• Nuclear Engineering and Technology
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• v.48 no.3
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.

• Journal of the Korean Society of Radiology
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• v.85 no.5
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• pp.834-847
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• 2024

Artificial intelligence (AI) is transforming radiology with improved diagnostic accuracy and efficiency, but prediction uncertainty remains a critical challenge. This review examines key sources of uncertainty-out-of-distribution, aleatoric, and model uncertainties-and highlights the importance of independent confidence metrics and explainable AI for safe integration. Independent confidence metrics assess the reliability of AI predictions, while explainable AI provides transparency, enhancing collaboration between AI and radiologists. The development of zero-error tolerance models, designed to minimize errors, sets new standards for safety. Addressing these challenges will enable AI to become a trusted partner in radiology, advancing care standards and patient outcomes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1376-1391
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• 2015

In bus delay-tolerant networks, the route of bus is determinate but its arrival time is indeterminate. However, most conventional approaches predict future contact without considering its uncertainty, which makes a limitation on routing performance. A novel approach is proposed by employing interval algebra to characterize the contact's uncertainty and time-varying nature. The contact is predicted by using the Bayesian estimation to achieve a better routing performance. Simulation results show that this approach achieves a good balance between delivery latency and delivery ratio.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.11a
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• pp.459-468
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• 2009

Analysis of the uncertainty to have engineering solution of gas-dynamic and hydrodynamic problems is based on the comparison the prospective engineering solution with experimental result. In this paper, the mathematical model to estimate heat flux along gas-dynamic channel wall and the solution sequence are shown. Statistical information and generalizing experimental characteristics about gas- and hydro-dynamic channels were applied to the mathematical model. As the results, it is possible to draw a conclusion that models of the integrated approach, using the averaged statistical data of generalizing characteristics for a turbulent flow, without consideration of the turbulent mechanism (characteristic pulsations), can predict a nominal operating regime for gas-dynamic and hydrodynamic systems. The probable deviation of operating regime for newly designed the gas-dynamic channel can achieve 20% from a regime predicted on a basis 1-D or 3-D modelling irrespective of a kind of used models.

• Journal of the Korea Fashion and Costume Design Association
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• v.11 no.3
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• pp.17-25
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• 2009

The purpose of this study is to help understanding of abstract patterns and to play a guideline's role in the development of designs and the prediction of trends for present and future fashion designers and textile designers. The summary of this study's results is like followings. Formative characteristics of abstract pattern in 2000's fashion are Impromptu, Anti-mechanism, Superimposing, Disorder. First, Impromptu is rebounce against uniformity, mechanism, man-created beauty Second, Anti-mechanism represents unfinishing, unbalance, inaccuracy and relates with each traditional of nation or ethnic group. Third, Presupposing transparency Superimposing offsets each patterns. So Their images are ambiguity, ununiformity, unequality, incompletion, uncertainty and so on. Fourth, Disorder breaks and ignores physical balance, rule, order and so on. These images represent uncertainty, freedom, naturality. From this result, I can interpret that these images are representing of humanism reacting about uniformity, mechanism, man-created beauty, completeness of modernism since the Industrial Revolution.

• Geomechanics and Engineering
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• v.2 no.1
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• pp.57-69
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• 2010

A major consideration in the design of tunnels in urban areas is the prediction of the ground movements and surface settlements associated with the tunneling operations. Excessive ground movements can damage adjacent building and utilities. In this paper, a neural network model is used to predict the maximum surface settlement, based on instrumented results from three separate EPB tunneling projects in Singapore. This paper demonstrates that by coupling the trained neural network model to a spreadsheet optimization technique, the reliability assessment of the settlement serviceability limit state can be carried out using the first-order reliability method. With this method, it is possible to carry out sensitivity studies to examine the effect of the level of uncertainty of each parameter uncertainty on the probability that the serviceability limit state has been exceeded.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.307-307
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• 2019

