SEARCH · Engineering Papers

Results for “Data Digitization”

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.

SourceResearch connectionFrom yearThrough year

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

At least 1,459 records · Page 81

All sources NASA NTRS DOE OSTI

DOE OSTI2023

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Ferreras, Ignacio (ORCID:0000000345843127)↗

DOE OSTI2020

Industry 4.0 and Intelligent Manufacturing Processes: A Review of Modern Sensing Technologies

Digital Manufacturing technologies have quickly become ubiquitous in the manufacturing industry. The transformation commonly referred to as the fourth industrial revolution, or Industry 4.0, has ushered in a wide range of communication technologies, connection mechanisms, and data analysis capabilities. These technologies provide powerful tools to create a more lean, profitable, and data-driven manufacturing processes. In this paper, we review modern communication technologies and connection architectures for Digital Manufacturing and Industry 4.0 applications. An introduction to Cyber-Physical Systems and a review of digital manufacturing trends is followed by an overview of data acquisition methods for manufacturing processes. Numerous communication protocols are presented and discussed for connecting disparate machines and processes. Flexible data architectures are discussed and examples of machine monitoring implementations are provided. Finally, select implementations of these communication protocols and architectures are surveyed with recommendations for future architecture implementations.

42 ENGINEERING↗

DOE OSTI2026

Data Center Cybersecurity, Supply Chain Risk Management, and Emerging Regulation Cohort Summary: Takeaways and Action Plans

This report summarizes the outcomes of the Data Center Cohort under the Department of Energy’s Technical Assistance for Digital Assurance (TADA) initiative, aimed at enhancing grid resilience through cybersecurity, supply chain risk management (SCRM), and Cyber-Informed Engineering (CIE). The cohort engaged 17 organizations across utilities, data center operators, vendors, and technology providers in three sessions combining presentations, discussions, and exercises. Key topics included AI-driven load behavior, cybersecurity vulnerabilities in UPS/BESS and cooling systems, governance gaps at utility–data center boundaries, and supply chain integrity. Five cross-cutting themes emerged: interconnection architecture vulnerabilities, fragmented governance, AI-driven stability risks, lack of regulatory frameworks, and long-term supply chain concerns. Actionable recommendations were developed, including implementing DMZ segmentation, formalizing vendor access agreements, designing AI workload limits, and advancing standards through NERC and state-level programs. These strategies aim to strengthen resilience, clarify responsibilities, and ensure secure integration of data centers into the grid.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Data Unfolding with Wiener-SVD Method

Here, data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Troubleshooting deep-learner training data problems using an evolutionary algorithm on Summit

Architectural and hyper-parameter design choices can influence deep-learner (DL) model fidelity but can also be affected by malformed training and validation data. However, practitioners may spend significant time refining layers and hyper-parameters before discovering that distorted training data was impeding training progress. We found that an evolutionary algorithm (EA) can be used to diagnose this kind of DL problem. An EA evaluated thousands of DL configurations on Summit that yielded no overall improvement in DL performance, which suggested problems with the training and validation data. We suspected that Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement that was applied to previously generated digital surface models (DSMs), for which we were training DLs to find errors, had damaged the training data. Subsequent runs with an alternative global normalization yielded significantly improved DL performance. However, the DL Intersection Over Union (IOU) still exhibited consistent sub-par performance, which suggested further problems with the training data and DL approach. Nonetheless, we were able to diagnose this problem within a 12-hour span via Summit runs, which prevented several weeks of unproductive trial-and-error DL configuration refinement, and allowed for a more timely convergence on an ultimately viable solution.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗

DOE OSTI2015

GRAPhEME: a setup to measure (n, xn γ) reaction cross sections

Most of nuclear reactor developments are using evaluated data base for numerical simulations. However, the considered databases present still large uncertainties and disagreements. To improve their level of precision, new measurements are needed, in particular for (n, xn) reactions, which are of great importance as they modify the neutron spectrum, the neutron population, and produce radioactive species. The IPHC group started an experimental program to measure (n, xn gamma) reaction cross sections using prompt gamma spectroscopy and neutron energy determination by time of flight. Measurements of (n, xn gamma) cross section have been performed for {sup 235,238}U, {sup 232}Th, {sup nat,182,183,184,186}W, {sup nat}Zr. The experimental setup is installed at the neutron beam at GELINA (Geel, Belgium). The setup has recently been upgraded with the addition of a highly segmented 36 pixels planar HPGe detector. Significant efforts have been made to reduce radiation background and electromagnetic perturbations. The setup is equipped with a high rate digital acquisition system. The analysis of the segmented detector data requires a specific procedure to account for cross signals between pixels. An overall attention is paid to the precision of the measurement. The setup characteristic and the analysis procedure will be presented along with the acquisition and analysis challenges. Examples of results and their impact on models will be discussed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Geophysical estimation of shallow permafrost distribution and properties in an ice-wedge polygon-dominated Arctic tundra region

Shallow permafrost distribution and characteristics are important for predicting ecosystem feedbacks to a changing climate over decadal to century timescales because they can drive active layer deepening and land surface deformation, which in turn can significantly affect hydrologic and biogeochemical responses, including greenhouse gas dynamics. As part of the U.S. Department of Energy Next-Generation Ecosystem Experiments- Arctic, we have investigated shallow Arctic permafrost characteristics at a site in Barrow, Alaska, with the objective of improving our understanding of the spatial distribution of shallow permafrost, its associated properties, and its links with landscape microtopography. To meet this objective, we have acquired and integrated a variety of information, including electric resistance tomography data, frequency-domain electromagnetic induction data, laboratory core analysis, petrophysical studies, high-resolution digital surface models, and color mosaics inferred from kite-based landscape imaging. The results of our study provide a comprehensive and high-resolution examination of the distribution and nature of shallow permafrost in the Arctic tundra, including the estimation of ice content, porosity, and salinity. Among other results, porosity in the top 2 m varied between 85% (besides ice wedges) and 40%, and was negatively correlated with fluid salinity. Salinity directly influenced ice and unfrozen water content and indirectly influenced the soil organic matter content. A relatively continuous but depth-variable increase in salinity led to a partially unfrozen saline layer (cryopeg) located below the top of the permafrost. The cryopeg environment could lead to year-round microbial production of greenhouse gases. Results also indicated a covariability between topography and permafrost characteristics including icewedge and salinity distribution. In addition to providing insight about the Arctic ecosystem, through integration of lab-based petrophysical results with field data, this study also quantified the key controls on electric resistivity at this Arctic permafrost site, including salinity, porosity, water content, ice content, soil organic matter content, and lithologic properties.

58 GEOSCIENCES↗

DOE OSTI2026

Alabama Carbon Storage: Data Sharing and Engagement (Final Report)

This report is the final technical report on Alabama Carbon Storage: Data Sharing Engagement (ACS:DSE) project activities. The goals of the ACS:DSE project are to compile geologic, geophysical, infrastructure, and other relevant CCUS datasets for the study area and develop a geologic model of the study area; develop an online platform to serve data to stakeholders; engage with the public, students, and industry to educate them about CCUS and the data platform; and ensure energy and environmental justice is central to all aspects of the project. Datasets compiled and expanded include formation depths and elevations, digital geophysical well logs, reservoir properties, geologic structures, and geologic models. The geologic data were used to create a three-dimensional geologic model, structure grids, structure contour maps, and fault trace maps. In addition to downloadable datasets, links to CCUS relevant regulatory agencies (e.g., OGB, U.S. Environmental Protection Agency) and sources for infrastructure and educational information were included on the website Educational materials on CCUS for use by K-12 teachers were produced as part of the ACS:DSE project.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2018

A General Framework for Monitoring Image Acquisition Workflow in the Radiology Environment: Timeliness for Acute Stroke CT Imaging

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Viability of Data Analytics to Ascertain Component Performance for Additive Manufacturing

