• Journal of KSNVE
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• v.7 no.5
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• pp.747-756
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• 1997

Wigner-Ville distribution which is a time-frequency analysis has a fatal drawback, when the signal has multiple components. This is the cross-talk and often causes a neagative value in the distribution. Wingner-Ville distriution is an expression of power, therefore the cross-talk must be avoided. Smoothing the Wigner-Ville distribution by convoluting it with a window, is most commonly used to reduce the cross-talk. There can be infinite number of distributions depending on the windows. But, the smoothing reduces resolution in time-frequency plane; this motives to design a more effective window in reducing cross-talk while remaining resolution. The domain in which the cross-talk and legitimate components can be easily distinguished, is the ambiguity function. In the ambiguity function domain, the legitimate components appear as linear lines passing through the orgine. But, the cross-talk is widely distributes in the ambiguity function plane. Based on the relative distributions of cross-talk and legitimate components, rotating window can be designed to minimize cross-talk. Applying the rotating window to the ambiguity function corresponds to smoothing the Wigner-Ville distribution. Therefore, the effects of rotating window is estimated in terms of the bias error due to smooting the Wigner-Ville distribution. By applying the rotating window, not only the Wigner-Ville distribution but also its properties are changed. The properties of the new distribution are checked, in order to complete analyzing the rotating window.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.268-275
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• 2008

In this paper, we introduce a new method for detecting and estimating faults on a power line using the time-frequency domain reflectometry system. The system rests upon time-frequency signal analysis and uses a chirp signal which is multiplied by Gaussian envelope. The chirp signal is used as a reference signal, and we can get the reflected signal from a fault on a wire. To detect and estimate faults, we analyze the reflected signal by Wigner time-frequency distribution function and normalized time-frequency cross correlation function. In this paper we design an optimal reference signal for power line and implement a system for estimating fault distance on a power line with the TFDR implemented by PXI equipments. This approach is verified by some experiments with HIV 2.25mm power lines.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.12
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• pp.1683-1689
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• 2014

In this paper, we propose an image processing based time-frequency domain reflectometry(TFDR) in order to estimate the fault location of a cable. The Wigner-Ville distribution is used for analysis in both the time domain and the frequency domain when the conventional TFDR estimates the fault location in a cable. However, the Winger-Ville distribution is a bi-linear function, and hence the cross-term is occurred. The conventional TFDR cannot estimate the accurate fault location due to the cross-term in case the fault location is close to the position where the reference signal is applied to the cable. The proposed method can reduce the cross-term effectively using binarization and morphological image processing, and can estimate the fault location more accurately using the template matching based cross correlation compared to the conventional TFDR. To prove the performance of the proposed method, the actual experiments are carried out in some cases.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.991-993
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• 1999

This study proposed a new algorithm to assess autonomic function activity using Time-Frequency Representation(TFR). TFR is a way of describing the time-valiant energy of a signal. A discrete Wigner representation that is capable of filtering out any cross terms occuring in the Wigner-Ville Distribution(WVD) is used for time-variant energy distribution of heart rate variability(HRV) signals. And the marginal condition are evaluated to estimate power spectrum of HRV signals. The proposed algorithm showed that estimated power spectrum of HRV signals well describe the autonomic nerve system function and also showed the dynamics of autonomic nervous system response.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.48 no.2
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• pp.221-232
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• 2015

Broadband echoes measured in live chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, and fat greenling Hexagrammos otakii with different morphologies and internal characteristics were analyzed in time and frequency domains to understand the species-specific echo feature characteristics for classifying fish species. The mean echo image for each time-frequency representation dataset obtained as a function of orientation angle was extracted to mitigate the effect of fish orientation on acoustic scattering. The joint time-frequency content of the broadband echo signals was obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The SPWVDs were analyzed for each echo signature of the three fish species. The results show that the time-frequency analysis provided species-specific echo structure patterns and metrics of the broadband acoustic signals to facilitate fish species classification.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.181-188
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• 2006

The purpose of this study is to predict the defibrillation success of a ventricular Fibrillation ECG signal using time-frequency analysis. During CPR, coronary perfusion pressure and electrocardiogram were measured. Parameters extracted from time-frequency domain were served as predictor of resuscitation success. Time frequency distribution(TFD) of ECG signals was estimated from the smoothed pseudo Wigner-Ville distribution(SPWVD). Median frequency, peak frequency, 1/f slope, frequency band ratios$(2{\sim}4Hz,\;4{\sim}6Hz,\;6{\sim}8Hz,\;8{\sim}10Hz,\;10{\sim}12Hz,\;12{\sim}15Hz)$ were extracted from each TFD as function of time. Paired t-test was used to determine the differences in ROSC and non-ROSC groups. In the statistical results, we selected four significant parameters - median frequency, 1/f slope, $2{\sim}4Hz$ band ratio, $8{\sim}10Hz$ band ratio. We made an attempt to predict defibrillation success by combining features extracted from time frequency distribution. Independent t-test was used to determine the differences ROSC and non-ROSC groups. Consequently, we selected four significant parameters-median frequency, 1/f slope, $2{\sim}4Hz$ band ratio, $8{\sim}10Hz$ band ratio. The relationship between coronary perfusion pressure and ECG parameters was analyzed with linear regression analysis. R-square value was 55%. 1/f slope and $8{\sim}10Hz$ band ratio had the significant relationship with coronary perfusion pressure.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.501-510
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• 2000

Recently, advanced signal analysis which is called "time-frequency analysis" has been used widely in nondestructive evaluation applications. Wavelet transform(WT) and Wigner Distribution are the most advanced techniques for processing signals with time-varying spectra. Wavelet analysis method is an attractive technique for evaluation of material characterization nondestructively. Wavelet transform is applied to the time-frequency analysis of ultrasonic echo waveform obtained by an ultrasonic pulse-echo technique. In this study, the feasibility of noise suppression of ultrasonic flaw signal and frequency-dependent ultrasonic group velocity and attenuation coefficient using wavelet analysis of ultrasonic echo waveform have been verified experimentally. The Gabor function is adopted the analyzing wavelet. The wavelet analysis shows that the variations of ultrasonic group velocity and attenuation coefficient due to the change of material characterization can be evaluated at each frequency. Furthermore, to assure the enhancement of detectability and naw sizing performance, both computer simulated results and experimental measurements using wavelet signal processing are used to demonstrate the effectiveness of the noise suppression of ultrasonic flaw signal obtained from austenitic stainless steel weld including EDM notch.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.10a
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• pp.80-85
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• 1995

Too see the effects of finite record on the estimation of WVD in practice, a window which has time varying length is examined. Its length increases linearly with time in the first half of the record, and decreases from the center of the record. The bias error due to this window decreases inversely proportionally to the window length as time increases in the first half. In the second half, the bias error increases and the resolution decreases as time increases. The bias error due to the smoothing of WVD, which is obtained by two-dimensional convolution of the true WVD and the smoothing window, which has fixed lengths along time and frequency axes, is derived for arbitrary smoothing window function. In the case of using a Gaussian window as a smoothing window, the bias error is found to be expressed as an infinite summation of differential operators. It is demonstrated that the derived formula is well applicable to the continuous WVD, but when WVD has some discontinuities, it shows the trend of the error. This is a consequence of the assumption of the derivation, that is the continuity of WVD. For windows other than Gaussian window, the derived equation is shown to be well applicable for the prediction of the bias error.