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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Understanding EV Charging Pain Points Through Deep Learning Analysis

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
NASA NTRS1978

Using airborne radiometry to determine atmospheric effects in Landsat data

An empirical method was developed to measure how solar and atmospheric conditions affect Landsat images by comparing Landsat MSS data with terrain reflectance. Terrain reflectance was measured with a four-channel radiometer designed to measure radiance in wavelength bands matched to the Landsat MSS data. Other instrumentation included a digital data logger, an irradiance meter, and a video camera with recorder. Reflectance of many terrain elements which could be registered to the Landsat digital data were measured from a low-flying aircraft. Correlations of reflectance with Landsat radiance verified a linear atmospheric model with an additive (path radiance) term and a multiplicative (transmittance) term. Coefficients from this model permit the extension of spectral signatures in computer-aided classification of Landsat images.

Dana, R. W.↗
NASA NTRS1984

Comparison of Land Cover Information from LANDSAT Multispectral Scanner (MSS) and Airborne Thematic Mapper Simulator (TMS) Data for Hydrologic Applications

Detailed land cover classifications were performed on the Thematic Mapper Simulator (TMS) and MSS data of the Clinton River Basin (acquired on August 19, 1981, and June 28, 1980, respectively) using supervised classification techniques. Differences in interclass separability were compared to select several promising TMS band combinations, selected from the 27 covering the Clinton River Basin. The TMS data produced a more accurate and spatially contiguous classification than MSS for this study site. While the accuracy of the 4-band TM data set was as good as the 7-band, the 3-band TMS data sets were also better than the MSS. These results indicate that both the increased spectral discrimination and spatial resolution contribute to improved classification accuracy. The possibility of reducing the data analysis burden associated with large TM data volumes through effective band selection therefore appears promising. The implications of the improved classification accuracy of TMS data are important for hydrologic and economic modeling. In particular, the higher accuracies for the developed categories (residential and commercial) should improve the predictions of runoff in flood forecasting models and of flood damage for damage calculation models appreciably.

Gervin, J. C.↗
NASA NTRS1991

TEXSYS - A large scale demonstration of model-based real-time control of a Space Station subsystem

A hybrid approach to qualitative and temporal reasoning, using a device-oriented model-based representation in conjunction with both consistency-based and classification diagnosis methods, can be effectively used to monitor and control a complex electromechanical system. An example of this approach is given in some recent tests of the Thermal Expert System (TEXSYS) in control of the Boeing Aerospace Thermal Bus System (BATBS), a prototype two-phase Space Station Freedom thermal bus. The constraints of realtime performance and changing target hardware led to significant changes in the initial approach, including a reduction in the use of deep structural reasoning and the addition of temporal reasoning capabilities. TEXSYS test results show the successful completion of both nominal control and fault recovery actions with the BATBS.

Glass, B. J.↗
NASA NTRS1991

Higher-order neural network software for distortion invariant object recognition

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Reid, Max B.↗
NASA NTRS1992

Polarization effects and multipolarization SAR

Imaging radar polarimeters are usually implemented using a Synthetic Aperture Radar (SAR) approach to give a high resolution image in two dimensions: range and azimuth. For each pixel in the image a polarimetric SAR gives sufficient information to characterize the polarimetric scattering properties of the imaged area (or target) as seen by the radar. Using a polarimetric SAR system as opposed to a single-polarization SAR system provides significantly more information about the target scattering mechanisms and allows better discrimination between different types of surfaces. In these notes a brief overview of SAR polarimetry is offered. The notes are intended as a text to accompany a lecture on SAR polarimetry as part of an AGARD-NATO course. Covered in the notes are the following: the polarization properties of electromagnetic waves; the concepts of radar scattering and measuring radar backscatter with a SAR; polarization synthesis; scattering matrix, Stokes matrix, and covariance matrix representations of polarimetric SAR data; polarization signature plots; design and calibration of polarimetric SAR systems; polarization filtering for target detection; fitting a simple model to polarimetric SAR measurements of naturally occurring features; and supervised classification of polarimetric SAR data.

