• Journal of the Korean Society for Precision Engineering
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• v.18 no.9
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• pp.71-81
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• 2001

This paper analyzes the effectiveness of minimizing vibration and sound transmission on/through a thin rectangular plate by both feedback control and hybrid control which combines adaptive feedforward control with a feedback loop. An experimental system identification technique using the matrix-fractional curve-fitting of the frequency response data is introduced for complex shaped structures. This identification technique reduces the model order o the MIMO(Multi-Input Multi-Output) system which simplifies the practical implementation. The adaptive feedforward control uses a Multiple filtered-x LMS(Least Mean Square) algorithm and the feedback control uses a multivariable digital LQG(Linear Quadratic Gaussian) algorithm. Experimental results show that an effective reduction of sound transmission is achieved by the hybrid control scheme when both vibration and noise measurement signals are incorporated in the controller.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1650-1656
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• 2004

In this paper, a multi-adaptive filter is proposed for removing EOG and the 60 Hz power supply noise from EEG measured in the frontal lobe and the feedback output control method is implemented for biofeedback. The multi-adaptive filter has been implemented on the TMS320C6711 DSP system and the feedback output control algorithm has been realized by calculating the ratio of alpha wave on the TMS320C31 DSP system with real time performance. Through the experiment using the implemented multi-adaptive filter and feedback output controller, we demonstrate that the proposed adaptive filter effectively removes EOG and the 60 Hz power supply noise from the measured EEG in the frontal lobe and the feedback algorithm controls the level of stimulation by the ratio of the alpha wave.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.321-324
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• 1997

In this paper, Active Noise Control(ANC) algorithm is proposed based on the estimated frequency estimator of the reference signal. The conventional feedforward ANC algorithms should measure the reference and use it to calculate the gradient of the squared error and filter coefficients. For ANC systems applied to aircrafts and passenger ships, engines from which reference signal is usually measured is so far from seats where main part of controller is placed that the scheme might be difficult to implement or very costly. Feedback ANC algorithm which doesn't need to measure the reference uses the error signal to update the filter and is sensitive to unexpected transient noise like a sneeze, clapping of hands and so on The proposed algorithm estimates frequencies of the desired signal in real time using adaptive notch filter. New frequency estimation algorithm is proposed with the improved convergence rate, threshold SNR and computational simplicity. Reference is not measured but created with the estimated frequencies. It has strong similarity to the conventional feedback control because reference is made from error signal. Enhanced error signal is used to update the controller for better performance under the measurement noise and impact noise. The proposed ANC algorithm is compared with the conventional feedback control.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.437-442
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• 2000

An alternative inverse feedback structure for adaptive active control of periodic noise is introduced for systems with nonminimum phase cancellation path. To obtain the inverse model of the nonminimum phase cancellation path, the cancellation path model can be factorized into a minimum phase term and a maximum phase term. The maximum phase term containing unstable zeros makes the inverse model unstable. To avoid the instability, we alter the inverse model of the maximum phase system into an anti-causal FIR one. An LMS predictor estimates the future samples of the noise, which are necessary for causality of both anti-causal FIR approximation for the stable inverse of the maximum phase system and time-delay existing in the cancellation path. The proposed method has a faster convergence behavior and a better transient response than the conventional FX-LMS algorithms with the same internal model control structure since a filtered reference signal is not required. We compare the proposed methods with the conventional methods through simulation studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.623-625
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• 2012

The restoration of an image corrupted by Gaussian noise is an important task in image processing. There are many kinds of filters are proposed to remove Gaussian noise such as Gaussian filter, mean filter, weighted filter, etc. However, they perform not good enough for denoising and edge preservation. Hence, in this paper we proposed an adaptive weighted filter which considers spatial distance and the estimated variance of noise. We also compared the proposed method with existing methods through the simulation and used MSE(mean squared error) as the standard of judgement of improvement effect.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.433-434
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• 2007

In this paper, active control of noise in a HVAC duct is considered. Most adaptive control filters have used FIR structures based on filtered-x LMS algorithms. But, the IIR structures are more desirable for the active control of duct noise in order to remove the poles introduced by the acoustic feedback and presented an algorithm to adjust the coefficients of an IIR filter using the recursive least mean square (RLMS) algorithm. A smoothed LMS algorithm is proposed to improve a convergent speed of filter parameters when the noise is wide band and power of input is time varying. And computer simulations have performed to show the effectiveness of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3118-3120
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• 2000

In this paper. a stable IIR adaptive filter for active noise control is proposed. The IIR filter structure is more effective when acoustic feedback exists, in which case an order of a FIR filter must be very large if some of the poles of the ideal control filter are near the unit circle. But the IIR filter may have stability problems especially when the adaptive algorithm is not converged. A stabilizing procedure for IIR adaptive filter is presented in this paper, and computer simulation is performed to show the effectiveness of proposed schemes.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1131-1138
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• 2003

Active noise control(ANC) is an approach to noise reduction in which a secondary noise source destructively interferes with the unwanted noise is introduced. Generally, the performance of ANC is determined how well a secondary noise tracks noises. A secondary noise is generated from the cancelling speaker and a error sensor pick up error signal. The transfer function between the cancelling speaker and the error sensor is not flat and distorts secondary noises. Consequently, the performance of ANC is degraded by the transfer function. In this paper, a single sensor ANC which considers the characteristics of the speaker and the error sensor is proposed. To reduce distortion of secondary noises, the transfer function is estimated by adaptive inverse modelling and the primary noises are estimated by Kalman filter. Experimental results show that the proposed single sensor ANC effectively attenuates noises.

• Journal of electromagnetic engineering and science
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• v.5 no.2
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• pp.49-60
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• 2005

In this paper, an efficient performance enhancement scheme for an adaptive antenna array under the flat and the frequency-selective Rayleigh fadings is proposed. The proposed signal enhancement scheme is the modified linear signal estimator which combines the rank N approximation by reducing noise eigenvalues(RANE) and Toeplitz matrix approximation(TMA) methods into the linear signal estimator. The proposed performance enhancement scheme is performed by not only reducing the noise component from the signal-plus-noise subspace using RANE but also having the theoretical property of noise-free signal using TMA. Consequently, the key idea of the proposed performance enhancement scheme is to greatly enhance the performance of an adaptive antenna array by removing all undesired noise effects from the post-correlation received signal. The proposed performance enhancement scheme applies at the Wiener maximal ratio combining(MRC) method which has been widely used as the conventional adaptive antenna array. It is shown through several simulation results that the performance of an adaptive antenna array using the proposed signal enhancement scheme is much superior to that of a system using the conventional method under several environments, i.e., a flat Rayleigh fading, a fast frequency-selective Rayleigh fading, a perfect/imperfect power control, a single cell, and a multicell.