PGNAA Spectral Classification of Metal With Density Estimations
Author:
Affiliation:
1. Institute Industrial IT (inIT), OWL University of Applied Sciences and Arts, Lemgo, Germany
2. Department of Mathematical Methods and Software Development, AiNT GmbH, Stolberg, Germany
Funder
German Federal Ministry of Education and Research through the MetalClass Project
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Nuclear Energy and Engineering,Nuclear and High Energy Physics
Link
http://xplorestaging.ieee.org/ielx7/23/10154183/10036476.pdf?arnumber=10036476
Reference32 articles.
1. Special nuclear material classification and mass content estimation using temporal gamma-ray spectroscopy and machine learning methods;curtis,2019
2. High-Performance Remote Radioactive Material Identification of Mixtures
3. Deep convolutional neural networks for Raman spectrum recognition: a unified solution
4. Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach
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