• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.333-338
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• 2006

This paper proposes an improved do-noising method using multi-thresholding function based on translation-invariant (W) wavelet proposed by Donoho et al. for underwater radiated noise measurement. The traditional wavelet thresholding de-noising method causes Pseudo-Gibbs phenomena near singularities due to discrete wavelet transform. In order to suppress Pseudo-Gibbs Phenomena, a do-noising method combining multi-thresholding function with the translation-invariant wavelet transform is proposed in this paper. The multi-thresholding function is a modified soft-thresholding to each node according to the discriminated threshold so as to reject かon external noise and white gaussian noise. It is verified by numerical simulation. And the experimental results are confirmed through sea-trial using multi-single sensors.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1330-1341
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• 2010

Power analysis (PA) is known as a powerful physical attack method in the field of information security. This method uses the statistical characteristics of leaked power consumption signals measured from security devices to reveal the secret keys. However, when measuring a leakage power signal, it may be easily distorted by the noise due to its low magnitude values, and thus the PA attack shows different performances depending on the noise level of the measured signal. To overcome this vulnerability of the PA attack, we propose a noise-reduction method based on wavelet de-noising. Experimental results show that the proposed de-noising method improves the attack efficiency in terms of the number of signals required for the successful attack as well as the reliability on the guessing key.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.490-500
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• 2000

A new technique for the source location of acoustic emission (AE) in plates whose thichness are close to or thinner than the wavelength has been studied by introducing wavelet transform de-noising technique. The detected AE signals were pre-processed using wavelet transform to be decomposed into the low-frequency, high-amplitude flexural components and the high-frequency, low-amplitude extensional components. If the wavelet transform de-noising was employed, we could successfully filter out the extensional wave component, one of the critical errors of source location in plates by arrival time difference method. The accuracy of source location appeared to be significantly improved and independent of the setting of gain and threshold, plate thickness, sensor-to-sensor distance, and the relative position of source to sensors. Since the method utilizes the flexural component of relatively high amplitude, it could be applied to very large, thin-walled structures in practice.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.8
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• pp.914-916
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• 2016

The noise in a power signal degrades the detection rate of the power quality (PQ) event signals. We present a new wavelet de-noising technique for PQ event detection that employs the correlation-based thresholding instead of the wavelet-scale-based thresholding of existing schemes. The simulation results show that the proposed scheme is more robust to Gaussian and impulsive noisy conditions and has further improved detection ratio than existing schemes.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.67-72
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• 1996

Wavelets are localized in space and in frequency. This localization properties result from the multiresolution analysis of wavelets. The wavelet transform can be used to detect singularity of dynamic systems after the signal is de-noised. We applied the wavelet transform decomposition and do-noising procedures to the Hanaro dynamics consisting of 39 nonlinear differential equation plus Gaussian noise. The numerical tests demonstrate that the wavelet transform de-noising is effective for detection of the abrupt reactivity change and computationally efficient. Thus this wavelet theory could be profitably utilized in a real-time system for automatic event recognition (e.g., reactor condition monitoring).

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.5
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• pp.11-17
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• 2003

It is well known that the double integration of measured acceleration records is one of the most difficult signal processing, particularly in the measurements on civil engineering structures, The measured accelerations of civil engineering structures are usually non-stationary and contain non-gaussian low-frequency noises, which can be significant causes of numerical instabilities in double Integration, For the de-noising of this kind of signals, wavelet transform can be very effective because of its inherent processing features for non-stationary signals, In this paper, the de-noising algorithm for the double integration is proposed using the modified wavelet transform, which is extended version of ordinary wavelet transform to process non-gaussian and low-frequency noises, using the median filter concept, The example studies show that the integration can be improved by the proposed method.

• Nuclear Engineering and Technology
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• v.31 no.2
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• pp.192-201
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• 1999

This paper proposes an improved signal processing strategy for accurate feedwater flowrate estimation in nuclear power plants. It is generally known that ∼2% thermal power errors occur due to fouling Phenomena in feedwater flowmeters. In the strategy Proposed, the noises included in feedwater flowrate signal are classified into rapidly varying noises and gradually varying noises according to the characteristics in a frequency domain. The estimation precision is enhanced by introducing a low pass filter with the wavelet analysis against rapidly varying noises, and an autoassociative neural network which takes charge of the correction of only gradually varying noises. The modified multivariate stratification sampling using the concept of time stratification and MAXIMIN criteria is developed to overcome the shortcoming of a general random sampling. In addition the multi-stage robust training method is developed to increase the quality and reliability of training signals. Some validations using the simulated data from a micro-simulator were carried out. In the validation tests, the proposed methodology removed both rapidly varying noises and gradually varying noises respectively in each de-noising step, and 5.54% root mean square errors of initial noisy signals were decreased to 0.674% after de-noising. These results indicate that it is possible to estimate the reactor thermal power more elaborately by adopting this strategy.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1487-1496
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• 2013

In this paper, a novel algorithm for PD localization in power transformers based on wavelet de-noising technique and energy criterion is proposed. Partial discharge is one of the main failures in power transformers. The localization of which could be very useful for maintenance systems. Acoustic signals due to a PD event are transient, irregular and non-repetitive. So wavelet transform is an efficient tool for this signal processing problem that gives a time-frequency demonstration. First, different wavelet based de-noising methods are analyzed. Then, a reasonable structure for threshold value determining and applying manner on signals is presented. Evaluated errors are good evidences for choices. Next, applying the elimination low energy frequency bands is discussed and developed as a de-noising method. Time differences between signals are used for PD localization. Different ways in time arrival detection are introduced and a novel approach in energy criterion method is presented. At the end, the quality of algorithm is verified through the different assays in lab.

• Structural Engineering and Mechanics
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• v.40 no.1
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• pp.13-28
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• 2011

An effective method for detection linear flaws in plate structures via two-dimensional discrete wavelet transform is proposed in this study. The proposed method was applied to a four-fixed supported rectangular plate containing damage with arbitrary length, depth and location. Numerical results identifying the damage location are compared with the actual results to demonstrate the effectiveness of the proposed method. Also, a wavelet-based method presented for de-noising of mode shape of plate. Finally, the performance of the proposed method for de-noising and damage identification was verified using experimental data. Comparison between the location detected by the proposed method, and the plate's actual damage location revealed that the methodology can be used as an accessible and effective technique for damage identification of actual plate structures.