• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.60-60
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• 2022

본 연구에서는 적설 추정 알고리즘과 추계 일기 생성 모형을 활용하여 관측 적설의 특성을 재현하는 연속 적설심 자료 모의 방법을 소개한다. 적설 추정 알고리즘은 강수 유형 판단, Snow Ratio 추정, 그리고 적설 깊이 감소량 추정까지 총 3단계로 구성된다. 먼저 강수 발생시 지상기온과 상대습도를 지표로 활용하여 강수 유형을 판단하고, 강수가 적설로 판별되었을 때 강수량을 신적설심으로 환산하는 Snow Ratio를 추정한다. Snow Ratio는 지상 기온과의 sigmoid 함수 회귀분석을 통해 추정하였으며, precipitation rate 조건(5 mm/3hr 미만 및 이상)에 따라 두 가지 함수를 적용하였다. 마지막으로 적설 깊이 감소량은 온도 지표 snowmelt 식을 이용하여 추정하였으며, 매개변수는 적설 깊이 및 온도 관측 자료를 활용하여 보정하였다. 속초 관측소 자료를 활용하여 매개변수를 보정 및 검증하여 높은 NSE(보정기간 : 0.8671, 검증기간 : 0.7432)를 달성하였으며, 이 알고리즘을 추계 일기 생성 모형으로 모의한 합성 기상 자료(강수량, 지상기온, 습도)에 적용하여 합성 적설심 시계열을 모의하였다. 모의 자료는 관측 자료의 통계 및 극한값을 매우 정확하게 재현하였으며, 현행 건축구조기준과도 일치하는 것으로 나타났다. 이 모형을 통하여 적설 위험 분석 분야뿐 아니라 기후 전망 자료와의 결합, 미계측 지역에 대한 자료 모의 등에도 광범위하게 활용될 수 있을 것이다.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.602-607
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• 2013

Financial risks associated with capital investments are often measured with different feasibility indicators such as the net present value (NPV), the internal rate of return (IRR), the payback period (PBP), and the benefit-cost ratio (BCR). This paper aims at demonstrating practical applications of probabilistic feasibility analysis techniques for an integrated feasibility evaluation of the IRR and PBP. The IRR and PBP are concurrently analyzed in order to measure the profitability and liquidity, respectively, of a cash flow. The cash flow data of a real wind turbine project is used in the study. The presented approach consists of two phases. First, two newly reported analysis techniques are used to carry out a series of what-if analyses for the IRR and PBP. Second, the relationship between the IRR and PBP is identified using Monte Carlo simulation. The results demonstrate that the integrated feasibility evaluation of stochastic cash flows becomes a more viable option with the aide of newly developed probabilistic analysis techniques. It is also shown that the relationship between the IRR and PBP for the wind turbine project can be used as a predictive model for the actual IRR at the end of the service life based on the actual PBP of the project early in the service life.

• Journal of the Korean Society of Safety
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• v.35 no.3
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• pp.49-57
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• 2020

In comparison with the existing static reliability analysis methods, the dynamic reliability analysis(DyRA) method is more suitable for estimating the failure probability of a structure subjected to earthquake excitations because it can take into account the frequency characteristics and damping capacity of the structure. However, the DyRA is known to have an issue of numerical stability due to the uncertainty in random sampling of the earthquake excitations. In order to solve this numerical stability issue in the DyRA approach, this study proposed two earthquake-scale factors. The first factor is defined as the ratio of the first earthquake excitation over the maximum value of the remaining excitations, and the second factor is defined as the condition number of the matrix consisting of the earthquake excitations. Then, we have performed parametric studies of two factors on numerical stability of the DyRA method. In illustrative example, it was clearly confirmed that the two factors can be used to verify the numerical stability of the proposed DyRA method. However, there exists a difference between the two factors. The first factor showed some overlapping region between the stable results and the unstable results so that it requires some additional reliability analysis to guarantee the stability of the DyRA method. On the contrary, the second factor clearly distinguished the stable and unstable results of the DyRA method without any overlapping region. Therefore, the second factor can be said to be better than the first factor as the criterion to determine whether or not the proposed DyRA method guarantees its numerical stability. In addition, the accuracy of the numerical analysis results of the proposed DyRA has been verified in comparison with those of the existing first-order reliability method(FORM), Monte Carlo simulation(MCS) method and subset simulation method(SSM). The comparative results confirmed that the proposed DyRA method can provide accurate and reliable estimation of the structural failure probability while maintaining the superior numerical efficiency over the existing methods.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.481-491
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• 2020

The development of agricultural land at Saemangeum has required a significant increase in agricultural water use. It has been well acknowledged that salinity plays a critical role in the farming system. Therefore, a systematic study in salinity is necessary to better manage agricultural water. This study aims to develop a stochastic salinity simulation model that simultaneously simulates salinities obtained from different layers. More specifically, this study proposed a two-stage Autoregressive Exgeneous (ARX) model within a hierarchical Bayesian modeling framework. We derived posterior distributions of model parameters and further used them to obtain the predictive posterior distribution for salinities at three different layers. Here, the BIC values are used and compared to determine the optimal model from a set of candidate models. A detailed discussion of the model is provided.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.275-294
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• 2016

