• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.725-728
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• 2005

The scattering problem of the randomly rough surface is examined by the method of moments(MoM), small perturbation method (SPM), integral equation method (IEM) and the semi-empirical polarimetic model. To apply the numerical technique of the MoM to microwave scattering from a rough surface, at first, many independent randomly rough surfaces with a rms height and a correlation length are generated with Gaussian random deviate. Then, an efficient Monte Carlo simulation technique is applied to estimate the scattering coefficients of the surfaces.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.463-477
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• 2009

Parameter estimation methods such as maximum likelihood estimation method, probability weighted moments method, regression method have been popularly applied to various extreme value models in numerous literature. Among three methods above, the performance of regression method has not been rigorously investigated yet. In this paper the regression method is compared with the other methods via Monte Carlo simulation studies for estimation of parameters of the Generalized Extreme Value(GEV) distribution and the Generalized Pareto(GP) distribution. Our simulation results indicate that the regression method tends to outperform other methods under small samples by providing smaller biases and root mean square errors for estimation of location parameter of the GEV model. For the scale parameter estimation of the GP model under small samples, the regression method tends to report smaller biases than the other methods. The regression method tends to be superior to other methods for the shape parameter estimation of the GEV model and GP model when the shape parameter is -0.4 under small and moderately large samples.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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• pp.27-39
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• 2004

In the first part of this study, five homogeneous regions in view of topographical and geographically homogeneous aspects except Jeju and Ulreung islands in Korea were accomplished by K-means clustering method. A total of 57 rain gauges were used for the regional frequency analysis with minimum rainfall series for the consecutive durations. Generalized Extreme Value distribution was confirmed as an optimal one among applied distributions. Drought rainfalls following the return periods were estimated by at-site and regional frequency analysis using L-moments method. It was confirmed that the design drought rainfalls estimated by the regional frequency analysis were shown to be more appropriate than those by the at-site frequency analysis. In the second part of this study, LH-moment ratio diagram and the Kolmogorov-Smirnov test on the Gumbel (GUM), Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions were accomplished to get optimal probability distribution. Design drought rainfalls were estimated by both at-site and regional frequency analysis using LH-moments and GEV distribution, which was confirmed as an optimal one among applied distributions. Design rainfalls were estimated by at-site and regional frequency analysis using LH-moments, the observed and simulated data resulted from Monte Carlotechniques. Design drought rainfalls derived by regional frequency analysis using L1, L2, L3 and L4-moments (LH-moments) method have shown higher reliability than those of at-site frequency analysis in view of RRMSE (Relative Root-Mean-Square Error), RBIAS (Relative Bias) and RR (Relative Reduction) for the estimated design drought rainfalls. Relative efficiency were calculated for the judgment of relative merits and demerits for the design drought rainfalls derived by regional frequency analysis using L-moments and L1, L2, L3 and L4-moments applied in the first report and second report of this study, respectively. Consequently, design drought rainfalls derived by regional frequency analysis using L-moments were shown as more reliable than those using LH-moments. Finally, design drought rainfalls for the classified five homogeneous regions following the various consecutive durations were derived by regional frequency analysis using L-moments, which was confirmed as a more reliable method through this study. Maps for the design drought rainfalls for the classified five homogeneous regions following the various consecutive durations were accomplished by the method of inverse distance weight and Arc-View, which is one of GIS techniques.

• Wind and Structures
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• v.27 no.3
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• pp.149-161
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• 2018

A reliability analysis method is proposed in this paper based on the maximum entropy (MaxEnt) principle in which constraints are specified in terms of the fractional moments instead of integer moments. Then a multiplicative dimensional reduction method (M-DRM) is introduced to compute the fractional moments. The method is applicable for both explicit and implicit limit state functions of complex structures. After two examples illustrate the accuracy and efficiency of this method in comparison to the Monte Carlo simulation (MCS), the method is used to analyze the flutter reliability of suspension bridge. The results show that the empirical formula method in which the limit state function is explicitly represented as a function of variables is only a too conservative estimate for flutter reliability analysis but is not accurate adequately. So it is not suitable for reliability analysis of bridge flutter. The actual flutter reliability analysis should be conducted based on a finite element method in which limit state function is implicitly represented as a function of variables. The proposed M-DRM provide an alternate and efficient way to analyze a much more complicated flutter reliability of long span suspension bridge.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.237-240
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• 2002

Design rainfall using LH-moments following the consecutive duration were derived by the regional and at-site analysis using the observed and simulated data resulted from Monte Carlo techniques. RRMSE, RBIAS and RR in RRMSE for the design rainfall were computed and compared in the regional and at-site frequency analysis. Consequently, it was shown that the regional analysis can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than at-site analysis in the prediction of design rainfall. RE for an optimal order of L-moments was also computed by the methods of L, L1, L2, L3 and L4-moments for GEV distribution. It was found that the method of L-moments is more effective than the others for getting optimal design rainfall according to the regions and consecutive durations in the regional frequency analysis. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.817-827
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• 2000

In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4B
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• pp.345-352
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• 2002

In this paper, in order to evaluate the performance of IS-95 system reverse link in white gaussian noise and rayleigh fading environment, we suggest epochal proposal to improve computer run-time and its efficiency is verified in terms of the number of samples. MC(Monte Carlo) simulation is the most popular simulation technique lately, but MC simulation requires a number of samples at low bit error rate. Therefore, MC cannot avoid the limit of computer run-time. To alleviate these problems, we apply the suggested method called central moment technique to the reverse link of the IS-95 system and can obtain discrete probability mass functions from Nth order central moments of the less number of received signal samples than those required in MC. Continuous cumulative probability distribution function can be accurately estimated by using interpolation and the improvement effect for the number of samples is proven.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.1-7
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• 2007

Generally, an electric railroad feeding system has many problems due to the different characteristics in contrast with a load of general three-phase AC electric power system. One of them is harmonics problem caused by the switching device existing in the feeding system, and moreover, the time-varying dynamic loads of rail way is inherently another cause to increase this harmonics problem. In Korea power systems, the electric railroad feeding system is directly supplied from the substation of KEPCO. Therefore, if voltages fluctuation or unbalanced voltages are created by the voltage and current distortion or voltage drop during operation, it affects directly the source of supply. The trainloads of electric railway system have non-periodic but iterative harmonic characteristics as operating condition, because the electric characteristic of the electric railroad feeding system is changed by physical conditions of the each trainload. According to the traditional study, the estimation of harmonics has been performed by deterministic way using the steady state data at the specific time. This method is easy to analyze harmonics, but it has limits in some cases which needs an assessment of dynamic load and reliability. Therefore, this paper proposes the probabilistic estimation method, moments matching method(MW) in order to overcome the drawback of deterministic method. In this paper, distributions for each harmonics are convolved to obtain the moments and cumulants of TDD(Total Demand Distortion), and this can be generalized for any number of trains. For the case study, the electric railway system of LAT(Intra Airport Transit) in Incheon International Airport is modeled using PSCAD/EMTDC dynamic simulator. The raw data of harmonics for the moments matching method is acquired from simulation of the LAT model.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.319-323
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• 2001

This study was conducted to derive the regional design rainfall by the regional frequency analysis based on the regionalization of the precipitation. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the Generalized extreme value distribution among apt]lied distributions. regional and at-site parameters of the Generalized extreme value distribution were estimated by the method of L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error(RRMSE), relative bias(RBIAS) and relative reduction(RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.