New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation

Detecting nuclear materials in mixtures is challenging due to low concentration, environmental factors, sensor noise, source-detector distance variations, and others. This paper presents new results on nuclear material identification and relative count contribution (also known as mixing ratio) estimation for mixtures of materials in which there are multiple isotopes present. Conventional and deep-learning-based machine learning algorithms were compared. Realistic simulated data using Gamma Detector Response and Analysis Software (GADRAS) were used in our comparative studies. It was observed that a deep learning approach is highly promising.

[1]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[2]  Matthew Durbin,et al.  Machine Learning Applications for the Detection of Missing Radioactive Sources , 2019, 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[3]  Aaron R. Rababaah,et al.  Integration of Two Different Signal Processing Techniques with Artificial Neural Network for Stock Market Forecasting , 2015 .

[4]  Hairong Qi,et al.  Unsupervised Pansharpening Based on Self-Attention Mechanism , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Kiyoshi Shizuma,et al.  Application of neural networks for the analysis of gamma-ray spectra measured with a Ge spectrometer , 2002 .

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  J. Stinnett,et al.  Automated Isotope Identification Algorithm Using Artificial Neural Networks , 2017, IEEE Transactions on Nuclear Science.

[8]  Mark L. Berry,et al.  Regression for Radioactive Source Detection , 2017, 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).

[9]  Jing Wang,et al.  A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Chiman Kwan,et al.  Application of Deep Belief Network to Land Cover Classification Using Hyperspectral Images , 2017, ISNN.

[11]  Germà Garcia-Belmonte,et al.  A new approach to automatic radiation spectrum analysis , 1991 .

[12]  Vili Podgorelec,et al.  Decision trees , 2018, Encyclopedia of Database Systems.

[13]  Baoxin Li,et al.  Video Activity Recognition With Varying Rhythms , 2020, IEEE Access.

[14]  Liang Chen,et al.  Nuclide identification algorithm based on K–L transform and neural networks , 2009 .

[15]  M. Huver,et al.  Application of neural networks to quantitative spectrometry analysis , 1999 .

[16]  E. Choi,et al.  First experimental observation of plasma breakdown for detection of radioactive material using a gyrotron in real-time , 2017, 2017 Eighteenth International Vacuum Electronics Conference (IVEC).

[17]  Rasmus Berg Palm,et al.  Prediction as a candidate for learning deep hierarchical models of data , 2012 .

[18]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Kathryn D. Huff,et al.  Automated Isotope Identification and Quantification Using Artificial Neural Networks , 2019 .

[21]  Dimitrios Skarlatos,et al.  Vegetation Detection Using Deep Learning and Conventional Methods , 2020, Remote. Sens..

[22]  Chiman Kwan,et al.  Tracking and Classification of Multiple Human Objects Directly in Compressive Measurement Domain for Low Quality Optical Videos , 2019, 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

[23]  Cyrille. Eleon,et al.  Study of Boron Coated Straws and mixed (10B/3He) detectors for passive neutron measurements of radioactive waste drums , 2018, 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC).