• Korean Journal of Fisheries and Aquatic Sciences
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• v.55 no.2
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• pp.195-206
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• 2022

The separation of spectrally overlapped broadband echo signals from free-swimming Japanese needlefish Hypohamphus sajori using the fractional Fourier transform (FrFT) was investigated. The broadband echo signals were measured over frequency ranges of 40-80 and 110-220 kHz. The overlapped echo signals were separated after eliminating noise signals in the smoothed pseudo-Wigner-Ville distribution domain. The echo signal from a 40 mm WC sphere suspended just below a chirp transducer was used to calibrate the broadband of the chirp echo sounder and estimate the frequency dependence of target strength for the separated echo signals. The experimental results show that the proposed FrFT method can analyze the time-frequency image of broadband echo signals from free-swimming individual fish effectively and can be used as a quantitative tool for extracting the acoustic features used for fish species identification.

• Korean Journal of Optics and Photonics
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• v.14 no.1
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• pp.51-57
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• 2003

The hologram of an object contains the information of the object's depth distribution as well as the depth location of the object. However these pieces of information are blended together as a form of fringe pattern. This makes it hard to extract the depth location of the object directly from the hologram. In this paper, I propose a numerical method which separates the depth location information from the single-sideband hologram by gaussian low-pass filtering. The depth location of the object is extracted by numerical analysis of the filtered hologram. The hologram at the object's depth location is recovered by the extracted depth location.

• Geophysics and Geophysical Exploration
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• v.15 no.1
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• pp.39-46
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• 2012

Recently, stratigraphic reservoirs are getting more attention than structural reservoirs which have mostly developed. However, recognizing stratigraphic thin gas reservoirs in a stacked section is usually difficult because of tuning effects. Moreover, if the reflections from the brine-saturated region of a thin layer have the same polarity with those from the gas-saturated region, we could not easily identify the gas reservoir with conventional data processing technique. In this study, we introduced a way to delineate the gas-saturated region in a thin layer reservoir using a spectral decomposition method. First of all, amplitude spectrum with the variation of the frequency and the incident angle was investigated for the medium which represents property of Class 3, Class 1 or Class 4 AVO response. The results show that the maximum difference in the amplitude spectra between brine and gas-saturated thin layers occurs around the peak frequency independent of the incident angle and the type of AVO responses. In addition, the amplitude spectra of the gas-saturated zone are greater than those of brine-saturated one in Class 3 and Class 4 at the peak frequency while those of phenomenon occur oppositely in Class 1. Based on the results, we applied spectral decomposition method to the stacked section in order to distinguish the gas-saturated zone from the brine-saturated zone in a thin layer reservoir. To verify our new method, we constructed a thin-layer velocity model which contains both gas and brine-saturated zones which have the same reflection polarities. As a result, in the spectral decomposed sections near the peak frequency obtained by Wigner-Ville Distribution (WVD), we could identify the difference between reflections from gas- and brinesaturated region in the thin layer reservoir, which was hardly distinguishable in the stacked section.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.667-672
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• 2013

In this paper, we propose a multi-impedance changes localization method of on-voltage underground power cable using the wavelet transform based time-frequency domain reflectometry (WTFDR). To localize the impedance change in on-voltage power cable, the TFDR is the most suitable among reflectometries because the inductive coupler is used to inject the reference signal to the live cable. At this time, the actual on-voltage power cable has multi-impedance changes such as the automatic section switches and the auto load transfer switches. However, when the multi-impedance changes are generated in the close range, the conventional TFDR has the cross term interference problem because of the nonlinear characteristics of the Wigner-Ville distribution. To solve the problem, the wavelet transform (WT) is used because it has the linearity. That is, using WTFDR, the cross term interference is not generated in multi-impedance changes due to the linearity of the WT. To confirm the effectiveness and accuracy of the proposed method, the actual experiments are carried out for the on-voltage underground power cable.

• Proceedings of the Acoustical Society of Korea Conference
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• 1997.06a
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• pp.27-34
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• 1997

In this study , we tried to test the classification performance of a neural netow and thereby to examine its applicability to the signals distorted by a shallow water einvironment . We conducted an acoustic experiment iin a shallow sea near Pohang, Korea in which water depth is about 60m. The signals, on which the network has been tested, is ilinear frequency modulated ones centered on one of the frequencies, 200, 400, 600 and 800 Hz, each being swept up or down with bandwidth 100Hz. we considered two transforms, STFT(short-time Fourier transform) and PWVD (pseudo Wigner-Ville distribution), form which power spectra were derived. The training signals were simulated using an acoutic model based on the Fourier synthesis scheme. When the network has been trained on the measured signals of center frequency 600Hz,it gave a little better results than that trained onthe simulated . With the center frequencies varied, the overall performance reached over 90% except one case of center frequency 800Hz. With the feature extraction techniques(STFT and PWVD) varied,the network showed performance comparable to each other . In conclusion , the signals which have been simulated with water depth were successully applied to training a neural network, and the trained network performed well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.49 no.2
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• pp.214-223
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• 2016

Joint time-frequency images of the broadband acoustic echoes of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The acoustic features were extracted by changing the sliced window widths and dividing the time window by a 0.02-ms interval and the frequency window by a 20-kHz bandwidth. The 22 spectrum amplitudes obtained in the time and frequency domains of the SPWVD images were fed as input parameters into an artificial neural network (ANN) to verify the effectiveness for species-dependent features related to fish species identification. The results showed that the time-frequency approach improves the extraction of species-specific features for species identification from broadband echoes, compare with time-only or frequency-only features. The ANN classifier based on these acoustic feature components was correct in approximately 74.5% of the test cases. In the future, the identification rate will be improved using time-frequency images with reduced dimensions of the broadband acoustic echoes as input for the ANN classifier.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.54-65
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• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• Journal of Ocean Engineering and Technology
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• v.17 no.3 s.52
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• pp.46-51
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• 2003

Acoustic emission (AE) signals, emanated during local failure of aluminum alloys, have been the subject of numerous investigations. It is well known that the characteristics of AE are strongly influenced by the previous thermal and mechanical treatment of the sample. Possible sources of AE during deformation have been suggested as the avalanche motion of dislocations, fracture of brittle particles, and debonding of these particles from the alloy matrix. The goal of the present study is to determine if AE occurring as the result of fatigue crack propagation could be evaluated by the joint time-frequency analysis method, short time Fourier transform (STFT), and Wigner-Ville distribution (WVD). The time-frequency analysis methods can be used to analyze non-stationary AE more effectively than conventional techniques. STFT is more effective than WVD in analyzing AE signals. Noise and frequency characteristics of crack openings and closures could be separated using STFT. The influence of various fatigue parameters on the frequency characteristics of AE signals was investigated.