Use of artificial neural networks in measuring characteristics of shielded plutonium for arms control

Owen, M.; Chambers, David M.

doi:10.1039/c2ja10230g

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…We have demonstrated how this concept of direct provenancing of georeferenced values can be applied due to the unique spatial nature of soil pedogenesis processes. This has already been proven numerous times in the related context of digitally determining soil chemical and physical characteristics that are helpful in inferring soil provenance 3 , 4 , 17 – 19 . There is much scope for development of this approach.…”

Section: Discussionmentioning

confidence: 93%

Georeferenced soil provenancing with digital signatures

Tighe

Forster

Guppy

et al. 2018

Sci Rep

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The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply rapid and non-destructive spectral analysis to the question of direct soil provenancing. This approach is based on one of the underlying tenets of soil science – that soil pedogenesis is spatially unique, and thus digital spectral signatures of soil can be related directly, rather than via individual soil properties, to a georeferenced location. We examine three different multivariate regression techniques to predict GPS coordinates in two nested datasets. With a minimum of data processing, we show that in most instances Eastings and Northings can be predicted to within 20% of the range of each within the dataset using the spectral signatures produced via portable x-ray fluorescence. We also generate 50 and 95% confidence intervals of prediction and express these as a range of GPS coordinates. This approach has promise for future application in soil and environmental provenancing.

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Section: Discussionmentioning

confidence: 93%

Georeferenced soil provenancing with digital signatures

Tighe

Forster

Guppy

et al. 2018

Sci Rep

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“…The other paper relevant to this section was presented by Aitkenhead et al 187 These authors developed artificial neural networks for use as a potential ''information barrier'' technology in the verification of arms control treaty accountable items. Data obtained using over 400 g ray spectra were used to create the database.…”

Section: Nuclear Materialsmentioning

confidence: 99%

Atomic spectrometry update. Industrial analysis: metals, chemicals and advanced materials

Carter

Fisher

Hinds

et al. 2012

J. Anal. At. Spectrom.

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“…Intelligent nuclide identification methods have received more and more attention. Bayesian inference [3], support vector machines [4], and artificial neural networks [5][6][7][8][9][10][11] are applied to nuclide identification and quantification. Based on artificial neural networks, some variants of neural networks, such as convolutional neural network (CNN) [12][13][14][15], hybrid neural network [16], and generative adversarial network [17], have been applied to nuclide identification.…”

Section: Introductionmentioning

confidence: 99%

Multiple radionuclide identification using deep learning with channel attention module and visual explanation

et al. 2022

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As a rapid and automatic method, multiple radionuclide identification using deep learning has drawn wide interest from researchers in the field of nuclear safety and nuclear security. However, the network model in deep learning often appears in the form of a black box, which makes it difficult for people to understand its decision-making basis. It is necessary to develop an interpretable deep learning model for multiple nuclide identification. In the work on nuclide identification using deep learning, very few interpretable studies have been conducted. In this paper, channel attention weights are used for interpretable radionuclide identification for the first time. We propose a multiple radionuclide identification method using deep learning with channel attention module and visual explanation. A dataset of gamma spectra simulated by Geant4 was created, containing 256 combinations of 8 radionuclides. These gamma spectra were used to train using a convolutional neural network (CNN) with a channel attention module. The obtained accuracies on training, validation, and test sets are 97.8%, 97.6%, and 97.1%, respectively. The result of interpretation of spectral features show that based on the channel attention module, the CNN can make full use of the feature information of the photoelectric peak and Compton edge and suppress the background and noise interference. In addition, the t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the inner working process of the CNN and visually illustrate the correctness of feature extraction. This research will promote the application of artificial intelligence algorithms in nuclide identification instruments.

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Use of artificial neural networks in measuring characteristics of shielded plutonium for arms control

Cited by 12 publications

References 20 publications

Georeferenced soil provenancing with digital signatures

Georeferenced soil provenancing with digital signatures

Atomic spectrometry update. Industrial analysis: metals, chemicals and advanced materials

Multiple radionuclide identification using deep learning with channel attention module and visual explanation

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