Results for “Information extraction” — Page 35

NASA NTRSDate not supplied

Contextualizing Air Traffic Management Conversations using Natural Language Understanding

Efficient management of air traffic and mitigation of delays depend on extracting actionable information from unstructured data, such as dialogues from the Federal Aviation Administration’s (FAA’s) Air Traffic Control System Command Center (ATCSCC) telecons. This study presents a pipeline utilizing Natural Language Processing (NLP) methods for Intent Classification (IC) and Slot Filling (SF) to identify and extract Traffic Management Initiatives (TMIs) from aviation-specific dialogues. We leveraged DeBERTa, a pre-trained transformer model, and fine-tuned it to the nuances of the aviation domain. Despite challenges posed by annotation complexities, the IC model achieved promising results with a weighted average F1-score of 0.81. Our results are close to those of human annotators, which demonstrates the model’s strong alignment with human-level performance. The SF model also showed strong performance, achieving a weighted F1-score of 0.97, which demonstrates its effectiveness in accurately predicting key slots. Our analysis revealed limitations in handling less frequent intents and slot labels due to data sparsity, motivating future efforts to adopt joint IC-SF modeling and data augmentation strategies. This research highlights the potential of domain-specific NLP to streamline decision-making in the aviation industry and improve the management of TMIs.

Air Traffic Control Management↗

NASA NTRS1991

Science information systems: Visualization

Future programs in earth science, planetary science, and astrophysics will involve complex instruments that produce data at unprecedented rates and volumes. Current methods for data display, exploration, and discovery are inadequate. Visualization technology offers a means for the user to comprehend, explore, and examine complex data sets. The goal of this program is to increase the effectiveness and efficiency of scientists in extracting scientific information from large volumes of instrument data.

Wall, Ray J.↗

NASA NTRS2007

Information Metacatalog for a Grid

SWIM is a Software Information Metacatalog that gathers detailed information about the software components and packages installed on a grid resource. Information is currently gathered for Executable and Linking Format (ELF) executables and shared libraries, Java classes, shell scripts, and Perl and Python modules. SWIM is built on top of the POUR framework, which is described in the preceding article. SWIM consists of a set of Perl modules for extracting software information from a system, an XML schema defining the format of data that can be added by users, and a POUR XML configuration file that describes how these elements are used to generate periodic, on-demand, and user-specified information. Periodic software information is derived mainly from the package managers used on each system. SWIM collects information from native package managers in FreeBSD, Solaris, and IRX as well as the RPM, Perl, and Python package managers on multiple platforms. Because not all software is available, or installed in package form, SWIM also crawls the set of relevant paths from the File System Hierarchy Standard that defines the standard file system structure used by all major UNIX distributions. Using these two techniques, the vast majority of software installed on a system can be located. SWIM computes the same information gathered by the periodic routines for specific files on specific hosts, and locates software on a system given only its name and type.

Kolano, Paul↗

DOE OSTI2021

Gluon distributions and their applications to Ioffe-time distributions

We investigate unpolarized and polarized gluon distributions and their applications to the Ioffe-time distributions, which are related to lattice QCD calculations of parton distribution functions. Guided by the counting rules based on the perturbative QCD at large momentum fraction x and the color coherence of gluon couplings at small x, we parametrize gluon distributions in the helicity basis. By fitting the unpolarized gluon distribution, the inferred polarized gluon distribution from our parametrization agrees with the one from global analysis. A simultaneous fit to both unpolarized and polarized gluon distributions is also performed to explore the model uncertainty. The agreement with the global analysis supports the (1 - x) power suppression of the helicity-antialigned distribution relative to the helicity-aligned distribution. The corresponding Ioffe-time distributions and their asymptotic expansions are calculated from the gluon distributions. Our results of the Ioffe-time distributions can provide guidance to the extrapolation of lattice QCD data to the region lacking precise gluonic matrix elements. Therefore, they can help regulate the ill-posed inverse problem associated with extracting the gluon distributions from discrete data from first-principle calculations, which are available in a limited range of the nucleon momentum and the spatial separation between the gluonic currents. Given various limitations in obtaining lattice QCD data at large Ioffe time, phenomenological approaches can provide important complementary information to extract the gluon distributions in the entire momentum fraction region, especially at small x. The possibility of investigating higher-twist effects and other systematic uncertainties in the contemporary first-principle calculations of parton distributions from phenomenologically well-determined Ioffe-time distributions in the large Ioffe-time region is also discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1999