Freeman, Anthony↗
NASA NTRS1992

Signal processing for airborne doppler radar detection of hazardous wind shear as applied to NASA 1991 radar flight experiment data

Radar data collected during the 1991 NASA flight tests have been selectively analyzed to support research directed at developing both improved as well as new algorithms for detecting hazardous low-altitude windshear. Analysis of aircraft attitude data from several flights indicated that platform stability bandwidths were small compared to the data rate bandwidths which should support an assumption that radar returns can be treated as short time stationary. Various approaches at detection of weather returns in the presence of ground clutter are being investigated. Non-coventional clutter rejection through spectrum mode tracking and classification algorithms is a subject of continuing research. Based upon autoregressive modeling of the radar return time sequence, this approach may offer an alternative to overcome errors in conventional pulse-pair estimates. Adaptive filtering is being evaluated as a means of rejecting clutter with emphasis on low signal-to-clutter ratio situations, particularly in the presence of discrete clutter interference. An analysis of out-of-range clutter returns is included to illustrate effects of ground clutter interference due to range aliasing for aircraft on final approach. Data are presented to indicate how aircraft groundspeed might be corrected from the radar data as well as point to an observed problem of groundspeed estimate bias variation with radar antenna scan angle. A description of how recorded clutter return data are mixed with simulated weather returns is included. This enables the researcher to run controlled experiments to test signal processing algorithms. In the summary research efforts involving improved modelling of radar ground clutter returns and a Bayesian approach at hazard factor estimation are mentioned.

Baxa, Ernest G., Jr.↗
NASA NTRS1998

A Seasonal Air Transport Climatology for Kenya

A climatology of air transport to and from Kenya has been developed using kinematic trajectory modeling. Significant months for trajectory analysis have been determined from a classification of synoptic circulation fields. Five-point back and forward trajectory clusters to and from Kenya reveal that the transport corridors to Kenya are clearly bounded and well defined. Air reaching the country originates mainly from the Saharan region and northwestern Indian Ocean of the Arabian Sea in the northern hemisphere and from the Madagascan region of the Indian Ocean in the southern hemisphere. Transport from each of these source regions show distinctive annual cycles related to the northeasterly Asian monsoon and the southeasterly trade wind maximum over Kenya in May. The Saharan transport in the lower troposphere is at a maximum when the subtropical high over northern Africa is strongly developed in the boreal winter. Air reaching Kenya between 700 and 500 hPa is mainly from Sahara and northwest India Ocean flows in the months of January and March, which gives way to southwest Indian Ocean flow in May and November. In contrast, air reaching Kenya at 400 hPa is mainly from southwest Indian Ocean in January and March, which is replaced by Saharan transport in May and November. Transport of air from Kenya is invariant, both spatially and temporally, in the tropical easterlies to the Congo Basin and Atlantic Ocean in comparison to the transport to the country. Recirculation of air has also been observed, but on a limited and often local scale and not to the extent reported in southern Africa.

