• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.670-679
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• 2003

This paper deals with application of wavelet transform, which is known to be good for time-frequency analysis, in order to detect the underwater transient signals embedded in ambient noise. A new detector of acoustic transient signals is presented. It combines two detection tools: wavelet analysis and higher-order statistics. Using both techniques, the detection of the transient signal is possible in low signal to noise ratio condition. The proposed algorithm uses the wavelet transform of a partition of the signal on frequency domain, and then higher-order statistics tests the Gaussian nature of the segments.

• 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.

• Proceedings of the KIEE Conference
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• summer
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• pp.1235-1238
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• 2004

This paper presents the large-signal transient analysis of a self-driven active-clamp forward converter, eliminating the extra drive circuit for the active clamp switch. The operation principle of the converter was presented and experiential results were used to verify the analyzed results. A 50-W prototype converter built and tested it. From the tested results, input transient response and load transient response of the converter were established.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1442-1448
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• 2009

Transient characteristics of a signal can be effectively exhibited in time-frequency domain. Hilbert-Huang Transform (HHT) is one of the time-frequency domain analysis methods. HHT is known for its several advantages over other signal analysis methods. The capability of analyzing non-stationary or nonlinear characteristics of a signal is the primary advantage of HHT. Moreover, it is known that HHT can provide fine resolution in high frequency region and handle large size data efficiently. In this study, the effectiveness of Hilbert-Huang transform is illustrated by employing structural systems undergoing impact. A simple discrete system and an axially oscillating cantilever beam undertaking periodic impulsive force are chosen to show the effectiveness of HHT.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.752-757
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• 1994

Very often, one would like to have visual image of mechanical or acoustical events such as musical sound and transient vibrations. Conventional methods to visualize the signal, such as power spectrum, do not normally allow to cultivate the signal of interests due to their inherent limitation on transient signals. Other than the conventional method, one could use an instantaneous frequency which can reveal the variation of frequency in terms of time. Nevertheless it is quite sensitive to noise and can not resolve the frequency components of signals; normally produces additional components other than those of the signals. In this paper, we introduce the Wigner-Ville spectrum to see the transient characteristics of signal, especially musical sound and transient mechanical vibration signatures. For musical sound, several popular western classic music have been selected for the analysis. For the transient mechanical signature, the signals obtained from the car door experiment and the beam experiment are interpreted in terms of Wigner-Ville spectrum. Results demonstrate the visual expressions of transient signals; musical sound and vibrations.

• Journal of Power System Engineering
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• v.14 no.2
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• pp.40-47
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• 2010

This paper proposes a fault detection system to detect the strip rupture in six-high stand Cold Rolling Mills based on transient current signal of an electrical motor. For this work, signal smoothing technique is used to highlight precise feature between normal and fault condition. Subtracting the smoothed signal from the original signal gives the residuals that contains the information related to the normal or faulty condition. Using residual signal, discrete wavelet transform is performed and acquire the signal presenting fault feature well. Also, feature extraction and classification are executed by using PCA, KPCA and SVM. The actual data is acquired from POSCO for validating the proposed method.

• Journal of KSNVE
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• v.8 no.5
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• pp.957-963
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• 1998

A new signal processing technique, the directional harmonic wavelet map(dHWM), is presented to characterize the instantaneous planar motion of a measurement point in a structure from its transient complex-valued vibration signal. It is proven that the directional auto-HWM essentially tracks the shape and directively of the instantaneous planar motion, whereas the phase of the directional cross-HWM indicates its inclination angle. Finally, the technique is suessfully applied to an automobile engine for characterization of its transient motion during crank-on/idling/engine-off.

• Speech Sciences
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• v.3
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• pp.156-166
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• 1998

In this paper, I would like to propose a dividing method by estimating the inflection points and the average magnitude energy in speech signals. The method proposed in this paper gave not only a satisfactory solution for the problems on dividing method by zero-crossing rate, but could estimate the feature of the transient period after dividing the starting point and transient period in speech signals before steady state. In the results of the experiment carried out with monosyllabic speech, it was found that even through speech samples indicated in D.C. level, the staring and ending point of the speech signals were exactly divided by the method. In addition to the results, I could compare with the features, such as the length of transient period, the short term energy, the frequency characteristics, in each speech signal.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11b
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• pp.274-277
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• 2001

This paper describes the simulation study. conducted on the propagation characteristics of AE signal. In the case of gas insulation, such as $SF_6$, the equipment is less affected by the environment condition than air insulation, because the component parts of equipment were placed in the enclosure, which is filled with compressed gas. But. when the breakdown in the electric insulation occurs. it takes much time and economical efforts to repair. Therefore it is very important to diagnose the equipment before the accident. And. in general. UHF and AE signal is the most common transducer to diagnose the state of the power equipment, so, in this investigation, we make a experimental apparatus with aluminum plate and transient analysis with ANSYS to observe the propagation characteristics of AE signal. Through the result of the analysis. we can make a further understanding on the propagation characteristics of AE signal, and get the fundamental skills for the GIS diagnosis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11a
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• pp.274-277
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• 2001

This paper describes the simulation study, conducted on the propagation characteristics of AE signal. In the case of gas insulation, such as SF6, the equipment is less affected by the environment condition than air insulation, because the component parts of equipment were placed in the enclosure, which is filled with compressed gas. But, when the breakdown in the electric insulation occurs, it takes much time and economical efforts to repair. Therefore it is very important to diagnose the equipment before the accident. And, in general, UHF and AE signal is the most common transducer to diagnose the state of the power equipment, so, in this investigation, we make a experimental apparatus with aluminum plate and transient analysis with ANSYS to observe the propagation characteristics of AE signal. Through the result of the analysis, we can make a further understanding on the propagation characteristics of AE signal, and get the fundamental skills for the GIS diagnosis.