• Journal of Korean Society on Water Environment
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• v.24 no.2
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• pp.239-246
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• 2008

In this study, an optimized deterministic water-quality model was constructed to estimate water quality of a river and lake in the upstream basin of a dam. A stochastic water-quality analysis using reliability analysis technique was applied to the model. The model was tested in the 13.9 km reach from Maeil stage station of Kyechun to Hoengsung Dam of Sum River. After finding hydraulic characteristics from nonuniform flow analysis, Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization technique for model calibration was applied to determine optimum reaction parameters, and model verification was performed based on these. The stochastic model, using Mean First­Order Second­-Moment (MFOSM) and Monte-Carlo methods, was applied to the same reach as the deterministic study. Variations of discharge and water quality in headwater were considered, as well as variations of hydraulic coefficients and reaction coefficients. The statistical results of output variables from MFOSM were similar to those from the Monte-Carlo method. Risk analysis using MFOSM and Monte-Carlo methods presented the probabilities of some locations in the Hoengsung Lake violating existing water-quality standards in terms of DO and BOD.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.9
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• pp.3304-3310
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• 2010

This study solves the decision making problems for TFT-LCD manufacturing supply chain with demand and price uncertainties by establishing robust production and distribution strategies. In order to control the decisions regarding production graded by quality, inventory level and distribution, this study develop scenario model based stochastic mixed integer linear programs (SMILPs) that consider demand and price uncertainties as well as realistic constraints such as capacities etc. The performance of the solution obtained from the SMILPs using robust algorithms will be evaluated through various scenarios.

• IE interfaces
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• v.13 no.3
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• pp.424-430
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• 2000

Supply chain management (SCM) is an area that has recently received a great deal of attention in the business community. While SCM is relatively new, the idea of coordinated planning is not. During the last decades, many researchers have investigated multi-stage inventory problems. However, only a few papers address the problem of cost-optimal coordination of multi-stage inventory control with respect to service measures. Even published approaches have a shortcoming in dealing with a delivery lead time consisted of a shipping time and a waiting time. Assumed that there is no waiting time, or that the delivery lead time is implicitly compounded of a shipping time and a waiting time, the problem is often simplified into a multi-stage buffer allocation and a single-stage stochastic buffer sizing problem at all installations. This paper presents a simulation-based heuristic algorithm and a comparison with others for the problem that cannot be decomposed into a multi-stage buffer allocation and a single-stage stochastic buffer sizing problem because the waiting time ties together all stages. The comparison shows that the simulation-based heuristic algorithm performs better than other approaches in saving average inventory cost for both Poisson and Normal demands.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1559-1565
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• 2007

The many target-detection methods that use forward-looking infrared (FUR) images can deal with large targets measuring $70{\times}40$ pixels, utilizing their shape features. However, detection small targets is difficult because they are more obscure and there are many target-like objects. Therefore, few studies have examined how to detect small targets consisting of fewer than $30{\times}10$ pixels. This paper presents a small target detection method using clutter rejection with stochastic hypothesis testing for FLIR imagery. The proposed algorithm consists of two stages; detection and clutter rejection. In the detection stage, the mean of the input FLIR image is first removed and then the image is segmented using Otsu's method. A closing operation is also applied during the detection stage in order to merge any single targets detected separately. Then, the residual of the clutters is eliminated using statistical hypothesis testing based on the t-test. Several FLIR images are used to prove the performance of the proposed algorithm. The experimental results show that the proposed algorithm accurately detects small targets (Jess than $30{\times}10$ pixels) with a low false alarm rate compared to the center-surround difference method using the receiver operating characteristics (ROC) curve.

• Structural Engineering and Mechanics
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• v.46 no.1
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• pp.19-37
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• 2013

The optimum design of base isolation system considering model parameter uncertainty is usually performed by using the unconditional response of structure obtained by the total probability theory, as the performance index. Though, the probabilistic approach is powerful, it cannot be applied when the maximum possible ranges of variations are known and can be only modelled as uncertain but bounded type. In such cases, the interval analysis method is a viable alternative. The present study focuses on the bounded optimization of base isolation system to mitigate the seismic vibration effect of structures characterized by bounded type system parameters. With this intention in view, the conditional stochastic response quantities are obtained in random vibration framework using the state space formulation. Subsequently, with the aid of matrix perturbation theory using first order Taylor series expansion of dynamic response function and its interval extension, the vibration control problem is transformed to appropriate deterministic optimization problems correspond to a lower bound and upper bound optimum solutions. A lead rubber bearing isolating a multi-storeyed building frame is considered for numerical study to elucidate the proposed bounded optimization procedure and the optimum performance of the isolation system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.3
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• pp.117-129
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• 1993