Gatebe, C. K.↗
NASA NTRS2010

The Spectral Energy Distribution of Fermi Bright Blazars

We have conducted a detailed investigation of the broadband spectral properties of the gamma-ray selected blazars of the Fermi LAT Bright AGN Sample (LBAS). By combining our accurately estimated Fermi gamma-ray spectra with Swift, radio, infra-red, optical, and other hard X-ray /gamma-ray data, collected within 3 months of the LBAS data taking period, we were able to assemble high-quality and quasi-simultaneous spectral energy distributions (SED) for 48 LBAS blazars. The SED of these gamma-ray sources is similar to that of blazars discovered at other wavelengths, clearly showing, in the usual log v-log v Fv representation, the typical broadband spectral signatures normally attributed to a combination of low-energy synchrotron radiation followed by inverse Compton emission of one or more components. We have used these SED to characterize the peak intensity of both the low- and the high-energy components. The results have been used to derive empirical relationships that estimate the position of the two peaks from the broadband colors (i.e., the radio to optical, alpha(sub ro) , and optical to X-ray, alpha(sub ox), spectral slopes) and from the gamma-ray spectral index. Our data show that the synchrotron peak frequency (v(sup S) (sub peak)) is positioned between 10(exp 12.5) and 10(exp 14) Hz in broad-lined flat spectrum radio quasars (FSRQs) and between 10(exp 13) and 10(exp 17) Hz in featureless BL Lacertae objects. We find that the gamma-ray spectral slope is strongly correlated with the synchrotron peak energy and with the X-ray spectral index, as expected at first order in synchrotron-inverse Compton scenarios. However, simple homogeneous, one-zone, synchrotron self-Compton (SSC) models cannot explain most of our SED, especially in the case of FSRQs and low energy peaked (LBL) BL Lacs. More complex models involving external Compton radiation or multiple SSC components are required to reproduce the overall SED and the observed spectral variability. While more than 50% of known radio bright high energy peaked (HBL) BL Lacs are detected in the LBAS sample, only less than 13% of known bright FSRQs and LBL BL Lacs are included. This suggests that the latter sources, as a class, may be much fainter gamma-ray emitters than LBAS blazars, and could in fact radiate close to the expectations of simple SSC models. We categorized all our sources according to a new physical classification scheme based on the generally accepted paradigm for Active Galactic Nuclei and on the results of this SED study. Since the LAT detector is more sensitive to flat spectrum gamma-ray sources, the correlation between v(sup S) (sub peak) and gamma-ray spectral index strongly favors the detection of high energy peaked blazars, thus explaining the Fermi overabundance of this type of sources compared to radio and EGRET samples. This selection effect is similar to that experienced in the soft X-ray band where HBL BL Lacs are the dominant type of blazars.

Abdo, A. A.↗
NASA NTRS2020

2SXPS: An Improved and Expanded Swift X-Ray Telescope Point-source Catalog

We present the 2SXPS (Swift-XRT Point Source) catalog, containing 206,335 point sources detected by the Swift X-ray Telescope (XRT) in the 0.3–10 keV energy range. This catalog represents a significant improvement over 1SXPS, with double the sky coverage (now 3790 deg2), and several significant developments in source detection and classification. In particular, we present for the first time techniques to model the effect of stray light—significantly reducing the number of spurious sources detected. These techniques will be very important for future, large effective area X-ray missions, such as the forthcoming Athena X-ray observatory. We also present a new model of the XRT point-spread function and a method for correctly localizing and characterizing piled-up sources. We provide light curves—in four energy bands, two hardness ratios, and two binning timescales—for every source, and from these deduce that over 80,000 of the sources in 2SXPS are variable in at least one band or hardness ratio. The catalog data can be queried or downloaded via a web interface at https://www.swift.ac.uk/2SXPS, via HEASARC, or in Vizier (IX/58).

Catalogs↗
DOE OSTI2024

Theory for Equivariant Quantum Neural Networks

Quantum neural network architectures that have little to no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task. This is materialized through the usage of equivariant neural networks the action of which commutes with that of the symmetry. In this work, we import these ideas to the quantum realm by presenting a comprehensive theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group. We develop multiple methods to construct equivariant layers for EQNNs and analyze their advantages and drawbacks. Our methods can find unitary or general equivariant quantum channels efficiently even when the symmetry group is exponentially large or continuous. As a special implementation, we show how standard quantum convolutional neural networks (QCNNs) can be generalized to group-equivariant QCNNs where both the convolution and pooling layers are equivariant to the symmetry group. We then numerically demonstrate the effectiveness of a S U ( 2 ) -equivariant QCNN over symmetry-agnostic QCNN on a classification task of phases of matter in the bond-alternating Heisenberg model. Our framework can be readily applied to virtually all areas of quantum machine learning. Lastly, we discuss about how symmetry-informed models such as EQNNs provide hopes to alleviate central challenges such as barren plateaus, poor local minima, and sample complexity. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Sum rules in the standard model effective field theory from helicity amplitudes

The dispersion relation of an elastic 4-point amplitude in the forward direction leads to a sum rule that connects the low energy amplitude to the high energy observables. We perform a classification of these sum rules based on massless helicity amplitudes. With this classification, we are able to systematically write down the sum rules for the dimension-6 operators of the Standard Model Effective Field Theory (SMEFT), some of which are absent in previous literatures. These sum rules offer distinct insights on the relations between the operator coefficients in the EFT and the properties of the full theory that generates them. Their applicability goes beyond tree level, and in some cases can be used as a practical method of computing the one loop contributions to low energy observables. They also provide an interesting perspective for understanding the custodial symmetries of the SM Higgs and fermion sectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Chemical classification program synthesis using generative artificial intelligence

Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.

