• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.70-72
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• 2009

In this paper, nonlinear active noise control(ANC) of a 3rd-order Volterra system, which is robust to background noise variation, is proposed along with a variable step-size Gauss-Seidel pseudo affine projection(VSSGS-PAP) algorithm. Simulation results demonstrate that the proposed approach yields better nonlinear ANC performance in a background noise variation environment, compared with the conventional methods for the nonlinear ANC.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.81-84
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• 1998

For ANC systems applied to aircrafts or passenger ships, engines from which reference signals are usually measured are located so far from seats where main part of controllers are placed. It can make feedforward ANC scheme difficult to implement or very costly. Feedback ANC algorithms which do not require reference signals and use error signals alone to update the filter, are usually sensitive to measurement noise ' and impulse noise. In this paper, reference signal needed for the feedforward control is not measured directly but generated with the estimated frequencies. Cascade adaptive notch filter (ANF), which has the low computational burden, is used to implement ANC system in real time. Several ANFs of order 2 are connected in series to estimate multiple sinusoids. Computer simulations and experiments in the laboratory for verifying efficacy of the proposed algorithm are carried out.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2879-2881
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• 1999

This paper presents the adaptive Active Noise Control(ANC) system using the Neuro-Fuzzy controller. In general, the character of noise is time-varing and nonlinear Thus controller must have the adaptivness so that applied in Active Noise Control system to cancel the noise. This paper propose the Neuro-Fuzzy controller trained with back-propagation teaming algorithm to optimize the parameters of controller The objects of this paper are cancel the noise, extract the original(speech) signal polluted by noise and design the Neuro-Fuzzy controller.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.1016-1019
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• 2004

The study of the noise reduction of an automobile has been concentrated on the reduction of the automotive engine noise because the engine noise is the major cause of automotive noise. However, many studies of automotive engine noise led to the interest of the noise reduction of the exhaust and intake system. The method of the reduction of the 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, 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. So, in order to this problem, the modified FXLMS algorithm using Moving Bandpass Filter was proposed. In this study, MBPF was implemented and use ANC for automotive intake under revived rapidly accelerated driving conditions and it was verified its performance.

• Proceedings of the KIPE Conference
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• 1997.07a
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• pp.193-199
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• 1997

Until now, environmental problem has become more and more serious, and acoustic noise is one of environmental pollutions. For many years, ANC(Active Noise Control) has been made to reduce this noise. In this paper, active silencer using switching method is studied. This silencer is composed of single-phase full bridge inverter to drive cancelling loudspeaker. The inverter is controlled to reduce noise by ramp comparison control method. At the vacuum cleaner, noise control with FFCS(Feed-Forward Control Structure) is applied and geometric arrangement is replaced to avoid unstability of FFCS.

• Journal of KSNVE
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• v.11 no.2
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• pp.221-225
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• 2001

In order to make a quiet zone, one can consider various methods. Of the methods, this paper deals with the method using control materials such as absorption materials. This method controls sound fields by changing boundary conditions. First, this paper shows that the control material is essentially on the same road as active noise control (ANC) That is, we can consider the control material as the control source of ANC. However we cannot control the source strength. Second, this paper shows that the position of the control material is an important variable by a numerical simulation. And the strategy to optimize the position is addressed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.3
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• pp.157-162
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• 2014

The active noise control(ANC) method for noise problems solution generally uses filtered-X LMS algorithms. However, Filtered-X LMS algorithms were mainly used but these had a limitation that had to measure a transfer function of secondary noise path. However, newly proposed correlation-LMS algorithms have slightly much calculation and are minutely behind performance, these have a advantage not in measuring transfer function onerously so that we can easily adapt these in real time. Thus Co-LMS algorithm was developed to improve the real-time implementation performance under the variable input noise such as road noise environment. In this paper, the performance of the Co-LMS is presented in comparison with that of the Filtered-X LMS algorithm. Simulation results show that active noise control using Co-LMS have slightly much calculation and are minutely behind performance, these have a advantage not in measuring transfer function onerously so that we can easily adapt these in real time.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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• pp.983-990
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• 2011

This paper investigates active noise control(ANC) of a rectangular cavity using single channel filtered-x least mean square(FXLMS) algorithms to globally reduce the interior noise. To obtain the global reduction of the interior noise, multichannel active control should be incorporated in general. We, however, examined firstly the optimal location of the secondary source that produces a global reduction of the interior noise field using single channel control. We then investigated the frequency characteristics of the reduction to yield the effective frequency band of the active control system. It follows that the secondary source should be located as close to the primary source as possible in order to obtain the global reduction.