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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra

We present a library of empirical stellar spectra created using spectra from the Sloan Digital Sky Survey’s Baryon Oscillation Spectroscopic Survey. The templates cover spectral types O5 through L3, are binned by metallicity from −2.0 dex through +1.0 dex, and are separated into main-sequence (dwarf) stars and giant stars. With recently developed M dwarf metallicity indicators, we are able to extend the metallicity bins down through the spectral subtype M8, making this the first empirical library with this degree of temperature and metallicity coverage. The wavelength coverage for the templates is from 3650 to 10200 Å at a resolution of better than R ∼ 2000. Using the templates, we identify trends in color space with metallicity and surface gravity, which will be useful for analyzing large data sets from upcoming missions like the Large Synoptic Survey Telescope. Along with the templates, we are releasing a code for automatically (and/or visually) identifying the spectral type and metallicity of a star.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1994

McIntosh active-region class similarities and suggestions for mergers

McIntosh active-region classifications reported during a five-year period were examined to determine similarities among the classes. Two methods were used extensively to determine these similarities. The number of transitions among classes were used to determine the most frequent transitions out of each class, and the alternative classes reported for the same region by different sites were used to establish which classes were neighboring classes. These transition frequencies and neighboring classes were used to identify classes that could be eliminated or merged with other classes. Class similarities were used to investigate the relative importance of several pairs of decisions that occur within a single McIntosh parameter. In particular, the redundancy of parameters in some classes was examined, and the class similarities were used to identify which of these parameters could be eliminated. Infrequently reported classes were also considered, and suggestions for mergers were made when similarities between classes could be identified.

Bornmann, P. L.↗
DOE OSTI2014

A first class constraint generates not a gauge transformation, but a bad physical change: The case of electromagnetism

In Dirac–Bergmann constrained dynamics, a first-class constraint typically does not alone generate a gauge transformation. By direct calculation it is found that each first-class constraint in Maxwell’s theory generates a change in the electric field E{sup →} by an arbitrary gradient, spoiling Gauss’s law. The secondary first-class constraint p{sup i},{sub i}=0 still holds, but being a function of derivatives of momenta (mere auxiliary fields), it is not directly about the observable electric field (a function of derivatives of A{sub μ}), which couples to charge. Only a special combination of the two first-class constraints, the Anderson–Bergmann–Castellani gauge generator G, leaves E{sup →} unchanged. Likewise only that combination leaves the canonical action invariant—an argument independent of observables. If one uses a first-class constraint to generate instead a canonical transformation, one partly strips the canonical coordinates of physical meaning as electromagnetic potentials, vindicating the Anderson–Bergmann Lagrangian orientation of interesting canonical transformations. The need to keep gauge-invariant the relation q-dot −(δH)/(δp) =−E{sub i}−p{sup i}=0 supports using the gauge generator and primary Hamiltonian rather than the separate first-class constraints and the extended Hamiltonian. Partly paralleling Pons’s criticism, it is shown that Dirac’s proof that a first-class primary constraint generates a gauge transformation, by comparing evolutions from identical initial data, cancels out and hence fails to detect the alterations made to the initial state. It also neglects the arbitrary coordinates multiplying the secondary constraints inside the canonical Hamiltonian. Thus the gauge-generating property has been ascribed to the primaries alone, not the primary–secondary team G. Hence the Dirac conjecture about secondary first-class constraints as generating gauge transformations rests upon a false presupposition about primary first-class constraints. Clarity about Hamiltonian electromagnetism will be useful for an analogous treatment of GR. - Highlights: • A first-class constraint changes the electric field E, spoiling Gauss’s law. • A first-class constraint does not leave the action invariant or preserve q,0−dH/dp. • The gauge generator preserves E,q,0−dH/dp, and the canonical action. • The error in proofs that first-class primaries generating gauge is shown. • Dirac’s conjecture about secondary first-class constraints is blocked.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects

