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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning
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Automatic Particle Trajectory Classification in Plasma Simulations
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VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing
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Reactor Facility Operational State Classification Using Gas Effluents and Radiation Measurements
Inferring the operational state of a reactor facility, using measurements from an independent in-situ monitoring system, is critical to the assessment of its compliance to agreements. We consider the problem of inferring the on/off operational state of a facility by using gas effluents and radiation measurements collected at the reactor's off-gas ventilation stack and within its surrounding area. Effluent release concentrations of three noble gases, namely, Ar-41, Cs-138, and Xe-138, are collected inside the ventilation stack and provided every four hours. Gamma spectra is recorded every second by a radiation detector located about 100 meters offset from the stack, which is affected by weather and nearby conditions. We study classifiers based on the correlation coefficient (CC) method [1] applied to individual modalities and their combinations. We implement CC classifiers using ground truth measurements collected over 2 operational cycles of High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL) in year 2017.
A Multiresolution Technique for the Classification of Precipitation Echoes in Radar Data
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Constrained Classification for Infrastructure Threat Assessment
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Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information
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On/Off State Classification of a Reactor Facility Using Gas Effluence Measurements
Inferring on/off operational state of a reactor facility using measurements from an independent monitoring system is critical to the assessment of its compliance to agreements. In this paper, we consider the problem of inferring on/off state of a reactor facility using measurements of Ar-41, Cs-138, and Xe-138 gas effluence types collected at the facility’s off-gas stack. We present classifiers based on thresholding measurements of individual effluence types, and then present fusers that combine their outputs or measurements. We present five fusers based on simple majority rule, Chow’s pattern recognition function, Fisher’s combined p-value statistic, physics-based Poisson radiation counts model, and correlation coefficient method. In addition, we also test five machine learning methods based on non-linear classifiers, which are available as R packages. Finally, our results show that: (i) these gas effluence measurements are effective in inferring on/off state of a reactor facility; for example, best fusers achieve ~97% detection at ~1% false alarm rate, and (ii) fusers that combine all effluence types based on physics-based models, correlation coefficient and Fisher’s method outperform simple majority rule, Chow’s fusers and machine learning methods, as well as when they are applied to individual and pairs of effluence types.
Contrastive Knowledge-Augmented Meta-Learning for Few-Shot Classification
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Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies
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A call for standardized classification of metagenome projects
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The fall, recovery, classification, and initial characterization of the Hamburg, Michigan H4 chondrite
Abstract The Hamburg meteorite fell on January 16, 2018, near Hamburg, Michigan, after a fireball event widely observed in the U.S. Midwest and in Ontario, Canada. Several fragments fell onto frozen surfaces of lakes and, thanks to weather radar data, were recovered days after the fall. The studied rock fragments show no or little signs of terrestrial weathering. Here, we present the initial results from an international consortium study to describe the fall, characterize the meteorite, and probe the collision history of Hamburg. About 1 kg of recovered meteorites was initially reported. Petrology, mineral chemistry, trace element and organic chemistry, and O and Cr isotopic compositions are characteristic of H4 chondrites. Cosmic ray exposure ages based on cosmogenic 3 He, 21 Ne, and 38 Ar are ~12 Ma, and roughly agree with each other. Noble gas data as well as the cosmogenic 10 Be concentration point to a small 40–60 cm diameter meteoroid. An 40 Ar‐ 39 Ar age of 4532 ± 24 Ma indicates no major impact event occurring later in its evolutionary history, consistent with data of other H4 chondrites. Microanalyses of phosphates with LA‐ICPMS give an average Pb‐Pb age of 4549 ± 36 Ma. This is in good agreement with the average SIMS Pb‐Pb phosphate age of 4535.3 ± 9.5 Ma and U‐Pb Concordia age of 4535 ± 10 Ma. The weighted average age of 4541.6 ± 9.5 Ma reflects the metamorphic phosphate crystallization age after parent body formation in the early solar system.
Designing Remote Monitoring for Smart Manufacturing Facilities: Hazard Identification and Classification
This study investigates the process of hazard identification in complex manufacturing environments during the design phase, emphasizing the significance of the design process in developing designs that effectively mitigate hazards in contexts with numerous variables, such as a variety of machines, sensors, actuators, and agents. Through a mixed-methods approach, the objective of this work is to understand how the evolution of design outcomes across various stages might influence a designer’s ability to recognize both standard and novel hazards. To achieve this understanding, an experimental design task was conducted with six designers from a national lab specializing in manufacturing technologies. This approach combined qualitative and quantitative data analysis from a one-hour virtual session with participants. Findings suggest that the complexity of identifying hazards in a high-dimensional design space is challenging within a limited time frame and that the identification of hazards is significantly influenced by the stage of the design task and the initial design decisions, indicating the need for extended time and strategic initial planning in the design process to enhance hazard identification.