• Journal of muscle and joint health
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• v.4 no.2
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• pp.277-296
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• 1997

This study was conducted to investigate the influencing factors on the appraisal of uncertainty in patients having rheumatoid arthritis. Subjects of the study constituted 528 patients who visited outpatient clinics of two university hospitals and one general hospital in Seoul. Self report questionnaires were used to measure the variables influencing the appraisal of uncertainty. Reliability coefficients of these instruments were found Cronbach's Alpha=$.70{\sim}.96$. In data analysis, SPSS PC 6.0 program was utilized for descriptive statistics, Pearson's correlation, logistic and multiple regression analysis. The results of logistic and multiple regression analysis were as follows 1) Among the independent variables, significant factors to explain the appraisal of uncertainty in patients were uncertainty(p<.001), severity of illness(p<.05), educational level (p<.05) and age (p<.05). 2) When patients appraised uncertainty as "Danger", significant factors to explain the appraisal of uncertainty were uncertainty(p<.0001), age(p<.0005), severity of illness(p<.001), educational level (p<.05). 3) When patients appraised uncertainty as "Opportunity", significant factors to predict the appraisal of uncertainty were uncertainty(p<.0005), social support(p<.0005), severity of illness(p<.005), credible authority(p<.05), age(p<.05) and educational level (p<.05).

• Journal of Korea Game Society
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• v.18 no.1
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• pp.105-114
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• 2018

Various data analysis methods used for customer type analysis are very important for game companies to understand their type and characteristics in an attempt to plan customized content for our customers and to provide more convenient services. In this paper, we propose a k-mode cluster analysis algorithm that uses information uncertainty by extending information entropy to reduce information loss. Therefore, the measurement of the similarity of attributes is considered in two aspects. One is to measure the uncertainty between each attribute on the center of each partition and the other is to measure the uncertainty about the probability distribution of the uncertainty of each property. In particular, the uncertainty in attributes is taken into account in the non-probabilistic and probabilistic scales because the entropy of the attribute is transformed into probabilistic information to measure the uncertainty. The accuracy of the algorithm is observable to the result of cluster analysis based on the optimal initial value through extensive performance analysis and various indexes.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.353-362
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• 2014

The effects of nuclear data uncertainties are studied on a typical PWR fuel assembly model in the framework of the OECD Nuclear Energy Agency UAM (Uncertainty Analysis in Modeling) expert working group. The "Fast Total Monte Carlo" method is applied on a model for the Monte Carlo transport and burnup code SERPENT. Uncertainties on $k_{\infty}$, reaction rates, two-group cross sections, inventory and local pin power density during burnup are obtained, due to transport cross sections for the actinides and fission products, fission yields and thermal scattering data.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.396-404
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• 2021

It is important to verify that the contracted speed-power performance of a ship is satisfied in sea trials. International Organization for Standardization (ISO) has published the procedure for measuring and assessing ship speed during sea trials. The results obtained from actual sea conditions inevitably include various uncertainty factors. In this study, double run tests were performed on one container ship to analyze the uncertainty of sea trial on three maximum continuous rating conditions. The uncertainty factors and scale of uncertainty were examined based on the measured raw data during sea trial. The results indicate that the expanded uncertainty for ideal power performance is approximately ±1.4% at 95% confidence level (coverage factor k = 2) and most of the uncertainty factors were because of the shaft power measurement system.

• Journal of Environmental Impact Assessment
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• v.9 no.3
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• pp.185-202
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• 2000

Uncertainty and variability in Life Cycle Assessment(LCA) have been significant key issues in LCA methodology with techniques in other research area such as social and political science. Variability is understood as stemming from inherent variations in the real world, while uncertainty comes from inaccurate measurements, lack of data, model assumptions, etc. Related articles in this issues were reviewed for classification, distinguish and elaboration of probabilistic/stochastic health risk analysis application in LCA. Concept of focal zone, streamlining technique, scenario modelling and Monte Carlo/Latin Hypercube risk analysis were applied to the uncertainty/variability analysis of health risk in LCA. These results show that this general framework of multi-disciplinary methodology between probabilistic health risk assessment and LCA was of benefit to decision making process by suppling information about input/output data sensitivity, health effect priority and health risk distribution. There should be further research needs for case study using this methodology.

• Journal of Distribution Science
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• v.9 no.2
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• pp.123-129
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• 2011

The restaurant franchise industry is one that could benefit significantly from the use of intranet technology, from its potential for improving communications between franchisors and franchisees, to providing easier inventory and ordering processes. However, there is a level of trepidation among potential users about whether the technology would improve their work performance. This study sought to examine the relationships between perceived uncertainty and perceived usefulness of intranet technology in the restaurant franchise industry. Through a review of available literature, 10 sub-dimensions of perceived uncertainty (Duncan, 1972) and six sub-dimensions of perceived usefulness (Davis, 1989) were derived. Canonical correlation analysis was used to examine the relationships between these concepts using data collected from 163 franchising restaurant managers in South Korea. Findings from the data analysis demonstrates two negative factors and one positive factor in perceived uncertainty that influence perceived usefulness, thus offering some implications of what to consider when implementing an intranet system in a restaurant franchise.

