• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.6
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• pp.233-240
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• 2021

The stochastic method is applied to simulate strong ground motions at seismic stations of seven metropolises in South Korea, creating an earthquake scenario based on the causative fault of the 2016 Gyeongju earthquake. Input parameters are established according to what has been revealed so far for the causative fault of the Gyeongju earthquake, while the ratio of differences in response spectra between observed and simulated strong ground motions is assumed to be an adjustment factor. The calculations confirm the applicability and reproducibility of strong ground motion simulations based on the relatively small bias in response spectra between observed and simulated strong ground motions. Based on this result, strong ground motions by a scenario earthquake on the causative fault of the Gyeongju earthquake with moment magnitude 6.5 are simulated, assuming that the ratios of its fault length to width are 2:1, 3:1, and 4:1. The results are similar to those of the empirical Green's function method. Although actual site response factors of seismic stations should be supplemented later, the simulated strong ground motions can be used as input data for developing ground motion prediction equations and input data for calculating the design response spectra of major facilities in South Korea.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.6
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• pp.59-70
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• 2003

We estimated the spectrum decay parameter $\chi$ and the stress parameter ($\Delta$$\sigma$) in southeastern Korea. Especially, we propose a procedure to compute site-independent $\chi$$_{q}$ and dependent $\chi$$_{s}$ values, separately, This procedure is to use the coda normalization method for the computation of site independent Q or corresponding $\chi$$_{q}$ value as the first step followed by the next step, the computation of $\chi$$_{s}$ values for each site using the given $\chi$$_{q}$ value evaluated at the first step, For the estimation of stress parameter, we used seismic data monitored from three earthquakes occurred near Gyeongju in 1999 with the method of Jo and Baag, In addition, we simulated strong ground motion using the $\chi$ value and the stress parameter, In this case, we calculated the $\chi$ value with conventional method. The $\chi$ value of 0.016+0.000157R and the stress parameter of 92-bar was applied to the stochastic simulation, At last, we derived seismic attenuation equation using results of the stochastic prediction, and compared these results with some others reported previously.ported previously.

• Water Engineering Research
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• v.5 no.2
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• pp.101-109
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• 2004

Aggregating information by combining forecasts from two or more forecasting methods is an alternative to using forecasts from just a single method to improve forecast accuracy. This paper describes the development and use of a monthly inflow forecast model based on an optimal linear combination (OLC) of forecasts derived from naive, persistence, and Ensemble Streamflow Prediction (ESP) forecasts. Using the cross-validation technique, the OLC model made 1-month ahead probabilistic forecasts for the Chungju multi-purpose dam inflows for 15 years. For most of the verification months, the skill associated with the OLC forecast was superior to those drawn from the individual forecast techniques. Therefore this study demonstrates that OLC can improve the accuracy of the ESP forecast, especially during the dry season. This study also examined the value of the OLC forecasts in reservoir operations. Stochastic Dynamic Programming (SDP) derived the optimal operating policy for the Chungju multi-purpose dam operation and the derived policy was simulated using the 15-year observed inflows. The simulation results showed the SDP model that updated its probability from the new OLC forecast provided more efficient operation decisions than the conventional SDP model.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.329-340
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• 1999

We consider a Bayesian forcasting method for the analysis of repeated surveys. It is assumed that the parameters of the superpopulation model at each time follow a stochastic model. We propose Bayesian prediction procedures for the finite population total under multiprocess dynamic generalized linear models. The multiprocess dynamic model offers a powerful framework for the modelling and analysis of time series which are subject to a abrupt changes in pattern. Some numerical studies are provided to illustrate the behavior of the proposed predictors.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.351-353
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• 2006

Preventive maintenance can avail the generating unit to reduce cost and gain more profit in a competitive supply-side power market. so, it is necessary to perform reliability analysis on the systems in which reliability is essential. In this paper, FMECA assessment adopted using real historical failure data in Korean power plants for apply RCM analytical method. The stochastic FMECA is an engineering analysis and a core activity performed by reliability engineers to review the effects of probable failure modes of generating unit and assemblies of the power system on system performance. Optimal RCM schedule which is considered the severity level of each generating unit and failure probability from failure prediction of generating unit can be planned using proposed FMECA with IOE index.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.686-691
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• 1994

For evaluating the response fluctuation of the actual environmental acoustic system excited by arbitrary random inputs, it is important to predict a whole probability distribution form closely connected with evaluation indexes Lx, Leq and so on. In this paper, a new type evaluation method is proposed by introducing three functional models matched to the prediction of the response probability distribution from a problem-oriented viewpoint. Because of the positive variable of the sound intensity, the response probability density function can be reasonably expressed theoretically by a statistical Laguerre expansion series form. The relationship between input and output is described by the regression relationship between the distribution parameters(containing expansion coefficients of this expression) and the stochastic input. These regression functions are expressed in terms of the orthogonal series expansion and their parameters are determined based on the least-squares error criterion and the measure of statistical independency.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.301-312
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• 2015

The purpose of this paper is to establish the epidemic model to explain the process of disease spread. The process of disease spread can be classified into two types: deterministic process and stochastic process. Most studies supposed that the process follows the deterministic process and established the model using the ordinary differential equation. In this article, we try to build the disease spread prediction model based on the SIR (Suspectible - Infectious - Recovered) model. we first estimated the model parameters using least squared method and applied to a deterministic model using ordinary differential equation. we also applied to a stochastic model based on Gillespie algorithm. The methods introduced in this paper are applied to the data on the number of cases of malaria every week from January 2001 to March 2003, released by Korea Centers for Disease Control and Prevention. As a result, we conclude that our model explains well the process of disease spread.

• Computational Structural Engineering
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• v.3 no.4
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• pp.143-148
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the auto regressive and moving average model with auxiliary stochastic input(ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story budding model subject to ground exitations.

• Journal of Ocean Engineering and Technology
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• v.5 no.2
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• pp.51-57
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• 1991

불규칙 해상에서 선박의 큰 횡요각의 예측이 중요한 과제로 대두 되고 있다. 본 논문에서는 통계적 해석에 의한 이의 예측 방법을 제시한다. 즉 주어진 비 선형 횡요운동 방정식으로 부터 배의 횡요각과 각속도의 결합 확률 밀도 함수를 구하는 방법을 도입하고 각종 계수들의 값의 변화에 따른 예측 결과를 다른 논문에서 제시한 시뮬레이션 결과와 비교하였다.