• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.45-50
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• 1992

해양 가이드 타워를 대상으로 하여, 지진하중에 대한 심해응구조물의 비정상거동해법에 대하여 연구하였다. 지반운동의 비정상특성은 정상과정성분에 시간포락함수가 곱해진 형태로 모형화하였으며, 구조물의 비정상거동은 시간종속분산함수로 구하였다. 지반가속도에 대한 자기상관함수를 복소지수함수의 형태로 이상화함으로써, 구조물 거동의 시간종속함수가 해석적인 방법으로 쉽게 구할 수 있는 기법을 개발하였다. 지진의 발생시간 동안 예상되는 최대거동을 구하였으며. 이를 구조물 거동을 정상확률과정으로 가정하여 산정한 결과와 비교 분석하였다.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.773-794
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• 2021

Prediction of vehicle traffic volume is very important in planning municipal administration. It may help promote social and economic interests and also prevent traffic congestion costs. Traffic volume as a time-varying trajectory is considered as functional data. In this paper we study three functional regression models that can be used to predict an unseen trajectory of traffic volume based on already observed trajectories. We apply the methods to highway tollgate traffic volume data collected at some tollgates in Seoul, Chuncheon and Gangneung. We compare the prediction errors of the three models to find the best one for each of the three tollgate traffic volumes.

• Computational Structural Engineering
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• v.5 no.3
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• pp.127-137
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• 1992

A method for nonstationary response analysis of an offshore guyed tower subjected to earthquake loading is presented. The nonstationarity of the earthquake excitation is modeled by imposing a time varying envelope function onto a stationary random model. By taking the envelope function and the auto-correlation function of ground acceleration in terms of complex exponential functions of time, an analytical procedure is developed for computing time varying variances of the tower response. Example analysis indicates that the maximum responses estimated by considering nonstationary effect properly are significantly less than those obtained by the conventional frequency domain analysis method based upon the stationary assumption.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.201-202
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• 2006

To analyze the dynamic characteristics of the bundle drawing process, we employed a Random Phase Spectrum method to generate stochastic test signals that had a given autocorrelation function. And the spectra of the dynamics of the process outputs were obtained, based on the dynamic model of the bundle drawing process. Results showed that the RPS method was very effective to generate stochastic signals that had an exponential function form. The drawing process had the traits that there existed a special frequency range, incurring the process resonance.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.47-55
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• 1994

The purpose of the present study is to investigate the effect of specimen thickness on statistical properties of crack growth resistance. In this study, the resistance S$\delta$$_h$(x) to fatigue crack growth was treated as a spatial stochastic process. which varies randomly on the crack surface. The theoretical autocorrelation functions of the resistance to fatigue crack growth considering specimen thickness are discussed for several correlation lengths. The main results obtained are : (1) The theoretical autocorrelation functions of S$\delta$$_h$(x) are almost independent of specimen of specimen thickness except for the origin. (2) The variance increases with decreasing specimen thickness.

• Geophysics and Geophysical Exploration
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• v.15 no.1
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• pp.1-7
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• 2012

To evaluate methods of determining near-surface shear-wave velocities (${\nu}_s$), we derived dispersion curves of Rayleigh waves generated by both passive and active sources in Chuncheon, Korea. Microtremors were recorded for 5 minutes in each of four triangular arrays with radii of 5 ~ 40 m. Those data were analyzed using the Spatial Autocorrelation method. Rayleigh waves were also generated by a hammer source and recorded in the same area for 2 s using 24 4.5-Hz geophones. Multichannel Analysis of Surface Waves was applied to those data. Velocity spectra were derived with relatively high signal-to-noise ratios in the frequency ranges of 7 ~ 19 and 11 ~ 50 Hz for the microtremors and synthetically generated Rayleigh waves, respectively. The resultant dispersion curves were combined as one and then input to inversion to derive shear wave velocities that were compared with a lithology log from a nearby well. Shearwave velocities in the top soil and soft-rock layers are almost constant with values of 221 and 846 m/s, respectively; while the inverse-modeled ${\nu}_s$ increases linearly in the gravelly sand, cobbles, and weathered-rock layers. If rock type is classified based on shear-wave velocity, the inversion-derived boundary between weathered-rock and soft rock may be about 5 m deeper than in the well log.

