• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.681-690
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• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.177-186
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• 2016

Drought is a water-related natural disaster which can be simply described as spatially and temporally sequential absence of water. However, its characteristics are very difficult to define. For this reason, the preparation and mitigation from drought events have not been successful. In the current study, we illustrated a design drought estimation approach of water resources infrastructures as well as the existing theoretical one to prepare and mitigate drought disasters. Theoretical and simulation methods were tested including three time series models such as autoregressive (AR), Gamma AR, Copula AR models. The results indicated that for South Korea region, the simulation-based method to estimate drought frequency presented better performance and all the three time series models show similar performance to each other. The current drought event occurring in South Korea was investigated with dividing South Korea into four basins as Han River, Nakdong River, Geum River, and Nakdong River basins. The results showed that two middle and north basins presented significant drought events with 3 year drought duration and around 40 year return period while the other two southern regions illustrated relatively weaker drought events.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.53-62
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• 2013

Currently we have 11 climatological stations registered in World Meteorological Organization. Geographically, these stations are unevenly distributed in Korea and are mainly located on seaside. Therefore station's density analysis should be performed to produce the high-quality climatological data. Using the correlation integral method, the density of climatological stations can be measured by the estimation of fractal dimension. In this study, new climatological stations having the higher fractal dimension were selected. Sequential or simultaneous selection method were carried out until 3 new stations were selected based on the fractal dimension.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.525-534
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• 2017

The open set recognition method should be used for the cases that the classes of test data are not known completely in the training phase. So it is required to include two processes of classification and the validation test. This kind of research is very necessary for commercialization of face recognition modules, but few domestic researches results about it have been published. In this paper, we propose an open set face recognition method that includes two sequential validation phases. In the first phase, with dummy classes we perform classification based on sparse representation. Here, when the test data is classified into a dummy class, we conclude that the data is invalid. If the data is classified into one of the regular training classes, for second validation test we extract four features and apply them for the proposed decision function. In experiments, we proposed a simulation method for open set recognition and showed that the proposed validation test outperform SCI of the well-known validation method

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.31-44
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• 2014

In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.

• Journal of Astronomy and Space Sciences
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• v.37 no.4
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• pp.237-247
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• 2020

The ground tracking support is a critical factor for the navigation performance of spacecraft orbiting around the Moon. Because of the tracking limit of antennas, only a small number of facilities can support lunar missions. Therefore, case studies for various ground tracking support conditions are needed for lunar missions on the stage of preliminary mission analysis. This study analyzes the ground supporting condition effect on orbit determination (OD) of Korea Pathfinder Lunar Orbiter (KPLO) in the lunar orbit. For the assumption of ground support conditions, daily tracking frequency, cut-off angle for low elevation, tracking measurement accuracy, and tracking failure situations were considered. Two antennas of deep space network (DSN) and Korea Deep Space Antenna (KDSA) are utilized for various tracking conditions configuration. For the investigation of the daily tracking frequency effect, three cases (full support, DSN 4 pass/day and KDSA 4 pass/day, and DSN 2 pass/day and KDSA 2 pass/day) are prepared. For the elevation cut-off angle effect, two situations, which are 5 deg and 10 deg, are assumed. Three cases (0%, 30%, and 50% of degradation) were considered for the tracking measurement accuracy effect. Three cases such as no missing, 1-day KDSA missing, and 2-day KDSA missing are assumed for tracking failure effect. For OD, a sequential estimation algorithm was used, and for the OD performance evaluation, position uncertainty, position differences between true and estimated orbits, and orbit overlap precision according to various ground supporting conditions were investigated. Orbit prediction accuracy variations due to ground tracking conditions were also demonstrated. This study provides a guideline for selecting ground tracking support levels and preparing a backup plan for the KPLO lunar mission phase.

• Health Policy and Management
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• v.3 no.2
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• pp.159-176
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• 1993

The utilization of outpatient care services involves two steps of sequential decisions. The first step decision is about whether to initiate the utilization and the second one is about how many more visits to make after the initiation. Presumably, the initiation decision is largely made by the patient and his or her family, while the number of additional visits is decided under a strong influence of the physician. Implication is that the analysis of the outpatient care utilization requires to specify each of the two decisions underlying the utilization as a distinct stochastic process. This paper is concerned with the number of physician visits, which is, by definition, a discrete variable that can take only non-negative integer values. Since the initial visit is considered in the analysis of whether or not having made any physician visit, the focus on the number of visits made in addition to the initial one must be enough. The number of additional visits, being a kind of count data, could be assumed to exhibit a Poisson distribution. However, it is likely that the distribution is over dispersed since the number of physician visits tends to cluster around a few values but still vary widely. A recently reported study of outpatient care utilization employed an analysis based upon the assumption of a negative binomial distribution which is a type of overdispersed Poisson distribution. But there is an indication that the use of Poisson distribution making adjustments for over-dispersion results in less loss of efficiency in parameter estimation compared to the use of a certain type of distribution like a negative binomial distribution. An analysis of the data for outpatient care utilization was performed focusing on an assessment of appropriateness of available techniques. The data used in the analysis were collected by a community survey in Hwachon Gun, Kangwon Do in 1990. It was observed that a Poisson regression with adjustments for over-dispersion is superior to either an ordinary regression or a Poisson regression without adjustments oor over-dispersion. In conclusion, it seems the most approprite to assume that the number of physician visits made in addition to the initial visist exhibits an overdispersed Poisson distribution when outpatient care utilization is studied based upon a model which embodies the two-part character of the decision process uderlying the utilization.

• Computers and Concrete
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• v.31 no.5
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• pp.405-417
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• 2023

The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.103-118
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• 2024

The horseshoe prior is notably one of the most popular priors in sparse regression models, where only a small fraction of coefficients are nonzero. The parameter space of the horseshoe prior is much smaller than that of the spike and slab prior, so it enables us to efficiently explore the parameter space even in high-dimensions. However, on the other hand, the horseshoe prior has a high computational cost for each iteration in the Gibbs sampler. To overcome this issue, various MCMC algorithms for the horseshoe prior have been proposed to reduce the computational burden. Especially, Johndrow et al. (2020) recently proposes an approximate algorithm that can significantly improve the mixing and speed of the MCMC algorithm. In this paper, we compare (1) the traditional MCMC algorithm, (2) the approximate MCMC algorithm proposed by Johndrow et al. (2020) and (3) its variant in terms of computing times, estimation and variable selection performance. For the variable selection, we adopt the sequential clustering-based method suggested by Li and Pati (2017). Practical performances of the MCMC methods are demonstrated via numerical studies.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.43-50
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• 2024

Large planar military antenna boasts a range of electrical components, including TRA(Transmit-Receive Assembly), signal processors, etc. which engage in computations and calculations. These processes generate a significant amount of heat, leading to unforeseen consequences for the equipment. To mitigate these adverse effects, it's imperative to implement a cooling system that can effectively reduce heat-related issues. Given the antenna's intricate nature and the multitude of components it houses, a two-step estimation process is necessary. The first step involves a comprehensive model calculation to determine the total flow characteristics, while the second step entails a thermal analysis of individual TRA set. In this study, we depicted an antenna set using simplified 3D models of its components, considering their material and thermal properties. The sequential analysis process facilitated the calculation of branched flow rates, providing insights into the individual TRA. This approach also allowed us to design a cooling system for the TRA set, assessing its thermal stability in high-temperature environments. To ensure the optimal performance of TRA, breaking down the analysis into stages based on the cooling system's structure can assist operators in predicting numerical results more effectively.