• Journal of Environmental Science International
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• v.29 no.1
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• pp.15-23
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• 2020

The determination of soil characteristics is important in the simulation of rainfall runoff using a distributed FLO-2D model in catchment analysis. Digital maps acquired using remote sensing techniques have been widely used in modern hydrology. However, the determination of a representative parameter with spatial scaling mismatch is difficult. In this investigation, the FLO-2D rainfall-runoff model is utilized in the Yongdam catchment to test sensitivity based on three different methods (mosaic, arithmetic, and predominant) that describe soil surface characteristics in real systems. The results show that the mosaic method is costly, but provides a reasonably realistic description and exhibits superior performance compared to other methods in terms of both the amount and time to peak flow.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.12
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• pp.597-603
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• 2001

In the mathematical model for the transmission dynamics of HIV infection described in previous papers, the population under consideration is assumed to be homogeneous community of homosexual males for which the parameter x represents the constant rate at which individual members of the population acquire new sexual partners. This is a gross oversimplification since it is well known that individuals vary widely in their levels of sexual activity and in this papers the heterogeneous model is modified to allow for this variation. The pattern on the epidemic character of HIV, the causative agent of AIDS, was analysed by heterogeneous-mixing model. The computer simulation was performed using real date.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.8
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• pp.1222-1226
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• 2002

Estimates of genetic correlation between direct and maternal effects for weaning weight of beef cattle are often negative in field data. The biological existence of this genetic antagonism has been the point at issue. Some researchers perceived such negative estimate to be an artifact from poor modeling. Recent studies on sources affecting the genetic correlation estimates are reviewed in this article. They focus on heterogeneity of the correlation by sex, selection bias caused from selective reporting, selection bias caused from splitting data by sex, sire by year interaction variance, and sire misidentification and inbreeding depression as factors contributing sire by year interaction variance. A biological justification of the genetic antagonism is also discussed. It is proposed to include the direct-maternal genetic covariance in the analytical models.

• Communications for Statistical Applications and Methods
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• v.21 no.6
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• pp.501-512
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• 2014

Stylized facts on asset return are fat-tail, asymmetry, volatility clustering and structure changes. This paper simultaneously captures these characteristics by introducing a multi-regime models: Finite mixture distribution and regime switching GARCH model. Analyzing the daily KOSPI return from $4^{th}$ January 2000 to $30^{th}$ June 2014, we find that a two-component mixture of t distribution is a good candidate to describe the shape of the KOSPI return from unconditional and conditional perspectives. Empirical results suggest that the equality assumption on the shape parameter of t distribution yields better discrimination of heterogeneity component in return data. We report the strong regime-dependent characteristics in volatility dynamics with high persistence and asymmetry by employing a regime switching GJR-GARCH model with t innovation model. Compared to two sub-samples, Pre-Crisis (January 2003 ~ December 2007) and Post-Crisis (January 2010 ~ June 2014), we find that the degree of persistence in the Pre-Crisis is higher than in the Post-Crisis along with a strong asymmetry in the low-volatility (high-volatility) regime during the Pre-Crisis (Post-Crisis).

• Geomechanics and Engineering
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• v.23 no.5
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• pp.419-429
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• 2020

To investigate the effect of spatial variability on hydraulic properties of unsaturated soils, a numerical model is set up which can simulate seepage process in an unsaturated heterogeneous soil. The unsaturated heterogeneous soil is composed of matrix sand embedded with a small proportion of clay for simulating the heterogeneity. Soil-water characteristic curve and unsaturated hydraulic conductivity curve of the unsaturated soil are expressed by Van Genuchten model. Hydraulic parameters of the matrix sand are considered as random fields. Different autocorrelation lengths (ACLs) of hydraulic parameter of the matrix sand and different proportions of clay are assumed to investigate the influence of spatial variability on the equivalent hydraulic properties of the heterogeneous soil. Four model sizes are used in the numerical experiments to investigate the influence of scale effects and to determine the sizes of representative volume element (RVE) in the numerical simulations. Through a number of Monte Carlo simulations of unsaturated seepage analysis, the means and the coefficients of variations (COVs) of the equivalent hydraulic parameters of the heterogeneous soil are calculated. Simulations show that the ACL and model size has little influence on the means of the equivalent hydraulic parameters, but they have a large influence on the COVs of the equivalent hydraulic parameters. The size of an RVE is mainly affected by the ACL and the proportion of heterogeneity. The influence of spatial variability on the hydraulic parameters of the heterogeneous unsaturated soil can be used as a guidance for geotechnical reliability analysis and design related to unsaturated soils.

• Journal of Korean Society of Environmental Engineers
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• v.31 no.4
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• pp.300-307
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• 2009

The transport of landfill leachate in the subsurface formations of unlined landfill sites is considered. The impacts of hydrogeological and chemical heterogeneities on the leachate transport are assessed by examining the results from a series of Monte-Carlo simulations. The landfill system simulated in this study is hypothetically represented with three levels of spatial variability for the hydrogeological and chemical parameter; (1) homogeneous hydraulic conductivity (K) and distribution coefficient ($K_d$), (2) K heterogeneity only, and (3) combined heterogeneities of K and $K_d$. To calculate the transport of leachate through negatively-correlated random hypothetical K-$K_d$ fields generated using geostatistical input parameters, a saturated flow model is linked with a contaminant transport model. Point statistic values such as mean, standard deviation, and coefficient of variation of the concentration were obtained from 100 Monte-Carlo trials. Results of point statistics show that the heterogeneities of K and $K_d$ in the landfill site prove to be an important parameter in controlling leachate concentrations. Consideration of combined K and $K_d$ heterogeneities results in enhancing the variability of contaminant transport. The variability in the leachate concentration for different realizations also increases as the distance between source and monitoring well increase.

