• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.428-428
• /
• 2017

In this study, three optimization techniques efficiency is assessed for calibration of the GR4J model for streamflow simulation in Selmacheon, Boryeong Dam and Kyeongancheon watersheds located in South Korea. The Penman-Monteith equation is applied to estimate the potential evapotranspiration, model calibration, and validation is carried out using the readily available daily hydro-meteorological data. The Shuffled Complex Evolution-University of Arizona(SCE-UA), Uniform Adaptive Monte Carlo (UAMC), and Coupled Latin Hypercube and Rosenbrock (CLHR) optimization techniques has been used to evaluate the robustness, performance and optimized parameters of the three catchments. The result of the three algorithms performances and optimized parameters are within the recommended ranges in the tested watersheds. The SCE-UA and CLHR outputs are found to be similar both in efficiency and model parameters. However, the UAMC algorithms performances differently in the three tested watersheds.

• Geophysics and Geophysical Exploration
• /
• v.5 no.4
• /
• pp.262-271
• /
• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.

• Tunnel and Underground Space
• /
• v.29 no.2
• /
• pp.89-107
• /
• 2019

Rock joints have an extremely important role in analyzing the mechanical stability and hydraulic characteristics of rock mass structures. Most rock joint parameters are generally indicated as a distribution by statistical techniques. In this research, calculation technique of Joint Center Volume (JCV) is analyzed, which is required for estimating the size distribution having the largest uncertainty among the joint parameters, then a new technique is proposed which is applicable regardless of the shape of survey window. The existing theoretical JCV calculation technique can be applied only to the plane window, and the complete enumeration techniques show the limitations in joint trace type and analysis time. This research aims to overcome the limitations in survey window shape and joint trace type through calculating JCV by using Monte Carlo simulation. The applicability of proposed technique is validated through the estimation results at non-planar survey windows such as curved surface and tunnel surface.

• Journal of Korean Society of Steel Construction
• /
• v.13 no.5
• /
• pp.467-476
• /
• 2001

The construction of highway bridges is almost complete in many countries including the United States. The government and highway agencies change the focus from constructing to maintaining To maintain the bridges effectively there is an urgent need to assess actual bridge loading carrying capacity and to predict their remaining life. The system reliability techniques have to be used for this purpose. Based on lifetime distribution (function) techniques this study illustrates how typical highway bridges can be modeled to predict their remaining life. The parameters of lifetime distribution are generated by Monte. The results can be used for optimization of planning interventions on existing bridges.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.9 no.3 s.43
• /
• pp.1-8
• /
• 2005

This paper describes a new objective methodology of seismic building damage assessment which is called Advanced Component Method(ACM). ACM is a major attempt to replace the conventional loss estimation procedure, which is based on subjective measures and the opinions of experts, with one that objectively measures both earthquake intensity and the response ol buildings. First, response of typical buildings is obtained analytically by nonlinear seismic static analysis, push-over analyses. The spectral displacement Is used as a measure of earthquake intensity in order to use Capacity Spectrum Method and the damage functions for each building component, both structural and non-structural, are developed as a function of component deformation. Examples of components Include columns, beams, floors, partitions, glazing, etc. A repair/replacement cost model is developed that maps the physical damage to monetary damage for each component. Finally, building response, component damage functions, and cost model were combined probabilistically, using Wonte Carlo simulation techniques, to develop the final damage functions for each building type. Uncertainties in building response resulting from variability in material properties and load assumptions were incorporated in the Latin Hypercube sampling technique. The paper also presents and compares ACM and conventional building loss estimation based on historical damage data and reported loss data.

• 한국지구물리탐사학회:학술대회논문집
• /
• 2003.11a
• /
• pp.340-343
• /
• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

• Journal of the Korean Society for Precision Engineering
• /
• v.6 no.1
• /
• pp.33-44
• /
• 1989

A controller disign method is proposed for multivariable nonlinear stochastic systems with hard nonlinearities such as Coulomb friction, backlash and saturation. In order to take the nonlinearities into account statistical linearization techniques are used. And multi- variable pole placement techniques are applied to design controller for the statistically linearized multivariable systems. The basic concept of the controller design method is to solve two coupled equations, characteristic equation and Lyapunov equation, simultaneously and iteratively for statistically linearized multivariable stochastic systems. An aircraft with saturation serves as a design example. The design example illustrates the influence of nonlinear effects. The results of the analysis are compared to Monte Carlo simulation to test their accuracy.

• Proceedings of the Membrane Society of Korea Conference
• /
• 2004.05a
• /
• pp.33-38
• /
• 2004

Molecular modeling of gas permeation through zeolite membranes with/without intercrystalline region was carried out. Molecular dynamics (MD) and Monte Carlo (MC) simulations were performed to estimate the diffusion coefficient and adsorption parameters respectively, and our proposed combined method of molecular simulation techniques with a permeation theory (CMP) was used to estimate gas permeability. The calculated permeability of gases (Ar, He, Ne, $N_2$, $0_2$, $CH_4$) at 301 K for the single crystal membrane model was about one order of magnitude larger than the experiential values, although the dependence on the molecular weight of the permeating species agreed with experiments. On the other hand, the estimated permeability using the diffusivity and adsorption parameters of the intercrystalline region model was in good agreement with the experiments. The consistency between experiments and the estimated values means the importance of considering the intercrystalline region and the validity of CMP method to predict the performance of zeolite membranes.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.4
• /
• pp.961-971
• /
• 1996

The effect of inband TDMA narrow band jammers to DS-CDMA system performance and the suppression techniques are investigated using Monte Carlo simulations. TIA stantard North American Digital Cellular wea used as jammer. Levinson Dubin and conventional recursive least square algorithm were emphasized since these techniques can be implemented with a few DSPs for CDMA application. Two filter structures, i.e., complex suppression filter and real suppression filter in each inphase and quadrature channels, are investigated and their performances are compared. Complex suppression filter with Levinson Durbin algorithm of 20msec updata rate is the most promising with respect to implementation and performance poit of view. Implementation feasibility is discussed and the channel capacity lost by suppression is computed.

• Journal of Powder Materials
• /
• v.31 no.3
• /
• pp.213-219
• /
• 2024

Additive Manufacturing (AM) is a process that fabricates products by manufacturing materials according to a three-dimensional model. It has recently gained attention due to its environmental advantages, including reduced energy consumption and high material utilization rates. However, controlling defects such as melting issues and residual stress, which can occur during metal additive manufacturing, poses a challenge. The trial-and-error verification of these defects is both time-consuming and costly. Consequently, efforts have been made to develop phenomenological models that understand the influence of process variables on defects, and mechanical/ electrical/thermal properties of geometrically complex products. This paper introduces modeling techniques that can simulate the powder additive manufacturing process. The focus is on representative metal additive manufacturing processes such as Powder Bed Fusion (PBF), Direct Energy Deposition (DED), and Binder Jetting (BJ) method. To calculate thermal-stress history and the resulting deformations, modeling techniques based on Finite Element Method (FEM) are generally utilized. For simulating the movements and packing behavior of powders during powder classification, modeling techniques based on Discrete Element Method (DEM) are employed. Additionally, to simulate sintering and microstructural changes, techniques such as Monte Carlo (MC), Molecular Dynamics (MD), and Phase Field Modeling (PFM) are predominantly used.