• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.362.2-362
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• 2002

Bearing diagnostics is difficult in a gearbox because bearing signals are masked by the strong gear signals. Self adaptive noise cancellation(SANC) Is useful technique to seperate bearing signals from gear signals. While gear signals are correlated with a long correlation length, bearing signals are not correlated with a short length. SANC seperates two components on the basis of correlation length. (omitted)

• 한국초전도학회:학술대회논문집
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• v.13
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• pp.43-43
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• 2003

• 한국초전도학회:학술대회논문집
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• v.12
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• pp.50-50
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• 2002

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.184-190
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• 2008

The fault location algorithm based on stationary wavelet transform was developed to locate the fault point more accurately. The stationary wavelet transform(SWT) was introduced instead of conventional discrete wavelet transform(DWT) because SWT has redundancy properties which is more useful in noise signal processing. In previous paper, noise cancellation technique based on the correlation of wavelet coefficients at multi-scales was introduced, and the efficiency was also proved in full. In this paper, fault section discrimination and fault location algorithm using noise cancellation technique were tested by ATP simulation on real power cable systems. From these results, the fault can be located even in very difficult and complicated situations such as different inception angle and fault resistance.

• The Journal of the Acoustical Society of Korea
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• v.18 no.2E
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• pp.45-52
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• 1999

This paper presents the integrated speech enhancement system for hands-free mobile communication. The proposed integrated system incorporates both acoustic echo cancellation and engine noise reduction device to provide signal enhancement of desired speech signal from the echoed plus noisy environments. To implement the system, a delayless subband adaptive structure is used for acoustic echo cancellation operation. The NLMS based adaptive noise canceller then applied to the residual echo removed noisy signal to achieve the selective engine noise attenuation in dominant frequency component. Two sets of computer simulations are conducted to demonstrate the effectiveness of the system; one for the fixed acoustical environment condition, the other for the robustness of the system in which, more realistic situation, the acoustic transmission environment change. Simulation results confirm the system performance of 20-25dB ERLE in acoustic echo cancellation and 9-19 dB engine noise attenuation in dominant frequency component for both cases.

• Proceedings of the IEEK Conference
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• summer
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• pp.244-248
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• 2004

This paper proposes a new noise cancellation method for speech recognition in noise environments. We determine the time adaptive threshold value using standard deviations of wavelet coefficients after wavelet transform by frames. The time adaptive threshold value is set up by using sum of standard deviations of wavelet coefficients in cA3 and weighted cD1. cA3 coefficients represent the voiced sound with lower frequency components and cD1 coefficients represent the unvoiced sound with higher frequency components. In experiments, we removed noise after adding white Gaussian noise and colored noise to original speech. The proposed method improved SNR and MSE more than wavelet transform and wavelet packet transform does. As a result of speech recognition experiment using noise speech DB, recognition performance is improved by $2\sim4\;\%.$

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4334-4356
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• 2017

In orthogonal frequency-division multiplexing (OFDM) systems, phase noise introduced by the local oscillators can cause bit error rate (BER) performance degradation. To solve the phase noise problem, a novel orthogonal-polarization-based phase noise self-cancellation (OP-PNSC) scheme is proposed. First, the efficiency of canceling the phase noise of the OP-PNSC scheme in the AWGN channel is investigated. Then, the OP-PNSC scheme in the polarization-dependent loss (PDL) channel is investigated due to power imbalance caused by PDL, and a PDL pre-compensated OP-PNSC (PPC -OP-PNSC) scheme is proposed to mitigate the power imbalance caused by PDL. In addition, the performance of the PPC-OP-PNSC scheme is investigated, where the signal-to-interference-plus-noise ratio (SINR) and spectral efficiency (SE) performances are analyzed. Finally, a comparison between the OP-PNSC and polarization diversity scheme is discussed. The numerical results show that the BER and SINR performances of the OP-PNSC scheme outperform the case with the phase noise compensation and phase noise self-cancellation scheme.

• The Journal of the Acoustical Society of Korea
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• v.20 no.3
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• pp.8-14
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• 2001

In this paper, we present a new cost function for subband block adaptive algorithm and block conjugate gradient algorithm for noise cancellation of acoustic signal. For the cost function, we process the subband signals with data blocks for each subbands and recombine it a whole data block. After these process, the cost function has a quadratic form in adaptive filter coefficients, it guarantees the convergence of the suggested block conjugate gradient algorithm. And the block conjugate gradient algorithm which minimizes the suggested cost function has better performance than the case of full-band block conjugate gradient algorithm, the computer simulation results of noise cancellation show the efficiency of the suggested algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.51-55
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• 2021

In this paper, we propose the deep learning based interference cancellation scheme algorithm for visible light communication (VLC) systems in smart building. The proposed scheme estimates the channel noise information by applying a deep learning model. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the VLC performance is effectively removed through interference cancellation technique. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance. Consequently, the proposed interference cancellation with deep learning improves the signal quality of VLC systems by effectively removing the channel noise. The results of the paper can be applied to VLC for smart building and general communication systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.138-149
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• 2018

In this paper, we propose a system model which effectively mitigates impulsive noise that degrades the performance of power line communication. Recently, deep learning have shown effective performance improvement in various fields. In order to mitigate effective impulsive noise, we applied a convolution neural network which is one of deep learning algorithm to conventional system. Also, we used a successive interference cancellation scheme to mitigate impulsive noise generated from multi-users. We simulate the proposed model which can be applied to the power line communication in the Section V. The performance of the proposed system model is verified through bit error probability versus SNR graph. In addition, we compare ZF and MMSE successive interference cancellation scheme, successive interference cancellation with optimal ordering, and successive interference cancellation without optimal ordering. Then we confirm which schemes have better performance.