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Archer, Daniel

Publications and source records attributed to Archer, Daniel.

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A multi-modal scanning system to digitize CBRNE emergency response scenes

A handheld system developed to digitize a contextual understanding of the scene at a chemical, biological, radiological, nuclear and/or explosives (CBRNE) events is described. The system uses LiDAR and cameras to create a colorized 3D model of the environment, which helps domain experts that are supporting responders in the field. To generate the digitized model, a responder scans any suspicious objects and the surroundings by carrying the system through the scene. The scanning system provides a real-time user interface to inform the user about scanning progress and to indicate any areas that may have been missed either by the LiDAR sensors or the cameras. Currently, the collected data are post-processed on a different device, building a colorized triangular mesh of the encountered scene, with the intention of moving this pipeline to the scanner at a later point. The mesh is sufficiently compressed to be sent over a reduced bandwidth connection to a remote analyst. Furthermore, the system tracks fiducial markers attached to diagnostic equipment that is placed around the suspicious object. The resulting tracking information can be transmitted to remote analysts to further facilitate their supporting efforts. The paper will discuss the system's design, software components, the user interface used for scanning a scene, the necessary procedures for calibration of the sensors, and the processing steps of the resulting data. The discussion will close by evaluating the system's performance on 11 scenes.

Prins, Nicholas↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2022

Measurement Plan for Uncertainty Contributions to 252 Cf Waste Measurements

This measurement plan establishes the methodology for performing and analyzing mock-up measurements of Building 7930 Cell G Cf waste generated from 252 Cf product preparation to better determine the measurement uncertainty contributors. Understanding the uncertainty contributors will establish the total measurement uncertainty. This is critical because it will impact the administrative threshold at which personnel can discriminate between low-level waste (LLW) and transuranic (TRU) waste.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Cell G 7606A Mockup Measurement Analysis Methods and Results

The production of research isotopes at the Radiochemical Engineering Development Center (REDC) invariably leads to the accumulation of radioactive waste. Storage of this waste onsite at ORNL serves as an interim solution prior to shipment to long term storage facilities, however onsite capacity is limited. Proper characterization of the activity of waste products is essential for determining the appropriate waste stream and, ultimately, mitigating the cost of disposal. This is typically done via gamma spectrometry and use of the In-Situ Object Counting System (ISOCS), a software package from Mirion that serves as an accepted community standard. However, variability in the contents, density, and activity distribution in waste containers can introduce large errors in ISOCS quantification. A measurement campaign using a mockup of the proposed setup in Cell G seeks to quantify the magnitude and source of these systematic errors. Analysis of this data will guide the creation of ISOCS geometry templates and measurement methods designed to minimize overall uncertainty in reported activities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