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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Concurrent Image Processing Executive (CIPE). Volume 1: Design overview

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are described. The target machine for this software is a JPL/Caltech Mark 3fp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules: user interface, host-resident executive, hypercube-resident executive, and application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube, a data management method which distributes, redistributes, and tracks data set information was implemented. The data management also allows data sharing among application programs. The CIPE software architecture provides a flexible environment for scientific analysis of complex remote sensing image data, such as planetary data and imaging spectrometry, utilizing state-of-the-art concurrent computation capabilities.

Lee, Meemong↗
NASA NTRS1997

Mars Delay-Doppler Radar Observations With GSSR: Global Analysis for Landing Site Selection and Characterization

Earth-based radar data remain an important part of the information set used to select and certify spacecraft landing sites on Mars. Constraints on robotic landings on Mars include: terrain elevation, radar reflectivity. regional and local slopes, rock distribution and coverage, and surface roughness, all of which are addressed by radar data. Indeed, the usefulness of radar data for Mars exploration has been demonstrated in the past. Radar data were critical in assessing the Viking Lander I site, and more recently, the Mars Pathfinder landing site.

Haldemann, A. F. C.↗
NASA NTRS2000

Global Database of GSSR Mars Delay-Doppler Radar Obsevations: Analysis for Landing Site Characterization and Rover Trafficability

Earth-based radar data remain an important part of the information set used to select and certify spacecraft landing sites on Mars. Constraints on robotic landings on Mars include: terrain elevation, radar reflectivity, regional and local slopes, rock distribution and coverage, and surface roughness, all of which are addressed by radar data. Indeed, the usefulness of radar data for Mars exploration has been demonstrated in the past. Radar data were critical in assessing the Viking Lander, and more recently, the Mars Pathfinder landing.

Haldemann, A. F. C.↗
NASA NTRS1999

Global Database of GSSR Mars Delay-Doppler Radar Observations: Analysis for Landing Site Characterization and Rover Trafficability

Earth-based radar data remain an important part of the information set used to select and certify spacecraft landing sites on Mars. Constraints on robotic landings on Mars include: terrain elevation, radar reflectivity, regional and local slopes, rock distribution and coverage, and surface roughness, all of which are addressed by radar data. Indeed, the usefulness of radar data for Mars exploration has been demonstrated in the past. Radar data were critical in assessing the Viking Lander 1 site, and more recently, the Mars Pathfinder landing site.

Haldemann, A. F. C.↗
NASA NTRS2004

Lunar and Planetary Science XXXV: Education Programs Demonstrations

Reports from the session on Education Programs Demonstration include:Hands-On Activities for Exploring the Solar System in K-14; Formal Education and Informal Settings;Making Earth and Space Science and Exploration Accessible; New Thematic Solar System Exploration Products for Scientists and Educators Engaging Students of All Ages with Research-related Activities: Using the Levers of Museum Reach and Media Attention to Current Events; Astronomy Village: Use of Planetary Images in Educational Multimedia; ACUMEN: Astronomy Classes Unleashed: Meaningful Experiences for Neophytes; Unusual Guidebook to Terrestrial Field Work Studies: Microenvironmental Studies by Landers on Planetary Surfaces (New Atlas in the Series of the Solar System Notebooks on E tv s University, Hungary); and The NASA ADS: Searching, Linking and More.

Source record↗
NASA NTRS2011

Taxonomy, Ontology and Semantics at Johnson Space Center

At NASA Johnson Space Center (JSC), the Chief Knowledge Officer has been developing the JSC Taxonomy to capitalize on the accomplishments of yesterday while maintaining the flexibility needed for the evolving information environment of today. A clear vision and scope for the semantic system is integral to its success. The vision for the JSC Taxonomy is to connect information stovepipes to present a unified view for information and knowledge across the Center, across organizations, and across decades. Semantic search at JSC means seemless integration of disparate information sets into a single interface. Ever increasing use, interest, and organizational participation mark successful integration and provide the framework for future application.

Berndt, Sarah Ann↗
NASA NTRS2011

A Practitioner's Perspective on Taxonomy, Ontology and Findability

This slide presentation reviews the presenters perspective on developing a taxonomy for JSC to capitalize on the accomplishments of yesterday, while maintaining the flexibility needed for the evolving information of today. A clear vision and scope for the semantic system is integral to its success. The vision for the JSC Taxonomy is to connect information stovepipes to present a unified view for information and knowledge across the Center, across organizations, and across decades. Semantic search at JSC means seamless integration of disparate information sets into a single interface. Ever increasing use, interest, and organizational participation mark successful integration and provide the framework for future application.

Berndt, Sarah↗
NASA NTRS2008

Skill Assessment in Ocean Biological Data Assimilation

There is growing recognition that rigorous skill assessment is required to understand the ability of ocean biological models to represent ocean processes and distributions. Statistical analysis of model results with observations represents the most quantitative form of skill assessment, and this principle serves as well for data assimilation models. However, skill assessment for data assimilation requires special consideration. This is because there are three sets of information in the free-run model, data, and the assimilation model, which uses Data assimilation information from both the flee-run model and the data. Intercom parison of results among the three sets of information is important and useful for assessment, but is not conclusive since the three information sets are intertwined. An independent data set is necessary for an objective determination. Other useful measures of ocean biological data assimilation assessment include responses of unassimilated variables to the data assimilation, performance outside the prescribed region/time of interest, forecasting, and trend analysis. Examples of each approach from the literature are provided. A comprehensive list of ocean biological data assimilation and their applications of skill assessment, in both ecosystem/biogeochemical and fisheries efforts, is summarized.

