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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2000

Determination of anionic surfactants during wastewater recycling process by ion pair chromatography with suppressed conductivity detection

A direct approach utilizing ion pairing reversed-phase chromatography coupled with suppressed conductivity detection was developed to monitor biodegradation of anionic surfactants during wastewater recycling through hydroponic plant growth systems and fixed-film bioreactors. Samples of hydroponic nutrient solution and bioreactor effluent with high concentrations (up to 120 mS electrical conductance) of inorganic ions can be analyzed without pretreatment or interference. The presence of non-ionic surfactants did not significantly affect the analysis. Dynamic linear ranges for tested surfactants [Igepon TC-42, ammonium lauryl sulfate, sodium laureth sulfate and sodium alkyl (C10-C16) ether sulfate] were 2 to approximately 500, 1 to approximately 500, 2.5 to approximately 550 and 3.0 to approximately 630 microg/ml, respectively.

NASA Program Advanced Human Support Technology↗
DOE OSTI2023

A Fine-grained Asynchronous Bulk Synchronous parallelism model for PGAS applications

The Partitioned Global Address Space (PGAS) model is well suited for executing irregular applications on cluster-based systems, due to its efficient support for short, one-sided messages. Separately, the actor model has been gaining popularity as a productive asynchronous message-passing approach for distributed objects in enterprise and cloud computing platforms, typically implemented in languages such as Erlang, Scala or Rust. To the best of our knowledge, there has been no past work on using the actor model to deliver both productivity and scalability to irregular PGAS applications with large number of small messages. In this paper, we introduce a new programming system for PGAS applications, in which point-to-point remote operations can be expressed as fine-grained asynchronous actor messages. In our approach, the programmer does not need to worry about programming complexities related to message aggregation and termination detection. Our approach can be viewed as extending the classical Bulk Synchronous Parallelism model with fine-grained asynchronous communications within a phase or superstep. Here, we believe that our approach offers a desirable point in the productivity-performance space for PGAS applications, with more scalable performance and higher productivity relative to past approaches. Specifically, for seven irregular mini-applications from the Bale Kernels and three graph kernels executed using 2048 cores in the NERSC Cori system, our approach shows geometric mean performance improvements of ≥ 20X relative to standard PGAS versions (UPC and OpenSHMEM) while maintaining comparable productivity to those versions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Luo, Yufeng (ORCID:0000000246230683)↗
NASA NTRS1988

The prospects for detecting spectral shifts due to satellite sensor aging

Along with responsivity changes due to sensor aging, there may be concurrent spectral changes. A field-measurement approach for detecting postlaunch spectral changes is described. For illustration, one hypothetical model of change (spectral band shift) is explored through simulation for five satellite sensors. Two different types of natural terrain - vegetation and bare soil - are used as test targets.

Suits, G.↗
NASA NTRS1988

Neptune radio emission - Predictions based on planetary scaling laws

In this paper a prediction is advanced concerning Neptune's low-frequency radio emission based on the radiometric Bode's law for radio planets in combination with the magnetostrophic scaling law for magnetized planets. The total emitted radio power is predicted to be about 1.6 x 10 to the 7th W, very nearly the same as that predicted and observed for Uranus. Possible emission spectral shapes, based on Saturn and earth-like models, are shown. Using these models, the radio emission frequency range is predicted to extend from approximately 100 to just over 1000 kHz, with a spectral peak between 350 and 500 kHz. If radiation is beamed approximately in the sunward direction, Neptune should be detectable by the planetary radio astronomy experiment onboard the Voyager spacecraft sometime between 45 and 90 days before closest approach. This detection is likely to represent the first direct evidence of a Neptune magnetic field. Possible implications for Neptune's magnetosphere with regard to the time of first detection are discussed.

