The use of artificial neural networks in PVT-based radiation portal monitors

Kangas, Lars J.; Keller, Paul E.; Siciliano, Edward R.; Kouzes, Richard T.; Ely, James H.

doi:10.1016/j.nima.2008.01.065

Cited by 57 publications

(36 citation statements)

References 11 publications

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“…AbdelAal and Al-Haddad reported improved results in [3], when applying abductive machine learning to identify a small set of radioisotopes in gamma-ray spectroscopy. More recently, Kangas et al reported the results of applying multilayer perceptron neural networks in [4] to analyze the shape of low resolution polyvinyl toluene spectra data acquired from port monitoring technology. Multilayer perceptrons were also applied by Vignerson et al, in [5], to determine 235 U/U total ratios, and Yoshida et al for radionuclide detection in uranium ore [6].…”

Section: A Related Workmentioning

confidence: 99%

Anomaly detection in gamma ray spectra: A machine learning perspective

Sharma

Bellinger

Japkowicz

et al. 2012

2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications

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Abstract-With Canadian security and the safety of the general public in mind, physicists at Health Canada (HC) have begun to develop techniques to identify persons concealing radioactive material that may represent a threat to attendees at public gatherings, such as political proceedings and sporting events. To this end, Health Canada has initiated field trials that include the deployment of gamma-ray spectrometers. In particular, a series of these detectors, which take measurements every minute and produce 1,024 channel gamma-ray spectrum, were deployed during the Vancouver 2010 olympics. Simple computerized statistics and human expertise were used as the primary line of defence. More specifically, if a measured spectrum deviated significantly from the background, an internal alarm was sounded and an HC physicist undertook further analysis into the nature of the alarming spectrum. This strategy, however, lead to a significant number of costly and time consuming false positives. This research applies sophisticated machine learning algorithms to reduce the number of false positives to an acceptable level, the results of which are detailed in this paper. In addition, we emphasize the primary findings of our work and highlight avenues available to further improve upon our current results.

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Section: A Related Workmentioning

confidence: 99%

Anomaly detection in gamma ray spectra: A machine learning perspective

Sharma

Bellinger

Japkowicz

et al. 2012

2012 IEEE Symposium on Computational Intelligence for Security and Defence Applications

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“…ANN eliminates the limitations of classical approaches by extracting the desired information from the input data. Applying ANN to a spectrometry system needs sufficient input and output data instead of mathematical equations for performing the fit to nuclear spectra, including X-, gamma-ray and alpha-particles spectra (Baeza et al, 2011;Basheer and Hajmeer, 2000;Keller et al, 1995;Yoshida et al, 2002;Kangas et al, 2008;Chen and Wei, 2009;Medhat, 2012;Miranda et al, 2009;Doostmohammadi et al, 2010). For each nuclear spectrum, such as alpha spectrum, up to 2048 data points are selected as inputs.…”

Section: Introductionmentioning

confidence: 99%

“…The Multilayer feed forward error-back propagation neural network, as an estimating tool, is used to estimate the 234 U/ 238 U activity ratio, because it is a good choice to screen and distinguish between natural and anthropogenic spectra in a number of applications (Keller et al, 1995;Yoshida et al, 2002;Minteer et al, 2007;Kangas et al, 2008;Doostmohammadi et al, 2010;Medhat, 2012). The network was trained by an alpha spectrum library which has been developed in the present work.…”

Section: Introductionmentioning

confidence: 99%

Application of neural network method to detect type of uranium contamination by estimation of activity ratio in environmental alpha spectra

Einian

Aghamiri

Ghaderi

2016

Journal of Environmental Radioactivity

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“…ANN eliminates the limitations of classical approaches by extracting the desired information from the input data. Applying ANN to a spectrometry system needs sufficient input and output data instead of mathematical equations for performing the fit to nuclear spectra, including X-, gamma-ray and alphaparticles spectra (Baeza et al, 2011;Basheer and Hajmeer, 2000;Keller et al, 1995;Yoshida et al,2002;Kangas et al 2008;Chen and Wei, 2009;Medhat, 2012;Miranda et al, 2009;Doostmohammadi et al, 2010). For each nuclear spectrum, such as alpha spectrum, up to 2048 data points are selected as inputs.…”

Section: Introductionmentioning

confidence: 99%