Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network
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2005
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An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PBR) as a function of measurement time in gamma-ray spectrometry. In order to make the ANN model with good predictive power, the ANN parameters were optimized simultaneously employing a variable-size simplex method. Most of the predicted and the experimental PBR values for eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137, and Be-7) commonly detected in soil samples agreed to within +/- 19.4% of the expanded uncertainty and 2.61% of average bias.
Ključne reči:
ANN / radionuclides / PBR / soil / measurement timeIzvor:
Applied Radiation and Isotopes, 2005, 63, 3, 363-366Izdavač:
- Pergamon-Elsevier Science Ltd, Oxford
DOI: 10.1016/j.apradiso.2005.03.009
ISSN: 0969-8043
PubMed: 15927476
WoS: 000231043100010
Scopus: 2-s2.0-21844453922
Institucija/grupa
Tehnološko-metalurški fakultetTY - JOUR AU - Dragović, Snežana D. AU - Onjia, Antonije PY - 2005 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/853 AB - An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PBR) as a function of measurement time in gamma-ray spectrometry. In order to make the ANN model with good predictive power, the ANN parameters were optimized simultaneously employing a variable-size simplex method. Most of the predicted and the experimental PBR values for eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137, and Be-7) commonly detected in soil samples agreed to within +/- 19.4% of the expanded uncertainty and 2.61% of average bias. PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Applied Radiation and Isotopes T1 - Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network EP - 366 IS - 3 SP - 363 VL - 63 DO - 10.1016/j.apradiso.2005.03.009 ER -
@article{ author = "Dragović, Snežana D. and Onjia, Antonije", year = "2005", abstract = "An artificial neural network (ANN) model was used for the prediction of peak-to-background ratio (PBR) as a function of measurement time in gamma-ray spectrometry. In order to make the ANN model with good predictive power, the ANN parameters were optimized simultaneously employing a variable-size simplex method. Most of the predicted and the experimental PBR values for eight radionuclides (Ra-226, U-238, U-235, K-40, Th-232, Cs-134, Cs-137, and Be-7) commonly detected in soil samples agreed to within +/- 19.4% of the expanded uncertainty and 2.61% of average bias.", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Applied Radiation and Isotopes", title = "Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network", pages = "366-363", number = "3", volume = "63", doi = "10.1016/j.apradiso.2005.03.009" }
Dragović, S. D.,& Onjia, A.. (2005). Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network. in Applied Radiation and Isotopes Pergamon-Elsevier Science Ltd, Oxford., 63(3), 363-366. https://doi.org/10.1016/j.apradiso.2005.03.009
Dragović SD, Onjia A. Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network. in Applied Radiation and Isotopes. 2005;63(3):363-366. doi:10.1016/j.apradiso.2005.03.009 .
Dragović, Snežana D., Onjia, Antonije, "Prediction of peak-to-background ratio in gamma-ray spectrometry using simplex optimized artificial neural network" in Applied Radiation and Isotopes, 63, no. 3 (2005):363-366, https://doi.org/10.1016/j.apradiso.2005.03.009 . .