• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.857-861
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• 2004

An active method that improves low frequency characteristics of a reverberation chamber has been proposed. Spatial uniformity of sound pressure at low frequency has been increased by applying active noise control system. The system acts as frequency selective damping materials, which decreases resonance at low frequency and leave high frequency sound field as it is. Linearity of the chamber, which is essential for sound power measurement in the chamber, is guaranteed since fixed control logic has been adopted. Simple application and tests have been done in a small sized rectangular reverberation chamber which has 400Hz cutoff, Loud speakers were used to simulate a noise source and to construct an active noise control system. The spatial distribution of a sound field at 250Hz, 315Hz and 400Hz bands has been measured before and after control. The standard deviation of sound field has been decreased from 3.4dB to 2.5dB. The results show the possibility of active control system as a sound diffuser.

• 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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.4
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• pp.223-231
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• 2015

Enclosures are widely used to alleviate the contribution of machinery noise. It has been long concerned with the noise transmission through the access openings of the enclosures. In this study, we investigate active noise control technology for reduction of the transmission. A numerical model based on the acoustic boundary element method is first established. Using the numerical model, the acoustic transfer functions of the field points over the opening to the primary source at arbitrary locations are estimated. The feedforward control to minimize the acoustic power through the opening is then numerically implemented. The controller drives the secondary source to destructively interfere the noise transmission through the opening. Finally, a parametric study is conducted to evaluate the effects of the location and the number of the microphones on the control performance. Furthermore, the effects of the location of the secondary source on the performance of active noise control are investigated. It is followed that the control system implemented in this study leads to a significant reduction of about 31.5 dB in the sound power through the opening using only one secondary source located at the optimized position.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.3-8
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• 1999

In this paper, we propose a new active noise control system that cancels the only noise signal from the mixture selectively. A blind source separation realized by a dynamic recurrent neural network is used as a preprocessor of the active noise control system and separates the desired signal and the noise signal. The active noise control system adaptively generates an anti-noise signal to remove the only noise signal separated by the blind source separation. Computer simulation results show that the proposed scheme is effective to construct a selective attention system.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.3
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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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.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.37 no.5
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• pp.331-337
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• 2018

In this paper, we apply the NR (Noise Rating) to the evaluation of the residual noise of the window type ANC (Active Noise Control) and study the active noise controller for minimizing the NR value in rooms. We proposed a shape of a noise shaping filter of the Filtered-E LMS (Least Mean Square) algorithm that reduces the NR value within the effective operating frequency band of the ANC. The usefulness of the proposed scheme is verified by simulation and showed that the NR value of the residual noise is lower than the filters used in the conventional psychoacoustic ANC.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.10
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• pp.1489-1495
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• 2012

In this study, the active noise control experiment has been performed using induction motor noises. While the noises were measured, a induction motor was operated in different speed. For the simulation of ANC(Active Noise Control), test-bed is composed a multi-channel ANC system was constructed. In order to compare the control performance, we performed noise reduction simulations of ANC by Co-FXLMS algorithm and FXLMS algorithm. Through the simulation results, we confirmed that convergence performance and noise decrease effect of the proposed Co-FXLMS algorithm have been improved from existing FXLMS algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.13 no.7
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• pp.499-507
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• 2003

This paper presents a method for active noise control (ANC) in a duct by using a ring-tyPe smart foam. The ring-type smart foam consists of an elastic porous material of lining shape and a PVDF film embedded In the material. The PVDF element acts as a secondary sound source to reduce the noise. Active noise control using a ring-type smart foam is only effective locally because of the way to excite radially. To enlarge the quiet zone, the duct Is lined with additional acoustic foam between the smart foam and the error microphone. When cancellation path ks optimized by the LMS/RLS algorithm, the computation power is reduced while control performance Is maintained. The filtered-x LMS algorithm is used to minimize the error signal.