• Phonetics and Speech Sciences
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• v.3 no.3
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• pp.99-108
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• 2011

The perturbation parameters like jitter, shimmer, and signal-to-noise ratio (SNR) are largely estimated in the particular segment from the subjective or whole portion of the given pathological voice signal although there are many possible regions to be able to analyze the voice signals. In this paper, the pathological voice signals were classified as type 1, 2, 3, or 4 according to narrow band spectrogram and the value differences of the perturbation parameters extracted in the subjective and entire portion tended to be getting bigger as from type 1 to type 4 signals. Therefore, sample selection method based on moving window to analyze type 2 and 3 signals as well as type 1 signals is proposed. Although type 3 signals cannot be analyzed using the perturbation analysis, the type 3 signals by selecting out the samples in which error count is less than 10 through moving window were analyzed. At present, there is no method to be able to analyze the type 4 signals. Future research will endeavor to determine the best way to evaluate such voices.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.942-944
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• 2005

In this study, eddy current signals from various anomalous defects in the heat exchanger tube are predicted af their signal slope characteristics no analyzed. The signal changes due to frequency increase are also observed. Based in the accumulated knowledge, the analysis of superimposed signal is attempted which includes the effects of support plate. Both differential and absolute bobbin probe signals are analyzed. For the prediction of signals, axisymmetric finite element modeling is used and this leads us to the utilization of slope angle analysis of the signal. Results show that differential signals are useful to locate the position of defect under the support plate and absolute signals no easy to predict and analyze even though they no superimposed signals. Combined use of these two types of signals will accomplish a reliable inspection.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.838-842
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• 2007

A brain-computer interface(BCI) system is a communication channel which transforms a subject's thought process into command signals to control various devices. These systems use electroencephalographic signals or the neuronal activity of many single neurons. The presented study deals with an efficient analysis method of neuronal signals from a BCI System using an independent component analysis(ICA) algorithm. The BCI system was implemented to generate event signals coding movement information of the subject. To apply the ICA algorithm, we obtained the perievent histograms of neuronal signals recorded from prefrontal cortex(PFC) region during target-to-goal(TG) task trials in the BCI system. The neuronal signals were then smoothed over 5ms intervals by low-pass filtering. The matrix of smoothed signals was then rearranged such that each signal was represented as a column and each bin as a row. Each column was also normalized to have a unit variance. As a result, we verified that different patterns of the neuronal signals are dependent on the target position and predefined event signals.

• Proceedings of the KIEE Conference
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• 2005.10c
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• pp.50-52
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• 2005

In this paper, the analysis of the eddy current testing(ECT) signals under thc Influence of the ferromagnetic support plate was performed in steam generator(SG) tube of nuclear power plant. In order to remove the influence of the ferromagnetic support plate, a multi-frequency ECT was used. The models which was established for the analysis of the signals is calculated using numerical analysis of finite element method. Through the result of numerical analysis, improved signals is acquired considering the influence of the ferromagnetic support plate using mixing of multi-frequency This paper is presented the residual errors and the phase changes for analysis of the defect signals which should be considered when conducting a ECT using multi-frequency.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.391-394
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• 1997

In this paper, we considered newly the use of wavelet transform in order to improve the troubles of the established methods for the analysis of ultrasound echo signals. We made the phantoms of 13.2g, 19.8g, 26.4g, 33.0g, 39.8g by ourselves, and extracted the only pulse-echo signals that reflected through the mediums using windowing technique. For determining the characterized value, the signals were wavelet transformed, absoluted, and integral calculated. As the result, we acquired characterized value of each signals, and acknowledged the differences among them except of some datas. But this will be improved by advanced work as sellecting a proper mother wavelet, a method of making phantoms, correcting the various errors, etc. We expect that wavelet transform is powerful for analysis of ultrasound signals.

