• Atmosphere
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• v.25 no.3
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• pp.511-520
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• 2015

Existing studies on radar rainfall uncertainties were performed to reduce the uncertainty for each stage by using bias correction during the quantitative radar rainfall estimation process. However, the studies do not provide quantitative comparison with the uncertainties for all stages. Consequently, this study proposes a suitable approach that can quantify the uncertainties at each stage of the quantitative radar rainfall estimation process. First, the new approach can present initial and final uncertainties, increasing or decreasing the uncertainty, and the uncertainty percentage at each stage. Furthermore, Maximum Entropy (ME) was applied to quantify the uncertainty in the entire process. Second, for the uncertainty quantification of radar rainfall estimation at each stage, this study used two quality control algorithms, two rainfall estimation relations, and two bias correction techniques as post-processing and progressed through all stages of the radar rainfall estimation. For the proposed approach, the final uncertainty (ME = 3.81) from the ME of the bias correction stage was the smallest while the uncertainty of the rainfall estimation stage was higher because of the use of an unsuitable relation. Additionally, the ME of the quality control was at 4.28 (112.34%), while that of the rainfall estimation was at 4.53 (118.90%), and that of the bias correction at 3.81 (100%). However, this study also determined that selecting the appropriate method for each stage would gradually reduce the uncertainty at each stage. Finally, the uncertainty due to natural variability was 93.70% of the final uncertainty. Thus, the results indicate that this new approach can contribute significantly to the field of uncertainty estimation and help with estimating more accurate radar rainfall.

• Bulletin of the Korean Chemical Society
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• v.35 no.12
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• pp.3482-3488
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• 2014

Matrix-induced mass bias and its effect on the accuracy of isotope ratio measurements have been examined for a quadrupole-based inductively coupled plasma-mass spectrometer (Q ICP-MS). Matrix-induced mass bias effect was directly proportional to % mass difference, and its magnitude varied for element and nebulizer flow rate. For a given element and conditions in a day, the effect was consistent. The isotope ratio of Cd106/Cd114 under $200{\mu}g\;g^{-1}$ U matrix deviated from the natural value significantly by 3.5%. When Cd 111 and Cd114 were used for the quantification of Cd with isotope dilution (ID) method, the average of differences between the calculated and measured concentrations was -0.034% for samples without matrix ($0.076{\mu}g\;g^{-1}$ to $0.21{\mu}g\;g^{-1}$ for the period of 6 months). However, the error was as large as 1.5% for samples with $200{\mu}g\;g^{-1}$ U. The error in ID caused by matrix could be larger when larger mass difference isotopes are used.

• Journal of KSNVE
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• v.8 no.6
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• pp.1023-1029
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• 1998

When one attempts to construct a hologram. one finds that there are many sources of measurement errors. These errors are even amplified if one predicts the pressures close to the sources. The pressure estimation errors depend on the following parameters: the measurement spacing on the hologram plane. the prediction spacing on the prediction plane. and the distance between the hologram and the prediction plane. This raper analyzes quantitatively the errors when these are distributed irregularly on the hologram plane The sensor mismatch and inaccurate measurement location. position mismatch. are mainly addressed. In these cases. one can assume that the measurement is a sample of many measurement events. The bias and random error are derived theoretically. Then the relationship between the random error amplification ratio and the parameters mentioned above is examined quantitatively in terms of energy.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.2
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• pp.269-274
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• 2005

In this work, analytical bias arising from the gas chromatographic determination of sulfur compounds was evaluated by the application of direct loop injection method to the GC/PFPD detection of four sulfur compounds including H$_{2}$S, CH$_{3}$SH, DMS, and DMDS. For the proper evaluation of analytical uncertainties involved in GC calibration, we employed two comparative techniques of calibration at fxed concentration injection (CFCI) vs calibration at fixed volume injection (CFVI) method. The results of our study indicate that CFCI method exhibits very poor sensitivity due to the matrix effect with varying injection volumes. On the other hand, as CFVI method overcomes such limitation, it can be used to obtain very accurate quantification of S compounds at their high concentration levels above a few to a few tens ppb.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.158-166
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• 2022

