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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2022

LANL Contributions to pyiron: An integrated development environment for computational materials science

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.

Janssen, Jan↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1981

IGDS/TRAP Interface Program (ITIP). Software User Manual (SUM)

This specification establishes the requirements, concepts, and preliminary design for a set of software known as the IGDS/TRAP Interface Program (ITIP). This software provides the capability to develop at an Interactive Graphics Design System (IGDS) design station process flow diagrams for use by the NASA Coal Gasification Task Team. In addition, ITIP will use the Data Management and Retrieval System (DMRS) to maintain a data base from which a properly formatted input file to the Time-Line and Resources Analysis Program (TRAP) can be extracted. This set of software will reside on the PDP-11/70 and will become the primary interface between the Coal Gasification Task Team and IGDS, DMRS, and TRAP. The user manual for the computer program is presented.

Jefferys, S.↗
NASA NTRS1981

Interactive design and analysis of future large spacecraft concepts

Recent developments in interactive computer-aided design and analysis of future large spacecraft concepts are presented. The primary emphases are on automatic spacecraft modeling for lattice (truss-like) structures and simplified multidiscipline design and analysis modules. Capabilities and performance of the twenty some applications modules, the executive and data management software, and graphics display features are reviewed. Data generated for an earth-orbiting large diameter (750 meter) antenna satellite are used to illustrate current capabilities. A single user at an interactive terminal can create, design, analyze, and conduct parametric studies of earth-orbiting spacecraft with relative ease. The approach is particularly useful in the conceptual design phase of advanced space missions when a multiplicity of concepts must be evaluated in a cost-effective and timely manner

Garrett, L. B.↗
NASA NTRS1983

Stability Statistics Data Base System

Stability Statistics Data Base System is written entirely in FORTRAN and does not require use of outside data-management routines. Is used with directly-accessible data base containing simple card images. Four FORTRAN modules implement edit, sort, report-generation, and other functions.

Kwok, G. Y.↗
NASA NTRS1992

Data archaeology at ICES

This paper provides a brief overview of the function of the International Council for the Exploration of the Sea (ICES), both past and present, in particular in the context of its interest in compiling oceanographic data sets. Details are provided of the procedures it adopted to ensure adequate internationally collaborative marine investigations during the first part of the century, such as how it provided a forum for action by its member states, how it coordinated and published the results of scientific programs, and how it provided a foundation, through scientists employed in the ICES Office, for the establishment of the original oceanographic marine databases and associated products, and the scientific interpretation of the results. The growth and expansion of this area of ICES activity is then traced, taking into account the changing conditions for oceanographic data management resulting from the establishment of the National Data Centres, as well as the World Data Centres for Oceanography, which were created to meet the needs of the International Geophysical Year (IGY). Finally, there is a discussion of the way in which the very existence of ICES has proved to be a valuable source of old data, some of which have not yet been digitized, but which can be readily retrieved because they have been very carefully documented throughout the years. Lessons from this activity are noted, and suggestions are made on how the past experiences of ICES can be utilized to ensure the availability of marine data to present and future generations of scientists.

Dooley, Harry D.↗
NASA NTRS1993

Intelligent data presentation for real-time spacecraft monitoring

This paper describes an intelligent user interface that is currently under development. The interface serves as a front end for real-time spacecraft monitoring software. The software operates under circumstances in which neither an intelligent human without automated assistance nor an automated system without intelligence are sufficiently effective. The user interface is supported by dynamic trade-off evaluation (DTE), a new technique that has been developed to automate general types of performance trade-offs in real-time problem solving systems. In this application, DTE is used to perform dynamic input data management for the purpose of determining which input data should be monitored in time constrained situations and how to present the monitoring information to a human analyst who has the ultimate responsibility for the spacecraft. This application has demonstrated that DTE can be used to dynamically vary the data that is monitored, making it possible to detect and correctly analyze all anomalous data by examining only a subset of the total input data. In carefully structured experimental evaluations that use real spacecraft data and real decision making, DTE provides the ability to handle a three-fold increase in input data (in real-time) without loss of performance and to intelligently present the information to a mission analyst.

Schwuttke, U. M.↗
NASA NTRS2001

D3: A Collaborative Infrastructure for Aerospace Design

DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid dynamics) model executions. DARWIN captures, stores and indexes data, manages derived knowledge (such as visualizations across multiple data sets) and provides an environment for designers to collaborate in the analysis of the results of testing. DARWIN is an interesting application because it supports high volumes of data, integrates multiple modalities of data display (e.g. images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and view of data.

