• 지구물리와물리탐사
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• 제15권1호
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• pp.39-46
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• 2012

최근에는 지금까지 주로 탐사개발이 이루어진 구조적 저류층보다 층서적 저류층에 대한 관심이 높아지고 있다. 하지만 얇은 두께의 가스 저류층의 경우 동조효과로 인해 겹쌓기 단면도에서 탐지가 어렵다. 게다가 얇은 저류층 내 염수가 있는 부분과 가스로 치환된 부분으로부터의 반사파가 동일한 극성을 갖는 경우 일반적인 자료 처리 기술을 이용해서 가스가 있는 부분을 규명하는 것이 쉽지 않다. 본 연구에서는 빛띠 분해를 이용해서 얇은 저류층 내 가스로 치환된 부분을 나타내는 방법을 소개하고자 한다. 먼저, Class 1, Class 3 그리고 Class 4의 AVO 반응을 가지는 매질의 물성을 이용하여 다양한 입사각과 진동수에 따른 진폭 빛띠를 분석하였다. 그 결과 입사각과 AVO 종류에 무관하게 최대 진폭 빛띠 값을 갖는 꼭지 진동수 근처에서 염수와 가스로 치환된 얇은 층의 진폭 빛띠 값이 가장 크게 차이가 나는 것을 확인하였다. 또한 Class 3와 Class 4의 성질을 가지는 매질에서는 가스로 치환된 부분의 진폭 빛띠가 꼭지 진동수에서 염수로 치환된 부분의 진폭 빛띠보다 크게 나타나는 것을 확인하였고 이러한 현상은 Class 1에서는 반대로 일어나는 것을 확인하였다. 위의 결과를 토대로 얇은 저류층내에서 가스로 치환된 부분을 염수로 치환된 부분과 구분하기 위해서 겹쌓기 단면에 빛띠 분해법을 적용하였다. 위 방법에 대한 타당성을 검증하기 위해서 동일한 반사 극성을 가지면서 염수와 가스로 치환된 부분이 모두 있는 하나의 얇은 저류층 속도 모델을 설정하였다. 결과적으로 Wigner-Ville distribution을 사용해서 얻은 꼭지 진동수 근처에서의 빛띠 분해 단면도를 통해 겹쌓기 단면에서는 구분이 어려웠던 얇은 저류층 내에서의 염수와 가스로 치환된 부분을 구분할 수 있었다.

• 전기학회논문지
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• 제62권5호
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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.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1997년도 영남지회 학술발표회 논문집 Acoustic Society of Korean Youngnam Chapter Symposium Proceedings
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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.

• 한국수산과학회지
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• 제49권2호
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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.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1995년도 추계학술대회논문집; 한국종합전시장, 24 Nov. 1995
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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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• 제17권1E호
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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.

• 한국해양공학회지
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• 제17권3호
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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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.