Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “feature matching”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13

TraceContract: A Scala DSL for Trace Analysis

In this paper we describe TRACECONTRACT, an API for trace analysis, implemented in the SCALA programming language. We argue that for certain forms of trace analysis the best weapon is a high level programming language augmented with constructs for temporal reasoning. A trace is a sequence of events, which may for example be generated by a running program, instrumented appropriately to generate events. The API supports writing properties in a notation that combines an advanced form of data parameterized state machines with temporal logic. The implementation utilizes SCALA's support for defining internal Domain Specific Languages (DSLs). Furthermore SCALA's combination of object oriented and functional programming features, including partial functions and pattern matching, makes it an ideal host language for such an API.

log file analysis↗

Image processing in remote sensing data analysis - The state of the art

Image analysis techniques applicable to remote sensing data and covering image models, feature detection, segmentation and classification, texture analysis, and matching are studied. Model types for characterizing images examined include random-field, mosaic, and facet models. Edge and corner detection as well as global extraction of linear features are discussed. Pixel clustering and classification are covered in addition to the regional approach to segmentation. Autocorrelation, second-order gray level probability density, and the use of primitive element statistics are discussed in relation to texture analysis. Finally, reducing the cost of (sub)imaging matching methods (e.g., pixelwise comparison of gray levels and normalized cross-correlation between two images) as well as improving match sharpness is considered.

Rosenfeld, A.↗

Radiative lifetime-encoded unicolour security tags using perovskite nanocrystals

Traditional fluorescence-based tags, used for anticounterfeiting, rely on primitive pattern matching and visual identification; additional covert security features such as fluorescent lifetime or pattern masking are advantageous if fraud is to be deterred. Herein, we present an electrohydrodynamically printed unicolour multi-fluorescent-lifetime security tag system composed of lifetime-tunable lead-halide perovskite nanocrystals that can be deciphered with both existing time-correlated single-photon counting fluorescence-lifetime imaging microscopy and a novel time-of-flight prototype. We find that unicolour or matching emission wavelength materials can be prepared through cation-engineering with the partial substitution of formamidinium for ethylenediammonium to generate “hollow” formamidinium lead bromide perovskite nanocrystals; these materials can be successfully printed into fluorescence-lifetime-encoded-quick-read tags that are protected from conventional readers. Furthermore, we also demonstrate that a portable, cost-effective time-of-flight fluorescence-lifetime imaging prototype can also decipher these codes. A single comprehensive approach combining these innovations may be eventually deployed to protect both producers and consumers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Galactic Distribution of Phosphorus: A Survey of 163 Disk and Halo Stars*

Phosphorus (P) is a critical element for life on Earth, yet the cosmic production sites of P are relatively uncertain. To understand how P has evolved in the solar neighborhood, we measured abundances for 163 FGK stars over a range of –1.09 < [Fe/H] < 0.47 using observations from the Habitable-zone Planet Finder instrument on the Hobby–Eberly Telescope. Atmospheric parameters were calculated by fitting a combination of astrometry, photometry, and Fe I line equivalent widths. Phosphorus abundances were measured by matching synthetic spectra to a P I feature at 10529.52 Å. Our [P/Fe] ratios show that chemical evolution models generally underpredict P over the observed metallicity range. Additionally, we find that the [P/Fe] differs by ~0.1 dex between thin disk and thick disk stars that were identified with kinematics. The P abundances were compared with α-elements, iron-peak, odd-Z, and s-process elements, and we found that the evolution of P in the disk most strongly resembles that of the α-elements. We also find that molar P/C and N/C ratios for our sample match the scatter seen from other abundance studies. Finally, we measure a [P/Fe] = 0.09 ± 0.1 ratio in one low-α halo star and probable Gaia–Sausage–Enceladus member, an abundance ratio ~0.3–0.5 dex lower than the other Milky Way disk and halo stars at similar metallicities. Overall, we find that P is likely most significantly produced by massive stars in core-collapse supernovae, based on the largest P abundance survey to date.