Gregg, Watson W.↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
NASA NTRS1991

Directory interchange format manual, version 4.0

The Directory Interchange Format (DIF) is a data structure used to exchange directory-level information about data sets among information systems. In general the format consists of a number of fields that describe the attributes of a directory entry and text blocks that contain a descriptive summary of and references for the directory entry. All fields and the summary are preceded by labels identifying their contents. All values are ASCII character strings. The structure is intended to be flexible, allowing for future changes in the contents of directory entries. The manual is structured as follows: section 1 is a general description of what constitutes a directory entry; section 2 describes the content of the individual fields within the data structure, together with some examples. Also included in the six appendices is a description of the syntax used within the examples; samples of the directory interchange format applied to different data sets; the allowable discipline keywords; a current list of valid location keywords; a list of allowable parameter keywords; a list of acronyns and a glossary of terms used; and a description of the Standard Formatted Data Unit header, which may be added to the front of a DIF file to identify the file as a registered standard format.

Source record↗
NASA NTRS1990

Directory interchange format manual, version 3.0

The Directory Interchange Format (DIF) is a data structure used to exchange directory level information about data sets among information systems. The format consists of a number of fields that describe the attributes of a directory entry and text blocks that contain a descriptive summary of and references for the directory entry. All fields and the summary are preceded by labels identifying their contents. All values are ASCII character strings. The structure is intended to be flexible, allowing for future changes in the contents of directory entries.

Source record↗
NASA NTRSDate not supplied

EARLY INFORMATION PARAMETER-SET ANALYSIS FOR SATELLITE CLOSE APPROACHES USING MACHINE LEARNING

In spaceflight navigation applications, understanding and accurately applying orbital mechanics by leveraging force models for trajectory predictions will always remain an important aspect in space mission design and operations. In the process of capturing the dynamics and perturbations in the space environment, the force models are not all encompassing in that these models are subject to errors, commonly referred to as process noise. Therefore, in predicting state vectors of space objects such as spacecraft or debris over long periods of time, these errors in the process noise tend to grow over time.

machine learning↗
NASA NTRSDate not supplied

Analytical Framework for Rodent/NHP Data Set Utilization to Inform Human CNS Risk Estimation

Objectives Review and inform on tactics to analyze & communicate data from animal research (e.g., rodents, mini-pigs, NHPs, et al) exposed to single and combinations of spaceflight hazards, using NASA’s Human System Risk Board (HSRB) process for the Behavioral Medicine Risk - To quantitatively inform Behavioral Medicine Risk posture - i.e., likelihood (L) and consequence (C) - To help inform estimation of crew health standards for operational task performance (i.e., Fitness For Duty, Space Permissible Exposure Levels, Permissible Operational Limits).

Brain↗
NASA NTRS2015

Fast Grasp Contact Computation for a Serial Robot

A system includes a controller and a serial robot having links that are interconnected by a joint, wherein the robot can grasp a three-dimensional (3D) object in response to a commanded grasp pose. The controller receives input information, including the commanded grasp pose, a first set of information describing the kinematics of the robot, and a second set of information describing the position of the object to be grasped. The controller also calculates, in a two-dimensional (2D) plane, a set of contact points between the serial robot and a surface of the 3D object needed for the serial robot to achieve the commanded grasp pose. A required joint angle is then calculated in the 2D plane between the pair of links using the set of contact points. A control action is then executed with respect to the motion of the serial robot using the required joint angle.

Shi, Jianying↗
NASA NTRS2008

Multiple Miniature Avionic Displays

A display screen for displaying multiple sets of information is provided. In one embodiment, an aviation display screen includes a main window and a plurality of miniature windows. The main window is adapted to illustrate one set of information. Each miniature window is adapted to display a set of avionic information. The avionic display is further adapted to toggle a select set of avionic information in one of the miniature windows into the main window.

Rye, Jeffrey M.↗
NASA NTRS1976

Development of Alabama Resources Information System (ARIS)

A formal, organized set of information concerning the development status of the Alabama Resources Information System (ARIS) as of September 1976 is provided. A series of computer source language programs, and flow charts related to each of the computer programs to provide greater ease in performing future change are presented. Listings of the variable names, and their meanings, used in the various source code programs, and copies of the various user manuals which were prepared through this time are given.

Herring, B. E.↗
NASA NTRS1997

Intelligent System Development Using a Rough Sets Methodology

The purpose of this research was to examine the potential of the rough sets technique for developing intelligent models of complex systems from limited information. Rough sets a simple but promising technology to extract easily understood rules from data. The rough set methodology has been shown to perform well when used with a large set of exemplars, but its performance with sparse data sets is less certain. The difficulty is that rules will be developed based on just a few examples, each of which might have a large amount of noise associated with them. The question then becomes, what is the probability of a useful rule being developed from such limited information? One nice feature of rough sets is that in unusual situations, the technique can give an answer of 'I don't know'. That is, if a case arises that is different from the cases the rough set rules were developed on, the methodology can recognize this and alert human operators of it. It can also be trained to do this when the desired action is unknown because conflicting examples apply to the same set of inputs. This summer's project was to look at combining rough set theory with statistical theory to develop confidence limits in rules developed by rough sets. Often it is important not to make a certain type of mistake (e.g., false positives or false negatives), so the rules must be biased toward preventing a catastrophic error, rather than giving the most likely course of action. A method to determine the best course of action in the light of such constraints was examined. The resulting technique was tested with files containing electrical power line 'signatures' from the space shuttle and with decompression sickness data.

Anderson, Gray T.↗