Guide for Application of Radiation Monitors to the Control and Physical Security of Special Nuclear Material

10.1520/c1112 ◽  
2014 ◽  
Author(s):  
Keyword(s):  
Nuclear Material ◽  
Physical Security ◽  
Radiation Monitors

Mobile Detection Assessment and Response Systems (MDARS): A Force Protection, Physical Security Operational Success

2006 ◽  
Author(s):  
Brian Shoop ◽  
Michael Johnston ◽  
Richard Goehring ◽  
Jon Moneyhun ◽  
Brian Skibba
Keyword(s):  
Physical Security ◽  
Response Systems

Evaluating Corrosion Effects on the Stainless Steel Components of the SAVY-4000/Hagan Nuclear Material Storage Containers

2018 ◽  
Author(s):  
Kirk Patrick Reeves ◽  
Tristan Karns ◽  
Timothy Amos Stone ◽  
Joshua Edward Narlesky ◽  
Holden Christopher Hyer ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Nuclear Material ◽  
Storage Containers

Physical security of deep learning on edge devices: Comprehensive evaluation of fault injection attack vectors

2021 ◽  
Vol 120 ◽  
pp. 114116
Author(s):  
Xiaolu Hou ◽  
Jakub Breier ◽  
Dirmanto Jap ◽  
Lei Ma ◽  
Shivam Bhasin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Fault Injection ◽  
Physical Security ◽  
Fault Injection Attack

scholarly journals New Results on Radioactive Mixture Identification and Relative Count Contribution Estimation

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4155
Author(s):  
Bulent Ayhan ◽  
Chiman Kwan
Keyword(s):  
Deep Learning ◽  
Noise Source ◽  
Simulated Data ◽  
Nuclear Material ◽  
Machine Learning Algorithms ◽  
Detector Response ◽  
Nuclear Materials ◽  
Ratio Estimation ◽  
Analysis Software ◽  
Detector Distance

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.

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