• Journal of Power System Engineering
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• v.14 no.5
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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.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2265-2271
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• 1994

With the improvement of standard of living, requirement for comfortable and quiet environment has been increased and, therefore, there has been a many researches for active noise reduction to overcome the limit of passive control method. In this study, active noise control is performed in a duct system using intelligent control technique which needs not decide the coefficients of high order filter and the mathematical modeling of a system. Back propagation algorithm is applied as an intelligent control technique and control system is organized to exclude the error microphone and high speed operational device which are indispensable for conventional active noise control techniques. Furthermore, learning is performed by organizing acoustic feedback model, and the effect of the proposed control technique is verified via computer simulation and experiment of active noise control in a duct system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1759-1766
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• 2010

The performance of noise attenuation of the adaptive active noise control algorithm is improved using the recurrent neural network. The FXLMS that has been frequently used in the active noise control is simple and has low computational load, but this method is weak to nonlinearity of the main or secondary path since it is based on the FIR linear filter method. In this paper, the recurrent neural network filter has been developed and applied to improvement of the active noise attenuation by simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.343-348
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• 2011

This paper introduces a design for real time wireless headset using ANC (active noise control) system based on NFxLMS adaptive filter algorithm. The training time of the proposed system is significantly reduced by using the RMS delay spread of a channel as an error correction parameter, and convergence rate of the FxLMS filter has been improved with updating the coefficients of the NFxLMS filter, which we have got during the training process. Our system has shorter training time and better convergence rate at the same noise reduction level than the conventional system under real noisy environment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.5
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• pp.34-40
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• 2001

In this paper, a narrowband active noise control system to reduce the noise in thermal power plants is proposed. The narrowband active noise control system contains rectangular wave generator and has a multi channel feed forward adaptive algorithms which uses the adjoint LMS algorithm. Although the effectiveness have been proven in the filtered-X LMS broadband active noise control system, this algorithm has much more computational complexity than that of narrowband active noise control system. The proposed active control system that uses the adjoint LMS algorithm, compared to the previous broadband active noise control system, not only is more effective in controlling narrowband noise but also has a more stable structure. Adaptive filter contains the FIR structure and IIR structure for primary and secondary path models. The simulation proves the effectiveness of the proposed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.179-184
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• 2019

In this paper, we propose a design method of Active Noise Control (ANC) that reduces perceived level of the residual noise. A FELMS (Filtered-E Least Mean Squares) algorithm is used for the ANC system and the NC (noise criteria) is applied as an evaluation criterion of the residual noise. With this structure, we present the allowable spectral shape of the noise shaping filter that minimizes the NC index within the effective operating frequency band of the ANC, and showed that the filter satisfying in the criterion has a lower NC value than the psychoacoustic-based filter used in the previous studies.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.231-239
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• 2009

The filtered-x LMS (FX-LMS) algorithm has been applied to the active noise control (ANC) system in an acoustic duct. This algorithm is designed based on the FIR (finite impulse response) filter, but it has a slow convergence problem because of a large number of zero coefficients. In order to improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct since the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filteredu LMS algorithm (FU-LMS) based on infinite impulse response (IIR) is developed by considering the backward acoustic propagation. This algorithm, unfortunately, generally has a stability problem. The stability problem was improved by using an error smoothing filter. In this paper, the recursive LMS algorithm with variable step size and smoothing error filter is designed. This recursive LMS algorithm, called FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct such as the intake system of an automotive. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal of which the instantaneous frequency is variable. Test results demonstrate that the FU-VSSLMS algorithm has superior convergence performance to the FX-LMS algorithm and FX-LMS algorithm. It is successfully applied to the ANC system in a short duct.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.2
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• pp.146-155
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• 2012

The filtered-x LMS(FX-LMS) algorithm has been applied to the active noise control(ANC) system in an acoustic duct. This algorithm is designed based on the FIR(finite impulse response) filter, but it has a slow convergence problem because of a large number of zero coefficients. In order to improve the convergence performance, the step size of the LMS algorithm was modified from fixed to variable. However, this algorithm is still not suitable for the ANC system of a short acoustic duct since the reference signal is affected by the backward acoustic wave propagated from a secondary source. Therefore, the recursive filtered-u LMS algorithm(FU-LMS) based on infinite impulse response(IIR) is developed by considering the backward acoustic propagation. This algorithm, unfortunately, generally has a stability problem. The stability problem was improved by using an error smoothing filter. In this paper, the recursive LMS algorithm with variable step size and smoothing error filter is designed. This recursive LMS algorithm, called FU-VSSLMS algorithm, uses an IIR filter. With fast convergence and good stability, this algorithm is suitable for the ANC system in a short acoustic duct such as the intake system of an automotive. This algorithm is applied to the ANC system of a short acoustic duct. The disturbance signals used as primary noise source are a sinusoidal signal embedded in white noise and the chirp signal of which the instantaneous frequency is variable. Test results demonstrate that the FU-VSSLMS algorithm has superior convergence performance to the FX-LMS algorithm and FX-LMS algorithm. It is successfully applied to the ANC system in a short duct.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.57-65
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• 2017

This paper presented a method of designing low-power feedback active noise control filter optimized for headphones/earphones. Using constrained optimization, we obtained a high order FIR noise control filter to ensure reasonable noise attenuation performance at high sampling frequency environment. Then using infinite impulse response (IIR) approximation method called Balanced Model Truncation (BMT), we obtained a low order IIR noise control filter suitable for low-power digital signal processing system like headphones/earphones. For further performance improvement, we utilized frequency warping method so that we could obtain more accurately approximated IIR filter and we ensured system stability by reconstructing the low order IIR filter in form of cascaded second order IIR filters. ANC simulation with white noise and stability test verified that the proposed algorithm had superior attenuation performance and better robustness compared to the conventional algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.233-236
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• 2005

Phase noise in a phase-locked-loop (PLL) is unwanted and unavoidable. It is a main concern in oscillation system especially PLL. The phase noise is derived in term of power spectrum density by using a reliable phase noise model. There are four noise sources being considered in this paper, which are generated by reference oscillator, voltage controlled oscillator, filter, and main divider. The major concern for this paper is the noise from the filter. Two types of second order low pass filter are used in the PLL system. Applying the mathematical phase noise model, the output noises are compared. The total noise from the passive filter is lower than the active filter at the offset frequency range between 1 Hz to 33 kHz.