This study was conducted to get reasonable and abundant hydrological time series of monthly flows simulated by a best fitting stochastic simulation model for the establishment of rational design and the rationalization of management for agricultural hydraulic structures including reservoirs. Comparative analysis carried out for both statistical characteristics and synthetic monthly flows simulated by the multi-season first order Markov model based on Gamma distribution which is confirmed as good one in the first report of this study and by Harmonic synthetic model analyzed in this report for the six watersheds of Yeong San and Seom Jin river systems. 1.Arithmetic mean values of synthetic monthly flows simulated by Gamma distribution are much closer to the results of the observed data than those of Harmonic synthetic model in the applied watersheds. 2.In comparison with the coefficients of variation, index of fluctuation for monthly flows simulated by two kinds of synthetic models, those based on Gamma distribution are appeared closer to the observed data than those of Harmonic synthetic model both in Yeong San and Seom Jin river systems. 3.It was found that synthetic monthly flows based on Gamma distribution are considered to give better results than those of Harmonic synthetic model in the applied watersheds. 4.Continuation studies by comparison with other simulation techniques are to be desired for getting reasonable generation technique of synthetic monthly flows for the various river systems in Korea.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.424-429
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• 2020

An Energy Storage System (ESS) is recently drawing an increasing attention as an efficient tool to cope with variation in the energy system. In order to take the best utilization of ESS, the inherent variation of energy supply and demand should be properly addressed. This paper is concerned with computing the stochastic capacity of ESS in the face of such variations by way of Monte Carlo simulation. The issue of uncertainty in energy systems will be given further focus. More works are expected to be followed to address the issues in academia and industry.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1167-1176
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• 2009

Few studies have investigated the quantitative relationship between port ownership structure and port efficiency with mixed results. This paper therefore contributes to the empirical literature by investigating the impact of port privatization on port efficiency using sample data drawn from the world's major ports. Moreover, this study applies the Bayesian approach to estimate the impact of port ownership on port efficiency. We fit Bayesian stochastic frontier model which is introduced by Griffin and Steel (2007) by WinBUGS. World's 25 main ports data are used for analysis. Based on MCMC sampling, we estimate parameters of the model and efficiency index of each ports. Moreover, we add estimates from package Frontier 4.1c in order to compare them with Bayesian results.

• Journal of Environmental Policy
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• v.11 no.1
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• pp.75-97
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• 2012

The purpose of the paper is to estimate the functions of the energy input efficiency and the energy intensity efficiency, and measure their energy efficiencies for the reduction of greenhouse gases focusing on OECD countries. The efficiency of the traditional energy intensity was rarely connected with the energy efficiencies of the stochastic frontier function. It seems that the energy efficiency by the function of energy input efficiency sensitively responds to the order of GDP, capital stock, labor, and energy input quantity as explanatory variables. In the future, we need to reduce energy quantities by the optimal mix of inputs, and pursuit low-carbon economic growth through the production of the goods consuming small energy.

• Computers and Concrete
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• v.20 no.1
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• pp.99-110
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• 2017

Service life assessments which do not include the synergy between mechanical and environmental loading are neglecting a factor that can have a significant impact on structural safety and durability assessment. The degradation of concrete structure is a result of the combined effect of environmental and mechanical factors. In order to make service life design realistic it is necessary to consider both of these factors acting simultaneously. This paper deals with the advanced modelling of concrete carbonation and chloride ingress into concrete using stochastic 1D and 2D models. Widely accepted models incorporated into the new fib Model Code 2010 are extended to include factors that reflect the coupled effects of mechanical and environmental loads on the durability and reliability of reinforced concrete structures. An example of cooling tower degradation by carbonation and an example of a bended reinforced concrete beam kept for several years in salt fog are numerically studied to show the capability of the stochastic approach. The modelled degradation measures are compared with experimental results, leading to good agreement.