• Journal of Korean Society for Atmospheric Environment
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• v.17 no.E4
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• pp.163-168
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• 2001

Estimating the emission rate of VOCs from a gasoline industry at national level can be a challenging take even though the estimation is mean-based. However, using the procedures in the US EPA AP-42 guidelines, it is possible to approximate the mean industry emission rate once enough data are available. However, this estimate can be misled in the sense that there exist many stochastic factors in the EPA\\`s estimation procedures and also throughout the marketing channels of gasoline industry. Addressing the stochasticity problem in EPA\\`s procedure is hard to tackle because the detailed data needed to execute the estimation are not usually available even from refiners. Instead, this research tries to stay focused on the second type of stochasticity issue, raised from the mean0based metrological and marketing practice data collected from the 4 major refiners. To do so emission raters from each marketing channels (8 marketing points by 3 transportation types and by storage facilities of 4 refiners) are estimated monthly, following AP-42 procedures and using Tank 4.0. Once these estimates are acquired, the distribution of VOC emission rate for each marketing channel of all 4 refiners is estimated through simulation method using @Risk. The mean-based emission rates are weighted by company quantities to estimate the emission rate from the whole gasoline industry. Simple economic implication is provided, based on the result. This study found that, on the mean-bases, about 0.66% of gasoline marketed are evaporated into air. Considering the stochasticity in the estimation, about 90% of simulation results fell into the range of 0.65 to 0.68%. For 90% chance, the estimated economic loss is $54.65 million to $57.17 million, not counting the cost caused by air quality degradation and associated health impact.

• Wind and Structures
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• v.14 no.5
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• pp.413-434
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• 2011

Without output covariance estimation, one reference-based Stochastic Subspace Technique (SST) for extracting modal parameters and flutter derivatives of bridge deck is developed and programmed. Compared with the covariance-driven SST and the oscillation signals incurred by oncoming or signature turbulence that adopted by previous investigators, the newly-presented identification scheme is less time-consuming in computation and a more desired accuracy should be contributed to high-quality free oscillated signals excited by specific initial displacement. The reliability and identification precision of this technique are confirmed by a numerical example. For the 3-DOF sectional models of Sutong Bridge deck (streamlined) and Suramadu Bridge deck (bluff) in wind tunnel tests, with different wind velocities, the lateral bending, vertical bending, torsional frequencies and damping ratios as well as 18 flutter derivatives are extracted by using SST. The flutter derivatives of two kinds of typical decks are compared with the pseudo-steady theoretical values, and the performance of $H_1{^*}$, $H_3{^*}$, $A_1{^*}$, $A_3{^*}$ is very stable and well-matched with each other, respectively. The lateral direct flutter derivatives $P_5{^*}$, $P_6{^*}$ are comparatively more accurate than other relevant lateral components. Experimental procedure seems to be more critical than identification technique for refining the estimation precision.

• Proceedings of the Technology Innovation Conference
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• 2004.02a
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• pp.222-245
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• 2004

This paper has an intention of proposing useful parametric tables of each industry group within Korea. These parametric tables can be insightful criteria for those who are dealing with the exact valuation of company, technology or industry through Real Option Analysis (ROA) since the identification of the movement of underlying asset is the very first step to be done. To give the exact estimations of parameters and the most preferred model in each industry group, we cover topics on ROA, stochastic process, and parametric estimation method like Generalized Method of Moments (GMM) and Maximum Likelihood Estimation (MLE). Additionally, specific industry groups, such as, Internet service group and mobile telecommunication service group defined independently in this paper are also examined in terms of its property of movement with the suggesting of the most fitting stochastic model.

• Journal of Korea Water Resources Association
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• v.35 no.2
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• pp.187-194
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• 2002

To improve the accuracy of the areal rainfall estimates over a river basin, the optimal design method of rainfall network was studied using the stochastic characteristics of measured rainfall data. The objective function was constructed with the estimation error of areal rainfall and observation cost of point rainfall and the observation sites with minimum objective function value were selected as the optimal network. As a stochastic variance estimator, kriging model was selected to minimize the error terms. The annual operation cost including the installation cost was considered as the cost terms and an accuracy equivalent parameter was used to combine the error and cost terms. The optimal design method of rainfall network was studied in the Yongdam dam basin whose raingauge numbers need to be enlarged for the optimal rainfall networks of the basin.

• International Journal of Highway Engineering
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• v.16 no.1
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• pp.57-62
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• 2014

PURPOSES : Capacity is a main factor of determining the number of lane in highway design or the level of service in road on operation. Previous studies showed that breakdown may occur before capacity is reached, and then it was concluded that capacity is a stochastic value rather than a deterministic one. In general, estimating capacity is based on average over maximum traffic volume observed for capacity state. This method includes the empirical distribution method(EDM) and would underestimate capacity. This study estimated existing empirical methods of estimating stochastic highway capacity. Among the studied methods are the product limit method(PLM) and the selected method(SM). METHODS : Speed and volume data were collected at three freeway bottleneck sites in Cheonan-Nonsan and West Sea Freeway. The data were grouped into a free-flow state or capacity state with speeds observed in the bottlenecks and the upstream. The data were applied to the empirical methods. RESULTS : The results show that the PLM and SM estimated capacity higher than EDM. The reason is that while the EDM is based on capacity observations only, the PLM and SM are based on free-flow high volumes and capacity observations. CONCLUSIONS : The PLM and SM using both free-flow and capacity observations would be improved to enhance the reliability of the capacity estimation.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.413-428
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• 2003

If a restriction is imposed only to a (proper) subset of parameters of interest, we call it a local restriction. Statistical inference under a local restriction in multinomial setting is studied. The maximum likelihood estimation under a local restriction and likelihood ratio tests for and against a local restriction are discussed. A real data is analyzed for illustrative purpose.

• Korean Management Science Review
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• v.11 no.2
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• pp.29-41
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• 1994

This paper analyses the stochastic nature of the Permanent Income Hypothesis (PIH) by specifying the variance-covariance structure of PIH based on Hall and Mishkin[3]. Maximum likelihood is employed to estimate the model by explicitely incorporating the heteroscedastic nature of the data into the likelihood. The data used are individual Korean household consumption and income data. The results indicate that the data are generally consistent with the Permanent Income Hypothesis, and about 11 percent of the total variation in consumption may be attributable to the excess sensitivity of consumption to income.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.3C
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• pp.141-146
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• 2005

The authors investigate the convergence speed problem of nonlinear adaptive equalization. Convergence constraints and time constant of radial basis function network using stochastic gradient (RBF-SG) algorithm is analyzed and a method of making time constant independent of hidden-node output power by using sample-by-sample node output power estimation is derived. The method for estimating the node power is to use a single-pole low-pass filter. It is shown by simulation that the proposed algorithm gives faster convergence and lower minimum MSE than the RBF-SG algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.168-173
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• 1987

The optimal Input design problem for linear system Which have the common parameters in the system and noise transfer functions. Exploiting the assumed Model structure and deriving the information matrix structure in detail, D-optimal open-loop stochastic input can be realized as an ARMA process under the Input or output variance constraints. In spite of the reduced order, It Is necessary to develop an efficient algorithms for the optimation with respect to the .rho..