Abstract Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing problem by, e.g., encrypting camera images or obscuring/blurring the imaged information through digital algorithms. Here, we demonstrate a camera design that performs class-specific imaging of target objects with instantaneous all-optical erasure of other classes of objects. This diffractive camera consists of transmissive surfaces structured using deep learning to perform selective imaging of target classes of objects positioned at its input field-of-view. After their fabrication, the thin diffractive layers collectively perform optical mode filtering to accurately form images of the objects that belong to a target data class or group of classes, while instantaneously erasing objects of the other data classes at the output field-of-view. Using the same framework, we also demonstrate the design of class-specific permutation and class-specific linear transformation cameras, where the objects of a target data class are pixel-wise permuted or linearly transformed following an arbitrarily selected transformation matrix for all-optical class-specific encryption, while the other classes of objects are irreversibly erased from the output image. The success of class-specific diffractive cameras was experimentally demonstrated using terahertz (THz) waves and 3D-printed diffractive layers that selectively imaged only one class of the MNIST handwritten digit dataset, all-optically erasing the other handwritten digits. This diffractive camera design can be scaled to different parts of the electromagnetic spectrum, including, e.g., the visible and infrared wavelengths, to provide transformative opportunities for privacy-preserving digital cameras and task-specific data-efficient imaging.

47 OTHER INSTRUMENTATION↗
NASA NTRS1999

Gamma-Ray Burst Class Properties

Guided by the Supervised pattern recognition algorithm C4.5, we examine the three gamma-ray burst classes identified by Mukherjee et al. C4.5 provides strong statistical support for this classification. However, with C4.5 and our knowledge of the BATSE instrument, we demonstrate that Class 3 (intermediate fluence, intermediate duration, soft) does not have to be a distinct source population: statistical/systematic errors in measuring burst attributes combined with the well-known hardness/intensity correlation can cause low peak flux Class I (high fluence, long, intermediate hardness) bursts to take on Class 3 characteristics naturally. Based on our hypothesis that the third class is not a distinct one, we provide rules so that future events can be placed in either Class I or Class 2 (low fluence, short, hard). Using classified bursts from the BATSE 4B Catalog, we plot log(N>P) vs. log(P) curves and study spectral features of each class. We find that the two classes are relatively distinct on the basis of spectral parameters, alpha, Beta, and E(sub peak) alone. Although this does not indicate a better basis for classification, it does suggest that different physical conditions exist for Class I and Class 2 bursts. In the process of studying burst class characteristics, we identify a new bias that affects measurement of burst fluences and durations. Using a simple model of how burst duration can be underestimated, we generally characterize how this fluence duration bias affects BATSE measurements, and demonstrate the type of effect it can have on the BATSE fluence vs. peak flux diagram.

Hakkila, Jon↗
NASA NTRS2014

Aeroflex Technology as Class-Y Demonstrator

Modern space field programmable gate array (FPGA) devices with increased functional density and operational frequency, such as Xilinx Virtex 4 (V4) and S (V5), are packaged in non-hermetic ceramic flip chip forms. These next generation space parts were not qualified to the MIL-PRF-38535 Qualified Manufacturer Listing (QML) class-V when they were released because class-V was only intended for hermetic parts. In order to bring Xilinx V5 type packages into the QML system, it was suggested that class-Y be set up as a new category. From 2010 through 2014, a JEDEC G12 task group developed screening and qualification requirements for Class-Y products. The Document Standardization Division of the Defense Logistics Agency (DLA) has completed an engineering practice study. In parallel with the class-Y efforts, the NASA Electronic Parts and Packaging (NEPP) program has funded JPL to study potential reliability issues of the class-Y products. The major hurdle of this task was the absence of adequate research samples. Figure 1-1 shows schematic diagrams of typical structures of class-Y type products. Typically, class-Y products are either in ceramic flip chip column grid array (CGA) or land grid array (LGA) form. In class-Y packages, underfill and heat spread adhesive materials are directly exposed to the spacecraft environment due to their non-hermeticity. One of the concerns originally raised was that the underfill material could degrade due to the spacecraft environment and negatively impact the reliability of the package. In order to study such issues, it was necessary to use ceramic daisy chain flip chip package samples so that continuity of flip chip solder bumps could be monitored during the reliability tests. However, none of the commercially available class-Y daisy chain parts had electrical connections through flip chip solder bumps; only solder columns were daisy chained, which made it impossible to test continuity of flip chip solder bumps without using extremely costly functional parts. Among space parts manufacturers who were interested in producing class-Y products, Aeroflex Microelectronic Solutions-HiRel had been developing assembly processes using their internal R&D classy type samples. In early 2012, JPL and Aeroflex initiated a collaboration to study reliability of the Aeroflex technology as a class-Y demonstrator.

