• Geophysics and Geophysical Exploration
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• v.22 no.4
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• pp.225-238
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• 2019

Migration velocity analysis (MVA) for creating optimum depth-domain velocities in seismic imaging was applied to marine long-offset multi-channel data, and the effectiveness of the MVA approach was demonstrated by the combinations of conventional data processing procedures. The time-domain images generated by conventional time-processing scheme has been considered to be sufficient so far for the seismic stratigraphic interpretation. However, when the purpose of the seismic imaging moves to the hydrocarbon exploration, especially in the geologic modeling of the oil and gas play or lead area, drilling prognosis, in-place hydrocarbon volume estimation, the seismic images should be converted into depth domain or depth processing should be applied in the processing phase. CMP-based velocity analysis, which is mainly based on several approximations in the data domain, inherently contains errors and thus has high uncertainties. On the other hand, the MVA provides efficient and somewhat real-scale (in depth) images even if there are no logging data available. In this study, marine long-offset multi-channel seismic data were optimally processed in time domain to establish the most qualified dataset for the usage of the iterative MVA. Then, the depth-domain velocity profile was updated several times and the final velocity-in-depth was used for generating depth images (CRP gather and stack) and compared with the images obtained from the velocity-in-time. From the results, we were able to confirm the depth-domain results are more reasonable than the time-domain results. The spurious local minima, which can be occurred during the implementation of full waveform inversion, can be reduced when the result of MVA is used as an initial velocity model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• Geophysics and Geophysical Exploration
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• v.24 no.4
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• pp.149-157
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• 2021

Given that induced polarization (IP) and direct current (DC) resistivity surveys are similar in terms of data acquisition, most DC resistivity systems are equipped with a time-domain IP data acquisition function. In addition, the time-domain IP data include the DC resistivity values. As such, IP and DC resistivity data are intimately linked, and the inversion of IP data is a two-step process based on DC resistivity inversions. Nevertheless, IP surveys are rarely applied, in contrast to DC resistivity surveys, as proper inversion software is unavailable. In this study, through numerical modeling and inversion experiments, we analyze the problems with the conventional inverse mapping technique used to invert time-domain IP data. Furthermore, we propose a modified inverse mapping technique that can effectively suppress inversion artifacts. The performance of the technique is confirmed through inversions applied to synthetic IP data.

• Journal of the Ergonomics Society of Korea
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• v.16 no.1
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• pp.119-126
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• 1997

Most of human factors experiments in nuclear industry domain produe lots of experimental data, thus much time is reauired to analyze the data. DAEXESS was developed to reduce resource demands necessary for the analysis work through systematic data analysis requirements and automated data processing based on computer technology. Physilolgical data, human behavior recording data, system log data and verbal protocl can be collected, synthesized and easily analyzed with with respect to time domain in DAEXESS so that analyser is able to look into inte- grated information on operating context. DAEXESS assists analyser to carry out qualitative and quantitative data analysis easily.

• Tunnel and Underground Space
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• v.15 no.5 s.58
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• pp.352-358
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• 2005

For non-destructive testing of concrete structures, time and frequency domain method were applied to detect cavity in underground model and pier model. To interpret the measured data, time domain method made use of tomography which was completed with first arrivaltime and inversion method. In this steady, frequency domain method using Fourier transform was tried. Maximum frequency in the frequency domain was analyzed to calculate location of cavity.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.7 no.3
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• pp.239-245
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• 1996

The time-domain response of a two-conductor-structure transmission line excited by an incident electromagnetic pulse is numerically analyzed using the Finite-Difference Time-Domain (FDTD) method. The external electromagnetic pulse is generated by ultilizing a TEM cell. The simulated time-domain response is compared with the time-domain response which is obtained by the Inverse Fast Fourier Transform(IFFT) of the frequency domain measurement data.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.84-93
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• 2023

The supervised learning-based deep-learning seismic inversion techniques have demonstrated successful performance in synthetic data examples targeting small-scale areas. The supervised learning-based deep-learning seismic inversion uses time-domain wavefields as input and subsurface velocity models as output. Because the time-domain wavefields contain various types of wave information, the data size is considerably large. Therefore, research applying supervised learning-based deep-learning seismic inversion trained with a significant amount of field-scale data has not yet been conducted. In this study, we predict subsurface velocity models using Laplace-domain wavefields as input instead of time-domain wavefields to apply a supervised learning-based deep-learning seismic inversion technique to field-scale data. Using Laplace-domain wavefields instead of time-domain wavefields significantly reduces the size of the input data, thereby accelerating the neural network training, although the resolution of the results is reduced. Additionally, a large grid interval can be used to efficiently predict the velocity model of the field data size, and the results obtained can be used as the initial model for subsequent inversions. The neural network is trained using only synthetic data by generating a massive synthetic velocity model and Laplace-domain wavefields of the same size as the field-scale data. In addition, we adopt a towed-streamer acquisition geometry to simulate a marine seismic survey. Testing the trained network on numerical examples using the test data and a benchmark model yielded appropriate background velocity models.

• Journal of Communications and Networks
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• v.9 no.1
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• pp.75-83
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• 2007

Orthogonal frequency division multiplexing (OFDM) systems with multiple transmit antennas can exploit space-time block coding on each subchannel for reliable data transmission. Spacetime coded OFDM systems, however, are very sensitive to time variant channels because the channels need to be static over multiple OFDM symbol periods. In this paper, we propose to mitigate the channel variations in the frequency domain using a linear filter in the frequency domain that exploits the sparse structure of the system matrix in the frequency domain. Our approach has reduced complexity compared with alternative approaches based on time domain block-linear filters. Simulation results demonstrate that our proposed frequency domain block-linear filter reduces computational complexity by more than a factor of ten at the cost of small performance degradation, compared with a time domain block-linear filter.

• Geophysics and Geophysical Exploration
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• v.20 no.3
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• pp.121-128
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• 2017

Interpretation of time-domain electromagnetic (TEM) data has been made mostly based on one-dimensional (1-D) inversion scheme in Korea. A proper interpretation of TEM data should employ 3-D TEM forward and inverse modeling algorithms. This study developed a 3-D TEM modeling algorithm using a finite difference time-domain (FDTD) method with staggered grid. In numerically solving Maxwell equations, fictitious displacement current is included based on an explicit FDTD method using a central difference approximation scheme. The developed modeling algorithm simulated a small-coil source configuration to be verified against analytic solutions for homogeneous half-space models. Further, TEM responses for a 3-D anomaly are modeled and analyzed. We expect that it will contribute greatly to the precise interpretation of TEM data.

• International Journal of Naval Architecture and Ocean Engineering
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• v.3 no.1
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• pp.72-79
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• 2011

Typical results obtained by a newly developed, nonlinear time domain hybrid method for simulating large amplitude motions of ships advancing with constant forward speed in waves are presented. The method is hybrid in the way of combining a time-domain transient Green function method and a Rankine source method. The present approach employs a simple double integration algorithm with respect to time to simulate the free-surface boundary condition. During the simulation, the diffraction and radiation forces are computed by pressure integration over the mean wetted surface, whereas the incident wave and hydrostatic restoring forces/moments are calculated on the instantaneously wetted surface of the hull. Typical numerical results of application of the method to the seakeeping performance of a standard containership, namely the ITTC S175, are herein presented. Comparisons have been made between the results from the present method, the frequency domain 3D panel method (NEWDRIFT) of NTUA-SDL and available experimental data and good agreement has been observed for all studied cases between the results of the present method and comparable other data.