• Clean Technology
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• v.17 no.2
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• pp.150-155
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• 2011

The removal process of heavy metal ion in aqueous solution by the functionalized silica bead was simulated using the finite difference method. Equilibrium model and non-equilibrium model were proposed and the effects of dimensionless groups and various parameters were investigated. Freundlich isotherm was used in equilibrium model and 1st order adsorption rate expression was assumed in non-equilibrium model. The comparison results by the predictions of equilibrium and non-equilibrium models showed good agreement. The predictions of equilibrium model were compared with experimental results reported in literature and showed the marginal agreement.

• Journal of Environmental Impact Assessment
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• v.5 no.1
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• pp.31-45
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• 1996

This paper presents the results from a hydrodynamic model study to predict the impacts of coastal reclamation in Pusan harbor system using RMA2 which is a tidal flow model of the US Army Corps of Engineers' TABS-2 system. A finite element mesh was constructed and refined to cover the complicated geometry of Pusan harbor system and the proposed reclamation area. The model was calibrated to tidal elevations and currents measured during spring fall syzygys. Under the three different tidal conditions including summer winter syzygys, spring fall quarters, and summer winter quarters, the model predictions were compared with the field measurements both in tidal elevation and current. In all cases, there were excellent agreements between the model predictions and the field measurements. The validated model was then used to predict the changes in tidal current and elevation that might occur due to the coastal reclamation. It was predicted that there would be no change in tidal elevation of this system after the reclamation. In tidal current, however, discernible changes were predicted near the proposed reclamation area both in magnitude and direction.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.15-38
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• 2010

In order to predict the shear strengths of reinforced concrete beams, many deterministic models have been developed based on rules of mechanics and on experimental test results. While the constant and variable angle truss models are known to provide reliable bases and to give reasonable predictions for the shear strengths of members with shear reinforcement, in the case of members without shear reinforcement, even advanced models with complicated procedures may show lack of accuracy or lead to fairly different predictions from other similar models. For this reason, many research efforts have been made for more accurate predictions, which resulted in important recent publications. This paper develops probabilistic shear strength models for reinforced concrete beams without shear reinforcement based on deterministic shear strength models, understanding of shear transfer mechanisms and influential parameters, and experimental test results reported in the literature. Using a Bayesian parameter estimation method, the biases of base deterministic models are identified as algebraic functions of input parameters and the errors of the developed models remaining after the bias-correction are quantified in a stochastic manner. The proposed probabilistic models predict the shear strengths with improved accuracy and help incorporate the model uncertainties into vulnerability estimations and risk-quantified designs.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.422-426
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• 2003

Model soil tank tests were conducted in sandy soil to investigate the effect of surcharge strip loads on vertical stress distribution in soil. A total number of 6 tests were performed using one loading plate and two relative density(55%, 65%). The soil was considered as an elastic material, while no friction was allowed between the wall and the soil. Measured stress values were compared to predictions defined by Frohlich, Boussinesq and Westergaard. The comparison of measured values and predictions used the ratio between the soil pressure and load value. Results of this study demonstrated that experimental values were generally larger than predictions. The Frohlich analysis provided the best prediction, while the Boussinesq analysis and Westergaard theory not presented a satisfactional result.

• Structural Engineering and Mechanics
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• v.23 no.3
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• pp.249-262
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• 2006

In this paper, a simplified mathematical procedure is presented to incorporate nonlinearity in soil material to predict the deceleration time history, penetration depth and other relevant parameters for normal impact of missiles into soil targets. Numerical method is employed for these predictions. The results of the study are compared with experimental observations and predictions available in the literature. A good agreement is found with experimental observations and an improvement is observed with existing predictions. A comparison is also made with linear soil model. Some parametric studies are also carried out to obtain the results of practical interest.

• Journal of Welding and Joining
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• v.15 no.6
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• pp.105-115
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• 1997

A numerical study is performed to predict the thermal response of a detailed card assembly during infrared reflow soldering. The card assembly is exposed to discontinuous infrared panel heater temperature distributions and high radiative/convective heating and cooling rates at the inlet and exit of the oven. The convective, radiative and conduction heat transfer within the reflow oven as well as within the card assembly are simulated and the predictions illustrate the detailed thermal responses. The predictions show that mixed convection plays an important role with relatively high frequency effects attributed to buoyancy forces, however the thermal response of the card assembly is dominated by radiation. The predictions of the detailed card assembly thermal response can be used to select the oven operating conditions to ensure proper solder melting and minimization of thermally induced card assembly tresses and warpage.

• Transactions of Materials Processing
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• v.21 no.1
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• pp.19-23
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• 2012

In this paper, a Mannesmann roll piercing process equipped with Diecher's guiding discs is investigated using a rigid-thermoviscoplastic finite elements method with intelligent remeshing capability and tetrahedral elements. The analysis model is presented and the approach is applied to a Mannesmann roll piercing process found in the literature. Details about the remeshing criterion as well as mesh density control are given. The present predictions are compared with the predictions found in the literature, showing that the two predictions are in close agreement in terms of the deformed shape. However, it is emphasized that the present approach has the distinct strength in predicting details of final shape.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.27-50
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• 2009

This paper describes an adaptive management approach for predicting, monitoring, and controlling ground movements associated with excavations in urban areas. Successful use of monitoring data to update performance predictions of supported excavations depends equally on reasonable numerical simulations of performance, the type of monitoring data used as observations, and the optimization techniques used to minimize the difference between predictions and observed performance. This paper summarizes each of these factors and emphasizes their inter-dependence. Numerical considerations are described, including the initial stress and boundary conditions, the importance of reasonable representation of the construction process, and factors affecting the selection of the constitutive model. Monitoring data that can be used in conjunction with current numerical capabilities are discussed, including laser scanning and webcams for developing an accurate record of construction activities, and automated and remote instrumentations to measure movements. Self-updating numerical models that have been successfully used to compute anticipated ground movements, update predictions of field observations and to learn from field observations are summarized. Applications of these techniques from case studies are presented to illustrate the capabilities of this approach.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.271-280
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• 2018

Recently, big data has been growing rapidly due to the development of IT technology. Especially in the medical field, big data is utilized to provide services such as patient-customized medical care, disease management and disease prediction. In Korea, 'National Health Alarm Service' is provided by National Health Insurance Corporation. However, the prediction model has a problem of short-term prediction within 3 days and unreliability of social data used in prediction model. In order to solve these problems, this paper proposes a disease prediction model using medicine prescription data generated from actual patients. This model predicts the total number of patients and the risk of disease in each region and uses the ARIMA model for long-term predictions.