• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.1
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• pp.62-68
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• 2014

Recently, MRI(magnetic resonance imager) scanner is continually used for medical diagnosis and many biomedical researches. When it operates, however, intense noise is generated. The SPL(sound pressure level) of the noise approaches 130 dB especially in 3 T(Tesla) MRI. Meanwhile, more than 3 T MRI scanners have been developed to get higher-resolution images, so louder noise is expected in the future. The intense noise makes patients feel nervous and uncomfortable. Moreover, it could possibly cause hearing loss to patient in extreme cases. For this reason, some active noise control systems have been researched. One of them used feedback Filtered-X LMS(FXLMS) algorithm which is able to control only narrowband noises and possible to diverge in severe case. In this paper, we determine the property of MRI noise. Using the property, we applied a method of open-loop and adaptive control for reducing MRI noise at target point inside bore. We verified performance of the method with computer simulation and preliminary experiment. The results demonstrate that the method can effectively reduce MRI noise at target point.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.362-367
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• 2010

This paper deals with the correlation between the interior noise and the floor vibration of the train from rolling, impulse and friction in Busan Metro line 3. The correlation is verified by sound and vibration measurement causing friction between the railway and the wheel. If ANC(Active Noise Control) system can reduce 5 dB in below 500 Hz, the sound pressure level of the whole band pass can be reduced about 1.8-4.8 dB in squeal noise. Curve squeal noise is the intense high frequency tonal that can occur when a railway vehicle transverses a curve. The frequency range is from around 500 to almost 20,000 Hz, with noise levels up to about 15 dB in curve.

• Journal of KSNVE
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• v.8 no.5
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• pp.914-921
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• 1998

Combusion instability due to thermoacoustic feedback in a ducted combustor usually excites severe noise and vibration, which could lead to result in the failure of the system or environmental dispute. In the present study, an active noise control(ANC) method with an adaptive algotithm is hired to suppress instability which has very discrete behavior in the frequency domain. Especially a feedback system is composed to evade hot environment of the combustor, and as a preliminary, the performance and stability of the controller is chekced by simulating the real situation with harmonic waves. Application to the real combustor showed serious reductions in sound pressure level by 20∼30 dB. It was also shown that the selected control system was very stable and effective.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.2002_2003
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• 2009

본 논문은 실시간 능동소음 제어시스템(ANC)을 위한 위상 반전에 관한 연구를 하였다. ANC 기술은 수동적 방법론 보다 적응적으로 그리고 다양한 주파수 대역폭의 소음을 저감할 수 있는 장점으로 다양한 시스템에 적용되고 있다. 본 논문의 목적은 ANC 관련 기술을DSP에 구현하여 실시간 ANC 적용에 대한 가능성을 제시하고자 하였다.

• The Journal of the Acoustical Society of Korea
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• v.35 no.3
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• pp.216-222
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• 2016

Generation of a quiet zone in noisy environment is undoubtedly of considerable realistic significance. This paper describes development and implementation of a multichannel real-time active noise control (ANC) system for 3 dimensional noisy environment to enhance the quiet zone performance in terms of size and noise cancellation gain. The proposed ANC system employes a multichannel delay-compensated filtered-X least mean square (FXLMS) algorithm; its real-time implementation is designed in TMS320C6713 digital signal processor (DSP) board. The system is evaluated for cancelling various tonal frequency noises in the range from 100 to 500 Hz, and the performance is then illustrated by measuring the quiet zone in terms of sound pressure level (SPL) attenuation. Experiment results show that a quiet zone of quiet with satisfactory size and maximum 24 dB noise attenuation is successfully generated.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.22.1-22
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• 2001

In this paper we present Multiple-Channel Active Noise Control[ANC] system by employing Independent Component Analysis[ICA] and Adaptive Network Fuzzy Inference System[ANFIS]. ICA is widely used in signal processing and communication and it use prewhiting and appropriate choice of non-linearities, ICA can separate mixed signal. ANFIS controller is trained with the hybrid learning algorithm to optimize its parameters for adaptively canceling noise. This new method which minimizes a statistical dependency of mutual information(MI) in mixed low frequency noise signal and there is no need to secondary path modeling. The proposed implementations achieve more powerful and stable noise reduction than Filtered-X LMS algorithms which is needed for LTI assumption and precise secondary error

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.551-554
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• 2005

The method of the reduction of the automotive induction 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 of the engine room. Whereas, the active control method can overcome the demerit of passive control method. The algorithm of active control is mostly used the LMS (Least-Mean-Square) 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. However, the convergence performance of LMS algorithm goes bad when the FXLMS algorithm is applied to an active control of the induction noise under rapidly accelerated driving conditions. Thus Normalized FXLMS algorithm was developed to improve the control performance under the rapid acceleration. The advantage of Normalized FXLMS algorithm is that the step size is no longer constant. Instead, it varies with time. But there is one additional practical difficulty that can arise when a nonstationary input is used. If the input is zero for consecutive samples, then the step size becomes unbounded. So, in order to solve this problem. the Correlation FXLMS algorithm was developed. The Correlation 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 Correlation FXLMS Is presented in comparison with that of the other FXLMS algorithms based on computer simulations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.263-268
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• 2000

In this paper, a new indirect feedback active noise control (ANC) scheme based on the fundamental frequency estimation is proposed for systems with a harmonic noise. When reference signals necessary for feedforward ANC configuration is difficult to obtain, the conventional ANC algorithms for multi-tonal noise do not measure the reference signals but generate them with the estimated frequencies. However, the beating phenomena, in which certain frequency components of the noise vanish intermittently, may make the adaptive frequency estimation difficult. The confusion in the estimated frequencies due to the beating phenomena makes the generated reference signals worthless. The proposed algorithm consists of two parts. The first part is a reference generator using the fundamental frequency estimation and the second one is the conventional feedforward control. We propose the fundamental frequency estimation algorithm using decision rules, which is insensitive to the beating phenomena. In addition, the proposed fundamental frequency estimation algorithm has good tracking capability and lower variance of frequency estimation error than that of the conventional cascade ANF method. We are also able to control all interested modes of the noise, even which cannot be estimated by the conventional frequency estimation method because of the poor SIN ratio. We verify the performance of the proposed ANC method through simulations for the measured cabin noise of a passenger ship and the measured time-varying engine booming noise of a passenger vehicle.

• The Journal of the Acoustical Society of Korea
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• v.32 no.5
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• pp.408-414
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• 2013

The famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems may become unstable in non-stationary noise environment. To solve this problem, Sun's algorithm and Akhtar's algorithm are developed based on modifying the reference signal in update of FxLMS algorithm, but these two algorithms have dissatisfactory stability in dealing with sustaining impulsive noise. In proposed algorithm, probability estimation and zero-crossing rate (ZCR) control are used to improve the stability and performance, at the same time, an optimal parameter selection based on fuzzy system is utilized. Computer simulation results prove the proposed algorithm has faster convergence and better stability in non-stationary noise environment.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.66-73
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• 1996

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise control(ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.