• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.109-118
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• 2004

Life cycle assessment (LCA) usually involves some uncertainty. These uncertainties are generally divided in two categories such lack of data and data inaccuracy in life cycle inventory (LCI). This paper explo.es a methodology on dealing with uncertainty due to lack of data in LCI. In order to treat uncertainty of LCI data, a model for data uncertainty is proposed. The model works with probabilistic curves as inputs and with Monte Carlo Simulation techniques to propagate uncertainty. The probabilistic curves were derived from the results of survey in expert network and Monte Carlo Simulation was performed using the derived probabilistic curves. The results of Monte Carlo Simulation were verified by statistical test. The proposed approach should serve as a guide to improve data quality and deal with uncertainty of LCI data in LCA projects.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.1
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• pp.26-36
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• 2014

The uncertainty assessment is important to improve the reliability of emission inventory data. The DARS (Data Attribute Rating System) have recommended as the uncertainty assessment technic of emission inventory by U.S. EPA (Environmental Protection Agency) EIIP (Emission Inventory Improvement Program). The DARS score is based on the perceived quality of the emission factor and activity data. Scores are assigned to four attributes; measurement/method, source specificity, spatial congruity and temporal congruity. The resulting emission factor and activity rate scores are combined to arrive at an overall confidence rating for the inventory. So DARS is believed to be a useful tool and may provide more information about inventories than the usual qualitative grading procedures (e.g. A through E). In this study, the uncertainty assessment for 2009 CAPSS (Clean Air Policy Support System) emission inventory is conducted by DARS. According to the result of this uncertainty assessment, the uncertainty for fugitive dust emission data is higher than other sources, the uncertainty of emission factor for surface coating is the highest value, and the uncertainty of activity data for motor cycle is the highest value. Also it is analysed that the improvement of uncertainty for activity data is as much important as the improvement for emission factor to upgrade the reliability of CAPSS emission inventory.

• Smart Structures and Systems
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• v.15 no.3
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• pp.751-767
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• 2015

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.619-632
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• 2014

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1148-1155
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• 2020

This work presents two different methods for quantifying and propagating the uncertainty associated with fuel composition at end of life for cask criticality calculations. The first approach, the computational approach uses parametric uncertainty including those associated with nuclear data, fuel geometry, material composition, and plant operation to perform forward depletion on Monte-Carlo sampled inputs. These uncertainties are based on experimental and prior experience in criticality safety. The second approach, the data-driven approach relies on using radiochemcial assay data to derive code bias information. The code bias data is used to perturb the isotopic inventory in the data-driven approach. For both approaches, the uncertainty in k_eff for the cask is propagated by performing forward criticality calculations on sampled inputs using the distributions obtained from each approach. It is found that the data driven approach yielded a higher uncertainty than the computational approach by about 500 pcm. An exploration is also done to see if considering correlation between isotopes at end of life affects k_eff uncertainty, and the results demonstrate an effect of about 100 pcm.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.323-330
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• 2012

The development of data-based models requires uncertainty analysis to explain the accuracy of their predictions. In this paper, an uncertainty analysis of the support vector regression (SVR) model, which is a data-based model, was performed because previous research showed that the SVR method accurately estimates the collapse moments of wall-thinned pipe bends and elbows. The uncertainty analysis method used in this study was an analytic uncertainty analysis method, and estimates with a 95% confidence interval were obtained for 370 test data points. From the results, the prediction interval (PI) was very narrow, which means that the predicted values are quite accurate. Therefore, the proposed SVR method can be used effectively to assess and validate the integrity of the wall-thinned pipe bends and elbows.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.109-128
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• 2020

A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

• Asian Oncology Nursing
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• v.12 no.2
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• pp.139-146
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• 2012

Purpose: This study was designed to identify the impact of uncertainty degree and uncertainty appraisal on cancer patients resilience. Methods: A sample of 181 patients with cancer was recruited from a hospital in Incheon. Data were collected from May 20 to August 25, 2011. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient and multiple regression with the SPSS/WIN 12.0 program. Results: The resilience for cancer patients showed a significant relationship with uncertainty degree and uncertainty appraisal. The significant factors influencing resilience were uncertainty degree and uncertainty appraisal, they explained 26.5% of the variance. Conclusion: Patients with cancer were adversely affected by uncertaint which led to a negative effect on resilience. The result suggests that intervention programs to reduce the level of uncertainty among patients could improve the resilience of cancer patients.

• Child Health Nursing Research
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• v.8 no.1
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• pp.67-76
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• 2002

The purpose of this study was to investigate the level of uncertainty and anxiety in families of hospitalized children. Data were collected through self-report questionnaires which were constructed to include parent's perception of uncertainty and state anxiety. The subjects consisted of 126 families of hospitalized children in one university-affiliated hospital in Daegu. The data were analyzed by the SPSS program. The results were as follows; 1. The mean score of uncertainty was 64.70 (Range=31-95). The mean scores of subsets of the uncertainty were followed as: lack of clarity (2.59), unpredictability (2.46), lack of information (2.22) and ambiguity (2.14). 2. The mean score of state anxiety was 47.93 (Range=20-67). 3.The level of uncertainty was positively correlated to the level of state anxiety. 4. The level of anxiety was different depending on their religion and monthly income. The above findings indicated that the level of uncertainty and the state anxiety in families of hospitalized children were positively correlated. Therefore, nursing intervention for reducing uncertainty and anxiety and improving coping method should be provided for families of hospitalized children.