• Smart Structures and Systems
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• 제24권4호
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• 한국대기환경학회지
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• 제3권1호
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• pp.34-40
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• 1987

The Texas Climatological Model (TCM) Predicts long-term pollutant concentrations for a rectilinear array or receptors defined by the user. This paper describes the TCM and compares predictions from TCM with predictions from the Climatological Dispersion Model (CDMQC). A number of model runs have been made with the TCM and CDMQC using the same source inventories and sets of climatology. The concentrations predicted by these two models are compared and the result of several types of statistical analyses are reported. In most cases, the TCM predicts concentrations that are equivalent to those predicted by the CDMQC. However, in certain cases, the CDMQC tends to predict concentrations that are unrealistically high. In the computer time, the TCM requires about one-eights of the computer time used by the CDMQC.

• 상하수도학회지
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• 제30권2호
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• pp.179-185
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• 2016

In this study, the RDII predictions were compared using two methodologies, i.e., the RTK-based and regression methods. Long-term (1/1/2011~12/31/2011) monitoring data, which consists of 10-min interval streamflow and the amount of precipitation, were collected at the domestic study area (1.36 km2 located in H county), and used for the construction of the RDII prediction models. The RTK method employs super position of tri-triangles, and each triangle (called, unit hydrograph) is defined by three parameters (i.e., R, T and K) determined/optimized using Genetic Algorithm (GA). In regression method, the MovingAverage (MA) filtering was used for data processing. Accuracies of RDII predictions from these two approaches were evaluated by comparing the root mean square error (RMSE) values from each model, in which the values were calculated to 320.613 (RTK method) and 420.653 (regression method), respectively. As a results, the RTK method was found to be more suitable for RDII prediction during extreme rainfall event, than the regression method.

• Ocean and Polar Research
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• 제37권1호
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• pp.1-9
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• 2015

An approach based on the combined use of a 2D shallow water model and analytical 1D long wave run-up theory is proposed which facilitates the forecasting of tsunami run-up heights in a more rapid way, compared with the statistical or empirical run-up ratio method or resorting to complicated coastal inundation models. Its application is advantageous for long-term tsunami predictions based on the modeling of many prognostic tsunami scenarios. The modeling of the Chilean tsunami on February 27, 2010 has been performed, and the estimations of run-up heights are found to be in good agreement with available observations.

• Nuclear Engineering and Technology
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• 제55권9호
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4722-4730
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• 2022

This paper introduces a new point depletion-based source term calculation code named BESNA (Bateman Equation Solver for Nuclear Applications), which is aimed to estimate nuclide inventories and source terms from spent nuclear fuels. The BESNA code employs a new modified CE/CM (Constant Extrapolation - Constant Midpoint) predictor-corrector scheme in depletion calculations for improving computational efficiency. In this modified CE/CM scheme, the decay components leading to the large norm of the depletion matrix are excluded in the corrector, and hence the corrector calculation involves only the reaction components, which can be efficiently solved with the Talyor Expansion Method (TEM). The numerical test shows that the new scheme substantially reduces computing time without loss of accuracy in comparison with the conventional scheme using CRAM (Chebyshev Rational Approximation Method), especially when the substep calculations are applied. The depletion calculation and source term estimation capability of BESNA are verified and validated through several problems, where results from BESNA are compared with those calculated by other codes as well as measured data. The analysis results show the computational efficiency of the new modified scheme and the reliability of BESNA in both isotopic predictions and source term estimations.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.22-34
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• 2017

K-water의 분포형 강우-유출모형인 K-DRUM(K-water hydrologic & hydraulic Distributed RUnoff Model)은 단기예측 강수자료를 통해 댐의 예측 유출량 및 수위를 산출하는 모형으로, 장기적인 수문기상정보를 획득하기 위해서는 장기예측 강수자료를 입력자료로 사용할 필요가 있다. 본 연구에서는 2014년 국내에 도입된 기상청의 계절예측시스템인 GloSea5(Global Seasonal Forecast System version 5) 예측 강수량 앙상블을 K-DRUM의 입력자료로 사용하는 프로그램을 개발하였으며, 이를 통해 산출된 예측 유출량 앙상블 자료를 기반으로 댐 운영자에게 수문기상정보를 제공하는 웹 기반 확률장기예보 활용 물관리 의사결정지원시스템을 함께 구축하였다. GloSea5의 예측 결과를 입력자료로 사용하기 위하여 대상 댐 유역에 대해 전처리 과정을 수행한 후 편의보정기법을 적용하여 예측 강수 앙상블 자료를 산출하였으며, 이를 K-DRUM에 입력하여 수행하여 예측 유출량을 산출하였다. 이 과정에서 편의보정된 강수량과 강우-유출모형에서 산정된 예측 유출량은 그래프와 테이블로 함께 표출할 수 있도록 하였다. 본 연구의 결과를 통해 시스템의 사용자는 예측 강수량과 유출량을 토대로 댐의 방류량을 조정함으로써 댐 수위 모의 운영을 수행할 수 있게 되어 장기적인 물관리 의사결정에 도움이 될 것으로 기대된다.

