• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.9-17
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• 2019

In order to deal with high uncertainty and variability in emergency medical centers, many researchers have developed various models for their operational planning and scheduling. However, most of the models just provide static plans without any risk measures as their results, and thus the users often lose the opportunity to analyze how much risk the patients have, whether the plan is still implementable or how the plan should be changed when an unexpected event happens. In this study, we construct a simulation model combined with a risk-based planning and scheduling module designed by Simio LLC. In addition to static schedules, it provides possibility of treatment delay for each patient as a risk measure, and updates the schedule to avoid the risk when it is needed. By using the simulation model, the users can experiment various scenarios in operations quickly, and also can make a decision not based on their past experience or intuition but based on scientific estimation of risks even in urgent situations. An example of such an operational decision making process is demonstrated for a real mid-size emergency medical center located in Seoul, Republic of Korea. The model is designed for temporal short-term planning especially, but it can be expanded for long-term planning also with some appropriate adjustments.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.926-939
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• 2022

The area of this study will cover the location-wise seismic response variation of an electrical cabinet in nuclear power point (NPP) based on classical reliability analysis. The location-based seismic ground motion (GM) selection is carried out with the help of probabilistic seismic hazard analysis using PSHRisktool, where the variation of reliability analysis can be understood from the relation between the reliability index and intensity measure. Two different approaches such as the first-order second moment method (FOSM) and Monte Carlo Simulation (MCS) are helped to evaluate and compare the reliability assessment of the cabinet. The cabinet is modeled with material uncertainty utilizing Steel01 as the material model and the fiber section modeling approach is considered to characterize the section's nonlinear reaction behavior. To verify the modal frequency, this study compares the FEM result with recorded data using Least-Squares Complex Exponential (LSCE) method from the impact hammer test. In spite of a few investigations, the main novelty of this study is to introduce the reader to check and compare the seismic reliability assessment variation in different seismic locations and for different earthquake levels. Alongside, the betterment can be found by comparing the result between two considered reliability estimation methods.

• New & Renewable Energy
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• v.18 no.3
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• pp.32-42
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• 2022

Rapid spread of intermittent renewable energy has amplified the instability and uncertainty of power systems. The Korea Power Exchange (KPX) promoted efficient management by opening the power brokerage market in 2019. By combining small-scale intermittent renewable energy with a flexible facility through the power brokerage market, the KPX aims to develop a virtual power plant system that will allow the conversion of existing intermittent renewable energy into collective power plants. However, the participation rate of renewable power owners in the power brokerage market is relatively low because other markets such as the small solar power contract market or the Korea Electric Power Corporation power purchase agreement are more profitable. In this study, we used a choice experiment to determine the attributes affecting the participation rate in the power brokerage market for 113 renewable power owners and estimate the value of the power brokerage market. According to the estimation results, a low smart meter installation cost, low profit variations, long contract periods, and few clearances increased the probability of participation. Moreover, the average value of the power brokerage market was estimated to be 2.63 million KRW per power owner.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1331-1344
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• 2021

This study is to evaluate the seismic capacity of the fire-damaged cabinet facility in a nuclear power plant (NPP). A prototype of an electrical cabinet is modeled using OpenSees for the numerical simulation. To capture the nonlinear behavior of the cabinet, the constitutive law of the material model under the fire environment is considered. The experimental record from the impact hammer test is extracted trough the frequency-domain decomposition (FDD) method, which is used to verify the effectiveness of the numerical model through modal assurance criteria (MAC). Assuming different temperatures, the nonlinear time history analysis is conducted using a set of fifty earthquakes and the seismic outputs are investigated by the fragility analysis. To get a threshold of intensity measure, the Monte Carlo Simulation (MCS) is adopted for uncertainty reduction purposes. Finally, a capacity estimation model has been proposed through the investigation, which will be helpful for the engineer or NPP operator to evaluate the fire-damaged cabinet strength under seismic excitation. This capacity model is presented in terms of the High Confidence of Low Probability of Failure (HCLPF) point. The results are validated by the proper judgment and can be used to analyze the influences of fire on the electrical cabinet.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.347-347
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• 2023

홍수 예·경보 시에는 시간-단위 또는 그 이하의 시간 척도에서 작용하는 강우에 대한 고도의 영향이 중요하게 되며, 특히 상대적으로 더 드문 관측 밀도가 있는 산악지역에서 강우의 공간분포에 대한 산악 효과의 중요도가 더 높아지게 된다. 일반적으로 1시간 시간스케일에서 강우-고도의 관계를 살펴보기 위해서는 대략 5km 내외의 관측 밀도를 가져야 하는 것으로 알려져 있으나 이러한 지역은 매우 드물다. 최근 기상 예측 수치모델로부터 모의된 강우량의 품질이 눈에 띄게 향상됨에 따라 국내에도 다양한 연구가 수행된 바 있다. 본 연구에서는 WRF를 이용하여 남강댐 지역의 과거 호우 사상을 재현한 후, 이로부터 생산된 공간적인 강우장을 이용하여 시간-단위의 시간 척도에서 강우량과 고도 사이의 관계를 고려할 수 있는 WREPN(WRF Rainfall-Elevation Parameterized Nowcasting) 모형을 제안한다. 홍수량 분석을 위해 WREPN 모형을 이용하였으며, 비교군으로 실무적으로 많이 사용되는 IDW, Kriging 기반 격자강우가 사용되었다. 격자기반 분포형 강우자료로부터 홍수량을 분석하기 위해 Mod-Clark 모형이 적용되었으며, 입력된 강우자료별매개변수의 불확실성을 분석하기 위해 베이지안 기법이 적용되었다. 매개변수의 불확실성 분석으로부터 강우-고도 관계가 고려된 WREPN 모형의 강우자료가 상대적으로 불확실성이 낮다는 것을 확인할 수 있었다.

