• Journal of Korean Society for Quality Management
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• v.51 no.3
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• pp.419-434
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• 2023

Purpose: Measurement results obtained under non-ideal measurement environment conditions may contain uncertain factors. As a result, the reliability of measurement results may be deteriorated. In this study, we tried to find ways to improve quality by evaluating and applying measurement uncertainty based on GUM. Methods: In the flatness measurement of semiconductor parts, uncertainty factors that could occur under actual environmental conditions of workers were derived, and measurement uncertainties were calculated, and methods for minimizing the main factors affecting the measurement results were analyzed. Results: Depending on the part and the coordinate measuring machine, it was shown that the effect of dispersion caused by repeated measurements as type A uncertainty and the effect of the calibration results of equipment as type B uncertainty have the main influence. Conclusion: Depending on the uncertainty factors of type A and type B and the influence of the total expanded uncertainty, the central value and confidence interval of the initial measurement results showed fluctuations. It is considered that analysis and measures for the main uncertainty factors are needed as quality improvement in the industrial field.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• International Journal of Aeronautical and Space Sciences
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• v.5 no.1
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• pp.46-56
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• 2004

The verification and improvement of the measurement uncertainty have beenperformed in the altitude test facility for small gas turbine engines, which was built atthe Korea Aerospace Research Institute (KARI) in October 1999. This test is performedwith a single spool turbojet engine at several flight conditions. This paper discussesthe evaluation and validation process for the measurement uncertainty improvements usedin the altitude test facility. The evaluation process, defined as tests before the facilitymodification, shows that the major contnbutors to the measurement uncertainty are theflow meter discharge coefficient, the inlet static and total pressures, the cell pressureand the fuel flow rate. The measurement uncertainty is focused on the primary parametersof the engine performance such as airflow rate, thrust and specific fuel consumption (SFC).The validation process, defined as tests after the facility modification, shows that themeasurement uncertainty, in seal level condition, is tmproved to the acceptable level throughthe facility modification. In altitude test conditions, the measurement uncertainties arenot improved as much as the uncertainty in sea level condition.

• Journal of muscle and joint health
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• v.25 no.2
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• pp.148-156
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• 2018

Purpose: The purposes of this study were to examine health-related quality of life and to identify the mediating effect of resilience on the relationship among uncertainty, distress, and health-related quality of life in lung cancer patients. Methods: A total of 149 lung cancer patients visiting the D hospital in B city completed a questionnaire, including demographic and disease-specific characteristics, uncertainty, distress, resilience, and health-related quality of life. Data were analyzed with descriptive analysis, t-tests, ANOVA, and multiple regression analyses via SPSS 24. Results: Health-related quality of life was $81.00{\pm}21.39$ (range 0~136) in lung cancer patients. In the results of hierarchical regression analyses, the health-related quality of life was associated with education, uncertainty, distress, and resilience. However, there was no mediating effect of resilience on the relationship among uncertainty, distress, and health-related quality of life. Conclusion: Lung cancer patients with high uncertainty and distress and low resilience could experience low health-related quality of life. In order to reduce uncertainty and distress, it is necessary to provide more detailed, systematic information and support, while reinforcing positive thinking.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.155-155
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• 2018

Uncertainty in flood forecasting using a coupled meteorological and hydrological model is arisen from various sources, especially the uncertainty comes from the inaccuracy of Quantitative Precipitation Forecasts (QPFs). In order to improve the capability of flood forecast, the uncertainty estimation and mitigation are required to perform. This study is conducted to investigate and reduce such uncertainty. First, ensemble QPFs are generated by using Monte - Carlo simulation, then each ensemble member is forced as input for a hydrological model to obtain ensemble streamflow prediction. Likelihood measures are evaluated to identify feasible member. These members are retained to define upper and lower limits of the uncertainty interval and assess the uncertainty. To mitigate the uncertainty for very short lead time, a blending method, which merges the ensemble QPFs with radar-based rainfall prediction considering both qualitative and quantitative skills, is proposed. Finally, blending bias ratios, which are estimated from previous time step, are used to update the members over total lead time. The proposed method is verified for the two flood events in 2013 and 2016 in the Yeonguol and Soyang watersheds that are located in the Han River basin, South Korea. The uncertainty in flood forecasting using a coupled Local Data Assimilation and Prediction System (LDAPS) and Sejong University Rainfall - Runoff (SURR) model is investigated and then mitigated by blending the generated ensemble LDAPS members with radar-based rainfall prediction that uses McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE). The results show that the uncertainty of flood forecasting using the coupled model increases when the lead time is longer. The mitigation method indicates its effectiveness for mitigating the uncertainty with the increases of the percentage of feasible member (POFM) and the ratio of the number of observations that fall into the uncertainty interval (p-factor).

