IMPLEMENTATION OF MACHINE LEARNING TECHNOLOGIES FOR DESIGNING THE IONIZING RADIATIONS SMART DETECTING DEVICES CONCEPT
https://doi.org/10.56304/S2079562924050130
EDN: MOESPW
Abstract
The paper presents the suggestions about using the machine learning technologies for modern radiation monitoring software and hardware designing during the full production life cycle and the evaluations of the expected technical results deriving from such using illustrated there. There are experimental research schemes shown to confirm the applicability of the self-learning neural networks for the ionizing radiations detecting devices and automated systems based on them.
About the Authors
A. S. Gordeev
Specialized Scientific Research Institute of Instrumentation
Russian Federation
Russian Federation
F. Yu. Ipatov
Specialized Scientific Research Institute of Instrumentation
Russian Federation
Russian Federation
A. R. Kuztetsov
Specialized Scientific Research Institute of Instrumentation
Russian Federation
Russian Federation
References
1. Nakhostin M. Signal Processing for Radiation Detectors. 2018. New York: Wiley
2. Griths J., Kleinegesse S., Saunders D., Taylor R., Vacheret A. Pulse Shape Discrimination and Exploration of Scintillation Signals Using Convolutional Neural Networks. Machine Learning Science and Technology. 2020.
Review
For citations:
Gordeev A.S., Ipatov F.Yu., Kuztetsov A.R. IMPLEMENTATION OF MACHINE LEARNING TECHNOLOGIES FOR DESIGNING THE IONIZING RADIATIONS SMART DETECTING DEVICES CONCEPT. Nuclear Physics and Engineering. 2025;16(3):269-272. (In Russ.) https://doi.org/10.56304/S2079562924050130. EDN: MOESPW
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