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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

System architecture and method of processing data therein

A system architecture encoded on a non-transitory computer readable medium, the system architecture includes a first protocol. The first protocol is configured to receive a plurality of outputs from an ICS used in controlling an industrial system. The first protocol is configured to receive a plurality of inputs from a physical module. The physical module includes at least one of a component, a sensor, or the ICS. Additionally, the system architecture includes a second protocol, wherein the second protocol is configured to validate the plurality of inputs from the first protocol. Moreover, the system architectures includes a third protocol, wherein the third protocol is configured to validate the plurality of outputs from the first protocol. Further, the system architecture includes a fourth protocol, wherein the fourth protocol is configured to manage the ICS based on the second protocol and the third protocol.

Abdel-Khalik, Hany S.↗
DOE OSTI2011

Data Access Performance Through Parallelization and Vectored Access: Some Results

High Energy Physics data processing and analysis applications typically deal with the problem of accessing and processing data at high speed. Recent studies, development and test work have shown that the latencies due to data access can often be hidden by parallelizing them with the data processing, thus giving the ability to have applications which process remote data with a high level of efficiency. Techniques and algorithms able to reach this result have been implemented in the client side of the Scalla/xrootd system, and in this contribution we describe the results of some tests done in order to compare their performance and characteristics. These techniques, if used together with multiple streams data access, can also be effective in allowing to efficiently and transparently deal with data repositories accessible via a Wide Area Network.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Aerial Captured Data and Processed Models in Beaumont-Port Arthur Region in Feb and Oct, 2023

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We used a DJI Mavic 2 Pro to capture aerial photos in Beaumont-Port Arthur, TX, in February 2023, including:I. Beaumont Soccer ClubII. Corps’ Port Arthur Resident OfficeIII. Halbouty Pump Station comprises its vicinityIV. Lamar University (Including Exxon Power Plants close to Lamar Univ.)V. MLK Boulevard for aerial images of the industry and the ship channelVI. Salt Water Barrier (include some aerial images about the Big Thicket)Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each location.The processed data package including 3D models, geospatial data, mappings, point clouds, and the animation video of Halbouty Pump Station has various file types:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.In October 2023, we had our follow-up data collection, including:I. Beaumont Soccer ClubII. Shipping and Receiving Center at Lamar UniversityAfter the aerial collection, we obtained aerial photos of those two locations mentioned above, as well as processed data (such as point clouds and models).

2D mapping↗
DOE OSTI2016

Neurogenesis Deep Learning: Extending deep networks to accommodate new classes

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing – data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Real-time kinematic (RTK) Drone-collected Data and Processed Models of Port Arthur Coastal Neighborhood and Pleasure Island Golf Course, June 2024

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset will support researchers' decision-making processes under uncertainties.

2D mapping↗
DOE OSTI2024

Aerial Data and Processed Models of Port Arthur Coastal Neighborhood and Pleasure Island Golf Course, June 2024

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

2D mapping↗
DOE OSTI2016

Planck 2015 results: XI. CMB power spectra, likelihoods, and robustness of parameters

This study presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlationfunctions of the cosmic microwave background (CMB) temperature and polarization fluctuations that account for relevant uncertainties, both instrumental and astrophysical in nature. They are based on the same hybrid approach used for the previous release, i.e., a pixel-based likelihood at low multipoles (ℓ< 30) and a Gaussian approximation to the distribution of cross-power spectra at higher multipoles. The main improvements are the use of more and better processed data and of Planck polarization information, along with more detailed models of foregrounds and instrumental uncertainties. The increased redundancy brought by more than doubling the amount of data analysed enables further consistency checks and enhanced immunity to systematic effects. It also improves the constraining power of Planck, in particular with regard to small-scale foreground properties. Progress in the modelling of foreground emission enables the retention of a larger fraction of the sky to determine the properties of the CMB, which also contributes to the enhanced precision of the spectra. Improvements in data processing and instrumental modelling further reduce uncertainties. Extensive tests establish the robustness and accuracy of the likelihood results, from temperature alone, from polarization alone, and from their combination. For temperature, we also perform a full likelihood analysis of realistic end-to-end simulations of the instrumental response to the sky, which were fed into the actual data processing pipeline; this does not reveal biases from residual low-level instrumental systematics. Even with the increase in precision and robustness, the ΛCDM cosmological model continues to offer a very good fit to the Planck data. The slope of the primordial scalar fluctuations, n s , is confirmed smaller than unity at more than 5σ from Planck alone. We further validate the robustness of the likelihood results against specific extensions to the baseline cosmology, which are particularly sensitive to data at high multipoles. For instance, the effective number of neutrino species remains compatible with the canonical value of 3.046. For this first detailed analysis of Planck polarization spectra, we concentrate at high multipoles on the E modes, leaving the analysis of the weaker B modes to future work. At low multipoles we use temperature maps at all Planck frequencies along with a subset of polarization data. These data take advantage of Planck’s wide frequency coverage to improve the separation of CMB and foreground emission. Within the baseline ΛCDM cosmology this requires τ = 0.078 ± 0.019 for the reionization optical depth, which is significantly lower than estimates without the use of high-frequency data for explicit monitoring of dust emission. At high multipoles we detect residual systematic errors in E polarization, typically at the μK 2 level; we therefore choose to retain temperature information alone for high multipoles as the recommended baseline, in particular for testing non-minimal models. Finally and nevertheless, the high-multipole polarization spectra from Planck are already good enough to enable a separate high-precision determination of the parameters of the ΛCDM model, showing consistency with those established independently from temperature information alone.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Integration of Experimental Hydroprocessing and FCC Data with Process (Aspen Plus) and Refinery Optimization (Aspen PIMS) Models

