• Journal of KIISE:Information Networking
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• v.34 no.3
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• pp.156-167
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• 2007

In this paper, we develop the model based prediction algorithm for Variable-Bit-Rate(VBR) video traffic with regular Group of Picture(GOP) pattern. We use multiplicative ARIMA process called GOP ARIMA (ARIMA for Group Of Pictures) as a base stochastic model. Kalman Filter based prediction algorithm consists of two process: GOP ARIMA modeling and prediction. In performance study, we produce three video traces (news, drama, sports) and we compare the accuracy of three different prediction schemes: Kalman Filter based prediction, linear prediction, and double exponential smoothing. The proposed prediction algorithm yields superior prediction accuracy than the other two. We also show that confidence interval analysis can effectively detect scene changes of the sample video sequence. The Kalman filter based prediction algorithm proposed in this work makes significant contributions to various aspects of network traffic engineering and resource allocation.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.153-161
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• 2017

Branching processes which is used for epidemic dispersion as stochastic process model have advantages to estimate parameters by real data. We have to estimate both mean and dispersion parameter in order to use the negative binomial distribution as an offspring distribution on branching processes. In existing studies on biology and epidemiology, it is estimated using maximum-likelihood methods. However, for most of epidemic data, it is hard to get the best precision of maximum-likelihood estimator. We suggest a Bayesian inference that have good properties of statistics for small-sample. After estimating dispersion parameter we modelled the posterior distribution for 2015 Korea MERS cases. As the result, we found that the estimated dispersion parameter is relatively stable no matter how we assume prior distribution. We also computed extinction probabilities on branching processes using estimated dispersion parameters.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.327-340
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• 2015

General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.147-156
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• 2009

We present a methodology for modeling and solving the transit frequency design problem with variable demand. The problem is described as a bi-level model based on a non-cooperative Stackelberg game. The upper-level operator problem is formulated as a non-linear optimization model to minimize net cost, which includes operating cost, travel cost and revenue, with fleet size and frequency constraints. The lower-level user problem is formulated as a capacity-constrained stochastic user equilibrium assignment model with variable demand, considering transfer delay between transit lines. An efficient algorithm is also presented for solving the proposed model. The upper-level model is solved by a gradient projection method, and the lower-level model is solved by an existing iterative balancing method. An application of the proposed model and algorithm is presented using a small test network. The results of this application show that the proposed algorithm converges well to an optimal point. The methodology of this study is expected to contribute to form a theoretical basis for diagnosing the problems of current transit systems and for improving its operational efficiency to increase the demand as well as the level of service.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.451-463
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• 2007

This paper describes issues of GPS network adjustment to determine coordinate sets of the $2^{nd}$ order national geodetic control points based on the Korean Geodetic Datum (KGD2002) which has been newly adopted in 2003, After outlining theoretical background of the GPS network processing, the adjustment procedure applied for this project is detailed. Throughout performing a series of minimally constrained adjustments, some outliers have been removed and magnitude of absolute and relative error for a stochastic modeling has been determined as 4mm+0.4ppm and 8mm+0.8ppm in the horizontal and vertical component, respectively. The over constrained adjustment by fixing the $1^{st}$ order control points was performed to derive final solution, indicating that the accuracy of the estimated coordinates was 2cm and 4cm in the horizontal and vertical component.

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.421-426
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• 2010

In view of free from bluetongue (BT) in the domestic cattle population in Korea, the key of quarantine testing for BT virus (BTV) infection is detection of cattle previously exposed to the virus. The objective of this study was to estimate the probability of detecting a cattle infected with BTV using a stochastic modeling analysis of existing quarantine testing data. Three testing scenarios were considered in this study: serological testing of all animals in all imported lots (scenario 1), serological testing of a sample of cattle from all imported lots (scenario 2), and serological testing of 50% of imported lots (scenario 3). In scenario 2 and 3, it was assumed that cattle were sampled (sample size) within each lot to detect 5% of the cattle in each lot with a 95% confidence, taking into account diagnostic sensitivity of the ELISA (enzyme-linked immunosorbent assay). The model output was the total number of BTV-infected cattle and the prevalence of BTV infection in imported cattle from the US, Australia, Canada and Japan. Compared to the scenario 1, the probability of detecting a BTV-infected cattle was estimated to be 19% and 1.6% in scenario 2 and 3, respectively. Furthermore, the analyses showed a 95% confidence that BTV prevalence was less or equal to $9.7{\times}10^{-4}$ (median = $1.5{\times}10^{-5}$), indicating that, for the scenario 2 and 3 with serological testing for a sample of cattle, the risk of introducing an exotic strain of BTV into Korea through the importation of live cattle would not be acceptable.

• Journal of Korea Technology Innovation Society
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• v.14 no.3
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• pp.578-598
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• 2011

This study estimates the value of photovoltaic core material technology, which is getting attention as a clean energy source. The estimation is based on the real option pricing model (ROPM). This study has two main contributions. The first is in the methodology. The process of modeling volatility, which is the most complicated stage in ROPM, is greatly simplified by using the stock price as a covariate representing the volatility of the real option's basic asset. The second contribution is the application of technology. In this study, the economic value of poly-silicon, a core material in the photovoltaic industry and recently surging in demand, is evaluated as a manufacturing technology. In a case study of a company in the photovoltaic industry, the stochastic process of a basic asset follows geometric Brownian motion (GBM), and the option value of firm A's poly-silicon manufacturing technology is estimated at 3.4 trillion won.

• Water for future
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• v.27 no.3
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• pp.45-54
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• 1994

Basic theory of fractal dimension is introduced and performed for the generated time series using the water balance model. The water balance equation over a large area is analyzed at seasonal time scales. In the generation and modification of mesoscale circulation local recycling of precipitation and dynamic effects of soil moisture are explicitly included. Time delay is incorporated in the analysis. Depending on the parameter values, the system showed different senarios in the evolution such as fixed point, limit cycle, and chaotic types of behavior. The stochastic behavior of the generated time series is due to deterministic chaos which arises from a nonlinear dynamic system with a limited number of equations whose trajectories are highly sensitive to initial conditions. The presence of noise arose from the characterization of the incoming precipitation, destroys the organized structure of the attractor. The existence of the attractor although noise is present is very important to the short-term prediction of the evolution. The implications of this nonlinear dynamics are important for the interpretation and modeling of hydrologic records and phenomena.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1069-1078
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• 2018

Demand response (DR) programs give opportunity to consumers to manage their electricity bills. Besides, distribution system operator (DSO) is interested in using DR programs to obtain technical and economic benefits for distribution network. Since small consumers have difficulties to individually take part in the electricity market, an entity named demand response provider (DRP) has been recently defined to aggregate the DR of small consumers. However, implementing DR programs face challenges to fairly allocate benefits and payments between DRP and DSO. This paper presents a procedure for modeling the interaction between DRP and DSO based on a bilevel programming model. Both DSO and DRP behave from their own viewpoint with different objective functions. On the one hand, DRP bids the potential of DR programs, which are load shifting and load curtailment, to maximize its expected profit and on the other hand, DSO purchases electric power from either the electricity market or DRP to supply its consumers by minimizing its overall cost. In the proposed bilevel programming approach, the upper level problem represents the DRP decisions, while the lower level problem represents the DSO behavior. The obtained bilevel programming problem (BPP) is converted into a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Furthermore, point estimate method (PEM) is employed to model the uncertainties of the power demands and the electricity market prices. The efficiency of the presented model is verified through the case studies and analysis of the obtained results.