• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.197-197
• /
• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

• Journal of Radiation Protection and Research
• /
• v.46 no.2
• /
• pp.39-47
• /
• 2021

Background: The National Institutes for Quantum and Radiological Science and Technology-National Institute of Radiological Sciences (QST-NIRS) has continuously investigated the undesired radiation exposure in ion beam radiotherapy mainly in carbon-ion radiotherapy (CIRT). This review introduces our investigations on the secondary neutron dose in CIRT with the broad and scanning beam methods. Materials and Methods: The neutron ambient dose equivalents in CIRT are evaluated based on rem meter (WENDI-II) measurements. The out-of-field organ doses assuming prostate cancer and pediatric brain tumor treatments are also evaluated through the Monte Carlo simulation. This evaluation of the out-of-field dose includes contributions from secondary neutrons and secondary charged particles. Results and Discussion: The measurements of the neutron ambient dose equivalents at a 90#x00B0; angle to the beam axis in CIRT with the broad beam method show that the neutron dose per treatment dose in CIRT is lower than that in proton radiotherapy (PRT). For the scanning beam with the energy scanning technique, the neutron dose per treatment dose in CIRT is lower than that in PRT. Moreover, the out-of-field organ doses in CIRT decreased with distance to the target and are less than the lower bound in intensity-modulated radiotherapy (IMRT) shown in AAPM TG-158 (American Association of Physicists in Medicine Task Group). Conclusion: The evaluation of the out-of-field doses is important from the viewpoint of secondary cancer risk after radiotherapy. Secondary neutrons are the major source in CIRT, especially in the distant area from the target volume. However, the dose level in CIRT is similar or lower than that in PRT and IMRT, even if the contributions from all radiation species are included in the evaluation.

• Journal of the Korean Society of Systems Engineering
• /
• v.18 no.2
• /
• pp.75-93
• /
• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.6
• /
• pp.51-61
• /
• 2006

An efficient and accurate hybrid reliability analysis method is proposed in this paper to quantify the risk of an axially loaded single pile considering pile-soil interaction behavior and uncertainties in various design variables. The proposed method intelligently integrates the concepts of the response surface method, the finite difference method, the first-order reliability method, and the iterative linear interpolation scheme. The load transfer method is incorporated into the finite difference method for the deterministic analysis of a single pile-soil system. The uncertainties associated with load conditions, material and section properties of a pile and soil properties are explicitly considered. The risk corresponding to both serviceability limit state and strength limit state of the pile and soil is estimated. Applicability, accuracy and efficiency of the proposed method in the safety assessment of a realistic pile-soil system subjected to axial loads are verified by comparing it with the results of the Monte Carlo simulation technique.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.6A
• /
• pp.1001-1011
• /
• 2006

It is a general agreement that exact statistical solutions can be found by a Monte Carlo technique. Due to difficulties, however, in the numerical generation of random fields, which satisfy not only the probabilistic distribution but the spectral characteristics as well, it is recognized as relatively difficult to find an exact response variability of a structural response. In this study, recognizing that the random field assumes a constant over the domain under consideration when the correlation distance tends to infinity, a semi-theoretical solution of response variability is proposed for general structures. In this procedure, the probability density function is directly used. It is particularly noteworthy that the proposed methodology provides response variability for virtually any type of probability density function, and has capability of considering correlations between multiple random variables.

• Nuclear Engineering and Technology
• /
• v.56 no.7
• /
• pp.2711-2717
• /
• 2024

The current work aims to fabricate metal oxide-doped (PbO, Fe₂O₃, MgO, and Al₂O₃, each of which boasts a purity of 99%) zinc barium borate glasses through the melt quenching technique at the 1000 ℃ melting temperature. The results showed that adding 5 mol.% of metal oxides PbO, Fe₂O₃, Al₂O₃, and MgO increases the density of the zinc barium borate glasses. Additionally, the fabricated glasses' mechanical properties were determined based on the Makishima-Mackenzie model, which proved that the highest mechanical properties were achieved for glasses doped with Al₂O₃ compounds. The mechanical moduli for the glasses doped with Al₂O₃ reach 80.95 GPa (Young), 59.90 GPa (bulk), 31.75 GPa (shear), and 102.23 GPa (longitudinal). Additionally, the Al₂O₃-doped glasses' microhardness reaches 4.77 GPa. Moreover, estimation of the fabricated glasses' gamma-ray shielding capacity utilized Monte Carlo simulation. The highest linear attenuation coefficients are 29.132, 19.906, 19.243, and 18.923 cm^-1 obtained at 0.033 MeV for glasses dopped by PbO, Fe₂O₃, MgO, and Al₂O₃, respectively. Therefore, glasses doped with 5 mol.% of PbO have high gamma-ray shielding capacities followed by glasses doped by Fe₂O₃.