NREL's Economic, Sustainability, and Market Analysis (ESMA) team develops process models in Aspen Plus to support techno-economic analysis (TEA) and life-cycle assessment (LCA) of conversion pathways from renewable and circular resources to fuels and chemicals. These tools are being applied to assess probable operational constraints or bottlenecks associated with refinery co-processing and repurposing opportunities. The team also utilizes optimizable refinery models to quantify opportunities for biofuel and bioproduct pathways through integration with existing refining infrastructure. The goals of analysis are to model the transition of refineries to renewable and circular feedstocks and quantify the costs production and CO2 abatement through utilization of existing refineries. At the core of the ESMA team's work is the process data derived from conversion experiments on hydroprocessing and fluid catalytic cracking performed at NREL. The objective of this presentation is to highlight new analysis approaches and capabilities in pathway analysis and refinery optimization modeling with specific emphasis on integration of experimental data.

BIOMASS FUELS,ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

THE BLANCO COSMOLOGY SURVEY: DATA ACQUISITION, PROCESSING, CALIBRATION, QUALITY DIAGNOSTICS, AND DATA RELEASE

The Blanco Cosmology Survey (BCS) is a 60 night imaging survey of {approx}80 deg{sup 2} of the southern sky located in two fields: ({alpha}, {delta}) = (5 hr, -55 Degree-Sign ) and (23 hr, -55 Degree-Sign ). The survey was carried out between 2005 and 2008 in griz bands with the Mosaic2 imager on the Blanco 4 m telescope. The primary aim of the BCS survey is to provide the data required to optically confirm and measure photometric redshifts for Sunyaev-Zel'dovich effect selected galaxy clusters from the South Pole Telescope and the Atacama Cosmology Telescope. We process and calibrate the BCS data, carrying out point-spread function-corrected model-fitting photometry for all detected objects. The median 10{sigma} galaxy (point-source) depths over the survey in griz are approximately 23.3 (23.9), 23.4 (24.0), 23.0 (23.6), and 21.3 (22.1), respectively. The astrometric accuracy relative to the USNO-B survey is {approx}45 mas. We calibrate our absolute photometry using the stellar locus in grizJ bands, and thus our absolute photometric scale derives from the Two Micron All Sky Survey, which has {approx}2% accuracy. The scatter of stars about the stellar locus indicates a systematic floor in the relative stellar photometric scatter in griz that is {approx}1.9%, {approx}2.2%, {approx}2.7%, and {approx}2.7%, respectively. A simple cut in the AstrOmatic star-galaxy classifier spread{sub m}odel produces a star sample with good spatial uniformity. We use the resulting photometric catalogs to calibrate photometric redshifts for the survey and demonstrate scatter {delta}z/(1 + z) = 0.054 with an outlier fraction {eta} < 5% to z {approx} 1. We highlight some selected science results to date and provide a full description of the released data products.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.

Yarbrough, John↗
DOE OSTI2022

BFSVBF (BatFinder Smart Video BioFilter) [SWR-22-87] and Multi-class BatFinder Smart Video BioFilter Keras

Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The code has gone through five rounds of development with images used to train the models. This advancement allows for automated real-time data collection, identification, and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. We will discuss the two open source and publicly available machine learning models developed within this scope of this work: 1) a binary model with a 97.5% accuracy in identifying the difference between an object and an empty scene, including wind turbine and clouds; and 2) a multiple classification model with the capability of identifying the type of object detected: bats (90% accuracy), birds (83% accuracy), insects (69% accuracy) and non-biological (99% accuracy).