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to produce multiple nuclear components to be assembled into a fully functional microreactor core. AM was selected as the main manufacturing technology for TCR because it has the potential to disrupt the nuclear industry on two fronts: (1) it enables the manufacturing of very complex geometries with optimized and tailored material properties for the intended use of the component, opening up new options for reactor designs, and (2) it allows for a better understanding of the manufacturing process through real-time in situ monitoring, data analytics, and artificial intelligence, which can lead to a streamlined qualification and certification process. Within TCR, the development and deployment of a digital platform aims at addressing the latter opportunity. As part of this effort, the collection of pedigree datasets is vital, should these data be generated before, during or after the manufacturing process. This report focuses on the development of data analytics tools enabling qualitative assessment of the manufacturing processes to automate the identification of flaws in in situ monitoring imagery and to correlate those flaws to the resulting mechanical testing data. Through examples, the report gives an overview of the data collected and presents a path forward for the digital platform.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

NBIC Biofeeds: Deploying a New, Digital Tool for Open Source Biosurveillance across Federal Agencies

The National Biosurveillance Integration Center (NBIC) is deploying a scalable, flexible open source data collection, analysis, and dissemination tool to support biosurveillance operations by the U.S. Department of Homeland Security (DHS) and its federal interagency partners.IntroductionNBIC integrates, analyzes, and distributes key information about health and disease events to help ensure the nation’s responses are well-informed, save lives, and minimize economic impact. To meet its mission objectives, NBIC utilizes a variety of data sets, including open source information, to provide comprehensive coverage of biological events occurring across the globe. NBIC Biofeeds is a digital tool designed to improve the efficiency of analyzing large volumes of open source reporting and increase the number of relevant insights gleaned from this dataset. Moreover, the tool provides a mechanism to disseminate tailored, electronic message notifications in near-real time so that NBIC can share specific information of interest to its interagency partners in a timely manner. NBIC intends to implement operational use of the capability in FY 2018. The core components of the system are data collection, curation, and dissemination of information deemed important by NBIC subject matter experts. NBIC Biofeeds has captured information from more than 70,000 unique sources published from around the globe and presents, on average, 9,000 new biosurveillance-relevant articles to users each day. NBIC leverages a variety of data feeds, including third party aggregators like Google and subscription-based feeds such as HealthMap, as well as Really Simple Syndication (RSS) feeds and web-scraping of highly relevant sources. The NBIC biosurveillance taxonomy imbedded in the tool consists of more than 600 metadata targets that cover key information for understanding the significance of an active biological event, including etiologic agents, impact to humans and animals (e.g., infection severity, healthcare workers involved, type of host), social disruption, infrastructure strain, countermeasures engaged, and ‘red flag’ characteristics (e.g., pathogen appearance in a new geographic area, unusual clinical signs). This taxonomy serves as a foundation for data curation and can be tailored by NBIC partners to more directly meet their own mission objectives. At this time, metadata is predominately captured by NBIC analysts, who manually tag information, which triggers the population of three automatically-disseminated products from the tool: 1) the NBIC Daily Biosurveillance Review, 2) immediate and daily summary email notifications, and 3) custom-designed RSS feeds. These products are meant for individual recipients in the federal interagency and for consumption by other biosurveillance information technology systems, such as the Department of Defense, Defense Threat Reduction Agency (DTRA) Biosurveillance Ecosystem (BSVE). NBIC is working in partnership with DTRA to integrate NBIC Biofeeds as an application directly into the BSVE and further develop the BSVE as an all-in-one platform for biosurveillance data analytics. To improve the efficiency and effectiveness of gaining insights using NBIC Biofeeds, developers of the tool at the Pacific Northwest National Laboratory (PNNL) are researching and testing a variety of advanced analytics techniques focused on: 1) article relevancy ratings to improve the review of queried data, 2) significance ratings to elucidate the perceived severity of an event based on reported characteristics, 3) full-text article retrieval and storage for improved machine-tagging, and 4) anomaly detection for emerging threats. Testing and implementation of new analytic capabilities in NBIC Biofeeds is planned for this fiscal year. NBIC Biofeeds was developed to serve as a sophisticated and powerful open source biosurveillance technology of value to the federal government by providing information to stakeholders conducting open source biosurveillance as well as those consuming biosurveillance information. In FY 2018, NBIC Biofeeds will begin operational use by NBIC and an initial set of users in various federal agencies. User accounts for testing purposes will be available to other federal partners, and a broad scope of federal stakeholders can receive products directly from NBIC Biofeeds based on their interests. NBIC Biofeeds is expected to enable more rapid recognition and enhanced analysis of emerging biological events by NBIC analysts. NBIC anticipates other federal agencies with biosurveillance missions will find this technology of value and intends to offer use of the platform to those federal partners that can benefit from access to the tool and information generated from NBIC Biofeeds.