79 ASTRONOMY AND ASTROPHYSICS↗

Feature selection with distance correlation

Choosing which properties of the data to use as input to multivariate decision algorithms—also known as feature selection—is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-called automated feature engineering), for many tasks in physics, sets of theoretically well-motivated and well-understood features already exist. Working with such features can bring many benefits, including greater interpretability, reduced training and run time, and enhanced stability and robustness. We develop a new feature selection method based on distance correlation, and demonstrate its effectiveness on the tasks of boosted top- and W -tagging. Using our method to select features from a set of over 7,000 energy flow polynomials, we show that we can match the performance of much deeper architectures, by using only ten features and two orders-of-magnitude fewer model parameters. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

URANIUM HOLDUP MONITORING WITH COMPTON IMAGING AS FUNCTION OF DEPTH AND MASS

Mass quantification using standoff gamma detection is a logical progression for MC&A applications in uranium processing facilities. While obtaining a ground truth from physical collection and weighing of masses is ideal and routine, it poses a logistical problem from both a standpoint of ALARA programs as well as interruptions to operations. The standard approach is the Generalized Geometry in Holdup (GGH) method that can apply for many cases where geometries of the holdup match simulated models; however, there are many cases where a model may not exist or a feature of the holdup geometry is such that it does not match a model particularly well.1 Gamma imaging at standoff distances and outside process equipment has potential to address these shortcomings and improve mass estimations while reducing personnel exposure; however, algorithms still require a significant degree of benchmarking to build sufficient confidence in algorithms before displacing GGH in some procedures. This paper presents the creation of a set of laboratory standards of depleted U3O8 in fixed size containers covering a range of masses and effective thicknesses up to the infinite thickness for the 185-keV line. These blocks are identically sized aluminum boxes loaded such that a thin, flat face is placed towards the imager, while the back side contains closed cell foam to apply pressure to keep the powder in a fixed position at the front face as the sources are manipulated for measurements. Included is an analysis of the mass estimation using a commercially available Compton imaging instrument where multiple geometries are created showing limitations in current methodologies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Thermal Management and Thermal Protection Systems

During my internship in the Thermal Design Branch (ES3), I contributed to two main projects: i) novel passive thermal management system for future human exploration, ii) AVCOAT undercut thermal analysis. i) As NASA prepares to further expand human and robotic presence in space, it is well known that spacecraft architectures will be challenged with unprecedented thermal environments. Future exploration activities will have the need of thermal management systems that can provide higher reliability, mass and power reduction and increased performance. In an effort to start addressing the current technical gaps the NASA Johnson Space Center Passive Thermal Discipline has engaged in technology development activities. One of these activities was done through an in-house Passive Thermal Management System (PTMS) design for a lunar lander. The proposed PTMS, functional in both microgravity and gravity environments, consists of three main components: a heat spreader, a novel hybrid wick Variable Conductance Heat Pipe (VCHP), and a radiator. The aim of this PTMS is to keep electronics on a vehicle within their temperature limits (0 and 50 C for the current design) during all mission phases including multiple lunar day/night cycles. The VCHP was tested to verify its thermal performance. I created a thermal math model using Thermal Desktop (TD) and analyzed it to predict the PTMS performance. After testing, the test data provided a means to correlate the thermal math model. This correlation took into account conduction and convection heat transfer, representing the actual benchtop test. Since this PTMS is proposed for space missions, a vacuum test will be taking place to provide confidence that the system is functional in space environments. Therefore, the model was modified to include a vacuum chamber with a liquid nitrogen shroud while taking into account conduction and radiation heat transfer. Infrared Lamps were modelled and introduced into the model to simulate the sun's rays directly impinging on the system. Heating rate of the lamps were calculated by knowing fraction of emitted energy in a wavelength interval and the filament temperature. This version of the model can be used to predict performance of the system under vacuum with extreme cold or hot conditions. Initial testing of the PTMS showed promise, and the thermal math model predicts even better performance in thermal vacuum testing. ii) Thermal Protection Systems (TPS) are required for vehicles which enter earth's atmosphere to protect from aerodynamic heating caused by the friction between the vehicle and atmospheric gases. Orion's heat shield design has two aspects which needed to be analyzed thermally: i) a small excess of adhesive used to bond the outer AVCOAT layer to the inner composite structure tends to seep from under the AVCOAT and form a small bead in between two bricks of AVCOAT, ii) a silicone rubber with different thermophysical properties than AVCOAT fills the gap between two bricks of AVCOAT. I created a thermal model using TD to determine temperature differences that are caused by these two features. To prevent false results, all TD models must be verified against something known. In this case, the TD model was correlated to CHAR, an ablation modelling software used to analyze TPS. Analyzing a node far from the concerning features, we saw that the TD model data match CHAR data, verifying the TD model. Next, the temperature of the silicone rubber as well as the bead of adhesive were analyzed to determine if they exceeded allowable temperatures. It was determined that these two features do not have a significant effect on the max temperature of the heat shield. This model can be modified to check temperatures at various locations of the heat shield where the composite thickness varies.

