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Title: Neural networks for nuclear spectroscopy

Abstract

In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the wholemore » spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.« less

Authors:
; ; ;  [1]
  1. Pacific Northwest Lab., Richland, WA (United States); and others
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
OSTI Identifier:
377048
Report Number(s):
PNL-SA-26375; CONF-9503142-
ON: DE96009360; TRN: 96:003982-0004
DOE Contract Number:  
AC06-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Workshop on environmental and energy applications of neural networks conference, Richland, WA (United States), 30-31 Mar 1995; Other Information: PBD: [1995]; Related Information: Is Part Of Applications of neural networks in environmental and energy sciences and engineering. Proceedings of the 1995 workshop on environmental and energy applications of neural networks; Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.; PB: 193 p.
Country of Publication:
United States
Language:
English
Subject:
05 NUCLEAR FUELS; 40 CHEMISTRY; 44 INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; RADIOACTIVE WASTES; ALPHA SPECTROSCOPY; GAMMA SPECTROSCOPY; NEURAL NETWORKS; PLUTONIUM; EXPERT SYSTEMS

Citation Formats

Keller, P E, Kangas, L J, Hashem, S, and Kouzes, R T. Neural networks for nuclear spectroscopy. United States: N. p., 1995. Web.
Keller, P E, Kangas, L J, Hashem, S, & Kouzes, R T. Neural networks for nuclear spectroscopy. United States.
Keller, P E, Kangas, L J, Hashem, S, and Kouzes, R T. 1995. "Neural networks for nuclear spectroscopy". United States. https://www.osti.gov/servlets/purl/377048.
@article{osti_377048,
title = {Neural networks for nuclear spectroscopy},
author = {Keller, P E and Kangas, L J and Hashem, S and Kouzes, R T},
abstractNote = {In this paper two applications of artificial neural networks (ANNs) in nuclear spectroscopy analysis are discussed. In the first application, an ANN assigns quality coefficients to alpha particle energy spectra. These spectra are used to detect plutonium contamination in the work environment. The quality coefficients represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with quality coefficients by an expert and used to train the ANN expert system. Our investigation shows that the expert knowledge of spectral quality can be transferred to an ANN system. The second application combines a portable gamma-ray spectrometer with an ANN. In this system the ANN is used to automatically identify, radioactive isotopes in real-time from their gamma-ray spectra. Two neural network paradigms are examined: the linear perception and the optimal linear associative memory (OLAM). A comparison of the two paradigms shows that OLAM is superior to linear perception for this application. Both networks have a linear response and are useful in determining the composition of an unknown sample when the spectrum of the unknown is a linear superposition of known spectra. One feature of this technique is that it uses the whole spectrum in the identification process instead of only the individual photo-peaks. For this reason, it is potentially more useful for processing data from lower resolution gamma-ray spectrometers. This approach has been tested with data generated by Monte Carlo simulations and with field data from sodium iodide and Germanium detectors. With the ANN approach, the intense computation takes place during the training process. Once the network is trained, normal operation consists of propagating the data through the network, which results in rapid identification of samples. This approach is useful in situations that require fast response where precise quantification is less important.},
doi = {},
url = {https://www.osti.gov/biblio/377048}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Dec 31 00:00:00 EST 1995},
month = {Sun Dec 31 00:00:00 EST 1995}
}

Conference:
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