Desch, Michael D.↗
NASA NTRS1999

Remote Thermal IR Spectroscopy of our Solar System

Indirect methods to detect extrasolar planets have been successful in identifying a number of stars with companion planets. No direct detection of an extrasolar planet has yet been reported. Spectroscopy in the thermal infrared region provides a potentially powerful approach to detection and characterization of planets and planetary systems. We can use knowledge of our own solar system, its planets and their atmospheres to model spectral characteristics of planets around other stars. Spectra derived from modeling our own solar system seen from an extrasolar perspective can be used to constrain detection strategies, identification of planetary class (terrestrial vs. gaseous) and retrieval of chemical, thermal and dynamical information. Emission from planets in our solar system peaks in the thermal infrared region, approximately 10 - 30 microns, substantially displaced from the maximum of the much brighter solar emission in the visible near 0.5 microns. This fact provides a relatively good contrast ratio to discriminate between stellar (solar) and planetary emission and optimize the delectability of planetary spectra. Important molecular constituents in planetary atmospheres have rotational-vibrational spectra in the thermal infrared region. Spectra from these molecules have been well characterized in the laboratory and studied in the atmospheres of solar system planets from ground-based and space platforms. The best example of such measurements are the studies with Fourier transform spectrometers, the Infrared Interferometer Spectrometers (IRIS), from spacecraft: Earth observed from NIMBUS 8, Mars observed from Mariner 9, and the outer planets observed from Voyager spacecraft. An Earth-like planet is characterized by atmospheric spectra of ozone, carbon dioxide, and water. Terrestrial planets have oxidizing atmospheres which are easily distinguished from reducing atmospheres of gaseous giant planets which lack oxygen-bearing species and are characterized by spectra containing hydrocarbons such as methane and ethane. Spectroscopic information on extrasolar planets thus can permit their classification. Spectra and spectral lines contain information on the temperature structure of the atmosphere. Line and band spectra can be used to identify the molecular constituents and retrieve species abundances, thereby classifying and characterizing the planet. At high enough spectral resolution characteristic planetary atmospheric dynamics and unique phenomena such as failure of local thermodynamic equilibrium can be identified. Dynamically induced effects such as planetary rotation and orbital velocity shift and change the shape of spectral features and must be modeled in detailed spectral studies. We will use our knowledge of the compositional, thermal and dynamical characteristics of planetary atmospheres in our own solar system to model spectra observed remotely on similar planets in extrasolar planetary systems. We will use a detailed radiative transfer and beam integration program developed for the modeling and interpretation of thermal infrared spectra measured from nearby planet planets to generate models of an extra-solar "Earth" and "Jupiter". From these models we will show how key spectral features distinguish between terrestrial and gaseous planets, what information can be obtained with different spectral resolution, what spectral features can be used to search for conditions for biogenic activity, and how dynamics and distance modify the observed spectra. We also will look at unique planetary phenomena such as atmospheric lasing and discuss their utility as probes for detection and identification of planets. Results of such studies will provide information to constrain design for instrumentation needed to directly detect extrasolar planets.

Kostiuk, Theodor↗
NASA NTRS2016

L-Band RFI in Japan

In recent years, three instruments have been launched into orbit with the aim of producing global maps of sea surface salinity and soil moisture using the 1400-1427 MHz band: SMOS, Aquarius and SMAP. Although this frequency band is allocated to passive measurements only, RFI (Radio-Frequency Interference) is present in the data of all three missions. On a global scale, the three sensors have observed approximately the same distribution of RFI. Japan is an important exception that has implications for the design of RFI detection algorithms. RFI in Japan is caused by a large number of emitters belonging to the same system (TV receivers) and for this reason some traditional RFI detection strategies detect little to no RFI over Japan. The study of this case has led to an improvement of the approach to detect RFI in Aquarius data.

Aquarius↗
NASA NTRS2021

Detection and Height Measurement of Tenuous Clouds and Blowing Snow in ICESat-2 ATLAS Data

Tenuous atmospheric layers, such as high, thin cirrus clouds and blowing snow over Antarctica, play an important role in the climate system, affecting energy fluxes between the Earth and the atmosphere. Our knowledge of the structure of the atmosphere is largely derived from atmospheric satellite measurements. Yet tenuous layers can be hard to detect in satellite lidar data, especially in daylight data characterized by high solar background. In this study, we introduce an approach to detect tenuous atmospheric layers in the atmospheric lidar measurements of NASA's ICESat-2. The density-dimension algorithm for ICESat-2 atmospheric data (DDA-atmos) identifies atmospheric layers while automatically adapting to different background conditions of night, twilight, and daylight data. This capability, demonstrated for tenuous clouds and blowing snow, offers a data-based solution to an important climate modeling problem. Atmospheric layer boundaries, detection confidence, and density fields resultant from the DDA-atmos are reported in ICESat-2 atmospheric data product ATL09.