• Geomechanics and Engineering
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• v.32 no.3
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• pp.271-291
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• 2023

Settlement estimation in cohesion materials is a crucial topic to tackle because of the complexity of the cohesion soil texture, which could be solved roughly by substituted solutions. The goal of this research was to implement recently developed machine learning features as effective methods to predict settlement (S_m) of shallow foundations over cohesion soil properties. These models include hybridized support vector regression (SVR), random forests (RF), and coot optimization algorithm (COM), and black widow optimization algorithm (BWOA). The results indicate that all created systems accurately simulated the S_m, with an R² of better than 0.979 and 0.9765 for the train and test data phases, respectively. This indicates extraordinary efficiency and a good correlation between the experimental and simulated S_m. The model's results outperformed those of ANFIS - PSO, and COM - RF findings were much outstanding to those of the literature. By analyzing established designs utilizing different analysis aspects, such as various error criteria, Taylor diagrams, uncertainty analyses, and error distribution, it was feasible to arrive at the final result that the recommended COM - RF was the outperformed approach in the forecasting process of S_m of shallow foundation, while other techniques were also reliable.

• Journal of Environmental Health Sciences
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• v.49 no.1
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• pp.1-10
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• 2023

Low-dose radiation exposure has received considerable attention because it reflects the general public's type and level of exposure. Still, controversy remains due to the relatively unclear results and uncertainty in risk estimation compared to high-dose radiation. However, recent epidemiological studies report direct evidence of health effects for various types of low-dose radiation exposure. In particular, international nuclear workers' studies, CT exposure studies, and children's cancer studies on natural radiation showed significantly increased cancer risk among the study populations despite their low-dose radiation exposure. These studies showed similar results even when the cumulative radiation dose was limited to an exposure group of less than 100 mGy, demonstrating that the observed excess risk was not affected by high exposure. A linear dose-response relationship between radiation exposure and cancer incidence has been observed, even at the low-dose interval. These recent epidemiological studies include relatively large populations, and findings are broadly consistent with previous studies on Japanese atomic bomb survivors. However, the health effects of low-dose radiation are assumed to be small compared to the risks that may arise from other lifestyle factors; therefore, the benefits of radiation use should be considered at the individual level through a balanced interpretation. Further low-dose radiation studies are essential to accurately determining the benefits and risks of radiation.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.72-81
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• 2023

We introduce tightly-coupled GNSS-LiDAR-Inertial state estimator, which is capable of SLAM (Simultaneously Localization and Mapping) and autonomous driving. Long term drift is one of the main sources of estimation error, and some LiDAR SLAM framework utilize loop closure to overcome this error. However, when loop closing event happens, one's current state could change abruptly and pose some safety issues on drivers. Directly utilizing GNSS (Global Navigation Satellite System) positioning information could help alleviating this problem, but accurate information is not always available and inaccurate vertical positioning issues still exist. We thus propose our method which tightly couples raw GNSS measurements into LiDAR-Inertial SLAM framework which can handle satellite positioning information regardless of its uncertainty. Also, with NLOS (Non-light-of-sight) satellite signal handling, we can estimate our states more smoothly and accurately. With several autonomous driving tests on AGV (Autonomous Ground Vehicle), we verified that our method can be applied to real-world problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.63-63
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• 2021

Bed shear stress is important variable in river flow analysis. The bed shear stress has an effects on bed erosion, sediment transport, and mean flow characteristics. Quadratic formula to estimate bed shear stress is widely used, 𝜏=𝜌c_fu|u| in which friction coefficient, c_f, needs to be assigned to numerical models. The aim of this study is to estimate Chezy coefficient using bathymetry data measured by ADCP. Bed form geometry variables will be estimated form bed profile, then Chezy coefficient will be determined using estimated bed form geometry variables in order to set friction coefficient to numerical model. From the probability density function obtained from the bathymetry data, Chezy coefficient will be randomly generated since Chezy coefficient is not uniform over the space and it does not depend on spatial variables such as water depth and distance from river bank. Numerical test will be performed to find to demonstrate randomly extracted Chezy coefficient is appropriate. The result of this study is valuable in that the friction coefficient is estimated in consideration of the bed profile, and as a result, uncertainty of the friction coefficient can be reduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.