• 한국사회정책
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• 제25권1호
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• pp.287-320
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• 2018

이 연구는 노인장기요양서비스 분야에서 복지기술 준시장(quasi-market)의 성격과 문제점을 파악하고 향후 복지기술 발전에의 함의를 도출하고자 하였다. 개발자들에 대한 질적 인터뷰를 분석한 결과, 우리나라의 노인장기요양보험제도가 가지는 제도적 특성은 그 자체로 복지기술 시장을 제약하고 있음을 확인하였다. 즉 노인장기요양보험제도는 복지용구에 대한 제도적 지원을 통해 중증 장기요양보험대상자에 대한 제한된 유효수요 창출과 낮은 복지기술 수준의 복지용구 시장형성에 도움이 되었으나, 노인장기요양보험이 취하고 있는 정책 패러다임은 오히려 복지기술 시장의 확대와 질적 성장을 제약하는 왜곡된 관계의 양면성을 보이고 있다. 구체적으로 보면, 중증요양대상자 중심의 일방적 돌봄과 의존, 제도 내외 서비스 제공자간의 단절적 보호, 낮은 수가, 지원품목 지정 등의 엄격한 규제, 급여방식 등의 노인장기요양보험의 특징으로 인해, 복지용구 시장은 급여지원이 되는 범위 내에서 협소하게 형성되어 그 틀을 벗어나지 못하고 있다. 또한 노인들의 자립을 유도하는 복지기술보다는 중증 노인들의 수발에 필요한 최소한의 획일적인 복지용구가 주로 개발되고 있었다. 정부지원이 낮은 수준이면서도 엄격한 규제로 인해 질적 경쟁이 발생할 수 없는 구조인 것이다. 본 연구는 결론에서 이러한 연구결과를 토대로 복지기술 시장에 대한 발전 전망과 정책적 방향을 제시하고자 하였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2014년도 춘계학술대회
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• pp.241-243
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• 2014

최근, 해안 침식 현상은 사회적, 경제적 측면에서 큰 영향을 미칠 수 있기 때문에, 각 지자체마다 각종 방지대책이 실시되고 있다. 그러나, 방지시설을 설치하기 전에 표사이동특성 등의 변화를 면밀히 검토하고, 이를 토대로 장기모니터링을 통한 지속적인 변화검토가 필수적이다. 본 연구에서는 현재 백사장 침식이 문제가 되고 있는 서해 중부 연안의 임의의 해빈(사빈)을 대상으로 장기 파랑특성분석을 통한 이상파랑, 계절별 탁월 파향, 파고, 주기 등을 고려한 평상파랑을 산정하고 이를 근거하여 수치모형실험을 통하여 파랑변형 및 표사이동에 대한 경향을 파악하였다.

• Nuclear Engineering and Technology
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• 제35권2호
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• pp.108-120
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• 2003

In assessing the long term post closure radiological safety assessment of a potential HLW repository in Korea, three categories of uncertainties exist. The first one is the scenario uncertainty where series of different natural events are translated into written statements. The second one is the modeling uncertatinty where different mathematical models are applied for an identical scenario. The last one is the data uncertainty which can be expressed in terms of probabilistic density functions. In this analysis, three different scenarios are seleceted; a small well scenario, a radiolysis scenario, and a naturally discharged scenario. The MASCOT-K and the AMBER, probabilistic safety assessment codes based on connection of sub-modules and a compartment theory respectively, are applied to assess annual individual doses for a generic biosphere. Results illustrate that for a given scenario, predictions from two different codes fairly match well each other But the discrepancies for the different scenarios are significant. However, total doses are still well below the guideline of 2 mRem/yr. Detailed analyses with model and data uncertainties are underway to further assure the safety of a Korean reference dispsoal concept.