Ute Herzfeld↗
DOE OSTI2025

STILGAR End-of-Project Report

The Subsurface Tunnel Imaging LeveraGed by Analysis of Rayleigh wave ellipticity (STILGAR) project demonstrated an integrated geophysical approach for detecting, locating, and characterizing underground structural changes using dense seismic arrays and advanced inversion techniques. Field campaigns were conducted at two operational mines—the Redmond salt mine (Utah) and Graymont Pleasant Gap limestone mine (Pennsylvania)—providing real-world testbeds for monitoring anthropogenic subsurface activity. At the Redmond salt mine, seismic interferometry combined with back-projection inversion successfully identified continuous, low-amplitude signals from mining operations. The approach differentiated stationary from migrating anthropogenic sources, captured daily operational cycles, and validated the potential of passive seismic monitoring for remote detection of underground activity. At the Graymont Pleasant Gap mine, two dense seismic deployments in the spring and fall of 2023 generated over 4 TB of high-resolution data. Key outcomes included the relocation of 199 underground and 8 surface explosions with accuracies within tens of meters and the development of a 3D P-wave velocity model using the triple-difference tomography algorithm (tomoTD) that resolved major structural features such as the mine entrance, low-velocity tunnels, and roof-collapse areas. Ambient noise cross-correlation and back-projection analyses revealed persistent sources linked to ongoing mining activity, whereas horizontal-to-vertical spectral ratio (HVSR) and ellipticity studies confirmed stable site responses across seasons and identified soil thickness trends consistent with regional erosional and depositional processes. Checkerboard and sensitivity tests further validated the robustness of the tomographic results. Overall, the findings emphasize that although significant progress has been made in subsurface imaging, further work is needed to enhance the detection and localization of underground structures. Accurate imaging requires higher frequencies, yet anthropogenic sources tend to dominate the seismic record at those frequencies, and high-frequency surface waves are affected by higher modes that complicate interpretation. The improved detection and localization of human-induced signals enabled detailed temporal and spatial mapping of daily mine operations, demonstrating the feasibility of continuous anthropogenic source monitoring. Sensitivity to signals from nontraditional sources, such as fan operations, highlights the broader applicability of this approach to other industrial environments in which continuous and impulsive signals are present. The field campaigns produced a substantial volume of high-quality seismic data, supporting the development and testing of new methods for seismic source characterization and subsurface imaging. Future deployments should include sensors capable of recording lower frequencies to probe deeper structures, increase bandwidth to enhance resolution and sensitivity to both shallow and deep targets, and collect additional large-scale datasets to refine imaging and source characterization techniques. Moreover, conducting 3D modeling studies of seismic wavefields at higher frequencies will provide a better understanding of wave scattering and cavity–wavefield interactions in complex underground environments. In conclusion, the STILGAR project demonstrated that integrated seismic monitoring can effectively characterize underground operations, capturing both natural and anthropogenic signals. The approaches developed provide a foundation for improved detection, localization, and imaging of subsurface structures and are directly transferable to broader industrial monitoring applications.

58 GEOSCIENCES↗
NASA NTRS1992

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Keller, James M.↗
NASA NTRS1993

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Keller, James M.↗
DOE OSTI2024

Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches

This paper explores the detection and localization of cyber-attacks on power systems, focusing on comparing conventional machine learning (ML) and deep learning methods, and graph neural network (GNN)-based techniques. We assess the detection accuracy of these approaches and their potential to pinpoint the locations of specific buses under attack. Given the demonstrated success of GNNs in other time series anomaly detection applications, we aim to evaluate their performance within the context of power systems cyber-attack. Utilizing the IEEE 68-bus system, we simulated four types of attacks to test the selected approaches. Our results indicate that GNN-based methods outperform conventional machine learning and deep learning models in detection. Additionally, GNNs show promise in accurately localizing attacks for simple scenarios, although they still face challenges in more complex cases.

artificial intelligence↗
DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Covalent Drug Binding in Live Cells Monitored by Mid-Infrared Quantum Cascade Laser Spectroscopy: Photoactive Yellow Protein as a Model System

The detection of drug-target interactions in live cells enables analysis of therapeutic compounds in a native cellular environment. Recent advances in spectroscopy and molecular biology have facilitated the development of genetically encoded vibrational probes like nitriles that can sensitively report on molecular interactions. Nitriles are powerful tools for measuring electrostatic environments within condensed media like proteins, but such measurements in live cells have been hindered by low signal-to-noise ratios. In this study, we design a spectrometer based on a double-beam quantum cascade laser (QCL)-based transmission infrared (IR) source with balanced detection that can significantly enhance sensitivity to nitrile vibrational probes embedded in proteins within cells compared to a conventional FTIR spectrometer. Here, using this approach, we detect small-molecule binding in Escherichia coli, with particular focus on the interaction between para-Coumaric acid (pCA) and nitrile-incorporated photoactive yellow protein (PYP). This system effectively serves as a model for investigating covalent drug binding in a cellular environment. Notably, we observe large spectral shifts of up to 15 cm –1 for nitriles embedded in PYP between the unbound and drug-bound states directly within bacteria, in agreement with observations for purified proteins. Such large spectral shifts are ascribed to the changes in the hydrogen-bonding environment around the local environment of nitriles, accurately modeled through high-level molecular dynamics simulations using the AMOEBA force field. Our findings underscore the QCL spectrometer’s ability to enhance sensitivity for monitoring drug–protein interactions, offering new opportunities for advanced methodologies in drug development and biochemical research.

chromophores↗
DOE OSTI2021

Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings

Abstract We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring.

Szenicer, Alexandre↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