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Title: APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PROMPT GAMMA NEUTRON ACTIVATION ANALYSIS FOR CHEMICAL WARFARE AGENTS IDENTIFICATION

Abstract

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with a probabilistic decision tree (PDT). The effectiveness of the chemical identification algorithm is determined by the availability of a wide range of data to train the algorithm to identify chemical-fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to leverage such a database with the Artificial Neural Network (ANN) technique. Therefore, an ANN-based chemical identification algorithm was developed as an independent verification of the current algorithm. The ANN-based algorithm’s performance was evaluated against the U.S. Army blind test data, and results were presented and discussed in this study.

Authors:
ORCiD logo [1]
  1. Idaho National Laboratory
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1565918
Report Number(s):
INL/EXT-19-55616-Rev000
DOE Contract Number:  
DE-AC07-05ID14517
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
73 - NUCLEAR PHYSICS AND RADIATION PHYSICS; 38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEAR CHEMISTRY; 98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; 46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; chemical warfare agents; prompt gamma neutron activation analysis; High resolution gamma-ray spectroscopy; HPGe detector; Artificial neural network; Gamma-ray spectroscopy; Neutron activation; Neutron induced gamma-ray emission; Portable Isotopic Neutron Spectroscopy

Citation Formats

Lee, Dongwon. APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PROMPT GAMMA NEUTRON ACTIVATION ANALYSIS FOR CHEMICAL WARFARE AGENTS IDENTIFICATION. United States: N. p., 2019. Web. doi:10.2172/1565918.
Lee, Dongwon. APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PROMPT GAMMA NEUTRON ACTIVATION ANALYSIS FOR CHEMICAL WARFARE AGENTS IDENTIFICATION. United States. doi:10.2172/1565918.
Lee, Dongwon. Tue . "APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PROMPT GAMMA NEUTRON ACTIVATION ANALYSIS FOR CHEMICAL WARFARE AGENTS IDENTIFICATION". United States. doi:10.2172/1565918. https://www.osti.gov/servlets/purl/1565918.
@article{osti_1565918,
title = {APPLICATION OF ARTIFICIAL NEURAL NETWORK TO PROMPT GAMMA NEUTRON ACTIVATION ANALYSIS FOR CHEMICAL WARFARE AGENTS IDENTIFICATION},
author = {Lee, Dongwon},
abstractNote = {The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with a probabilistic decision tree (PDT). The effectiveness of the chemical identification algorithm is determined by the availability of a wide range of data to train the algorithm to identify chemical-fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to leverage such a database with the Artificial Neural Network (ANN) technique. Therefore, an ANN-based chemical identification algorithm was developed as an independent verification of the current algorithm. The ANN-based algorithm’s performance was evaluated against the U.S. Army blind test data, and results were presented and discussed in this study.},
doi = {10.2172/1565918},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

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