• 한국정보전자통신기술학회논문지
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• 제8권1호
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• pp.45-50
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• 2015

본 논문은 능동 소음 제어를 위한 적응 알고리즘들의 성능을 비교함으로써 효과적인 적응 알고리즘을 보인다. 일반적인 적응 알고리즘으로는 normalized least mean square (NLMS) 알고리즘이 있다. NLMS는 구조가 간단하고 수렴 속도가 빠르다는 장점이 있어서 널리 사용되고 있다. 하지만 상관도가 높은 신호에 대해서는 수렴 성능이 떨어지는 문제가 발생한다. 이에 수렴 성능을 개선하기 위해 affine projection (AP) 알고리즘을 사용하고 있다. 하지만 연산량의 문제로 AP 알고리즘의 사용이 제한적이다. 이러한 사실을 바탕으로 협대역 소음 제어를 위한 능동 소음 제어 시스템에서 NLMS와 AP 알고리즘을 연산량과 수렴 성능을 비교함으로써 효과적인 알고리즘을 도출하였다. 실험을 통해 NLMS와 AP 알고리즘의 소음 제어 성능이 차이가 크게 발생하지 않는 것을 확인함으로써 NLMS가 AP 알고리즘에 비해 소음 제어에 효과적임을 확인하였다.

• 한국소음진동공학회논문집
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• 제19권3호
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• pp.251-260
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• 2009

In this paper, we studied on an attenuation effect of automobile exhaust noise according to the direction of canceling speaker in ANC system. Automobile exhaust noise was recorded at 800 rpm, 3500 rpm and 5000 rpm of a diesel engine. Directions of canceling speaker can be set to $30^{\circ}$, $90^{\circ}$ and $150^{\circ}$ against the primary noise flow by acrylic ducts to be made for the experimentation. DSP board with TMS320C6416 chip of Texas Instrument Co. used to control the ANC system. The algorithm of this ANC system applied the Filtered-x-LMS algorithm that is modified to compensate for a property of DSP input signal and the secondary-path effect. As an experiment result, the direction of canceling speaker was proved to influence the reduction effect of noise. The $150^{\circ}$ duct in the attenuation effect of noise showed a better result than the $90^{\circ}$ or $30^{\circ}$ duct.

• 소음진동
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• 제10권6호
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• pp.1048-1058
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• 2000

본 논문에서는 압전 세라믹 재료를 작동기 및 감지기로 이용한 사각 경계면이 고정된 얇은 사각평판의 능동 진동제어 방법을 제시한다. 실험 데이터에 기초한 다중 입출력계의 주파수영역 모델링방법을 이용하여 분포된 센서 및 구동기 특성이 포함된 구조물의 동적 특성이 규명된다. 제어기 구조로는 혼합형을 채택하고 되먹임 제어기는 LQG 제어기법에 의해 설계된다. 앞먹임 제어기는 다중 filtered-$x$ 최소자승오차법에 의해 적응되도록 한다. 진동제어에 대한 시뮬레이션 및 실험결과는 제안된 제어기법이 지속적 외란 및 과도적 외란에 효율적으로 적용될 수 있음을 보인다.

• 한국통신학회논문지
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• 제36권6C호
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• pp.343-348
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• 2011

이 논문은 NFxLMS (normalized filtered-x least mean square) 적응 필터기반의 실시간 ANC (active noise control) 무선 헤드셋에 관해서 연구한다. RMS (root mean square) 지연 분포를 측정한 후 채널을 보정해서 학습시간을 줄이고, 학습 시간동안 NFxLMS 필터 계수를 갱신해서 잡음 제거 필터의 수렴속도를 개선하는 방법을 제안한다. 제안한 방법을 실제 잡음 환경에서 이용할 경우에 짧은 학습 시간과 빠른 수렴속도를 가지면서 기존 잡음 제거기와 비슷한 성능을 가지는 잡음 제거기를 구성할 수 있다.

• 한국소음진동공학회논문집
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• 제18권3호
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• pp.284-292
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• 2008

The active control technique mostly uses the least-mean-square(LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS(FXLMS) algorithm is applied to an active noise control(ANC) system. However, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation and experimental results show that active noise control using Co-FXLMS is effective in reducing the noise in duct system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 추계학술대회논문집
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• pp.737-742
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• 2007

The method of the reducing duct noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequencies (below 500Hz) and is limited by the space. On the other hand, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases slightly so it may delay the convergence time when the FXLMS algorithm is applied to the active control of duct noise. Thus the Co-FXLMS algorithm was developed to improve the control performance in order to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing duct noise.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.598-603
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• 2007

The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, FXLMS algorithm has the demerit that stability of the control is decreased when the step size become larger but the convergence speed is faster because the step size of FXLMS algorithm is fixed. As a result, the system has higher probability which the divergence occurs. Thus the Co-FXLMS algorithm was developed to solve this problem. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. In this paper, the performance of the Co-FXLMS algorithm is presented in comparison with the FXLMS algorithm. Simulation results show that active noise control using Co-FXLMS is effective in reducing the noise in duct system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 추계학술대회 논문집
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• pp.240-241
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• 2009

The method of the reduction of the duct noise can be classified by the method of passive control and the method of active control. However, the passive control method has a demerit to reduce the effect of noise reduction at low frequency (below 500Hz) range and to be limited by a space. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the Least-Mean-Square (LMS) algorithm because the LMS algorithm can easily obtain the complex transfer function in real-time. Especially, When the Filtered-X LMS (FXLMS) algorithm is applied to an ANC system.

• 전자공학회논문지S
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• 제34S권12호
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• pp.106-113
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• 1997

While driving, the low frequency interior noise below 200Hz causes the main component that irritates the auditory acoustic sense. But these passive control methods bring out increment in cost and weight of the vehicle and result in low efficiency. Recently, various ANC(Active Noise Control) methos to suppress the low frequency noise began to launch into application. In this study, we implemented the active noise control system for passenger vehicle to cancel the engine booming noise using DSP-based control unit, 4 micorphones, and 2 speakers. We used MEFX-LMS (Multiple Error Filtered X-Least Mean Square) algorithm since it can be easily implemented in real time. Also, MEFX-RLS algorithm was taken to enhance the suppression of the harmonic components of the engine booming noise inspite of its computational complexity. The performance of two adaptive algorithms were analyzed with experimental resutls.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1998년도 춘계학술대회논문집; 용평리조트 타워콘도, 21-22 May 1998
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• pp.670-677
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• 1998

Many of the adaptive noise control systems utilize a form of the least mean square (LMS) algorithm. In the active control of noise, it is common practice to locate an error microphone far from the control source to avoid the near-field effects by evanescent waves. Such a distance between the control source and the error microphone makes a certain level of time-delay inevitable and, hence, may yield undesirable effects on the convergence properties of control algorithms such as filtered-x LMS. This paper discusses the dependence of the convergence rate on the acoustic error path in these popular algorithms and introduces new algorithms which increase the convergence region regardless of the time-delay in the acoustic error path. Performances of the new LMS algorithms are presented in comparison with those by the conventional algorithms based on computer stimulations and experiments.