首页|Characterization of uncertainties and model generalizability for convolutional neural network predictions of uranium ore concentrate morphology

Characterization of uncertainties and model generalizability for convolutional neural network predictions of uranium ore concentrate morphology

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  • NSTL
  • Elsevier
As the capabilities of convolutional neural networks (CNNs) for image classification tasks have advanced, interest in applying deep learning techniques for determining the natural and anthropogenic origins of uranium ore concentrates (UOCs) and other unknown nuclear materials by their surface morphology characteristics has grown. But before CNNs can join the nuclear forensics toolbox along more traditional analytical techniques - such as scanning electron microscopy (SEM), X-ray diffractometry, mass spectrometry, radiation counting, and any number of spectroscopic methods - a deeper understanding of "black box" image classification will be required. This paper explores uncertainty quantification for convolutional neural networks and their ability to generalize to out-of-distribution (OOD) image data sets. For prediction uncertainty, Monte Carlo (MC) dropout and random image crops as variational inference techniques are implemented and characterized. Convolutional neural networks and classifiers using image features from unsupervised vector-quantized variational autoencoders (VQVAE) are trained using SEM images of pure, unaged, unmixed uranium ore concentrates considered "unperturbed." OOD data sets are developed containing perturbations from the training data with respect to the chemical and physical properties of the UOCs or data collection parameters; predictions made on the perturbation sets identify where significant shortcomings exist in the current training data and techniques used to develop models for classifying uranium process history, and provides valuable insights into how datasets and classification models can be improved for better generalizability to out-of-distribution examples.

Uranium chemistryNuclear forensicsMachine learningImage classificationUncertainty quantificationUranium chemistryNuclear forensicsMachine learningImage classificationUncertainty quantificationTHERMAL-DECOMPOSITIONSURFACE-MORPHOLOGYNUCLEAR FORENSICSTEMPERATURESIGNATURESQUANTIFICATIONHYDROLYSISORIGIN

McDonald, Luther W.、Nizinski, Cody A.、Ly, Cuong、Vachet, Clement、Hagen, Alex、Tasdizen, Tolga

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Univ Utah

Pacific Northwest Natl Lab

Sci Comp & Imaging Inst

2022

10.1016/j.chemolab.2022.104556

Chemometrics and Intelligent Laboratory Systems

Chemometrics and Intelligent Laboratory Systems

EISCI
ISSN：0169-7439
年,卷(期)：2022.225
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