UNCERTAINTY EVALUATION USING LAW OF PROPAGATION AND MONTE CARLO SIMULATION METHODS WITH THE AUTORFPOWER MEASUREMENT SOFTWARE
Year 2024,
, 596 - 607, 01.09.2024
Erkan Danacı
,
Aliye Kartal Doğan
,
Engin Can Çiçek
,
Anıl Çetinkaya
,
Muhammed Çağrı Kaya
,
M. S. Halit Oğuztüzün
,
Gülsün Tünay
Abstract
RF power measurement is essential in RF and microwave metrology. For reliable and accurate power measurement, automatic measurement is preferred. A software application in C#, named AutoRFPower, was developed for automatic RF power measurement and uncertainty calculations at this study. According to the GUM document, this application is enhanced for uncertainty calculations by utilizing the Law of Propagation method and the Monte Carlo Simulation method. Trial measurements were performed at different RF power levels and frequencies between 50 MHz and 18 GHz using the AutoRFPower software. Law of Propagation and Monte Carlo Simulation uncertainty calculations were carried out by AutoRFPower based on the trial measurements and by the Oracle Crystal Ball simulation application. All measurements and their uncertainty calculations were compared with each other, and this study validated the uncertainty calculation of AutoRFPower. In addition, it was observed that in the Monte Carlo Simulation, uncertainty calculation results were non-symmetrical normal distribution, contrary to the assumption of symmetrical normal distribution according to the Low of Propagation method. Moreover, it has been observed that the statistical distribution of uncertainty changes depending on the dominant component of the parameters in the model function used for the uncertainty calculation with the Monte Carlo Simulation method.
Ethical Statement
The authors of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission.
Supporting Institution
This work is supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant No. 5200040 entitled “A New Method and Software Development for Automatic RF Power Measurement and RF Power Meter Calibration”.
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Year 2024,
, 596 - 607, 01.09.2024
Erkan Danacı
,
Aliye Kartal Doğan
,
Engin Can Çiçek
,
Anıl Çetinkaya
,
Muhammed Çağrı Kaya
,
M. S. Halit Oğuztüzün
,
Gülsün Tünay
References
- BIPM, “Evaluation of measurement data – Guide to the expression of the uncertainty in measurement”, Bureau Int. des Poids et Measures, JCGM 100:2008, 1st ed., Sep. 2008. [Online]. Available: https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6 [Accessed: August 06, 2024].
- BIPM, “Evaluation of measurement data — Supplement 1 to the “Guide to the expression of uncertainty in measurement” — Propagation of distributions using a Monte Carlo method”, Bureau Int. des Poids et Measures, JCGM 101:2008, 1st ed., Sep. 2008. [Online]. Available: https://www.bipm.org/documents/20126/2071204/JCGM_101_2008_E.pdf/325dcaad-c15a-407c-1105-8b7f322d651c [Accessed: August 06, 2024].
- P. R. G. Couto, J. Carreteiro, and S. P. de Oliveira, Monte Carlo Simulations Applied to Uncertainty in Measurement, Theory and Applications of Monte Carlo Simulations. Intech, March 06, 2013. [E-Book]. Available: https://www.intechopen.com/chapters/43533. doi: 10.5772/53014.
- C.F. Dietrich, Uncertainty, calibration and probability, 2nd edition, Adam-Hilger (Bristol), 1991.
- G. M. Mahmoud, and R. S. Hegazy, “Comparison of GUM and Monte Carlo methods for the uncertainty estimation in hardness measurements”, International Journal of Metrology and Quality Engineering, vol. 8, no. 9, May 24, Article 14, 2017. https://doi.org/10.1051/ijmqe/2017014
- O. Ibe, Markov Processes for Stochastic Modelling, Basic Concepts in Probability, 2nd edition, Elsevier, pp. 1-27, 2013.
- J. Han, H. Chen, and Y. Cao, “Uncertainty Evaluation Using Monte Carlo Method with MATLAB”, presented at IEEE 10th International Conference on Electronic Measurement & Instruments, vol. 2. August 2011, pp. 282-286.
- C. E. Papadopoulos, H. Yeung, “Uncertainty estimation and Monte Carlo simulation method”, Flow Measurement and Instrumentation, vol. 12, issue 4, 2001, pp. 291-298. https://doi.org/10.1016/S0955-5986(01)00015-2.
- M. Á. Herrador, A. G. Asuero, A. G. González, “Estimation of the uncertainty of indirect measurements from the propagation of distributions by using the Monte-Carlo method: An overview”, Chemometrics and Intelligent Laboratory Systems, vol. 79, issue 1-2, 2005, pp. 115-122. https://doi.org/10.1016/j.chemolab.2005.04.010.
- I. Farrance, R. Frenkel, “Uncertainty in measurement: a review of monte carlo simulation using microsoft excel for the calculation of uncertainties through functional relationships, including uncertainties in empirically derived constants”, Clin Biochem Rev. vol. 35, no. 1, Feb. 2014, pp. 37-61. PMID: 24659835; PMCID: PMC3961998.
- A. Yugruk, E. Danaci, A. K. Dogan and A. O. Salman, "The Effects of Sequential and Multiple Measurement on RF Power," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477768.
- A. Cetinkaya, A. K. Dogan, E. Danaci and H. Oguztuzun, "AUTORFPOWER: Automatic RF Power Measurement Software for Metrological Applications," 2021 2nd International Informatics and Software Engineering Conference (IISEC), 2021, pp. 1-4, doi: 10.1109/IISEC54230.2021.9672386.
- A. Cetinkaya, M.C. Kaya, E. Danaci and H. Oguztuzun, “Uncertainty Calculation-As-A-Service: An IIot Application For Automated RF Power Sensor Calibration”, IMEKO TC6, International Conference on Metrology and Digital Transformation, September 2022, Berlin.
- D. M. Pozar, Microwave Engineering, John Wiley & Sons 4th Edition, 2011. ISBN: 1118213637, 9781118213636.
- J. Jia, J. Kuang, Z. He and J. Fang, "Design of automated test system based on GPIB," 2009 9th International Conference on Electronic Measurement & Instruments, 2009, pp. 1-943-1-948, doi: 10.1109/ICEMI.2009.5274384.