• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.04a
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• pp.70-80
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• 2000

In order to reduce seismic hazard the characteristics of strong earthquakes are required. In the region where strong earthquakes do not happen frequently the stochastic simulation of strong motion is an alternative way to predict strong motions. this simulation required input parameters such as the quality factor the corner frequency the moment magnitude the stress drop and so on which can be obtained from analyses of records of small and intermediate earthquakes. Using those parameters obtained in the previous work the strong ground motions are predicted employing the stochastic method, . The results are compared to the two observed earthquakes-the Ulsan Offshore Earthquake and the Kyungju Earthquake. Although some deviations are found the predictions are similar to the observed data. Finally we computed attenuation equations for PGA PGV and ground accelerations for some frequencies using the results of predictions. These results can be used for earthquake engineering and more reliable results will come out as earthquake observations continue.

• Communications of the Korean Mathematical Society
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• v.26 no.4
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• pp.709-720
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• 2011

We obtain special type of differential equations which their solution are random variable with known continuous density function. Stochastic differential equations (SDE) of continuous distributions are determined by the Fokker-Planck theorem. We approximate solution of differential equation with numerical methods such as: the Euler-Maruyama and ten stages explicit Runge-Kutta method, and analysis error prediction statistically. Numerical results, show the performance of the Rung-Kutta method with respect to the Euler-Maruyama. The exponential two parameters, exponential, normal, uniform, beta, gamma and Parreto distributions are considered in this paper.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.5
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• pp.537-545
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• 2005

A new dispersion model for dense gas is constructed in the Lagrangian framework. Prediction of concentration by the proposed model is compared with measure data obtained in the experiment conducted in Thorney Island in 1984. Two major effects of dense gas dispersion, gravity slumping and stratification effect, are successfully incorporated into LDM (Lagrangian dense gas model). Entrainment effect is naturally modelled by introducing stochastic dispersion model with the effect of turbulence suppression by stratification. Not only various releasing conditions but also complex terrain can be extended to, although proposed model is appropriate for flat terrain.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.685-688
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• 2004

본 연구에서는 저수지군 연계운영을 위한 표본 추계학적 선형 계획(SSLP, Sampling Stochastic Linear Programming) 모형을 제안한다. 일반적 추계학적 모형은 과거 자료로부터 확률변수의 확률분포를 추정하고 이를 몇 개 구간으로 나누어 이산 확률 값을 산정하여 기댓값이 최대가 되는 운영방안을 도출하지만 저수지 유입량 예측시 고려되어야할 지속성 효과(Persistemcy Effect)와 유역간 또는 시점별 공분산 효과(The joint spatial and temporal correlations)를 반영하는데 많은 한계가 있다. 이를 극복하기 위하여 과거자료 자체를 유입량 시나리오로 적용하여 시${\cdot}$공간적 상관관계를 유지하는 표본 추계학적(Sampling Stochastic)기법을 바탕으로 Simple Recourse Model로 구성한 추계학적 선형 계획 모형을 제시한다. 이 모형은 미국 기상청(NWS)에서 발생 가능한 유입량의 시나리오를 예측하는 방법인 앙상블 유량 예측(ESP, Ensemble Streamflow Prediction)을 통한 시나리오를 적용함으로써 좀더 신뢰성 있는 저수지군 연계운영 계획을 도출 할 수 있을 것으로 기대된다.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.225-230
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• 2000

This paper presents the fatigue behavior of composite materials with impact-induced damage. The impact damage parameter is proposed to evaluate the effect of impact damage on fatigue life. Subsequently, a new model is developed to predict the fatigue life of impacted composite materials. Also, a stochastic model is proposed to describe the variation of fatigue life due to the material nonhomogeneity. For these models, the fatigue tests were performed on the unimpacted and impacted composite materials. The effect of impact damage on fatigue life can be characterized by the impact damage parameter. Additionally, the results by the present fatigue life prediction model agree well with experimental results regardless of applied impact energy. Also, the variation of fatigue life can be described by the present stochastic model and is reduced with applied impact energy.

• Ocean and Polar Research
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• v.23 no.2
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• pp.189-194
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• 2001

A practical algorithm for prediction of the sea surface temperatures (SST)from the satellite remote sensing data is presented in this paper. The fluctuations of SST consist of deterministic normals and stochastic anomalies. Due to large thermal inertia of sea water, the SST anomalies can be modelled by autoregressive or Markov process, and its near future values can be predicted provided the recent values of SST are available. The actual SST is predicted by superposing the pre-known SST normals and the predicted SST anomalies. We applied this prediction algorithm to the NOAA AVHRR weekly SST data for 18 years (1981-1998) in the seas adjacent to Korea (115-$145^{\circ}E$, 20-$55^{\circ}N$). The algorithm is applicable not only for prediction of SST in near future but also for nowcast of SST in the cloud covered regions.

• Atmosphere
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• v.25 no.1
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• pp.1-18
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• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.12
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• pp.1138-1145
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• 2011

The internal broadband noise of a centrifugal fan in a household refrigerator is predicted using hybrid CAA techniques based on stochastic turbulent synthetic model. First, the unsteady flow field around the centrifugal fan is predicted using computational fluid dynamics(CFD) method. Then, the turbulent flow field is synthesized by applying the stochastic turbulent synthetic technique to the predicted flow field. The aerodynamic noise sources of the centrifugal fan are modeled on a basis of the synthesized turbulent field. Finally, the internal broadband noise of the centrifugal fan is predicted using the boundary element method(BEM) and the modeled sources. The predicted noise spectrum is compared with the experimental data. It is found that the predicted result closely follows the experimental data. The proposed method can be used as an effective tool for designing low-noise fans without expensive computational cost required generally for the LES and DNS simulations to resolve the turbulence flow field responsible for the broadband noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.10a
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• pp.627-632
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• 2011

The internal broadband noise of a centrifugal fan in a household refrigerator is predicted by using hybrid CAA technique based on stochastic turbulent synthetic model. First, the unsteady flow field around the centrifugal fan is predicted using Computational Fluid Dynamics (CFD) method. Then, the turbulent flow field is synthesized by applying the stochastic turbulent synthetic technique to the predicted flow field. The aerodynamic noise sources of the centrifugal fan are modeled on a basis of the synthesized turbulent field. Finally, the broadband noise of the centrifugal fan is predicted using Boundary Element Method (BEM) and the modeled sources. The predicted result is compared with the experimental data. It is found that the predicted result closely follows the experimental data. The proposed method can be used as an effective tool for designing low-noise fans without expensive computational cost required generally for the LES and DNS simulations to resolve the turbulence flow field responsible for the broadband noise.