여러 개 사용하여 빠른 수렴 속도와 낮은 평균 자승 에러를 가지는 방법을 제안한다. 최소 평균 자승 알고리즘에서 적응화 상수는 수렴속도와 평균 자승 에러를 제거하는데, 적응화 상수가 증가할수록 수렴속도가 빨라지는 반면, 평균 자승 에러는 증가하게 된다. 이 논문에서는 수렴속도를 증가하는 동시에 평균 자승 에러를 줄이기 위해, 최소 평균 자승 알고리즘에서 세 개의 적응화 상수를 가지는 새로운 검출기를 제안한다. 이 구조에서, 매 반복횟수에 따른 각 그룹 출력 값들을 가지고, 선택(selection)부분에서 평균 자승 에러들을 비교하며, 가장 작은 평균 자승 에러를 나타내는 그룹의 에러 값과 필터 계수 값들이 선택되어져 여러 적응화 상수 최소 평균 자승 알고리즘(several step size LMS algorithm)부분에서 각 그룹의 필터 계수를 갱신하는데 필요한 정보로 이용된다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.357-363
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• 2009

This paper deals with the Filtered-x Least Mean Square algorithm for a active vibration control in vehicle vibration reduction. Before applying the proposed FxLMS algorithm to automobile, the performance of the FxLMS algorithm is simulated using sensor data of a vehicle. The FxLMS algorithm requires that reference signal be a representation of disturbance signal and the plant model be incorporated into the computation path. To this end, The system identification is carried out to obtain the plant model based on the measurement results. A tachometer signal is used as reference signal. The FxLMS control algorithm is first tested using simulation and applied to a vehicle. Experimental results show that the proposed control algorithm can reduce vibration level in a short period of time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9C
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• pp.735-742
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• 2010

In this paper, we propose adaptive algorithms for interference cancellation in RF repeaters with multiple transmit and receive antennas. When multiple antennas are used in a repeater, the imperfect isolation between transmit and receive antennas causes the feedback interference which is modeled as multi-input multi-output (MIMO) channel. To remove the feedback interference, we derive the least mean square (LMS) algorithm and the recursive least squares (RLS) algorithm for interference cancellation based on adaptive signal processing techniques. Through computer simulations for the proposed algorithms, we analyze the convergence characteristics and compare the steady-state performance for interference cancellation.

• The Journal of Natural Sciences
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• v.8 no.1
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• pp.5-15
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• 1995

The equation error formulation in the adaptive IIR filtering provides convergence to a global minimum regardless a local minimum with a large stability margin. However, the equation error formulation suffers from the bias in the coefficient estimates. In this paper, a new algorithm, which does not require a prespecification of the noise variance, is proposed for the equation error formulation. This algorithm is based on the equation error smoothing and provides an unbiased parameter estimate in the presence of white noise. Through simulations, it is demonstrated that the algorithm eliminates the bias in the parameter estimate while retaining good properties of the equation error formulation such as fast convergence speed and the large stability margin.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.8-15
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• 1992

This paper analyzes the properties of such algorithm that corresponds to the LMS algorithm with additional update terms, parameterized by the scalar factors $\alpha$ and $\beta$, and presents its structure. The analysis of convergence leads to complex eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The computational cmomplexities of MLMS algorithms are compared with those of MADF, sign and the conventional LMS algorithms. In application of the system identification the second order momentum MLMS algorithm has faster convergence speed than LMS and the first order MLMS algorithms.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.508-508
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• 2006

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.3
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• pp.248-258
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• 1991

In this paper, ISI cancellation capabilites in the frequency selective fading channels of the equalizer emplouing individual tap LMS(ITLMS) algorithm and of th equalizer using the lattice structure have been investigated through the computer simulations in terms of bit error rate and convergence speed.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.981-982
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• 2008

In this paper, Kalman-LMS algorithm is further extended to nonlinear system identification, whereby Kalman-LMSalgorithm and third-order Volterra filer are utilized.