• Journal of the Korean GEO-environmental Society
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• v.23 no.8
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• pp.29-36
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• 2022

The importance of subsurface information is becoming crucial in urban area due to increase of underground construction. The position of underground facilities should be identified precisely before excavation work. Geophyiscal exporation method such as ground penetration radar (GPR) can be useful to investigate the subsurface facilities. GPR transmits electromagnetic waves to the ground and analyzes the reflected signals to determine the location and depth of subsurface facilities. Unfortunately, the readability of GPR signal is not favorable. To overcome this deficiency and automate the GPR signal processing, deep learning technique has been introduced recently. The accuracy of deep learning model can be improved with abundant training data. The ground is inherently heteorogeneous and the spacially variable ground properties can affact on the GPR signal. However, the effect of ground heterogeneity on the GPR signal has yet to be fully investigated. In this study, ground heterogeneity is simulated based on the fractal theory and GPR simulation is carried out by using gprMax. It is found that as the fractal dimension increases exceed 2.0, the error of fitting parameter reduces significantly. And the range of water content should be less than 0.14 to secure the validity of analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5585-5593
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• 2014

This study tested the hypothesis that the significance of the heterogeneous dispersion parameter in safety performance function (SPF) used to estimate the expected crashes is affected by the endogenous heterogeneous prior distributions, and analyzed the impacts of the mis-specified dispersion parameter on the evaluation results for traffic safety countermeasures. In particular, this study simulated the Poisson means based on the heterogeneous dispersion parameters and estimated the SPFs using both the negative binomial (NB) model and the heterogeneous negative binomial (HNB) model for analyzing the impacts of the model mis-specification on the mean and dispersion functions in SPF. In addition, this study analyzed the characteristics of errors in the crash reduction factors (CRFs) obtained when the two models are used to estimate the posterior means and variances, which are essentially estimated through the estimated hyper-parameters in the heterogeneous prior distributions. The simulation study results showed that a mis-estimation on the heterogeneous dispersion parameters through the NB model does not affect the coefficient of the mean functions, but the variances of the prior distribution are seriously mis-estimated when the NB model is used to develop SPFs without considering the heterogeneity in dispersion. Consequently, when the NB model is used erroneously to estimate the prior distributions with heterogeneous dispersion parameters, the mis-estimated posterior mean can produce large errors in CRFs up to 120%.

• Journal of Veterinary Clinics
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• v.28 no.1
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• pp.101-107
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• 2011

This study aimed to determine dose-response (DR) curve of avian influenza (AI) virus to predict the probability of illness or adverse health effects that may result from exposure to a pathogenic microorganism in a quantitative microbial risk assessment. To determine the parametric DR relationship of several strains of AI virus, 7 feeding trial data sets challenging humans (5 sets) and chickens (2 sets) for strains of H3N2 (4 sets), H5N1 (2 sets) and H1N1 (1 set) from the published literatures. Except for one data set (study with intra-tracheal inoculation for data set no. 6), all were obtained from the studies with intranasal inoculation. The data were analyzed using three types of DR model as the basis of heterogeneity in infectivity of AI strains in humans and chickens: exponential, beta-binomial and beta-Poisson. We fitted to the data using maximum likelihood estimation to get the parameter estimates of each model. The alpha and beta values of the beta-Poisson DR model ranged 0.06-0.19 and 1.7-48.8, respectively for H3N2 strain. Corresponding values for H5N1 ranged 0.464-0.563 and 97.3-99.4, respectively. For H1N1 the parameter values were 0.103 and 12.7, respectively. Using the exponential model, r (infectivity parameter) ranged from $1.6{\times}10^{-8}$ to $1.2{\times}10^{-5}$ for H3N2 and from $7.5{\times}10^{-3}$ to $4.0{\times}10^{-2}$ for H5N1, while the value was $1.6{\times}10^{-8}$ for H1N1. The beta-Poisson DR model provided the best fit to five of 7 data sets tested, and the estimated parameter values in betabinomial model were very close to those of beta-Poisson. Our study indicated that beta-binomial or beta-Poisson model could be the choice for DR modeling of AI, even though DR relationship varied depending on the virus strains studied, as indicated in prior studies. Further DR modeling should be conducted to quantify the differences among AI virus strains.

• Tunnel and Underground Space
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• v.29 no.6
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• pp.542-557
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• 2019

The behavior of rock mass is influenced by its microscopic feature of internal structure generating from forming and metamorphic process. This study investigated a new methodology for characterization of rock based on the X-ray CT (computed tomography) images reflecting the spatial distribution characteristics of internal constituent materials. The X-ray image based analysis is capable of quantification of heterogeneity and anisotropy of rock fabric, size distribution and shape parameter analysis of rock mineral grains, fluid flow simulation based on pore geometry image and roughness evaluation of unexposed joint surface which are hardly acquired by conventional rock testing methods.