Suh, Jong-ook↗
NASA NTRS2000

Gamma-Ray Burst Class Properties

Guided by the supervised pattern recognition algorithm C4.5 developed by Quinlan in 1986, we examine the three gamma-ray burst classes identified by Mukherjee et al. in 1998. C4.5 provides strong statistical support for this classification. However, with C4.5 and our knowledge of the Burst and Transient Source Experiment (BATSE) instrument, we demonstrate that class 3 (intermediate fluence, intermediate duration, soft) does not have to be a distinct source population: statistical/systematic errors in measuring burst attributes combined with the well-known hardness/intensity correlation can cause low peak flux class 1 (high fluence, long, intermediate hardness) bursts to take on class 3 characteristics naturally. Based on our hypothesis that the third class is not a distinct one, we provide rules so that future events can be placed in either class 1 or class 2 (low fluence, short, hard). We find that the two classes are relatively distinct on the basis of Band's work in 1993 on spectral parameters alpha, beta, and E (sub peak) alone. Although this does not indicate a better basis for classification, it does suggest that different physical conditions exist for class 1 and class 2 bursts. In the process of studying burst class characteristics, we identify a new bias affecting burst fluence and duration measurements. Using a simple model of how burst duration can be underestimated, we show how this fluence duration bias can affect BATSE measurements and demonstrate the type of effect it can have on the BATSE fluence versus peak flux diagram.

Hakkila, Jon↗
DOE OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Structures of Class Id Ribonucleotide Reductase Catalytic Subunits Reveal a Minimal Architecture for Deoxynucleotide Biosynthesis

Class I ribonucleotide reductases (RNRs) share a common mechanism of nucleotide reduction in a catalytic α subunit. All RNRs initiate catalysis with a thiyl radical, generated in class I enzymes by a metallocofactor in a separate β subunit. Class Id RNRs use a simple mechanism of cofactor activation involving oxidation of a Mn II 2 cluster by free superoxide to yield a metal-based Mn III Mn IV oxidant. This simple cofactor assembly pathway suggests that class Id RNRs may be representative of the evolutionary precursors to more complex class Ia–c enzymes. X-ray crystal structures of two class Id α proteins from Flavobacterium johnsoniae (Fj) and Actinobacillus ureae (Au) reveal that this subunit is distinctly small. The enzyme completely lacks common N-terminal ATP-cone allosteric motifs that regulate overall activity, a process that normally occurs by dATP-induced formation of inhibitory quaternary structures to prevent productive β subunit association. Class Id RNR activity is insensitive to dATP in the Fj and Au enzymes evaluated here, as expected. However, the class Id α protein from Fj adopts higher-order structures, detected crystallographically and in solution. The Au enzyme does not exhibit these quaternary forms. Our study reveals structural similarity between bacterial class Id and eukaryotic class Ia α subunits in conservation of an internal auxiliary domain. Overall, our findings with the Fj enzyme illustrate that nucleotide-independent higher-order quaternary structures can form in simple RNRs with truncated or missing allosteric motifs.

59 BASIC BIOLOGICAL SCIENCES↗