• Structural Engineering and Mechanics
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• v.63 no.1
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• pp.47-53
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• 2017

The fundamental goal of this study is to minimize the uncertainty of the median fragility curve and to assess the structural vulnerability under earthquake excitation. Bayesian Inference with Markov Chain Monte Carlo (MCMC) simulation has been presented for efficient collapse response assessment of the independent intake water tower. The intake tower is significantly used as a diversion type of the hydropower station for maintaining power plant, reservoir and spillway tunnel. Therefore, the seismic fragility assessment of the intake tower is a pivotal component for estimating total system risk of the reservoir. In this investigation, an asymmetrical independent slender reinforced concrete structure is considered. The Bayesian Inference method provides the flexibility to integrate the prior information of collapse response data with the numerical analysis results. The preliminary information of risk data can be obtained from various sources like experiments, existing studies, and simplified linear dynamic analysis or nonlinear static analysis. The conventional lognormal model is used for plotting the fragility curve using the data from time history simulation and nonlinear static pushover analysis respectively. The Bayesian Inference approach is applied for integrating the data from both analyses with the help of MCMC simulation. The method achieves meaningful improvement of uncertainty associated with the fragility curve, and provides significant statistical and computational efficiency.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.523-533
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• 2018

The objective of this study is to analyze uncertainties of ensemble-based streamflow prediction method for model parameters and input data. ESP (Ensemble Streamflow Prediction) and BAYES-ESP (Bayesian-ESP) based on ABCD rainfall-runoff model were selected as streamflow prediction method. GLUE (Generalized Likelihood Uncertainty Estimation) was applied for the analysis of parameter uncertainty. The analysis of input uncertainty was performed according to the duration of meteorological scenarios for ESP. The result showed that parameter uncertainty was much more significant than input uncertainty for the ensemble-based streamflow prediction. It also indicated that the duration of observed meteorological data was appropriate to using more than 20 years. And the BAYES-ESP was effective to reduce uncertainty of ESP method. It is concluded that this analysis is meaningful for elaborating characteristics of ESP method and error factors of ensemble-based streamflow prediction method.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1562-1573
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• 2024

This study rigorously examined uncertainty in the TMI-1 benchmark within the Uncertainty Analysis in Modeling (UAM) benchmark suite using the STREAM/RAST-K two-step method. It presents two pivotal advancements in computational techniques: (1) Development of an uncertainty quantification (UQ) module and a specialized library for the pin-based pointwise energy slowing-down method (PSM), and (2) Application of Principal Component Analysis (PCA) for UQ. To evaluate the new computational framework, we conducted verification tests using SCALE 6.2.2. Results demonstrated that STREAM's performance closely matched SCALE 6.2.2, with a negligible uncertainty discrepancy of ±0.0078% in TMI-1 pin cell calculations. To assess the reliability of the PSM covariance library, we performed verification tests, comparing calculations with Calvik's two-term rational approximation (EQ 2-term) covariance library. These calculations included both pin-based and fuel assembly (FA-wise) computations, encompassing hot zero-power and hot full-power operational conditions. The uncertainties calculated using both the EQ 2-term and PSM resonance treatments were consistent, showing a deviation within ±0.054%. Additionally, the data compression process yielded compression ratios of 88.210% and 92.926% for on-the-fly and data-saving approaches, respectively, in TMI fuel assembly calculations. In summary, this study provides a comprehensive explanation of the PCA process used for UQ calculations and offers valuable insights into the robustness and reliability of newly developed computational methods, supported by rigorous verification tests.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1158-1164
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• 2007

Recently, the demand on the practical application of life-cycle cost effectiveness for design and rehabilitation of civil infrastructure is rapidly growing unprecedentedly in civil engineering practice. Accordingly, it is expected that the life-cycle cost in the 21st century will become a new paradigm for all engineering decision problems in practice. However, in spite of impressive progress in the researches on the LCC, so far, most researches in Koreahave only focused on roadway bridges, which are not applicable to railway bridges. Thus, this paper presents the formulation models and methods for uncertainty-based LCCA for railroad bridges consideringboth objective statistical data available in the agency database of railroad bridges management and subjective data obtained form interviews with experts of the railway agency, which are used to anew uncertainty-based expected maintenance/repair costs including lifetime indirect costs. For reliable assessment of the life-cycle maintenance/repair costs, statistical analysis considering maintenance history data and survey data including the subjective judgments of railway experts on maintenance/management of railroad bridges, are performed to categorize critical maintenance items and associated expected costs and uncertainty-based deterioration models are developed. Finally, the formulation for simulation-based LCC analysis of railway bridges with uncertainty-based deterioration models are applied to the design-decision problem, which is to select an optimal bridge type having minimum Life-Cycle cost among various railway bridges types such as steel plate girder bridge, and prestressed concrete girder bridge in the basic design phase.