• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.2
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• pp.35-46
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• 2008

Soil moisture is one of the important components in hydrological processes and also controls the subsurface flow mechanism at a hillslope scale. In this study, time series of soil moisture were measured at a hillslope located in Gwangneung National Arboretum, Korea using a multiplex Time Domain Reflectometry(TDR) system measuring soil moisture with bi-hour interval. The Box-Jenkins transfer function and noise model was used to estimate spatial distributions of soil moisture histories between May and September, 2007. Rainfall was used as an input parameter and soil moisture at 10 cm depth was used as an output parameter in the model. The modeling process consisted of a series of procedures(e.g., data pretreatment, model identification, parameter estimation, and diagnostic checking of selected models), and the relationship between soil moisture and rainfall was assessed. The results indicated that the patterns of soil moisture at different locations and slopes along the hillslope were similar with those of rainfall during the measurment period. However, the spatial distribution of soil moisture was not associated with the slope of the monitored location. This implies that the variability of the soil moisture was determined more by rainfall than by the slope of the site. Due to the influence of vegetation activity on soil moisture flow in spring, the soil moisture prediction in spring showed higher variability and complexity than that in early autumn did. This indicates that vegetation activity is an important factor explaining the patterns of soil moisture for an upland forested hillslope.

• Geophysics and Geophysical Exploration
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• v.13 no.4
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• pp.315-322
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• 2010

The phase-screen and the split-step Fourier migrations, which are implemented in both the frequency-wavenumber and frequency-space domains by using one-way scalar wave equation, allow imaging in laterally heterogeneous media with less computing time and efficiency. The generalized-screen migration employs the series expansion of the exponential, unlike the phase-screen and the split-step Fourier migrations which assume the vertical propagation in frequency-wavenumber domain. In addition, since the generalized-screen migration generalizes the series expansion of the vertical slowness, it can utilize higher-order terms of that series expansion. As a result, the generalized-screen migration has higher accuracy in computing the propagation with wide angles than the phase-screen and split-step Fourier migrations for media with large and rapid lateral velocity variations. In this study, we developed a 2D prestack generalized-screen migration module for imaging a complex subsurface efficiently, which includes various dips and large lateral variations. We compared the generalized-screen propagator with the phase-screen propagator for a constant perturbation model and the SEG/EAGE salt dome model. The generalized-screen propagator was more accurate than the phase-screen propagator in computing the propagation with wide angles. Furthermore, the more the higher-order terms were added for the generalized-screen propagator, the more the accuracy was increased. Finally, we compared the results of the generalizedscreen migration with those of the phase-screen migration for a model which included various dips and large lateral velocity variations and the synthetic data of the SEG/EAGE salt dome model. In the generalized-screen migration section, reflectors were positioned more accurately than in the phase-screen migration section.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

• Proceedings of the KIEE Conference
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• 1995.07c
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• pp.1140-1142
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• 1995

The transformer is fabricated with two piezoelectric vibrator with the divided electrodes and adhesive insulator. We applied the electric input to the driving vibrator in parallel and connect the output voltage to the generating vibrator in series to the resistor load near its fundamental resonance frequency. Then we investigate output voltage in series twice as large as in parallal. Moreover we investigate the load characteristics at resonance frequencies under various resistor and the frequency characteristics near the resonance frequency under no load. Its equvalent circuit is derived from the Mason's model of a thickness-driven piezelectric vibrator. By its equevalent circuit, symbolic expressions for input impedances, voltage ratios, resonance frequencies, and bandwidths have been derived. The values calculated from those symbolic exprssions are shown to agree well with the measurement values.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.10
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• pp.42-50
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• 2005

Based upon the derived analytical model and equation of motion for the general rotor system with anisotropic stator and asymmetric rotor treated as a periodically time-varying system, the series of equations are structured by associating with the time modulated coefficients. The frequency response functions (FRFs) expressed by physical parameters are derived in such a convenient way from the direct inverse matrices of the Fourier transformation of those series of equations, from which the characteristics are analyzed and the properties are suggested.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.396-403
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• 1991

A solution is presented for the computation of the elastic-creep stresses in a hollow cylinder subjected to nonaxisymmetric temperature distribution. The creep problem is treated by the Maxwell creep model. Laplace transformation is used for reformation of the governing equation of elastic problem and Hooke's law in a function of .gamma. , .theta. , and creep constant. The governing equation is set up using the Airy stress function which leads to the biharmonic equation. The solution is obtained by using Fourer series method and Laplace inverse method used to obtain the stress components which include the variation of time. This solution shows excellent agreement with Lamkin's and Boley & Weiner's solution. The viscoelastic stresses are also obtained for the fuel rob tube subjecting nonaxisymmetric thermal load.

• Applied Microscopy
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• v.43 no.1
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• pp.1-8
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• 2013

We developed an improved method for tilt series alignment with fiducial markers in electron tomography. Based on previous works regarding alignment, we adapted the Levenberg-Marquardt method to solve the nonlinear least squares problem by incorporating a new formula for the alignment model. We also suggested a new method to estimate the initial value for inversion with higher accuracy. The proposed approach was applied to geopolymers. A better alignment of the tilt series was achieved than that by IMOD S/W. The initial value estimation provided both stability and a good rate of convergence since the new method uses all marker positions, including those partly covering the tilt images.

• Communications of the Korean Mathematical Society
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• v.18 no.3
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• pp.501-513
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• 2003

The Matsumoto metric is an ($\alpha,\;\bata$)-metric which is an exact formulation of the model of Finsler space. Lately, this metric was expressed as an infinite series form for $$\mid$\beat$\mid$\;<\;$\mid$\alpha$\mid$$ by the first author. He introduced an approximate Matsumoto metric as the ($\alpha,\;\bata$)-metric of finite series form and investigated it in [11]. The purpose of the present paper is devoted to finding the condition for a Finsler space with an approximate Matsumoto metric to be projectively flat.