• Ocean and Polar Research
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• v.28 no.4
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• pp.459-465
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• 2006

Ambient noise as a background noise in the ocean has been well known for its the various and irregular signal characteristics. Generally, these signals we treated as noise and they are analyzed through stochastical level if they don't include definite sinusoidal signals. This study is to see how ocean ambient noise can be analyzed by the chaotic analysis technique. The chaotic analysis is carried out with underwater ambient noise obtained in areas near the Korean Peninsula. The calculated physical parameters of time series signal are as follows: histogram, self-correlation coefficient, delay time, frequency spectrum, sonogram, return map, embedding dimension, correlation dimension, Lyapunov exponent, etc. We investigate the chaotic pattern of noises from these parameters. From the embedding dimensions of underwater noises, the assesment of underwater noise by chaotic analysis shows similar results if they don't include a definite sinusoidal signal. However, the values of Lyapunov exponent (divergence exponent) are smaller than that of random noise signal. As a result we confirm the possibility of classification of underwater noise using Lyapunov analysis.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.1
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• pp.135-141
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• 1999

Recently, advance signal analysis which is called "Time-Frequency Analysis" has been developed. Wavelet and Wigner Distribution are used to the method. Wavelet transform(WT) is applied to time-frequency analysis of waveforms obtained by an ultrasonic pulse-echo technique. The Gabor function is adopted as the analyzing wavelet. Wavelet analysis method is an attractive technique for evolution of material characterization evoluation. In this paper, the feasibility of suppression of ultrasonic background noise signal using WT has been presented. These results suggest that ultrasonic background noise ginal can be suppressed and enhanced even for SNR of 20.8 dB. This property of the WT is extremely useful for the detecting flaw echos embedded in background noise.und noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.721-726
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• 2008

Community Noise has been great concerned in public. A road traffic noise has affected too much damage on quiet living environment. In this paper, About measured from the measure point noise and the noise signal processing techniques applied to the occurrence of the noise source with a path to reach this measure point to the measured data to determine signal processor for Complex Cepstrum Analysis of how to apply for research.

• Journal of KSNVE
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• v.6 no.4
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• pp.499-502
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• 1996

Interior noise, engine speed and vehicle speed are measured under transient road-load condition and interior noise signal is transformed by using the transient signal analysis methods, such as the spectrogram and wavelet transform. Using the analyzed results, subjective noise metrics such as the loudness, sharpness and articulation index at each vehicle speed can be estimated and characteristics of interior noise for various running modes can be discussed in the viewpoint of noise quality.

• Geosystem Engineering
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• v.21 no.5
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• pp.251-261
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• 2018

With the urbanization in recent years, the power line interference noise in electromagnetic signal is increasing day by day, and has gradually become an unavoidable component of noises in magnetotelluric signal detection. Therefore, a kind of power line interference noise elimination method based on independent component analysis in wavelet domain for magnetotelluric signal is put forward in this paper. The method first uses wavelet decomposition to change single-channel signal into multi-channel signal, and then takes advantage of blind source separation principle of independent component analysis to eliminate power line interference noise. There is no need to choose the layer number of wavelet decomposition and the wavelet base of wavelet decomposition according to the observed signal. On the treatment effect, it is better than the previous power line interference removal method based on independent component analysis. Through the de-noising processing to actual magnetotelluric measuring data, it is shown that this method makes both the apparent resistivity curve near 50 Hz and the phase curve near 50 Hz become smoother and steadier than before processing, i.e., it effectively eliminates the power line interference noise.

• Journal of Ocean Engineering and Technology
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• v.25 no.2
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• pp.1-6
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• 2011

Vibration-based Structural Health Monitoring (SHM) systems use field measurements of operational signals, which are distorted by noise from many sources. Reducing this noise allows a more accurate assessment of the original "clean" signal and improves analysis results. The implementation of a noise reduction methodology for the Modal Distribution Method (MDM) is reported here. The spectral subtraction method is a popular broadband noise reduction technique used in speech signal processing. Its basic principle is to subtract the magnitude of the noise from the total noisy signal in the frequency domain. The underlying assumption of the method is that noise is additive and uncorrelated with the signal. In speech signal processing, noise can be measured when there is no signal. In the MDM, however, the magnitude of the noise profile can be estimated only from the magnitude of the Power Spectral Density (PSD) at higher frequencies than the frequency range of the true signal associated with structural vibrations under the additional assumption of white noise. The implementation of the spectral subtraction method to MDM may decrease the energy of the individual mode. In this work, a modification of the spectral subtraction method is introduced that enables the conservation of the energies of individual modes. The main difference is that any (negative) bars with a height below zero after subtraction are set to the absolute value of their height. Both noise reduction methods are implemented in the MDM, and an application example is presented that demonstrates its effectiveness when used with a signal corrupted by noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.266-270
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• 2006

Noise reduction the noise of a high precision multi-stage gearbox applied at the industrial robot is investigated by experiment such as the modal test and the signal analysis. The signal analysis performed with the waterfall plot representing the power spectrum as a function of the rotating speed. An eccentric load is installed at the gearbox in order to organize similar condition used in the industrial robot. Exciting sources are found out by the waterfall plot, and then the main factor to make the noise is distinguish. For the low-noise gearbox, the gear design parameter is modified and this gearbox is experimented in the same procedure. The results of the test show the noise level of gearbox reduced.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1303-1311
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• 2008

In this paper, we analyzed the error rate performance of convolution coded 16 QAM signal in impulsive noise Environments. We used convolution code and selective combining diversity for performance improvement. We analyzed the error rate performance of 16 QAM signal in impulsive noise environments compared with gaussian noise environments. As a result of analysis, there is a BER segment where the efficiency of system does not improve until which limit to raise a signal power potential from impulsive noise environment. when the signal power potential which goes over this limit is supplied, BER efficiency improve much more.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1183-1186
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• 1998

Adaptive nois canceller can extract the noiseremoved spech in noisy speech signal by adapting the filter-coefficients to the background noise environment. A kind of LMS algorithm is one of the most popular adaptive algorithm for noise cancellation due to low complexity, good numerical property and the merit of easy implementation. However there is the matter of increasing misadjustment at voiced speech signal. Therefore the demanded speech signal may be extracted. In this paper, we propose a fast and noise robust wavelet packet adaptive noise canceller with NLMS-SUM method LMS combined algorithm. That is, we decompose the frequency of noisy speech signal at the base of the proposed analysis tree structure. NLMS algorithm in low frequency band can efficiently dliminate the effect of the low frequency noise and SUM method LMS algorithm at each high frequency band can remove the high frequency nosie. The proposed wavelet packet adaptive noise canceller is enhanced the more in SNR and according to Itakura-Satio(IS) distance, it is closer to the clean speech signal than any other previous adaptive noise canceller.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.5
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• pp.447-453
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• 2009

Community noise has been great concerned in public. A traffic noise from a road or a railway has affected too mush damage on quiet living environment. In this paper, a measured noise signal on a street has been applied to extract a noise source and a path by using a complex cepstrum. An example shows that the waveform of the source and the path could be separated if a temporal windowing is properly applied.