• Journal of Korea Water Resources Association
• /
• v.53 no.2
• /
• pp.107-119
• /
• 2020

This study analyzed past drought characteristics based on the observed rainfall data and performed a long-term outlook for future extreme droughts using Representative Concentration Pathways 8.5 (RCP 8.5) climate change scenarios. Standardized Precipitation Index (SPI) used duration of 1, 3, 6, 9 and 12 months, a meteorological drought index, was applied for quantitative drought analysis. A single long-term time series was constructed by combining daily rainfall observation data and RCP scenario. The constructed data was used as SPI input factors for each different duration. For the analysis of meteorological drought observed relatively long-term since 1954 in Korea, 12 rainfall stations were selected and applied 10 general circulation models (GCM) at the same point. In order to analyze drought characteristics according to climate change, trend analysis and clustering were performed. For non-stationary frequency analysis using sampling technique, we adopted the technique DEMC that combines Bayesian-based differential evolution ("DE") and Markov chain Monte Carlo ("MCMC"). A non-stationary drought frequency analysis was used to derive Severity-Duration-Frequency (SDF) curves for the 12 locations. A quantitative outlook for future droughts was carried out by deriving SDF curves with long-term hydrologic data assuming non-stationarity, and by quantitatively identifying potential drought risks. As a result of performing cluster analysis to identify the spatial characteristics, it was analyzed that there is a high risk of drought in the future in Jeonju, Gwangju, Yeosun, Mokpo, and Chupyeongryeong except Jeju corresponding to Zone 1-2, 2, and 3-2. They could be efficiently utilized in future drought management policies.

• Journal of Radiation Protection and Research
• /
• v.25 no.4
• /
• pp.233-239
• /
• 2000

The sensitivity analysis of input parameters was Performed fer an ingestion dose assessment model (U. S. NRC's Regulatory Guide 1.109 model) from routine releases of radionuclides. In this study, three kinds of typical Korean foodstuffs (rice, leaff vegetables, milk) and two kinds of radionuclides $(^{l37}Cs,\;^{131}I)$ were considered. The values of input parameters were sampled using a Latin hypercube sampling technique based on Monte Carlo approach. Sensitivity indices, which represent the influence or the importance of input parameters for predictive results, were quantitatively expressed by the partial rank correlation coefficients. As the results, the ratio of the interception fraction to the yield of agricultural plants and the human consumption rate were sensitive input parameters for the considered foodstuffs and radionuclides. Additionally, in case of milk, the transfer factor of radionuclides from animal intake to milk and the daily intake rate of feedstuffs were sensitive input parameters. The weathering removal half-life and the delay time from food production to human consumption were relatively sensitive for $^{137}Cs$ and $^{131}I$ depositions, respectively.

• Journal of the Korean Geotechnical Society
• /
• v.26 no.3
• /
• pp.59-68
• /
• 2010

Systematic risk management is necessary in grand scaled urban construction because of the existence of complicated and various risk factors. Problems of obstructions, adjacent structures, safety, environment, traffic and geotechnical properties need to be solved because urban construction is progressed in limited space not as general earthwork. Therefore the establishment of special risk management system is necessary to manage not only geotechnical properties but also social and cultural uncertainties. This research presents the technique analysis by the current state of risk management technique. Risk factors were noticed and the importance of each factor was estimated through survey. The systemically categorized database was established. Risk extraction module, matrix and score module were developed based on the database. Expected construction budget and time distribution can be computed by Monte Carlo analysis of probabilities and influences. Construction budgets and time distributions of before and after response can be compared and analyzed 80 the risks are manageable for entire whole construction time. This system will be the foundation of standardization and integration. Procurement, efficiency improvement, effective time and resource management are available through integrated management technique development and application. Conclusively decrease in cost and time is expected by systemization of project management.

• Journal of the Korean Institute of Gas
• /
• v.21 no.5
• /
• pp.16-26
• /
• 2017

In chemical industry plants, the raw material, intermediate and final products can leak from unstable joints of flanges and valves as well as cracks of storage tanks. From the safety and economic standpoints, it is very important to understand whether leaks or not and leakage rate. The OGI(optical gas image) technique can tell gas leakages, but cannot give the leakage rate. Some special OGI devices can show the kind of gas in different color concentration in different darkness. Therefore the research on quantification of OGI is necessary. In this research, we have developed the practical method to quantify OGI of methane leakage. To estimate 3-dimensional gas leakages distribution from 2-dimensional OGI, the Monte Carlo Probability technique was applied. First the number of points in the area of width(2.54 cm) and length(2.54 cm) in OGI was counted. Total no of each experiment was compared with the measured flow rate. The correlation average between total points and measured flow rate was found to be 0.980. Reversely we estimated the leakage rate of OGI by use of the correlation table. The results showed good agreement between the estimation value and the measured value.