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.405-419
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• 2022

Flood frequency analysis commonly used to design the hydraulic structures to minimize flood damage includes uncertainty. Therefore, the most appropriate design flood within a uncertainty should be selected in the final stage of a hydraulic structure, but related studies were rarely carried out. The total expected cost function introduced into the flood frequency analysis is a new approach for determining the optimal design flood. This procedure has been used as UNCODE (UNcertainty COmpliant DEsign), but the application has not yet been introduced in South Korea. This study introduced the mathematical procedure of UNCODE and calculated the optimal design flood using the annual maximum inflow of hydroelectric dams located in the Bukhan River system and results were compared with that of the existing flood frequency. The parameter uncertainty was considered in the total expected cost function using the Gumbel and the GEV distribution, and the Metropolis-Hastings algorithm was used to sample the parameters. In this study, cost function and damage function were assumed to be a first-order linear function. It was found that the medians of the optimal design flood for 4 Hydroelectric dams, 2 probability distributions, and 2 return periods were calculated to be somewhat larger than the design flood by the existing flood frequency analysis. In the future, it is needed to develop the practical approximated procedure to UNCODE.

• 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.

• KDI Journal of Economic Policy
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• v.42 no.4
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• pp.117-138
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• 2020

Few studies have specifically focused on the uncertainty of demand forecasting despite the fact that uncertainty is the one of greatest risks for governments and private partners in PPP projects. This study presents a methodology for finding robust contract conditions considering uncertainty in travel demand forecasting in a PPP project. Through a case study of an urban railway PPP project in Korea, this study uncovered the risk of excessive government payments to private partners due to the uncertainty in contracted forecast ridership levels. The results allow the suggestion that robust contract conditions could reduce the expected total level of government payments and lower user fees while maintaining profitability of the project. This study offers a framework that assists contract negotiators and gives them more information regarding financial risks and vulnerabilities and helps them to quantify the likelihood of these vulnerabilities coming into play during PPP projects.

• Journal of Korean Academy of Nursing
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• v.33 no.8
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• pp.1127-1134
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• 2003

Background. Although there have been a great number of research studies based on the model of uncertainty in illness, few studies have considered the appraisal portion of model. Purpose. The purpose of this study was to test the mediating effect of appraisal in the model of uncertainty in illness. Additionally, this study aimed to examine the relationships among uncertainty, symptom severity, appraisal, and anxiety in patients newly diagnosed with atrial fibrillation. Methods. This study employed a descriptive correlational and cross-sectional survey design using a face-to-face interview method. Patients diagnosed with atrial fibrillation within the previous 6 months prior to data collection were interviewed by Mishel Uncertainty in Illness Scale-Community Form, appraisal scale, Symptom Checklist-Severity V.3, and State Anxiety Inventory. Results. A total of 81 patients with atrial fibrillation were recruited from two large urban medical centers in Cleveland, Ohio, U.S.A.. Symptom severity was the significant variable in explaining uncertainty ($\beta$=0.34). Individuals with greater symptom severity perceived more uncertainty. Uncertainty was appraised as a danger rather than opportunity, and those with greater uncertainty appraised a greater danger (p<.0l). While the appraisal of opportunity had the negative relationship with anxiety (r=-0.25), the appraisal of danger was positively associated with anxiety (r=0.78). The measure of goodness of fit (Q) of the model was .7863, and the significant test (X$^2$) for the Q was statistically significant (df =3, p<.00l). Accordingly, the overall mediating model of uncertainty in illness was proven not to be fit to the empirical data of patients with atrial fibrillation. Consequently, the mediating effect of appraisal was not supported by the empirical data of this study. Conclusion. The findings of this study were discussed in terms of their relevance compared with those of previous studies or theoretical framework and the plausible explanations on study findings. Lastly, in order to expand the present body of knowledge on uncertainty in illness model, recommendations for the future nursing studies were included.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1902-1908
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• 2022

In many of the complex systems modeled in the field of nuclear engineering, it is often useful to use linear regression-based analyses to analyze relationships between model parameters and responses of interests. In cases where the response of interest is calculated by a simulation which uses Monte Carlo methods, there will be some uncertainty in the responses. Further, the reduction of this uncertainty increases the time necessary to run each calculation. This paper presents some discussion on how the Monte Carlo error in the response of interest influences the error in computed linear regression coefficients. A mathematical justification is given that shows that when performing linear regression in these scenarios, the error in regression coefficients can be largely independent of the Monte Carlo error in each individual calculation. This condition is only true if the total number of calculations are scaled to have a constant total time, or amount of work, for all calculations. An application with a simple pin cell model is used to demonstrate these observations in a practical problem.