• Phonetics and Speech Sciences
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• v.6 no.3
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• pp.63-72
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• 2014

The current study assessed the utility of acoustic analyses the most commonly used in routine clinical voice assessment including perturbation, nonlinear dynamic analysis, and Spectral/Cepstrum analysis based on signal typing of dysphonic voices and investigated their applicability of clinical acoustic analysis methods. A total of 70 dysphonic voice samples were classified with signal typing using narrowband spectrogram. Traditional parameters of %jitter, %shimmer, and signal-to-noise ratio were calculated for the signals using TF32 and correlation dimension(D2) of nonlinear dynamic parameter and spectral/cepstral measures including mean CPP, CPP_sd, CPPf0, CPPf0_sd, L/H ratio, and L/H ratio_sd were also calculated with ADSV(Analysis of Dysphonia in Speech and VoiceTM). Auditory perceptual analysis was performed by two blinded speech-language pathologists with GRBAS. The results showed that nearly periodic Type 1 signals were all functional dysphonia and Type 4 signals were comprised of neurogenic and organic voice disorders. Only Type 1 voice signals were reliable for perturbation analysis in this study. Significant signal typing-related differences were found in all acoustic and auditory-perceptual measures. SNR, CPP, L/H ratio values for Type 4 were significantly lower than those of other voice signals and significant higher %jitter, %shimmer were observed in Type 4 voice signals(p<.001). Additionally, with increase of signal type, D2 values significantly increased and more complex and nonlinear patterns were represented. Nevertheless, voice signals with highly noise component associated with breathiness were not able to obtain D2. In particular, CPP, was highly sensitive with voice quality 'G', 'R', 'B' than any other acoustic measures. Thus, Spectral and cepstral analyses may be applied for more severe dysphonic voices such as Type 4 signals and CPP can be more accurate and predictive acoustic marker in measuring voice quality and severity in dysphonia.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.611-616
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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. Then we can find defect frequency component in the envelope spectrum of the bearing signals.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.2
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• pp.413-421
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• 1990

Acoustic emission signals due to leak from circular holes of 0.4, 1, 2 and 4mm diameter and rectangular slits of different geometry having the same cross section as 4mm diameter hole was studied both analytically and experimentally. Acoustic emission signals from a wide-band type transducer were transformed to digital signals through a digital oscilloscope, and $V_{rms}$ and frequency spectrum were obtained by processing digital signals. Relationships between acoustic parameters and fluid mechanical parameters were derived analytically. A quadrapole aerodynamic model was applied in the analysis of leak from the circular holes and $V_{rms}$ was found to be proportional to the root square of leak rate through the circular hole. A modified model based on dipole source mechanism and laminar equivalent diameter was applied in the analysis of leak signals from the rectangular slits. In the case of constant pressure, $V_{rms}$ increased as the laminar equivalent diameter of slit decreased. In the case of constant laminar equivalent diameter, however the result was similar to that for leak from the circular hole. The frequency spectra of leak signals shows the same frequency characteristics irrespective of the pressure difference.rence.

• 전기의세계
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• v.13 no.2
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• pp.5-10
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• 1964

Some nonlinearities in feedback control systems, such as relay servo, Coulomb friction, saturating amplifier of backlash in gears cause some undesirable effects. These may be static errors, limit cycles or jump phenomena. So, linearizing method of these nonlinearities using dither signals was first suggested by Loeb. He pointed out that the addition of high frequency signal to either the input or the output of a nonlinear system would make the nonlinear performance approximate the performance of linear systems. He used high-frequency sinusoidal wave as dither signal. But, in this thesis, general method of analysis is suggested for nonlinear control systmes using various dither signals, such as noise signal of Gaussian amplitude distribution, sinusoidal dither signal and sawtooth dither signal. Also, the advantage and disadvantage of these dither signals are compared. Throughout the analysis statistical method is adopted and lastly analog computer is used for the experiment of various nonlinear systems using dither signals.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.243-246
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• 2005

Using long-tenn monitoring AE techniques, two acoustic signals of wire fractures in a PSC beam are obtained. These data are compared to other noise signals. Based on the test results, the characteristics of the AE signals are classified and wire fracture signals are figured out among the other AE signals.