UNCERTAINTY EVALUATION FOR MULTIDETECTOR PASSIVE DOSEMETERS USING A TWO-STAGE MEASUREMENT MODEL AND THE MONTE CARLO METHOD

2015 ◽  
Vol 170 (1-4) ◽  
pp. 74-77 ◽  
Author(s):  
J. W. E. van Dijk
Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4472 ◽  
Author(s):  
Mingotti ◽  
Peretto ◽  
Tinarelli ◽  
Ghaderi

The paper addresses the evaluation of the uncertainty sources of a test bed system for calibrating voltage transformers vs. temperature. In particular, the Monte Carlo method has been applied in order to evaluate the effects of the uncertainty sources in two different conditions: by using the nominal accuracy specifications of the elements which compose the setup, or by exploiting the results of their metrological characterization. In addition, the influence of random effects on the system accuracy has been quantified and evaluated. From the results, it emerges that the choice of the uncertainty evaluation method affects the overall study. As a matter of fact, the use of a metrological characterization or of accuracy specifications provided by the manufacturers provides respectively an accuracy of 0.1 and 0.5 for the overall measurement setup.


2018 ◽  
Vol 15 (30) ◽  
pp. 252-258
Author(s):  
L. TREVISAN ◽  
D. A. K. FABRICIO

The Brinell hardness test is one of the most used mechanical tests in the industry to assure the quality of metallurgical processes. Based on the measured values, it is necessary to describe the measurement uncertainty values associated with the mathematical method used. Thus, measurement uncertainty values describe the reliability of the experimental results. The calculation of measurement uncertainty can be performed in several ways, and the method described by ISO/GUM is the most used by ISO/IEC 17025 accredited laboratories. The main objective of this work is to compare measurement uncertainty values based on different sources of uncertainty used in the measurement uncertainty evaluation for two Brazilian laboratories accredited by Cgcre/INMETRO. In addition, uncertainty values obtained by the GUM method and by the Monte Carlo method were compared. The results show that there is no great variation in the measurement uncertainty values as a function of the mathematical method used.


Metrologia ◽  
2010 ◽  
Vol 47 (3) ◽  
pp. 317-324 ◽  
Author(s):  
Gerd Wübbeler ◽  
Peter M Harris ◽  
Maurice G Cox ◽  
Clemens Elster

2020 ◽  
Vol 12 (5) ◽  
pp. 780 ◽  
Author(s):  
Agnieszka Białek ◽  
Sarah Douglas ◽  
Joel Kuusk ◽  
Ilmar Ansko ◽  
Viktor Vabson ◽  
...  

We describe a method to evaluate an uncertainly budget for the in situ Ocean Colour Radiometric measurements. A Monte Carlo approach is chosen to propagate the measurement uncertainty inputs through the measurements model. The measurement model is designed to address instrument characteristics and uncertainty associated with them. We present the results for a particular example when the radiometers were fully characterised and then use the same data to show a case when such characterisation is missing. This, depending on the measurement and the wavelength, can increase the uncertainty value significantly; for example, the downwelling irradiance at 442.5 nm with fully characterised instruments can reach uncertainty values of 1%, but for the instruments without such characterisation, that value could increase to almost 7%. The uncertainty values presented in this paper are not final, as some of the environmental contributors were not fully evaluated. The main conclusion of this work are the significance of thoughtful instrument characterisation and correction for the most significant uncertainty contributions in order to achieve a lower measurements uncertainty value.


10.14311/1590 ◽  
2012 ◽  
Vol 52 (4) ◽  
Author(s):  
Marcel Goliaš ◽  
Rudolf Palenčár

This paper presents the calculation of the uncertainty for distribution propagation by the Monte Carlo method for a measurement model with one output quantity. The procedure is shown on the basis of an example of the calculation of a rectangle by direct measurement of length by the same caliper. The measurements are correlated, and the uncertainties are calculated for three values of the correlation coefficients. Another part of the paper presents a validation of the law of propagation of uncertainties for distribution propagation by the Monte Carlo method.


Author(s):  
Qiang Na ◽  
Shurong Hu ◽  
Jianguo Tao ◽  
Yang Luo

The measurement of the centroid is of great significance to improve the control performance and reduce the energy consumption of the planetary rover (PR). The uncertainty is an essential indicator of the reliability of centroid measurement results. The purpose of the current study is to evaluate the uncertainty of centroid measurement in the multi-configuration rover. For the measurement of the centroid, the model with 37 parameters of two measurements as the input and the centroid coordinates as the output is derived. Further, the mechanical and electrical integrated system is developed, which can measure the centroid of PRs in different configurations and sizes. Moreover, to overcome the shortcomings of the Monte Carlo method (MCM) in uncertainty evaluation, an adaptive algorithm that automatically determines the number of input sequences is proposed. On this basis, an adaptive quasi-Monte Carlo method (AQMCM) is presented in order to accelerate the uncertainty evaluation, which is characterized by the randomized Sobol sequence. Besides, experiments are performed to compare the uncertainty evaluation process and results of the AQMCM and the adaptive Monte Carlo method (AMCM) in multiple configurations. The result shows that the standard uncertainty of the AQMCM is almost the same as that of the AMCM, but the sequence size of AQMCM is evidently smaller than that of AMCM. Taken together, we identify that the AQMCM evaluates the uncertainty of CM for the multi-configuration rover in an efficient and fast way. Furthermore, the AQMCM provides a new method for uncertainty evaluation, particularly for nonlinear models in different states.


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