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"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method

Ka Yung Cheng¹, Helmand Shayan, Kai Krycki, Markus Lange-Hegermann²
Aug 2022

There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer, e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article introduces a new development of the deep learning classification and contrive to reduce the test time for PGNAA machine. We propose both Random Sampling Methods and Class Activation Map (CAM) to generate "downsized" samples and train the CNN model continuously. Random Sampling Methods (RSM) aims to reduce the measuring time within a sample, and Class Activation Map (CAM) is for filtering out the less important energy range of the downsized samples. We shorten the overall PGNAA measuring time down to 2.5 seconds while ensuring the accuracy is around 96.88 % for our dataset with 12 different species of substances. Compared with classifying different species of materials, it requires more test time (sample count rate) for substances having the same elements to archive good accuracy. For example, the classification of copper alloys requires nearly 24 seconds test time to reach 98 % accuracy.

Literatur Beschaffung: IEEE
@misc{2560,
author= {Cheng, Ka Yung and Shayan, Helmand and Krycki, Kai and Lange-Hegermann, Markus},
title= {"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method},
abstract= {There is a pressing market demand to minimize the test time of Prompt Gamma Neutron Activation Analysis (PGNAA) spectra measurement machine, so that it could function as an instant material analyzer, e.g. to classify waste samples instantaneously and determine the best recycling method based on the detected compositions of the testing sample. This article introduces a new development of the deep learning classification and contrive to reduce the test time for PGNAA machine. We propose both Random Sampling Methods and Class Activation Map (CAM) to generate "downsized" samples and train the CNN model continuously. Random Sampling Methods (RSM) aims to reduce the measuring time within a sample, and Class Activation Map (CAM) is for filtering out the less important energy range of the downsized samples. We shorten the overall PGNAA measuring time down to 2.5 seconds while ensuring the accuracy is around 96.88 % for our dataset with 12 different species of substances. Compared with classifying different species of materials, it requires more test time (sample count rate) for substances having the same elements to archive good accuracy. For example, the classification of copper alloys requires nearly 24 seconds test time to reach 98 % accuracy.},
booktitle= {},
year= {2022},
month= {Aug},
publisher= {IEEE},
address= {https://indico.cern.ch/event/1109460/contributions/4892973/},
editor= {},
pages= {(Poster)},
organisation= {},
}

¹ Erstautoren
² Letztautoren