Filter by
Publications (194)
RSSExamining Autonomous Inspection of Geologic Repositories
ESARDA Bulletin N.64 (1) - This paper discusses the current state of the art in robotic autonomy for known or partially known environment mapping and patrolling, as well as shared autonomy, where humans collaborate with closed loop autonomation to complete tasks.
A Novel Approach for Detection of Illicit Nuclear Activities Using Optically Stimulated Dosimetry
ESARDA Bulletin N.64 (1) - This work aims to bring a novel approach to detecting illicit nuclear activities using commercially available optically OSLDs produced by LANDAUER®, leading to well-known and well-established techniques to determine the dose.
Studies of the impact of beta contributions on Cherenkov light emission by spent nuclear fuel
ESARDA Bulletin N.64 (1) - The methodology is based on comparing the measured Cherenkov light intensity with a predicted intensity, calculated with operator information.
Stochastic Approach to Inspection Evaluation: Methodology and Validation
ESARDA Bulletin N.64 (1) - The paper discusses the methodology for performing International Atomic Energy Agency’s (IAEA’s) postinspection analysis to assess the ef fectiveness of verification inspection plans using a stochastic method.
Connector N.6
Sixth issue of the ESARDA Connector newsletter released in spring 2022.
ESARDA Bulletin N.63 - Special issue
ESARDA Bulletin N.63 - Special issue on Data Analytics for International Nuclear Safeguards and Non-Proliferation.
Connector N.5
Fifth issue of the ESARDA Connector newsletter released in autumn 2021.
Artificial Judgement Assistance from teXt (AJAX): Applying Open Domain Question Answering to Nuclear Non-proliferation Analysis
ESARDA Bulletin N.63 - A unique Salt and Pepper strategy for testing the knowledge present in the language models, while also introducing auditability function in our pipeline.
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy
ESARDA Bulletin N.63 - NukeLM, a nuclear-domain language model pre-trained on 1.5 million abstracts from the U.S. Department of Energy Office of Scientific and Technical Information (OSTI) database. This NukeLM model is then fine-tuned for the classification of research articles.
Applied Machine Learning for Simulated Reprocessing Safeguards: Unsupervised Networks
ESARDA Bulletin N.63 - This work is part of a series of two documents that consider the use of ML to improve one aspect of safeguards, namely nuclear material accountancy.