• Journal of Power System Engineering
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• v.4 no.2
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• pp.40-45
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• 2000

Some engineering materials are often known to have considerable spatial variation in their resisting strength and other properties. The objective of this study is to investigate the averaging effect and the applicability of extremal statistic for the statistical size effect. In the present study, it is assumed that the material property is a stationary random process in space. The theoretical autocorrelation function of the material strength are discussed for several correlation lengths. And, in order to investigate the statistical size effect, the material properties was simulated by using the non-Gaussian random process method. The material properties were plotted on the Weibull probability papers. The main results are summarized as follows: The autocorrelation function of the material properties are almost independent of the averaging length. The variance decreases with increasing the averaging length. As correlation length is smaller, the slope is larger. And also, it was found that Weibull statistics based on the weakest-link model could not explain the spatial variation of material properties with respect to the size effect satisfactory.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.665-674
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• 2020

Low-frequency pressure fluctuations in an external-loop airlift reactor were investigated. Low-frequency pressure fluctuations could be measured by shooting videos about liquid levels in the four piezometric tubes which were installed at the lower and upper parts of the riser and downcomer using a cellular phone. The periodic characteristics of pressure fluctuations were proved by the calculation of their auto-correlation function and cross-correlation function. Even if the riser superficial gas velocity was constant, the riser and downcomer gas holdups as well as wall pressures were periodically changed due to the inertia of circulating liquid. In general, the intensity of pressure fluctuations increased with an increase in the gas velocity. When the unaerated liquid height was 0.04 m, the maximum period of pressure fluctuations was found at the specific gas velocity (0.14 ms^-1). It was because the maximum inertia of circulating liquid resulted from a reduction in the increasing rate of the liquid circulation velocity and a decrease in the volume of the effectively circulating liquid with an increase in the gas velocity.

• Water for future
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• v.17 no.4
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• pp.281-291
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• 1984

In our Koreans river basins there are many of monthly rainfall data, but unfortrnately streamflow data needed are rare. Analysing monthly rainfall data of Somjin river basin, the stochastic theory model for calculation of monthly streamflow series of that region is determined. The model is composed of Box & Jenkins stansfer function plus ARIMA residual models. This linear stochastic differenced time series equation models can adapt themselves to the structure and variety of rainfall, streamflow data on the assumption of the stationary covarience. The fiexibility of Box-Jenkins method consists mainly in the iterative technique of building an AIRMA model from observations and by the use of autocorrelation functions. The best models for Somjin river basin belong to the general calss: $Y_t=($\omega$o-$\omega$_1B) C_iX_t+$\varepsilon$t$ $Y_t$ monthly streamflow, $X_t$ : monthly rainfall, $C_i$ :monthly run-off, $$\omega$o-$\omega$_1$ : transfer parameter, $$\varepsilon$_t$ : residual The streamflow series resulted from the proposed model is satisfactory comparing with the exsting streamflow data of Somjin gauging station site.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.161-173
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• 2021

The shipping industry plummeted and was depressed due to the global economic crisis caused by the bankruptcy of Lehman Brothers in the US in 2008. In 2020, the shipping market also suffered from a collapse in the unstable global economic situation due to the COVID-19 pandemic, but unexpectedly, it changed to an upward trend from the end of 2020, and in 2021, it exceeded the market of the boom period of 2008. According to the Clarksons report published in May 2021, the decrease in cargo volume due to the COVID-19 pandemic in 2020 has returned to the pre-corona level by the end of 2020, and the tramper bulk carrier capacity of 103~104% of the Panamax has been in the ports due to congestion. Earnings across the bulker segments have risen to ten-year highs in recent months. In this study, as factors affecting BDI, the capacity and congestion ratio of Cape and Panamax ships on the supply side, iron ore and coal seaborne tonnge on the demand side and Granger causality test, IRF(Impulse Response Function) and FEVD(Forecast Error Variance Decomposition) were performed using VAR model to analyze the impact on BDI by congestion caused by strengthen quarantine at the port due to the COVID-19 pandemic and the loading and discharging operation delay due to the infection of the stevedore, etc and to predict the shipping market after the pandemic. As a result of the Granger causality test of variables and BDI using time series data from January 2016 to July 2021, causality was found in the Fleet and Congestion variables, and as a result of the Impulse Response Function, Congestion variable was found to have significant at both upper and lower limit of the confidence interval. As a result of the Forecast Error Variance Decomposition, Congestion variable showed an explanatory power upto 25% for the change in BDI. If the congestion in ports decreases after With Corona, it is expected that there is down-risk in the shipping market. The COVID-19 pandemic occurred not from economic factors but from an ecological factor by the pandemic is different from the past economic crisis. It is necessary to analyze from a different point of view than the past economic crisis. This study has meaningful to analyze the causality and explanatory power of Congestion factor by pandemic.