Comparing Deep Learning Architectures On Gamma-Spectroscopy Analysis For Nuclear Waste Characterization
| Autor: | Julio T. Marumo, Ademar Potiens Junior, Andre Gomes Lamas Otero |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Contextual image classification Computer science business.industry Deep learning Speech synthesis Image segmentation computer.software_genre Machine learning Field (computer science) Characterization (materials science) Artificial intelligence General Agricultural and Biological Sciences business computer Strengths and weaknesses |
| Zdroj: | Brazilian Journal of Radiation Sciences. 9 |
| ISSN: | 2319-0612 |
| Popis: | Neural networks, particularly deep neural networks, are used nowadays with great success in several tasks, such as image classification, image segmentation, translation, text to speech, speech to text, achieving super-human performance. In this study, we explore the capabilities of deep learning on a new field: gamma-spectroscopy analysis, comparing the classification performance of different deep neural network architectures. We choose VGG-16, VGG-19, Xception, ResNet, InceptionV3, and MobileNet architectures, which are available through the Keras Deep Learning framework to identify several different radionuclides (Am-241, Ba133, Cd-109, Co-60, Cs-137, Eu-152, Mn-54, Na-24, and Pb-210). Using an HPGe detector to acquire several gamma spectra from different sealed sources to create a dataset used for the training and validation of the comparison of the neural network. This study demonstrates the strengths and weaknesses of applying deep learning on gamma-spectroscopy analysis for nuclear waste characterization. |
| Databáze: | OpenAIRE |
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