Remote Sensing of Volcanic Clouds: Sulfur Gases and Plume Top Topography

New absorption line parameters for H2S were published and submitted to the Gestion et Etude des Informations Spectroscopiques Atmospheriques (GEISA) and high resolution transmission molecular absorption (HITRAN) databases. These new absorption line parameters will make it possible to use observations from the future Tropospheric Emission Spectrometer (TES) instrument [Earth Observing System (EOS) Chemistry Mission (CHEM) platform] to make more accurate H2S measurements if it observes an H2S-rich volcanic cloud. H2S is the second most abundant volcanic sulfur gas, and like SO2, it also converts to H2SO4 aerosols and can have a climate impact. A paper on the Moderate-resolution Imaging-Spectroradiometer (MODIS) SO2 alert is being revised. New aspects in the revision include verification of the SO2 alert during the EOS mission; factors affecting SO2 detection at thermal infrared, ultraviolet, and microwave wavelengths; radiative transfer tests; more description of satellite instruments; and thermal surface alert installed for MODIS. Her research involves the use of remote sensing to generate maps of plume top altitude. This parameter is important for models of volcanic eruption, aircraft hazards, and climate impact. The topographic shape of the top surface of a volcanic plume can provide information necessary to understand the physics controlling the injection and dispersal of a volcanic plume in the atmosphere. Glaze et al. describe the application of a photoclinometric technique to volcanic plumes. The software algorithm has been improved to account for more general plume and illumination geometries and for easily extracting position information directly from Advanced Very High-Resolution Radiometer (AVHRR) level 1B data. Testing of the algorithm has focused on acquiring AVHRR data for a variety of volcanic plumes in an effort to identify problems with the software as well as model sensitivities. The plumes chosen were erupted from volcanoes at a variety of latitudes, at different times of day, and in different seasons. Another important issue that is being investigated is the importance of plume opacity. A diagram illustrates the method for deriving phi, the angle a wind-blown plume makes with the horizontal, in the direction perpendicular to the remote sensing scan. A figure shows a sample output of the algorithm, a wire mesh map of plume top topography. Additional information is contained in the original.

Crisp, Joy A.↗

NASA NTRS2005

Monitoring Change Through Hierarchical Segmentation of Remotely Sensed Image Data

NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.

Tilton, James C.↗

NASA NTRS2004

Smart Camera Technology Increases Quality

When it comes to real-time image processing, everyone is an expert. People begin processing images at birth and rapidly learn to control their responses through the real-time processing of the human visual system. The human eye captures an enormous amount of information in the form of light images. In order to keep the brain from becoming overloaded with all the data, portions of an image are processed at a higher resolution than others, such as a traffic light changing colors. changing colors. In the same manner, image processing products strive to extract the information stored in light in the most efficient way possible. Digital cameras available today capture millions of pixels worth of information from incident light. However, at frame rates more than a few per second, existing digital interfaces are overwhelmed. All the user can do is store several frames to memory until that memory is full and then subsequent information is lost. New technology pairs existing digital interface technology with an off-the-shelf complementary metal oxide semiconductor (CMOS) imager to provide more than 500 frames per second of specialty image processing. The result is a cost-effective detection system unlike any other.

Source record↗

DOE OSTI2024

Foundations of automatic feature extraction at LHC–point clouds and graphs

Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Bhardwaj, Akanksha↗

DOE OSTI2024

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about distributions and locations of atoms, their coordination numbers and oxidation states, and the bonding characteristics [1]. However, analysis of XAS/EELS data often relies on matching the spectra of an unknown experimental sample to a series of simulated or experimental spectra of standard samples. Here, this limits analysis throughput and the ability to extract quantitative information from a sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2023

BERT-Based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

Hazard analysis↗

NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

hazard analysis↗

NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

hazard analysis↗

NASA NTRS2014

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach.

Light Detection & Ranging (LIDAR)↗

DOE OSTI2026

Leveraging Inequality-Constrained Data for Enhanced Liquidus Temperature Prediction in Nuclear Waste Glass Melts

Inequality-constrained data are frequently discarded in engineering, leading to significant information loss in data-scarce domains like glass characterization in nuclear waste vitrification. This paper presents a nonparametric censored-data regression framework based on an l1-norm optimization criterion that leverages slack variables to integrate left-, right-, and interval-constrained observations into training without distributional assumptions. Validated on synthetic data and a Physics-Informed Neural Network (PINN) for predicting liquidus temperature (TL), the method improved R2 from 0.60 to 0.89 and reduced Mean Absolute Error (MAE) by 48% (51.46 to 26.89?rC) on deterministic values. The traditional models failed to satisfy any inequality constraints while the proposed l1-norm PINN satisfies 81.25% of the constraints. The proposed framework effectively extracts actionable information from previously unusable data to enhance predictive accuracy, reduce epistemic uncertainty, and ensure physical consistency in complex industrial applications.