Artificial Intelligence↗
NASA NTRS2005

A Rapid Approach to Modeling Species-Habitat Relationships

A growing number of species require conservation or management efforts. Success of these activities requires knowledge of the species' occurrence pattern. Species-habitat models developed from GIS data sources are commonly used to predict species occurrence but commonly used data sources are often developed for purposes other than predicting species occurrence and are of inappropriate scale and the techniques used to extract predictor variables are often time consuming and cannot be repeated easily and thus cannot efficiently reflect changing conditions. We used digital orthophotographs and a grid cell classification scheme to develop an efficient technique to extract predictor variables. We combined our classification scheme with a priori hypothesis development using expert knowledge and a previously published habitat suitability index and used an objective model selection procedure to choose candidate models. We were able to classify a large area (57,000 ha) in a fraction of the time that would be required to map vegetation and were able to test models at varying scales using a windowing process. Interpretation of the selected models confirmed existing knowledge of factors important to Florida scrub-jay habitat occupancy. The potential uses and advantages of using a grid cell classification scheme in conjunction with expert knowledge or an habitat suitability index (HSI) and an objective model selection procedure are discussed.

Carter, Geoffrey M.↗
DOE OSTI2024

Toward Smart, Automated Junctional Tourniquets—AI Models to Interpret Vessel Occlusion at Physiological Pressure Points

Hemorrhage is the leading cause of preventable death in both civilian and military medicine. Junctional hemorrhages are especially difficult to manage since traditional tourniquet placement is often not possible. Ultrasound can be used to visualize and guide the caretaker to apply pressure at physiological pressure points to stop hemorrhage. However, this process is technically challenging, requiring the vessel to be properly positioned over rigid boney surfaces and applying sufficient pressure to maintain proper occlusion. As a first step toward automating this life-saving intervention, we demonstrate an artificial intelligence algorithm that classifies a vessel as patent or occluded, which can guide a user to apply the appropriate pressure required to stop flow. Neural network models were trained using images captured from a custom tissue-mimicking phantom and an ex vivo swine model of the inguinal region, as pressure was applied using an ultrasound probe with and without color Doppler overlays. Using these images, we developed an image classification algorithm suitable for the determination of patency or occlusion in an ultrasound image containing color Doppler overlay. Separate AI models for both test platforms were able to accurately detect occlusion status in test-image sets to more than 93% accuracy. In conclusion, this methodology can be utilized for guiding and monitoring proper vessel occlusion, which, when combined with automated actuation and other AI models, can allow for automated junctional tourniquet application.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Global flow structure and exact formal transseries of the Gubser flow in kinetic theory

In this work we introduce the generic conditions for the existence of a non-equilibrium attractor that is an invariant manifold determined by the long-wavelength modes of the physical system. We investigate the topological properties of the global flow structure of the Gubser flow for the Israel-Stewart theory and a kinetic model for the Boltzmann equation by employing Morse-Smale theory. We present a complete classification of the invariant submanifolds of the flow and determine all the possible flow lines connecting any pair of UV/IR fixed points. The formal transseries solutions to the Gubser dynamical system around the early-time (UV) and late-time (IR) fixed points are constructed and analyzed. It is proven that these solutions are purely perturbative (or power-law asymptotic) series with a finite radius of convergence. Based on these analyses, we find that Gubser-like expanding kinetic systems do not hydrodynamize owing to the failure of the hydrodynamization process which heavily relies on the classification of (non)hydrodynamic modes in the IR regime. This is in contrast to longitudinal boost-invariant plasmas where the asymptotic dynamics is described by a few terms of the hydrodynamic gradient expansion. We finally compare our results for both Bjorken and Gubser conformal kinetic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