Hasnain, Aqib↗

Evaluation of Digital Nautical Chart data for confirmation and expansion of GeoNames data

Here, this work examines how Digital Nautical Chart (DNC) data may contribute to the evolution and refinement of GeoNames data for near-shore features. GeoNames features are point data with one or more possible place names. DNC Earth Cover Text (ECRText) objects are map labels positioned nearby their real word counterpart. ECRText feature map position strikes a compromise between association with real features and cartographic readability. This work explores whether ECRText features can confirm (or expand names for) existing locations or contribute new locations through data conflation. Due to name variations and spatial position, conflating these data are nontrivial. Previous work engaged in a brief examination using the trigram string matching algorithm under coarse proximity constraints, indicating that ECRText could provide additional value to GeoNames. This work builds on that study, by engaging in a deeper examination of spatial proximity and exploring conflation agreement across an ensemble of string matching approaches. The result finds strong ensemble agreement about ECRText features which already exist in GeoNames but mixed results about which features contribute new information, as well as exploring why some of these matching techniques fail. With an eye toward automation, computational efficiency was found not to be a constraint in sustaining updates.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Laboratory comparisons of organic materials to interstellar dust and the Murchison meteorite

Spectra of objects which lie along several lines of sight through the diffuse interstellar medium (DISM) reveal an absorption feature near 3.4 micrometers, which has been attributed to saturated aliphatic hydrocarbons on interstellar grains. The similarity of the absorption bands near 3.4 micrometers (2950 cm-1) along different lines of sight indicates that the carrier of this band lies in the diffuse dust. Several materials have been proposed as "fits" to the 3.4 micrometers feature over the years. A comparison of these identifications is presented. These comparisons illustrate the need for high resolution, high signal-to-noise observational data as a means of distinguishing between laboratory organics as matches to the interstellar material. Although any material containing hydrocarbons will produce features in the 3.4 micrometers region, the proposed "matches" to the DISM do differ in detail. These differences may help in the analyses of the chemical composition and physical processes which led to the production of the DISM organics, although ISO Observations through the 5-8 micrometers spectral region are essential for a definitive identification. A remarkable similarity between the spectrum of the diffuse dust and an organic extract from the Murchison meteorite suggests that some of the interstellar organic material may be preserved in primitive solar system bodies. The 3.4 micrometers absorption feature (in the rest frame) has recently been detected in external galaxies, indicating the widespread availability of organic material for incorporation into planetary systems.

NASA Center ARC↗

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗

Nuclear masses learned from a probabilistic neural network

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian neural networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN provides not only mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface and command-line tool for high-dimensional mass spectrometry data analysis workflows, offering ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation: algorithm implementations utilize all dimensions simultaneously to (i) offer greater separation between features, improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data, demonstrating the advantages of a multidimensional approach in each data processing step.

