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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Russian Invasion of Ukraine and Nuclear Security

You have in your hands a unique publication. Voices of Ukraine is a collection of testimonies from people working in the nuclear and radioactive source sector in Ukraine. The five texts in this extract are a preview of the full volume that will be published in early 2023. The accounts presented in this extract are very different from the newspaper articles or reports you may have read about the Russian invasion of Ukraine and the attack on and occupation of the Chornobyl Exclusion Zone and Zaporizhzhia NPP. Five people share their own stories of the personal and professional dilemmas they faced: How to endure an unending shift under enemy occupation when replacement workers weren’t permitted to relieve the staff; whether to remain in your childhood home or to flee abroad; how to support your community and country while maintaining the safe operation of nuclear sites in wartime conditions? The narratives of these individuals have far-reaching consequences for the nuclear sector in Ukraine but also far beyond it.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Development of Hopfield Artificial Neural Network for Anomaly Detection in Environmental Gamma Radiation Background: Consortium on Nuclear Security Technologies (CONNECT) (Q2 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore supervised machine learning (ML) algorithms for development of a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm for detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Amendment to Programmatic Agreement among the U.S. Department of Energy, National Nuclear Security Administration, Los Alamos Field Office, the New Mexico State Historic Preservation Office and the Advisory Council on History Preservation Concerning Management of the Historic Properties of Los Alamos National Laboratory, Los Alamos, New Mexico (AGREEMENT)

The Field Office has a cultural resources program manager. The LANL Management and Operating Contractor has a staff of cultural resource specialists who meet the qualifications set forth in the Secretary of the Interior's Standards and Guidelines for Professional Qualifications (36 CFR Part 61), or work under the supervision of individuals who meet these qualifications. Any future Management and Operating Contractor will have a staff of Secretary of the Interior-qualified cultural resource specialists.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

A Nuclear Security Enterprise Study of High-Reliability Systems, Collaboration, and Data

It may seem simple and trivial, but defining the difference between data and information is contested and has implications that may affect the security of United States interests and even cost lives. For security, data are raw facts or figures without context, while information is the compilation or articulation of data that forms context. Security depends on clarity in the differences between data and information and controlling them. Control is necessary to ensure that data and information are not inadvertently released to foreign governments, the public, or those without Need-to-Know. A primary concern in the practice of security is the control of data to avoid the inadvertent conversion to sensitive information. The complexity of this concern is further augmented when institutions are part of tightly coupled networks that informally share data and information. Additionally, those that share data as a function of legislative action—and/or formally integrate data and information system infrastructures—may be a higher security risk. This paper will present a case study that utilizes elements of literature from Knowledge Management and networks to tell a story of an issue in security—specifically, controlling the conversion of data to information.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Joint Comments from the Department of Energy, National Nuclear Security Administration (DOE NNSA) Los Alamos Field Office and Triad National Security, LLC on the U.S. Environmental Protection Agency’s New Mexico State-Wide Municipal Separate Storm Sewer System Strawman General Permit

In December 2020 and March 2021, EPA issued two MS4 strawman permit drafts specific to the Los Alamos area. EPA made text changes in the 2 nd strawman permit based on stakeholder comments related to the initial draft. During the public meeting on the Los Alamos area designation decision held on February 13, 2024, EPA staff stated that comments from the past strawman permits would be incorporated into the new MS4 permit to be issued state-wide. However, it does not appear that any of the text changes from the 2 nd Los Alamos area strawman permit were included in this state-wide general MS4 strawman permit. For reference, the 2 nd Los Alamos area revised strawman permit is included with these comments as Attachment 2. Many of the following comments refer to or state EPA’s updated text included in the 2 nd Los Alamos area strawman permit. The updated text from this 2 nd strawman permit that are referenced in the following comments should be applicable state-wide and it is suggested that this past work be reconsidered and incorporated.

54 ENVIRONMENTAL SCIENCES↗