• Journal of Korea Water Resources Association
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• v.48 no.10
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• pp.793-806
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• 2015

The simulation of natural streamflow at ungauged basins is one of the fundamental challenges in hydrology community. The key to runoff simulation in ungauged basins is generally involved with a reliable parameter estimation in a rainfall-runoff model. However, the parameter estimation of the rainfall-runoff model is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of a continuous rainfall-runoff model in conjunction with a Bayesian statistical technique to consider uncertainty more precisely associated with the parameters. First, this study employed Bayesian Markov Chain Monte Carlo scheme for the estimation of the Sacramento rainfall-runoff model. The Sacramento model is calibrated against observed daily runoff data, and finally, the posterior density function of the parameters is derived. Second, we applied a multiple linear regression model to the set of the parameters with watershed characteristics, to obtain a functional relationship between pairs of variables. The proposed model was also validated with gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, index of agreement and the coefficient of correlation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.10A
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• pp.1131-1138
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• 2004

In Hierarchical Mobile Ipv6 (HMIPv6) networks, the mobility anchor point (MAP) handles binding update (BU) procedures locally to reduce signaling overhead for mobility. However, as the number of mobile nodes (MNs) handled by the MAP increases, the MAP suffers from the overhead not only to handle signaling traffic but also to Process data tunneling traffic. Therefore, it is important to control the number of MNs serviced by the MAP, in order to mitigate the burden of the MAP. We propose an adaptive load control scheme, which consists of two sub-algorithms: threshold-based admission control algorithm and session-to-mobility ratio (SMR) based replacement algorithm. When the number of MNs at a MAP reaches to the full capacity, the MAP replaces an existing MN at the MAP, whose SMR is high, with an MN that just requests binding update. The replaced MN is redirected to its home agent. We analyze the proposed load control scheme using the .Markov chain model in terms of the new MN and the ongoing MN blocking probabilities. Numerical results indicate that the above probabilities are lowered significantly compared to the threshold-based admission control alone.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.11 no.1
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• pp.25-39
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• 1986

In this paper, we analyze the performance, particularly the flow control mechanism, of the CCITT X.25 protocol in a packet-switched network. In this analysis, we consider the link and packet layers separately, and investigate the performance in three measures; normalized channel throughput, mean transmission time, and transmission efficiency. Each of these measures is formulated in terms of given protocol parameters such as windos size, $T_1$ and $T_2$ values, message length, and so forth. We model the service procedure of the inpur traffic based on the flow control mechanism of the X.25 protocol, and investigate the mechanism of the sliding window flow control with the piggybacked acknowlodgment scheme using a discrete-time Markov chain model. With this model, we study the effect of variation of the protoccol parameters on the performance of the X.25 protocol. From the numerical results of this analysis one can select the optimal valuse of the protocol parameters for different channel environments. it has been found that to maintain the trasnmission capacity satisfactorily, the window size must be greater than or equal to 7 in a high-speed channel. The time-out value, $T_1$, must carefully be selected in a noisy channel. In a normal condition, it should be in the order of ls. The value of $T_2$ has some effect on the transmission efficiency, but is not critical.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.267-280
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• 2013

College tuition is a significant economic, social, and political issue in Korea. We conduct a Bayesian analysis of a hierarchical model to address the factors related to college tuition based on a survey data collected by Statistics Korea. A binary response variable is selected depending on if more than 70% of tuition costs are supported by parents, and a hierarchical Probit model is constructed with areas as groups. A set of explanatory variables is selected from a factor analysis of available variables in the survey. A Markov chain Monte Carlo algorithm is used to estimate parameters. From the analysis results, income and stress are significantly related to college tuition support from parents. Parents with high income tend to support children's college tuition and students with parents' financial support tend to be mentally less stressed; subsequently, this shows that the economic status of parents significantly affects the mental health of college students. Gender, a healthy life style, and college satisfaction are not significant factors. Comparing areas in terms of the degrees of correlation between stress/income and tuition support from parents, students in Kangwon-do are the most mentally stressed when parents' support is limited; in addition, the positive correlation between parents support and income is stronger in big cities compared to provincial areas.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.733-745
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• 2010

Seasonality of hydrologic extreme variable is a significant element from a water resources managemental point of view. It is closely related with various fields such as dam operation, flood control, irrigation water management, and so on. Hydrological frequency analysis conjunction with partial duration series rather than block maxima, offers benefits that include data expansion, analysis of seasonality and occurrence. In this study, nonstationary frequency analysis based on the Bayesian model has been suggested which effectively linked with advantage of POT (peaks over threshold) analysis that contains seasonality information. A selected threshold that the value of upper 98% among the 24 hours duration rainfall was applied to extract POT series at Seoul station, and goodness-fit-test of selected GEV distribution has been examined through graphical representation. Seasonal variation of location and scale parameter ($\mu$ and $\sigma$) of GEV distribution were represented by Fourier series, and the posterior distributions were estimated by Bayesian Markov Chain Monte Carlo simulation. The design rainfall estimated by GEV quantile function and derived posterior distribution for the Fourier coefficients, were illustrated with a wide range of return periods. The nonstationary frequency analysis considering seasonality can reasonably reproduce underlying extreme distribution and simultaneously provide a full annual cycle of the design rainfall as well.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.545-554
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• 2019