Colby, Sean M↗

Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens

Multivalent presentation of viral glycoproteins can substantially increase the elicitation of antigen-specific antibodies. To enable a new generation of anti-viral vaccines, we designed self-assembling protein nanoparticles with geometries tailored to present the ectodomains of influenza, HIV, and RSV viral glycoprotein trimers. We first de novo designed trimers tailored for antigen fusion, featuring N-terminal helices positioned to match the C termini of the viral glycoproteins. Trimers that experimentally adopted their designed configurations were incorporated as components of tetrahedral, octahedral, and icosahedral nanoparticles, which were characterized by cryo-electron microscopy and assessed for their ability to present viral glycoproteins. Electron microscopy and antibody binding experiments demonstrated that the designed nanoparticles presented antigenically intact prefusion HIV-1 Env, influenza hemagglutinin, and RSV F trimers in the predicted geometries. This work demonstrates that antigen-displaying protein nanoparticles can be designed from scratch, and provides a systematic way to investigate the influence of antigen presentation geometry on the immune response to vaccination.

59 BASIC BIOLOGICAL SCIENCES↗

Mercury - Surface composition from the reflection spectrum.

The reflection spectrum for the integral disk of the planet Mercury was measured and was found to have a constant positive slope from 0.32 to 1.05 micrometers, except for absorption features in the infrared. The reflectivity curve matches closely the curve for the lunar upland and mare regions. Thus, the surface of Mercury is probably covered with a lunar-like soil rich in dark glasses of high iron and titanium content. Pyroxene is probably the dominant mafic mineral.

Mccord, T. B.↗

Mercury - Interpretation of optical observations.

The spectral reflectivity of Mercury has constant positive slope from 0.32 to 1.05 microns, except for the possibility of an absorption feature in the infrared. The reflectivity curve matches closely the curve for lunar upland and mare regions. Thus, the surface of Mercury is probably covered with lunar-like soil rich in dark glasses of high iron and titanium content. If the absorption band is real, pyroxene is the dominant mafic mineral.

Mccord, T. B.↗

A technique for 3-D robot vision for space applications

An extension of the MIAG algorithm for recognition and motion parameter determination of general 3-D polyhedral objects based on model matching techniques and using Moment Invariants as features of object representation is discussed. Results of tests conducted on the algorithm under conditions simulating space conditions are presented.

Markandey, V.↗

A corner-reflector mixer mount for far infrared wavelengths

A new type of corner-reflector mixer mount, which has the advantages of ease of fabrication and assembly as well as frequency versatility, has been designed and constructed. The predicted response patterns have been experimentally verified for various antenna lengths and operating frequencies between 800 and 2000 GHz. An important design feature is the incorporation of a microstrip matching network which eliminates IF impedance mismatch and provides mechanical isolation for the whisker antenna.

Zmuidzinas, J.↗

Analysis of coordinated observations of a giant stellar flare

Multi-wavelength observations of a giant flare on the star AD Leo were obtained with the 2.1 m and 0.9 m telescopes at McDonald Observatory and the International Ultraviolet Explorer satellite. The quality, spectral coverage, and time resolution of the data represented a major improvement over any published stellar flare data. Two theoretical, quantitative flare models were developed. Combining the models, the chromospheric emission model predictions in the hydrogen Balmer lines, Da II K, Mg II h + k and the optical continuum were compared to the observations, with the result that much of the gradual phase flare emission could be produced by the x ray and conductive heated atmospheres. The models lend insight into the impulsive phase flare emission, but do not reproduce it. Soft x ray and conductive heating of the chromosphere is a natural consequence of the coronal temperatures that have been observed during the gradual phase of flares on the sun and on M dwarf stars. The improved flare observations and quantitative flare models presented here show that these heating mechanisms can produce atmospheres whose emission matches many of the observed stellar flare features.

Lambert, David L.↗