Walton, Joan↗
NASA NTRS1998

Open Architecture Data System for NASA Langley Combined Loads Test System

The Combined Loads Test System (COLTS) is a new structures test complex that is being developed at NASA Langley Research Center (LaRC) to test large curved panels and cylindrical shell structures. These structural components are representative of aircraft fuselage sections of subsonic and supersonic transport aircraft and cryogenic tank structures of reusable launch vehicles. Test structures are subjected to combined loading conditions that simulate realistic flight load conditions. The facility consists of two pressure-box test machines and one combined loads test machine. Each test machine possesses a unique set of requirements or research data acquisition and real-time data display. Given the complex nature of the mechanical and thermal loads to be applied to the various research test articles, each data system has been designed with connectivity attributes that support both data acquisition and data management functions. This paper addresses the research driven data acquisition requirements for each test machine and demonstrates how an open architecture data system design not only meets those needs but provides robust data sharing between data systems including the various control systems which apply spectra of mechanical and thermal loading profiles.

Lightfoot, Michael C.↗
NASA NTRS2006

The Echoes of Earth Science

NASA s Earth Observing System Data and Information System (EOSDIS) acquires, archives, and manages data from all of NASA s Earth science satellites, for the benefit of the Space Agency and for the benefit of others, including local governments, first responders, the commercial remote sensing industry, teachers, museums, and the general public. EOSDIS is currently handling an extraordinary amount of NASA scientific data. To give an idea of the volume of information it receives, NASA s Terra Earth-observing satellite, just one of many NASA satellites sending down data, sends it hundreds of gigabytes a day, almost as much data as the Hubble Space Telescope acquires in an entire year, or about equal to the amount of information that could be found in hundreds of pickup trucks filled with books. To make EOSDIS data completely accessible to the Earth science community, NASA teamed up with private industry in 2000 to develop an Earth science "marketplace" registry that lets public users quickly drill down to the exact information they need. It also enables them to publish their research and resources alongside of NASA s research and resources. This registry is known as the Earth Observing System ClearingHOuse, or ECHO. The charter for this project focused on having an infrastructure completely independent from EOSDIS that would allow for more contributors and open up additional data access options. Accordingly, it is only fitting that the term ECHO is more than just an acronym; it represents the functionality of the system in that it can echo out and create interoperability among other systems, all while maturing with time as industry technologies and standards change and improve.

Source record↗
NASA NTRS2013

Integrated Lunar Information Architecture for Decision Support Version 3.0 (ILIADS 3.0)

ILIADS 3.0 provides the data management capabilities to access CxP-vetted lunar data sets from the LMMP-provided Data Portal and the LMMP-provided On-Moon lunar data product server. (LMMP stands for Lunar Mapping and Modeling Project.) It also provides specific quantitative analysis functions to meet the stated LMMP Level 3 functional and performance requirements specifications that were approved by the CxP. The purpose of ILIADS 3.0 is to provide an integrated, rich client lunar GIS software application

Talabac, Stephen↗
NASA NTRS2018

OMI/TROPOMI Data Support from GES DISC

The NASA GES DISC has been the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. In recent years, the GES DISC has been evolving and improving its data management and services in order to promote its data to be easily discovered and accessed, as well as to facilitate data "interoperability". This presentation will summarize metrics collected of OMI data usage and GES DISC data services. In order to support the recently released Sentinel-5P/TROPOMI data from ESA at the NASA GES DISC, a new End User License Agreement (EULA) has been implemented for users accessing these data from the GES DISC.

Johnson, J.↗
NASA NTRS2018

Exploring Synergistic Tools from NASA Satellite Data for Air Quality

Space-borne earth observation has been important to monitor the earth condition and played a critical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISC has been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Worldview to Giovanni, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

air quality↗
NASA NTRS2019

Orion Ascent Abort 2 Test - Benefits of a Digital Collaboration Environment

The Ascent Abort-2 Crew Module and Separation Ring (CSR) were developed internally to NASA, led by the JSC EA Integrated Product Team. The CSR IPT leveraged Sharepoint as an Integrated Data environment to link systems engineering and project management data element, enabling collaborative development and engagement from across the project (NASA-wide). This presentation describes the mission, the development approach, and the benefits of this approach. flight operations

Devolites, Jenny↗
NASA NTRS2019

Explore Air Quality from NASA Satellite Data

Space-borne earth observation has been important to monitor the earth condition and played acritical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISChas been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Giovanni to ArcGIS, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

Wei, J.↗
NASA NTRSDate not supplied

Opening Historical Airborne Data to Present Day Researchers

For more than 50 years, NASA has flown airborne sensors to carry out research, validate satellite sensors, and test new instrument capabilities. Data collected prior to 2000 are typically analog and difficult to locate and use. The Airborne Data Management Group (ADMG) facilitates rescue of these valuable data to ensure easier discovery, access, and use. But opening historical data comes at a cost of both time and money. Careful decisions are required in assessing the return on investment. - Is there interest in the science community? - Are there government data requirements? - What is the temporal / spatial value of the data? - Can data be transformed to a digital format? - What is cost of transformation? - What time period is needed for rescue? Converting the data to today’s digital storage standards increases value and provides data access. The addition of metadata makes the data easier to search for.

Deborah Smith↗