The most drought assessments are based on a drought index, which depends on univariate variables such as precipitation and soil moisture. However, there is a limitation in representing the drought conditions with single variables due to their complexity. It has been acknowledged that a multivariate drought index can more effectively describe the complex drought state. In this context, this study propose a Copula-based drought index that can jointly consider precipitation and soil moisture. Unlike precipitation data, long-term soil moisture data is not readily available so that this study utilized a Gaussian Mixture Non-Homogeneous Hidden Markov chain Model (GM-NHMM) model to simulate the soil moisture using the observed precipitation and temperature ranging from 1973 to 2014. The GM-NHMM model showed a better performance in terms of reproducing key statistics of soil moisture, compared to a multiple regression model. Finally, a bivariate frequency analysis was performed for the drought duration and severity, and it was confirmed that the recent droughts over Jeollabuk-do in 2015 have a 20-year return period.

• Journal of the Korean Regional Science Association
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• v.37 no.2
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• pp.63-82
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• 2021

This study aims to explore how manufacturing start-up activities from 2000 to 2018 have changed spatially and to predict changes in distribution patterns of future start-up activities. For the analysis, the Census on Establishments microdata from 2000 to 2018 were used, and the manufacturing industry was classified into four detailed industrial groups according to the 40 manufacturing standards presented by the Korea Institute for Industrial Economics and Trade's ISTANS. According to the results, start-up activities in industries that require high technology levels are concentrated in southern Gyeonggi region, and other start-up activities are concentrated outside of the metropolitan area. When the distribution change from 2018 to 2036, extending the trend from 2000 to 2018, it was confirmed that there was a high possibility of a rise in the hierarchy in the future in regions adjacent to regions where start-up activities occur. This study aimed to provide implications for regional policies related to fostering start-ups and creating jobs by dynamically analyzing the location pattern of manufacturing start-ups, which is a major source of job creation.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.77-86
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• 2021

A method of constructing a war simulation based on Bayesian Inference was proposed as a method of constructing heterogeneous historical war data obtained with a time difference into a single model. A method of applying a linear regression model can be considered as a method of predicting future battles by analyzing historical war results. However it is not appropriate for two heterogeneous types of historical data that reflect changes in the battlefield environment due to different times to be suitable as a single linear regression model and violation of the model's assumptions. To resolve these problems a Bayesian inference method was proposed to obtain a post-distribution by assuming the data from the previous era as a non-informative prior distribution and to infer the final posterior distribution by using it as a prior distribution to analyze the data obtained from the next era. Another advantage of the Bayesian inference method is that the results sampled by the Markov Chain Monte Carlo method can be used to infer posterior distribution or posterior predictive distribution reflecting uncertainty. In this way, it has the advantage of not only being able to utilize a variety of information rather than analyzing it with a classical linear regression model, but also continuing to update the model by reflecting additional data obtained in the future.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.229-236
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• 2021

There has been a lot of research on the real estate market, but a lack of research on the supply and demand of housing supply in each region, reflecting the changes in population growth and supply. It is calculated as the transition probability of the Markov chain model by reflecting the data on the number of houses per 1,000 people in the past 35 years and the forecast data for population change by region, in terms of supply (housing) to demand (population) for factors on the real estate market. According to the calculation results of the real estate market by region, the housing supply to the metropolitan area such as Gyeong-gi, Incheon, and Seoul is expected to be insufficient for a considerable period of time, considering the population changes by region. To stabilize the real estate market, it was confirmed that it was necessary to actively apply the differentiation of housing supply by region. It is meaningful in terms of verifying long term trends in the real estate market by region that reflect the prediction of population change, and it is expected that the methods used in this study will be practical through the analysis results using the historical data.

• Journal of Aerospace System Engineering
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• v.18 no.1
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• pp.11-20
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• 2024

In the recent era of NewSpace, unlike high-reliability satellites of the past, low-reliability satellites are being developed and mass-produced at a lower cost to launch constellations satellites. To achieve cost-effective cluster satellite development, satellite users and developers need to assess the feasibility of maintaining mission performance over the expected lifespan when cluster satellites are launched. Plans for replacements due to random failures should also be established to maintain performance. This study proposed a method for assessing system reliability and availability to maintain mission performance and establish replacement strategies for Earth observation constellation satellites. In this study, a constellation reliability and availability model considering mission performance required for a satellite constellation, situations of satellite backup, and additional ground backups was established. The reliability model was structured based on the concept of a k-out-of-n system and the availability model used a Markov chain model. Based on the proposed reliability model, the minimum number of satellites required to meet mission requirements was defined and satellites needed in orbit during the required mission period to satisfy mission reliability were calculated. This research also analyzed the number of spare satellites in orbit and on the ground required to meet the desired availability during required service period through availability analysis.