Yarbrough, John↗
DOE OSTI2026

Digital Twin Technology for Safety, Security, and Training in Spent Nuclear Fuel Handling

The increasing complexity of spent nuclear fuel handling requires significant resources to ensure safety, security, and personnel training. As nuclear facilities have continued to advance in scale and technology, the integration of digital tools has become indispensable. Among these tools, digital twins, which are virtual models of physical systems, are emerging as invaluable tools for enhancing safety protocols, security measures, and training in the nuclear sector. These models were conceptualized in the Industry 4.0 revolution. Digital twins can process data from physical systems in real time (by using sensors), include multiple code packages to enable simulations of different physics applications, and even implement artificial intelligence or machine learning techniques for advanced data processing. Despite the advantages that digital twins provide, challenges still exist regarding their widespread implementation. For instance, data used by a digital twin must be accurate to ensure that the digital twin is accurately tuned. Furthermore, if insecure digital twins are targeted by hackers, then they can pose serious risks to the security and safety of nuclear facilities.

Digital twins↗
DOE OSTI2025

Hahn-Echo Assisted Deconvolution (HEAD)

This repository contains the Bruker pulse program for acquire 2D HEAD data in addition to the C++ program used for data processing. Data must be acquired with identical digital resolution in both dimensions. The resulting 2D spectrum is converted to a ASCII file using the Topspin 'totxt' command. This file can be handled by the HEAD_processing program.

Perras, Frederic [Ames National Laboratory; Ames N↗
DOE OSTI2025

LCLS Big Data Handling – How I Learned to Stop Worrying and Love the Data Deluge

Advanced data and computing systems are vital to Linac Coherent Light Source (LCLS) operations, data interpretation and overall scientific productivity. The transition to MHz-era operation marks a fundamental change in scale that requires new infrastructure and architectures to link LCLS to the required scale of computing needed for scientific interpretation. The LCLS-II Data System meets big data challenges by implementing configurable data reduction that can adapt to multiple science areas, real-time analysis frameworks to provide visualization and fast feedback, and the ability to transfer data to local and remote computational facilities for near real time analysis at the appropriate scale. Feature extracted information generated in the data analysis pipeline - at the edge, local compute, or remote High-Performance Computing (HPC) resources - can be used to steer experiments and inform user decisions during beam time. Artificial Intelligence and Machine Learning (AI/ML) techniques present new opportunities to rapidly analyse large datasets and direct experiments, but create new challenges in scaling, adaptability, complexity, and trustworthiness. We describe how the LCLS-II Data System architecture addresses its data-driven challenges in the areas of data acquisition, data processing, data management, and workflow orchestration to decrease the overall time-to-science and provide a vision for future developments.

artificial intelligence↗
DOE OSTI2026

Searches for CE ν NS and physics beyond the standard model using Skipper-CCDs at CONNIE

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) aims to detect the coherent scattering ( CE ν NS ) of reactor antineutrinos off silicon nuclei using thick fully depleted high-resistivity silicon CCDs. Two Skipper-CCD sensors with subelectron readout noise capability were installed at the experiment next to the Angra-2 reactor in 2021, making CONNIE the first experiment to employ Skipper-CCDs for reactor neutrino detection. We report on the performance of the Skipper-CCDs, the new data processing, data quality, and event selection for CE ν NS interactions, which enable CONNIE to reach a record low detection threshold of 15 eV. The data were collected over 300 days in 2021–2022 and correspond to exposures of 14.9 g-days with the reactor-on and 3.5 g-days with the reactor-off. The difference between the reactor-on and off event rates shows no excess and yields upper limits for the neutrino interaction rates, comparable with previous CONNIE limits from standard CCDs and higher exposures. Searches for new neutrino interactions beyond the Standard Model improve the previous CONNIE limit on a simplified model with light vector mediators. A first dark matter (DM) search by diurnal modulation by CONNIE obtains the best limits on the DM-electron scattering cross section by a surface-level experiment. These promising results, obtained using a very small-mass sensor, illustrate the potential of Skipper-CCDs to probe rare neutrino interactions and motivate the plans to increase the detector mass in the near future.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2011

SAAF: SANS data Analysis using Analytical Functions

The recently completed Extended Q-Range Small Angle Scattering Diffractometer (EQ-SANS) has put the focus on its software needs with renewed urgency. In a series of efforts, we aim at providing a complete set of software solutions on the EQ-SANS instrument. These programs include initial data processing, data correction and reduction, analytical model fitting to the scattering data, Monte Carlo simulation for structure determination, and virtual instrument simulation for experiment planning. SAAF is one such program for analytical data modeling. It takes the reduced EQ-SANS data and allows users to fit the data to analytical models. These models are easy to write. They can either be user written, or from the pre-supplied model library.

Data Analysis↗
DOE OSTI2017

Data Processor 3 QASPR Project

The following discussion contains a high-level description of methods used to implement software for data processing. It describes the required directory structures and file handling required to use Excel's Visual Basic for Applications programming language and how to identify shot, test and capture types to appropriately process data. It also describes how to interface with the software.

97 MATHEMATICS AND COMPUTING↗