• 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 Home Health Care Nursing
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• v.24 no.1
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• pp.61-68
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• 2017

Purpose: The purpose of this study was to understand how disease activity and uncertainty influence anxiety and depression in patients with ankylosing spondylitis Methods: Participants were 125 patients with ankylosing spondylitis who had attended the rheumatology division of a university hospital. A structured questionnaire was used to assess disease activity, uncertainty, anxiety, and depression. The data gathered were analyzed using t-tests, ANOVAs, Pearson correlation coefficients, and a multiple regression. Results: Differences were observed in anxiety based on job status, and in depression based on age, marriage, and job status. Uncertainty and disease activity predicted patients' anxiety and explained 40% of the variance in this measure, and the relative influence of uncertainty (${\beta}=.38$, p<.001) was larger than that of disease activity (${\beta}=.30$, p<.001). Furthermore, uncertainty and disease activity predicted depression and explained 36% of the variance therein, and the relative influence of uncertainty (${\beta}=.27$, p=.002) and disease activity (${\beta}=.27$, p=.003) was similar. Conclusion: This study confirmed that disease activity and uncertainty influenced anxiety and depression in patients with ankylosing spondylitis. Therefore, efforts to decrease anxiety and depression in patients with this condition must take into consideration disease activity when implementing nursing interventions and should include strategies to lower uncertainty.

• Journal of KIISE:Databases
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• v.33 no.3
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• pp.261-270
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• 2006

Positional data of moving objects can be regularly sampled in order to minimize the cost of data collection in LBS. Since position data which are regularly sampled cannot include the changes of position occurred between sampling periods, sampled position data differ from the data predicted by a time parameterized linear function. Uncertain position data caused by these differences make the accuracy of the range queries for present positions diminish in the TPR tree. In this paper, we propose the uncertainty region to handle the range queries for uncertain position data. The uncertainty region is defined by the position data predicted by the time parameterized linear function and the estimated uncertainty error. We also present the weighted recent uncertainty error policy and the kalman filter policy to estimate the uncertainty error. For performance test, the query processor based by the uncertainty region is implemented in the TPR tree. The experiments show that the Proposed query processing methods are more accurate than the existing method by 15%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.12
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• pp.1107-1114
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• 2011

In this paper, the fatigue life of a laser peened structure was predicted. In order to calculate residual stress induced by laser peening finite element simulation was carried out. Modified Goodman equation was used to consider the effect of compressive residual stress induced by laser peening in fatigue analysis. In addition, additive adjustment factor approach was applied to consider S-N curve model uncertainty. Consequently, the reliable bounds of the predicted fatigue life of the laser peened structure was determined.

• 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.

• Structural Engineering and Mechanics
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• v.38 no.4
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• pp.503-516
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• 2011

Uncertainty in concrete properties, including concrete modulus of elasticity and modulus of rupture, are predicted by developing a microstructural homogenization model. The homogenization model is developed by analyzing a concrete representative volume element (RVE) using the finite element (FE) method. The concrete RVE considers concrete as a three phase composite material including: cement paste, aggregate and interfacial transition zone (ITZ). The homogenization model allows for considering two sources of variability in concrete, randomly dispersed aggregates in the concrete matrix and uncertain mechanical properties of composite phases of concrete. Using the proposed homogenization technique, the uncertainty in concrete modulus of elasticity and modulus of rupture (described by numerical cumulative probability density function) are determined. Deflection uncertainty of reinforced concrete (RC) beams, propagated from uncertainties in concrete properties, is quantified using Monte Carlo (MC) simulation. Cracked plane frame analysis is used to account for tension stiffening in concrete. Concrete homogenization enables a unique opportunity to bridge the gap between concrete materials and structural modeling, which is necessary for realistic serviceability prediction.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.7
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• pp.791-798
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• 2011

All mechanical-system parameters have uncertainty, and this uncertainty directly affects system performances and results in a decrease in the manufacturing outputs. In particular, since the size of a MEMS system is extremely small, the manufacturing tolerances of a MEMS system are relatively large when compared to the tolerances of a macro-scale system. High manufacturing tolerances result from an increase in the uncertainty of the system parameters, thereby affecting the performances and manufacturing yields. In this paper, the performance uncertainty of a MEMS accelerometer due to system parameter uncertainty is analyzed by using several uncertainty analysis methods. Finally, the performance distributions and manufacturing yields of the MEMS accelerometer are predicted.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.627-630
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• 2002

A methodology based on factorial design and Motto Carlo methods is developed and implemented for incorporating uncertainties within a multiphase subsurface flow and transport simulation system. Due to uncertainties in intrinsic permeability and longitudinal dispersivity, the predicted output is also uncertain based on the well-developed multiphase compositional simulator. The simulation results reveal that the uncertainties in input parameters pose considerable influences on the predicted output, and the mean and variance of permeability will have significant impacts on the modeling output. The proposed method offers an effective tool for evaluating uncertainty in multiphase flow simulation system.

• Journal of Environmental Health Sciences
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• v.46 no.2
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• pp.136-149
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• 2020

Objectives: The purpose of this study was to perform an aggregate human risk assessment for benzene in an industrial complex using the CalTOX model and to improve the reliability and predictability of the model by analyzing the uncertainty and sensitivity of the predicted assessment results. Methods: The CalTOX^TM 4.0 beta model was used to evaluate a selected region, and @Risk 7.6 software was used to analyze uncertainty and sensitivity. Results: As a result of performing the aggregate risk assessment on the assumption that 6.45E+04 g/d of benzene would be emitted into the atmosphere over two decades, 3% of the daily source term to air remained in the selected region, and 97% (6.26E+04 g/d) moved out of the region. As for exposure by breathing, the predicted LADD_inhalation was 2.14E-04 mg/kg-d, and that was assessed as making a 99.99% contribution to the LADD_total. Regarding human Risk_cancer assessment, the predicted human cancer risk was 5.19E-06 (95% CI; 4.07E-06-6.81E-06) (in the 95th percentile corresponding to the highest exposure level, a confidence interval of 90%). As a result of analyzing sensitivity, 'source term to air' was identified as the most influential variable, followed by 'exposure time, active indoors (h/day)', and 'exposure duration (years)'. Conclusions: As for the results of the human cancer risk assessment for the selected region, the predicted human cancer risk was 5.19E-06 (95% CI; 4.07E-06-6.81E-06) (in the 95th percentile, corresponding to the highest exposure level, a confidence interval of 90%). As a result of analyzing sensitivity, 'source term to air' was found to be most influential.

• Geomechanics and Engineering
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• v.11 no.2
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• pp.211-234
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• 2016

A total of 99 full-scale field load tests at 22 sites were compiled for this study to elucidate several issues related to the load-displacement behaviour of belled piers under axial uplift loading, including (1) interpretation criteria to define various elastic, inelastic, and "failure" states for each load test from the load-displacement curve; (2) generalized correlations among these states and determinations to the predicted ultimate uplift resistances; (3) uncertainty in the resistance model factor statistics required for reliability-based ultimate limit state (ULS) design; (4) uncertainty associated with the normalized load-displacement curves and the resulting model factor statistics required for reliability-based serviceability limit state (SLS) design; and (5) variations of the combined ULS and SLS model factor statistics for reliability-based limit state designs. The approaches discussed in this study are practical and grounded realistically on the load tests of belled piers with minimal assumptions. The results on the characterization and uncertainty of uplift load-displacement behaviour of belled piers could be served as to extend the early contributions for reliability-based ULS and SLS designs.