• 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)

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.3
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• pp.201-208
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• 2005

In this paper, absolute and differential eddy current signals from various defects in the steam generator tube are numerically predicted and their signal slope characteristics are investigated. The signal changes due to frequency increase are also observed. After studying signal patterns from various defects and frequencies, the analysis of mixed defect signals affected by the presence of a ferromagnetic support plate is attempted. For the signal prediction, axisymmetric finite element modeling is used and this leads us to the slope angle analysis of the signal. Results show that differential signals are useful for locating the position of a defect under the support plate, while absolute signals are easy to presume and interpret even though the effect of support plate is mixed. Combined use of these two types of signals will help us accomplish a more reliable inspection.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.59-62
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• 1997

In this paper, we used Wavelet Transform to analyze EMG signals. Wavelet transform has an advantage of dividing the nonstationary signals into the high frequency and low frequency band effectively. For determining the characterized value of EMG signals, it was wavelet-transformed, absoluted, and integral-calculated. As the result, we acquired characterized value of each signals, and acknowledged the differences among them. It was concluded that the results of this study using wavelet transform could be used to powerful tool or analysis of EMG signals.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.6
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• pp.520-530
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• 2007

Combination of the parametric and the wavelet analyses of acoustic emission (AE) signals was applied to identify the failure modes in carbon fiber reinforced plastic (CFRP) composite laminates during tensile testing. AE signals detected by surface mounted lead-zirconate-titanate (PZT) and polyvinylidene fluoride (PVDF) sensors were analyzed by parametric analysis based on the time of occurrence which classifies AE signals corresponding to failure modes. The frequency band level-energy analysis can distinguish the dominant frequency band for each failure mode. It was observed that the same type of failure mechanism produced signals with different characteristics depending on the stacking sequences and the type of sensors. This indicates that the proposed method can identify the failure modes of the signals if the stacking sequences and the sensors used are known.

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.329-336
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• 1998

The characteristics of vibration and sound signals which occurs at the driving system of electric train are investigated in this study since the vibration of driving system is one of the main sources of vibration and sound in electric train. The vibration signals are changed its signal patterns during the transmission from the source to passengers due to noise or several unknown factors. To avoid the complexity of actual signals of electric train, signals from experimental apparatus of motor/gear driving system are analyzed to find the appropriate method of analysis and to characterize the signal patterns. The used methods are waterfall diagram, transfer function and modal analysis. The results shows that the vibration signals are usually originated from motor bearing and gear meshing and these signals are transmitted to bottom or bogie. Also, the sound signal is similar to the vibration of bottom or bogie, but it is not so clear to figure out the source of vibration.

• 전자공학회논문지 IE
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• v.47 no.4
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• pp.10-17
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• 2010

There is no general method to find out from signals of the channel outputs of ICA(Independent Component Analysis) which is what you want. Assuming speech signals contaminated with the sound from the muffler of a car, this paper presents the method which shows what you want, It is anticipated that speech signals will show larger correlation coefficients for speech signals than others. Batch, maximum and average method were proposed using 'ah', 'oh', 'woo' vowels whose signals were spoken by the same person who spoke the speech signals and using the same vowels whose signals are by another person. With the correlation coefficients which were calculated for each vowel, voting and summation methods were added. This paper shows what the best is among several methods tried.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1312-1322
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• 1999

The world of linearity, which is regular, predictable and irrelevant to time sequence in most natural phenomenon, is a very small part. In fact, signals generated from natural phenomenon with which we're in contact are showed only slight linearity. Therefore it is very difficult to understand and analyze natural phenomenon with only predictable and regular linear systems. Due to these reasons researches concerning non-linear signals that of analysis were excluded being regarded as noise are being actively carried out. Countless signals generated from nonlinear system have the information about itself, and analyzing those signals and get information from it, that will be able to be used effectively in so may fields. Hence, in this paper we used a higher order spectrum, especially the bispectrum. After we prove the validity applying bispectrum to logistic map, which is typical chaotic signal. Subsequently by showing the result applying for actual signal analysis of EEG according to auditory stimuli, we show that higher order spectra is a very useful parameter in analysis of non-linear signals and the result of EEG analysis according to auditory stimuli.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.503-508
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• 2001

In this paper, time-fiequency analysis and multi-dimensional spectral analysis methods are applied for source identification and diagnosis of non-stationary sound/vibration signals. Sound or vibration problems of general vehicle and agricultural machinary are under 500 Hz. So We used linearly increased chirp signals under 500 Hz. By checking the coherences on concerned time, fur time-variant non-stationary signals, this simulation it very well coincident to expected results.

• Nuclear Engineering and Technology
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• v.29 no.5
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• pp.399-405
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• 1997

In this study a new phase separated spectral analysis algorithm is proposed to identify CSB vibration mode directly from ex-core neutron signals. Ex-core neutron signals can be decomposed into the global, core support barrel (CSB) beam mode, and CSB shell mode components by the new phase separation algorithm based on the characteristics of Fourier transform. By using the proposed algorithm and the conventional spectral analysis the vibration mode of the CSB and the fuel assembly of Ulchin-1 NPP were identified from measured ex-core neutron signals.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.52.5-52
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• 2002

In this paper, we develop analysis models for classification of temporal data from human subjects. The study focuses on the analysis of electroencephalogram (EEG) signals obtained during various emotional states. We demonstrate a generally applicable method of removing EOG and EMG artifacts from EEGs based on independent component analysis (ICA). All EEG channel maps were interpolated from 10 EEG subbands. ICA methods are based on the assumptions that the signals recorded on the scalp are mixtures of signals from independent cerebral and artifactual sources, that potentials arising from different parts of the brain, scalp and body are summed linearly at the electrodes and that prop...