Simulating the response of ionization chamber system to 137Cs irradiator using the artificial neural network modeling algorithm
The artificial neural network algorithm has been used to simulate the response of an ionization chamber dosimetric system to the 137 Cs irradiator facility at the National Institute for Standards, Egypt. The performance of the designed model has been investigated over different processes functions a...
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| Published in | SN applied sciences Vol. 2; no. 8; p. 1325 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.08.2020
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2523-3963 2523-3971 2523-3971 |
| DOI | 10.1007/s42452-020-3111-7 |
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| Summary: | The artificial neural network algorithm has been used to simulate the response of an ionization chamber dosimetric system to the
137
Cs irradiator facility at the National Institute for Standards, Egypt. The performance of the designed model has been investigated over different processes functions and a wide range of parameters till the optimum performance has been approached. The standard uncertainty of the designed model has been evaluated for different dose-rate levels. It has been found that the dose-rate can be evaluated using the designed model with an uncertainty < 2.0% for the dose-rate level
<
10
2
mGy/h
and with an uncertainty < 0.5% for the dose-rate level
[
10
2
-
10
3
]
mGy/h
. The bias of the designed model has been evaluated and compared with the usual interpolation algorithms. The errors in the evaluated dose-rate using these methods have exceeded the 100% error level and approached the 20% error level for the dose rates less and greater than
10
mGy/h
, respectively. While, the error in the evaluated dose-rate using the designed model did not exceed 7% and 2% for the same dose-rate levels, respectively. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2523-3963 2523-3971 2523-3971 |
| DOI: | 10.1007/s42452-020-3111-7 |