Garcia-Morado, Erick↗

NASA NTRS1975

Geology and forestry classification from ERTS-1 digital data

Computer classifications into seven and ten classes of two areas in central Oregon of interest to geology and forestry demonstrate the extraction of information from ERTS-1 data. The area around Newberry Caldera was classified into basalt, rhyolite obsidian, pumice flats, Newberry pumice, ponderosa pine, lodgepole pine, and water classes. The area around Mt. Washington was classified into two basalts, three forest, two clearcut, burn, snow, and water classes. Both also include an unclassified category. Significant details that cannot be extracted from photographic reconstitutions of the data emerge from these classifications, such as moraine locations and paleowind directions. Spectral signatures for the various rocks are comparable to those published elsewhere.

Lawrence, R. D.↗

DOE OSTI2023

Lost and Found Opportunities Around the Chlorine Worth Study

Los Alamos National Laboratory performed a series of critical experiments in 2021 to examine the worth of chlorine in plutonium-fueled systems. This series of experiments has been dubbed the “Chlorine Worth Study,” and the evaluation of the experiments was presented to the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Technical Review Group in April, 2023. The primary purpose of these experiments was to enable validation of aqueous solutions crediting neutron absorption in 35 Cl. An external, independent view of the events leading up to the design and execution of these experiments indicates a missed opportunity to leverage sensitivity/uncertainty (S/U) analysis to assert validation without the experiments by taking an additional margin for the lack of direct validation of chlorine. On the other hand, the execution of these experiments also presents a rare opportunity to examine the efficacy of the S/U approach and extract useful information about the evaluated chlorine covariance data. TSUNAMI-1D models of representative application solutions were created and used to generate sensitivity data. Varying plutonium and chlorine concentrations were considered to examine the impact of these differences on the chlorine sensitivities and uncertainties. The data-induced uncertainty in k eff resulting from chlorine was calculated directly from uncertainty information calculated in the TSUNAMI-1D sequence. In all cases, this uncertainty was less than 0.1 %Δk. This result could potentially be used to justify a reactivity margin to account for the validation gap related to chlorine in the validation set. On the other hand, given that the experiments were performed, the community should endeavor to extract as much value from them and their results as possible. The results can be used to examine the actual bias associated with chlorine in these systems once the evaluations have been released. These data can be compared with the data-induced uncertainty margin discussed above to test the sufficiency of the validation gap penalty. This result will provide an indication of the performance of the chlorine covariance data specifically and the S/U validation approach generally. More advanced S/U techniques may also be employed to determine reactivity sensitivities associated with the chlorine in the experiments, potentially generating a more robust test of the chlorine covariance data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

The Dark Energy Spectrographic Instrument (DESI) Guide-Focus Assembly

The specific research of the DESI project is to study in detail the expansion history of the Universe over the past 10 billion years. In order to do this, the project designed, fabricated, tested and commissioned the DESI instrument which has been deployed at the Mayall Telescope at the Kitt Peak National Observatory near Tucson, Arizona. The DESI project was conducted by approximately 30 US and foreign national laboratories. Our CRADA with the The Institut de Física d’Altes Energies (IFAE) in Barcelona (Spain) involved both research planning for eventual observing on the DESI telescope; and in providing hardware jointly designed by LBNL and IFAE; ten guide and focus sensor systems (GFAs) that either provide feedback to the Mayall telescope tracking system or provide focus position information to the DESI corrector hexapod. The key major goals of this CRADA were achieved.For survey planning, Drs. Fernandez, Fosalba, Garcia-Bellido, and Gaztañaga were all important contributors to the working groups to which they were assigned. Their application of redshift techniques, development of simulations to support target selection, exploration of the use of alternative observables to extract cosmological information like AP and void statistics, and the use of PAU data to assist in the optimization of targeting selections have been vital in the early planning for targets during commissioning. The major role in the project involved Drs. Castander, Miquel, Padilla, and Sànchez. They were all directly involved with the development of the Guide-Focus Assembly (GFA) hardware, software and filter validation. In order to accomplish the goals of the DESI Project, the Mayall telescope must track DESI targeted galaxies for up to 15 minutes and the DESI corrector must be repositioned on its hexapod system for different telescope orientations. The guide and focus assemblies (GFA) provide in this information. There are ten GFA modules on the focal plane, six providing tracking information and four providing focus information. A common GFA design supports both functions by a simple switching of its operating mode. IFAE designed, fabricated, tested and delivered twelve GFAs. Once received at LBNL, the GFAs were integrated with the focal plane. This was an iterative process as the GFA’s were delivered as they were made. Our working relationship with the IFAE scientists and engineers was remarkable given the distance and time zone issues. The GFAs have performed extremely well during the commissioning phase of the instrument.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Advances in solar forecasting: Computer vision with deep learning

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

14 SOLAR ENERGY↗