General Ciculation Model (GCM) 모형에 대한 평가를 본 연구에서 수행한다. 모형의 적용을 위해서는 국지적 일 강우량 및 기온자료를 이용한다. 31개의 GCM 모의를 통해 도출되는 결과가 성능 평가에서 활용되었다. 일 최대, 최소 기온와 강우량이 파키스탄 지역을 대상으로 모의되었다. 모의를 위해서는 Gridded 데이터가 적용되었으며 각각 Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation, Berkeley Earth Surface Temperature, Princeton Global Meteorological Forcing, Climate Prediction Centre에 해당된다. GCM의 순위를 결정하기 위해서는 Symmetrical Uncertainty 방법이 이용된다. 결과를 통해서 Gridded 데이터의 종류에 따라 가장 높은 효율을 나타내는 GCM의 공간 분포가 달라진다는 점을 확인하였다. 이러한 특성은 기온과 강우량 자료 모두에서 확인된다. 기온의 경우에는 Commonwealth Scientific and Industrial Research Organization, Australia-MK3-6-0과 Max Planck Institute-ESM-LR이 우수한 결과를 모의하는 것으로 나타났다. 반면 강우량의 경우에는 EC-Earth와 MIROC가 우수한 것으로 나타났다. 파키스탄 지역에서의 기온 및 강우량 자료의 합리적 반영을 위해서는 ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES, MIRCO5와 같은 6개 GCM을 이용하였을 때 다양한 기상 인자를 고려한 모의가 가능한 것으로 평가된다.

• Journal of Environmental Science International
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• v.24 no.9
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• pp.1131-1138
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• 2015

Radon exhalation rates have been determined for samples of concrete, gypsum board, marble, and tile among building materials that are used in domestic construction environment. Radon emanation was measured using the closed chamber method based on CR-39 nuclear track detectors. The radon concentrations in apartments of 100 households in Seoul, Busan and Gyeonggi Provinces were measured to verify the prediction model of indoor radon concentration. The results obtained by the four samples showed the largest radon exhalation rate of $0.34314Bq/m^2{\cdot}h$ for sample concrete. The radon concentration contribution to indoor radon in the house due to exhalation from the concrete was $31.006{\pm}7.529Bq/m^3$. The difference between the prediction concentration and actual measured concentration was believed to be due to the uncertainty resulting from the model implementation.

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.263-277
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• 2016

Recently, 3D printing technology has been considered as a core applicable technology because it brings many improvements such as the development of medical technology, medical customization, and reducing production cost and shortening treatment period. This research suggests a market prediction framework for medical 3D printing business. As an immature market situation, it is important to control some uncertainty for market prediction such as a customers' conversion rate. So we adopt decision making tree (DMT) model which used to choose an optimal decision making among diverse pathway. Among medical industries this paper just focuses on dentistry business. For predicting a 5 year period trend expected market size, we identified some replaceable denture procedure by 3D printing, collected related data, controlled uncertain variables. The result shows that medical 3D printing business could be a market of 28.2 billion won at 1st year and in the end of fifth year it could become on a scale of 61.1 billion won market.

• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.355-362
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• 2012

The aim of this study was to develop a bundle position-wise linear model (BPLM) to predict Pressure Tube (PT) diametral creep employing the previously measured PT diameters and operating conditions. There are twelve bundles in a fuel channel, and for each bundle a linear model was developed by using the dependent variables, such as the fast neutron fluences and the bundle coolant temperatures. The training data set was selected using the subtractive clustering method. The data of 39 channels that consist of 80 percent of a total of 49 measured channels from Units 2, 3, and 4 of the Wolsung nuclear plant in Korea were used to develop the BPLM. The data from the remaining 10 channels were used to test the developed BPLM. The BPLM was optimized by the maximum likelihood estimation method. The developed BPLM to predict PT diametral creep was verified using the operating data gathered from Units 2, 3, and 4. Two error components for the BPLM, which are the epistemic error and the aleatory error, were generated. The diametral creep prediction and two error components will be used for the generation of the regional overpower trip setpoint at the corresponding effective full power days. The root mean square (RMS) errors were also generated and compared to those from the current prediction method. The RMS errors were found to be less than the previous errors.