This dissertation deals with the problem of automated classification of the signals obtained
from certain radiation monitoring systems, specifically from the Core Discharge Monitor …

Automated analysis using pattern recognition and neural network software can help interpret
data, call attention to potential anomalies, and improve safeguards effectiveness. Automated …

This thesis serves as a compendium of instances where the application of machine learning
could further be used to better safeguard nuclear materials from current and future nuclear …

This paper presents two approaches based on different artificial intelligence techniques (AI),
genetic algorithms (GA) and neural networks (NN), for pattern recognition on the set of …

This paper summarizes a novel approach to improved localization of fuel defects by fusing
existing data sources and methods within a neural network model to make accurate and …

Nuclear safeguards verification of spent nuclear fuel (SNF) is imperative to ensure peaceful
use of nuclear material. To verify the correctness and completeness of operator declarations …

Unplanned outages can be especially costly for generation companies operating nuclear
facilities. Early detection of deviations from expected performance through condition …

Safeguards verification of spent nuclear fuel assemblies is frequently done by performing
non-destructive measurements, which are used to verify the completeness and correctness …

Monitoring the fueling process of an on-load nuclear reactor is a full-time job for nuclear
safeguarding agencies. Nuclear core discharge monitors (CDMs) can provide continuous …

A fundamental requirement on data to be used for the training of an empirical diagnostic
model is that the data has to be sufficiently similar and consistent with what will be observed …