Background: The effects of radiation on the health of radiation workers who are constantly susceptible to occupational exposure must be assessed based on an accurate and reliable reconstruction of organ-absorbed doses that can be calculated using personal dosimeter readings measured as H_p(10) and dose conversion coefficients. However, the data used in the dose reconstruction contain significant biases arising from the lack of reality and could result in an inaccurate measure of organ-absorbed doses. Therefore, this study quantified the biases involved in organ dose reconstruction and calculated the bias-corrected H_p(10)-to-organ-absorbed dose coefficients for the use in epidemiological studies of Korean radiation workers. Materials and Methods: Two major biases were considered: (a) the bias in H_p(10) arising from the difference between the dosimeter calibration geometry and the actual exposure geometry, and (b) the bias in air kerma-to-H_p(10) conversion coefficients resulting from geometric differences between the human body and slab phantom. The biases were quantified by implementing personal dosimeters on the slab and human phantoms coupled with a Monte Carlo method and considered to calculate the bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients. Results and Discussion: The bias in H_p(10) was significant for large incident angles and low energies (e.g., 0.32 for right lateral at 218 keV), whereas the bias in dose coefficients was significant for the posteroanterior (PA) geometry only (e.g., 0.79 at 218 keV). The bias-corrected H_p(10)-to-organ-absorbed dose conversion coefficients derived in this study were up to 3.09- fold greater than those from the International Commission on Radiological Protection publications without considering the biases. Conclusion: The obtained results will aid future studies in assessing the health effects of occupational exposure of Korean radiation workers. The bias-corrected dose coefficients of this study can be used to calculate organ doses for Korean radiation workers based on personal dose records.

• Analytical Science and Technology
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• v.36 no.6
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• pp.315-323
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• 2023

This study is to quantify α-quartz, cristobalite and kaolinite using by FTIR in respirable dust generated in the mining workplace. Various minerals in mines can interfere with peaks when quantifying respirable crystalline silica by FTIR. Therefore, for accurate quantification, it is necessary to remove interfering substances or correct the peaks that cause interference. To confirm the peaks occurring in α-quartz, cristobalite and kaolinite, each standard material was diluted with KBr and scanned in the range of 400 cm^-1 to 4000 cm^-1 using by FTIR. As a result of scanning the analytes, it was decided to use the peaks of 797.66 cm^-1 and 695.25 cm^-1 for α-quartz, 621.58 cm^-1 for cristobalite, and 3696.47 cm^-1 for kaolinite. When the above materials are mixed, interference occurs at the peak for quantification, which is corrected by the calculation formula. The analysis of the mixture of α-quartz and cristobalite shows the average bias (%) of 2.64 (corrected) at α-quartz (797.66 cm^-1), 5.61 (uncorrected) at α-quartz (695.25 cm^-1) and 1.51 (uncorrected) at cristobalite (621.58 cm^-1). The analysis of the mixture of α-quartz and kaolinite shows the average bias(%) of 1.79(corrected) at α-quartz (797.66 cm^-1), 3.92 (corrected) at α-quartz (695.25 cm^-1) and 2.58 (uncorrected) at kaolinite (3696.47 cm^-1). The analysis of the mixture of cristobalite and kaolinite shows the average bias (%) of 2.15 (corrected) at cristobalite (621.58 cm^-1), 4.32 (uncorrected) at kaolinite (3696.47 cm^-1). The analysis of the mixture of αquartz and cristobalite and kaolinite shows the average bias (%) of 1.93(corrected) at α-quartz (797.66 cm^-1), 6.47 (corrected) at α-quartz (695.25 cm^-1) and 1.77 (corrected) at cristobalite (621.58 cm^-1) and 2.61 (uncorrected) at kaolinite (3696.47 cm^-1). The experimental results showed that the deviation caused by peak interference by two or three substances could be corrected to less than 6 % of the average deviation. This study showed the possibility of correcting and quantifying when various interfering substances that are difficult to remove are mixed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1325-1328
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• 2007

When we visualize the sound field radiated from a spherical sound source, spherical acoustic holography is proper among acoustic holography methods. However, there are measurement errors due to sensor position mismatch, sensor mismatch, directivity of sensor, and background noise. These errors are amplified if one predicts the pressures close to the sources: backward prediction. The goal of this paper is to quantitatively examine the effects of the error due to sensor position mismatch on acoustic pressure estimation. This paper deals with the cases of which the measurement deviations are distributed irregularly on the hologram plane. In such cases, one can assume that the measurement is a sample of many measurement events, and the cause of the measurement error is white noise on the hologram plane. Then the bias and random error are derived mathematically. In the results, it is found that the random error is important in the backward prediction. The relationship between the random error amplification ratio and the measurement parameters is derived quantitatively in terms of their energies.

• 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.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.

• Korean Journal of Veterinary Research
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• v.47 no.2
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• pp.259-263
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• 2007

The choice of input distribution in quantitative risk assessments modeling is of great importance to get unbiased overall estimates, although it is difficult to characterize them in situations where data available are too sparse or small. The present study is particularly concerned with accommodation of uncertainties commonly encountered in the practice of modeling. The authors applied parametric and non-parametric bootstrap simulation methods which consist of re-sampling with replacement, in together with the classical Student-t statistics based on the normal distribution. The implications of these methods were demonstrated through an empirical analysis of trade volume from the amount of chicken and pork meat imported to Korea during the period of 1998-2005. The results of bootstrap method were comparable to the classical techniques, indicating that bootstrap can be an alternative approach in a specific context of trade volume. We also illustrated on what extent the bias corrected and accelerated non-parametric bootstrap method produces different estimate of interest, as compared by non-parametric bootstrap method.