A primary task in wireless sensor networks (WSNs) is data collection. The main objective of this task is to collect sensor readings from sensor fields at predetermined sinks using routing protocols without conducting network processing at intermediate nodes, which have been proved as being inefficient in many research studies using a static sink. The major drawback is that sensor nodes near a data sink are prone to dissipate more energy power than those far away due to their role as relay nodes. Recently, novel WSN architectures based on mobile sinks and mobile relay nodes, which are able to move inside the region of a deployed WSN, which has been developed in most research works related to mobile WSN mainly exploit mobility to reduce and balance energy consumption to enhance communication reliability among sensor nodes. Our main purpose in this paper is to propose a solution to the problem of deploying mobile data collectors for alleviating the high traffic load and resulting bottleneck in a sink's vicinity, which are caused by static approaches. For this reason, several WSNs based on mobile elements have been proposed. We studied two key issues in WSN mobility: the impact of the mobile element (sink or relay nodes) and the impact of the mobility model on WSN based on its performance expressed in terms of energy efficiency and reliability. We conducted an extensive set of simulation experiments. The results obtained reveal that the collection approach based on relay nodes and the mobility model based on stochastic perform better.

• Journal of Veterinary Clinics
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• v.31 no.1
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• pp.40-45
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• 2014

Abalone herpes-like virus (AbHV) is a fatal disease of abalones that impose severe economic impacts on the industry of infected regions due to high mortality. The aim of this study was to quantify the risk of introducing AbHV into Korea through the importation of live abalones for human consumption by import risk analysis (IRA). Monte Carlo simulation models were developed to provide estimates of the probability that a ton of imported abalone contains at least one AbHV-infected individual, using historical trade data and relevant literatures. A sensitivity analysis with 5,000 iterations was also conducted to determine the extent to which input parameters affect the outcome of the model. Although many uncertainties were present in the data, the results indicated that, if 5,000 tons of abalone were imported from a hypothetical exporting country with low prevalence of AbHV (model 1), there would be at least one AbHV-infected abalones in 4,816 of those tons (96.3%), while there would be at least one AbHV-infected abalones in 100% of those tons imported from country with high prevalence (model 2). Sensitivity analysis indicated that for model 1, prevalence was the strongest influence factor on the predicted number of infections. For model 2, background mortality and washing to reduce the risk of surface contamination during processing were the major contributing factors. Risk management strategies need to be enforced to reduce the risk of AbHV introduction in that at least one infected abalone would remain in a consignment from country even with a low prevalence of AbHV infection. The methodology and the results presented here will contribute to improve the development of AbHV management program, and with more accurate data this IRA model will aid science-based decision-making on mitigation strategies to reduce the risk of AbHV introduction in Korea.

• Smart Structures and Systems
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• v.14 no.1
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• pp.39-54
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• 2014

Node layout optimization of structural wireless systems is investigated as a means to prolong the network lifetime without, if possible, compromising information quality of the measurement data. The trade-off between these antagonistic objectives is studied within a multi-objective layout optimization framework. A Genetic Algorithm is adopted to obtain a set of Pareto-optimal solutions from which the end user can select the final layout. The information quality of the measurement data collected from a heterogeneous WSN is quantified from the placement quality indicators of strain and acceleration sensors. The network lifetime or equivalently the network energy consumption is estimated through WSN simulation that provides realistic results by capturing the dynamics of the wireless communication protocols. A layout optimization study of a monitoring system on the Great Belt Bridge is conducted to evaluate the proposed approach. The placement quality of strain gauges and accelerometers is obtained as a ratio of the Modal Clarity Index and Mode Shape Expansion values that are computed from a Finite Element model of the monitored bridge. To estimate the energy consumption of the WSN platform in a realistic scenario, we use a discrete-event simulator with stochastic communication models. Finally, we compare the optimization results with those obtained in a previous work where the network energy consumption is obtained via deterministic communication models.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.331-340
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• 2000

This study aims to develop a methodology of real time forecasting of mundation risk based on DAMBRK model and Kalman filter. The model is based on implicit, nonlinear finite difference approximatIons of the one-dimensional dynamic wave equations. The stochastic estimator uses on extended Kalman filter to provide optimal updating estimates. These are accomplished by combining the predictions of the determurustic model with real time observauons modified by the Kalman filter gain ractor. Inundation risks are also estimated by applying Monte Carlo simulation to consider the variability in cross section geometry and Manning's roughness coefficient. The model calibrated by applying to the floods ot South Han River on September, 1990 and August, 1995. The Kalman tilter model indicates that significant improvement compared to deteriministic analysis in flood routing predictions in the river. Overtopping risk of levee is also presented by comparing levee height with simulated flood level. level.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.135-143
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• 2014

In this study, Chungju inflow was simulated for climate change considering the uncertainties of GCMs and a stochastic model. TFN (Transfer Function Noise) model and 4 different GCMs (CNRM, CSIRO, CONS, UKMO) based on IPCC AR4 A2 scenario were used. In order to evaluate uncertainty of TFN model, 100 cases of noises are applied to the TFN model. Thus, 400 cases of inflow results are simulated. Future inflows according to the GCMs show different rates of changes for the future 3 periods relative to the past 30-years reference period. As the results, the summer inflow shows increasing trend and the spring inflow shows decreasing trend based on AR4 A2 scenario.