• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.35-37
• /
• 2007

Super resolution using stochastic approach which based on the Bayesian approach is to easy modeling for a priori knowledge. Generally, the Bayesian estimation is used when the posterior probability density function of the original image can be established. In this paper, we introduced the improved MAP algorithm based on Bayesian which is stochastic approach in spatial domain. And we presented the observation model between the HR images and LR images applied with MAP reconstruction method which is one of the major in the SR grid construction. Its test results, which are operation speed, chip size and output high resolution image Quality. are significantly improved.

• Water for future
• /
• v.25 no.4
• /
• pp.97-107
• /
• 1992

The purpose of this study is to estimate the flood hydrograph and runoff at ungaged small watershed by using interactive program with geomorphologic and climatic data obtained from the topographic maps following the law of stream classification and ordering by Horton and Strahler. The present model is modified from Allam's interactive program which derives the geomorphologic instantaneous unit hydrograph(GIUH). This program uses the results of Laplace transformation and convolution integral of probability density function in travel time at each station, This program is used to estimate the time to peak, the flood discharge and the direct runoff at San seong station in Bocheong Stream.

• Proceedings of the Earthquake Engineering Society of Korea Conference
• /
• 1999.04a
• /
• pp.178-185
• /
• 1999

Minimum life-cycle cost helps to evaluate cost effectiveness of base-isolated bridges under specific condition. Life-cycle cost mainly consists of the initial construction cost and the expected damage cost. Damage cost estimation needs proper model of input ground motion failure probability evaluation method and limit states definition. We model the input ground motion as spectral density function compatible with the response spectra defined at each seismicity and site condition. Spectrum analysis and crossing theory is suitable for reseating calculation of failure probabilities in the process of cost minimization. Limit states of base-isolated bridges re defined for superstructure isolator and pier respectively The method is applied to both base-isolated bridges and conventional bridges under the same conditions to investigate cost effectiveness of base isolation in low and moderate seismic region. the results show that base-isolation of bridges are more effective in low and moderate seismic region and that the site effects on the economical efficiency may not be negligible in such a region.

• International Journal of Reliability and Applications
• /
• v.13 no.2
• /
• pp.91-104
• /
• 2012

Sarhan and Kundu (2009) introduced a new distribution named as the generalized linear failure rate distribution. This distribution generalizes several well known distributions. The probability density function of the generalized linear failure rate distribution can be right skewed or unimodal and its hazard function can be increasing, decreasing or bathtub shaped. This distribution can be used quite effectively to analyze lifetime data in place of linear failure rate, generalized exponential and generalized Rayleigh distributions. In this paper, we apply the simulated annealing algorithm to obtain the maximum likelihood point estimates of the parameters of the generalized linear failure rate distribution. Simulated annealing algorithm can not only find the global optimum; it is also less likely to fail because it is a very robust algorithm. The estimators obtained using simulated annealing algorithm have been compared with the corresponding traditional maximum likelihood estimators for their risks.

• Structural Engineering and Mechanics
• /
• v.5 no.6
• /
• pp.839-851
• /
• 1997

Closed-form solutions are analytically derived for stochastic properties of earthquake ground motion fields, which are conditioned by an observed time series at certain observation sites and are characterized by spectra with uncertainties. The theoretical framework presented here can estimate not only the expectations of such simulated earthquake ground motions, but also the prediction errors which offer important information for the field of engineering. Before these derivations are made, the theory of conditional random fields is summarized for convenience in this study. Furthermore, a method for stochastic interpolation of power spectra is explained.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.4
• /
• pp.875-888
• /
• 2017

The Rayleigh distribution has been commonly used in life time testing studies of the probability of surviving until mission time. We focus on a reliability function of the Rayleigh distribution and deal with prior distribution on R(t). This paper is an effort to obtain Bayes estimators of rayleigh distribution with three different prior distribution on the reliability function; a noninformative prior, uniform prior and inverse gamma prior. We have found the Bayes estimator and predictive density function of a future observation y with each prior distribution. We compare the performance of the Bayes estimators under different sample size and in simulation study. We also derive the most plausible region, prediction intervals for a future observation.

• Wind and Structures
• /
• v.31 no.6
• /
• pp.549-560
• /
• 2020

Methods for stochastic simulation of non-Gaussian wind pressure have increasingly addressed the efficiency and accuracy contents to offer an accurate description of the extreme value estimation of the long-span and high-rise structures. This paper presents a linear prediction and z-transform (LPZ) based Cumulative distribution function (CDF) mapping algorithm for the simulation of multivariate non-Gaussian fluctuating wind pressure. The new algorithm generates realizations of non-Gaussian with prescribed marginal probability distribution function (PDF) and prescribed spectral density function (PSD). The inverse linear prediction and z-transform function (ILPZ) is deduced. LPZ is improved and applied to non-Gaussian wind pressure simulation for the first time. The new algorithm is demonstrated to be efficient, flexible, and more accurate in comparison with the FFT-based method and Hermite polynomial model method in two examples for transverse softening and longitudinal hardening non-Gaussian wind pressures.

• Communications for Statistical Applications and Methods
• /
• v.30 no.3
• /
• pp.259-272
• /
• 2023

In this paper, we suggest a new method for the prediction of sharp changes in particulate matter (PM₁₀) using quantile mapping. To predict the current PM₁₀ density in Seoul, we consider PM₁₀ and precipitation in Baengnyeong and Ganghwa monitoring stations observed a few hours before. For the PM₁₀ distribution estimation, we use the extreme value mixture model, which is a combination of conventional probability distributions and the generalized Pareto distribution. Furthermore, we also consider a quantile generalized additive model (QGAM) for the relationship modeling between precipitation and PM₁₀. To prove the validity of our proposed model, we conducted a simulation study and showed that the proposed method gives lower mean absolute differences. Real data analysis shows that the proposed method could give a more accurate prediction when there are sharp changes in PM₁₀ in Seoul.

• Journal of Industrial Technology
• /
• v.43 no.1
• /
• pp.57-62
• /
• 2023

In this review, we introduce the non-parametric Bayesian filtering algorithm known as the point-mass filter (PMF) and discuss recent studies related to it. PMF realizes Bayesian filtering by placing a deterministic grid on the state space and calculating the probability density at each grid point. PMF is known for its robustness and high accuracy compared to other nonparametric Bayesian filtering algorithms due to its uniform sampling. However, a drawback of PMF is its inherently high computational complexity in the prediction phase. In this review, we aim to understand the principles of the PMF algorithm and the reasons for the high computational complexity, and summarize recent research efforts to overcome this challenge. We hope that this review contributes to encouraging the consideration of PMF applications for various systems.

• The Korean Journal of Applied Statistics
• /
• v.27 no.6
• /
• pp.867-889
• /
• 2014

Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.