NBIC↗

DOE OSTI2017

Automated Blazar Light Curves Using Machine Learning

Every night in a remote clearing called Fenton Hill high in the Jemez Mountains of central New Mexico, a bank of robotically controlled telescopes tilt their lenses to the sky for another round of observation through digital imaging. Los Alamos National Laboratory’s Thinking Telescopes project is watching for celestial transients including high-power cosmic flashes called, and like all science, it can be messy work. To keep the project clicking along, Los Alamos scientists routinely install equipment upgrades, maintain the site, and refine the sophisticated machinelearning computer programs that process those images and extract useful data from them. Each week the system amasses 100,000 digital images of the heavens, some of which are compromised by clouds, wind gusts, focus problems, and so on. For a graduate student at the Lab taking a year’s break between master’s and Ph.D. studies, working with state-of-the-art autonomous telescopes that can make fundamental discoveries feels light years beyond the classroom.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Evolution of a Geological Model for Co-Producing Electricity at the Blackburn Oil Field, Nevada: Preprint

Oil fields characterized by significant proportions of hot water in the reservoir offer a unique opportunity for geothermal energy co-production and utilization. Development of an accurate geological model is essential in understanding the subsurface. However, this is a challenge at the preliminary stages of resource assessment when data on the subsurface parameters and subterranean fault network is scarce. Our focus in this paper is to explore a methodology for developing an accurate conceptual geological model of the complexly faulted reservoir at the Blackburn Field, Nevada, to create a numerical simulation that will eventually be used to assess the potential of co-producing electricity. There are four steps to the evolution of this model: (1) collection of public data and research on the geological setting of the system; (2) digitization of seismic time-interpretation and cross-sections to create the most representative model of the subsurface; and (3) creation of a natural-state numerical model to simulate the thermal gradient through the subsurface. Utilizing publicly available data of the geological system and Leapfrog conceptual modeling software, we refined a preliminary model to be more representative of the subsurface geology of an active geothermal exploration stage project. The conceptual geological model developed has been imported into VOLSUNG reservoir modeling software to create a natural-state model, further characterizing the Blackburn Field reservoir's thermal gradient. In the future, new seismic data will be utilized to further evolve the conceptual geological model for numerical simulations.

Basin and Range province↗

DOE OSTI2020

The Data Foundry: Secure Collaboration for the Geothermal Industry: Preprint

The Data Foundry provides secure, cloud-based storage and universal access to digital information, enabling the greater geothermal industry to collaborate seamlessly with the Department of Energy (DOE), national labs, universities, and private organizations. Originally developed to support the EGS Collab project, the Data Foundry has been expanded to support FORGE, EDGE, and other DOE-funded projects, some collaborative, some private, by providing each project with a secure space and the ability to fine-tune individual access controls. In response to user feedback, it now also features improved integration with DOE’s Geothermal Data Repository (GDR), to provide a clear and convenient pathway from collaboration to publication, and to register collaborative data projects with well-known data registries like Data.gov and the National Geothermal Data System (NGDS). This paper will explore how recent concerns raised by data-centric, proprietary projects have informed development on the Data Foundry and highlight improvements designed to streamline workflows, improve access control, and promote the timely dissemination of information to the geothermal industry.

Data Foundry↗

DOE OSTI2022

Optical authentication of images

Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Optical authentication of images

Murialdo, Maxwell R.↗