Search NASA⌕ Search

SEARCH · Search NASA

Results for “data learning”

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.

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

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

The NASA Mars Science Laboratory (MSL) and Mars 2020 entry vehicles included temperature measurements inside and direct heat flux on the surface of the thermal protection system (TPS) material. The instrumentation was included to begin addressing predictive capabilities for aerodynamic heating and ablative TPS material response. The MSL aeroshell included temperature measurements at seven heatshield locations, from which total aerodynamic heating was reconstructed. The Mars 2020 instrumentation suite included eleven heatshield measurement locations and new backshell measurements at nine locations: six with temperature sensors, two with sensors to directly measure total heat flux, and one to measure radiative heat flux. Both instrumentation suites returned full sets of data, with all temperature sensors nearest to the surface surviving entry, indicating minimal material recession. Heatshield boundary layer transition (BLT) was observed for both vehicles prior to the time of peak heating, with transition starting near the shoulder and progressing towards the stagnation area. MSL’s higher speed on approach to Mars resulted in higher total heating compared to Mars 2020, with the maximum occurring near the heatshield’s leeside shoulder. The Mars 2020 backshell heating data indicate significant contributions from shock layer radiation. Heating from Navier-Stokes flowfield calculations are in family with reconstructed heating. The paper covers the instrumentation, BLT and reconstructed heating on both heatshields, backshell heating for Mars 2020, TPS performance, and predicted versus reconstructed heat fluxes. Opportunities for improved design approaches for future Mars missions are discussed, especially for the upcoming Mars Sample Return mission.

Mars Science Laboratory↗
NASA NTRSDate not supplied

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

The NASA Mars Science Laboratory (MSL) and Mars 2020 entry vehicles included temperature measurements inside and direct heat flux on the surface of the thermal protection system (TPS) material. The instrumentation was included to begin addressing predictive capabilities for aerodynamic heating and ablative TPS material response. The MSL aeroshell included temperature measurements at seven heatshield locations, from which total aerodynamic heating was reconstructed. The Mars 2020 instrumentation suite included eleven heatshield measurement locations and new backshell measurements at nine locations: six with temperature sensors, two with sensors to directly measure total heat flux, and one to measure radiative heat flux. Both instrumentation suites returned full sets of data, with all temperature sensors nearest to the surface surviving entry, indicating minimal material recession. Heatshield boundary layer transition (BLT) was observed for both vehicles prior to the time of peak heating, with transition starting near the shoulder and progressing towards the stagnation area. MSL’s higher speed on approach to Mars resulted in higher total heating compared to Mars 2020, with the maximum occurring near the heatshield’s leeside shoulder. The Mars 2020 backshell heating data indicate significant contributions from shock layer radiation. Heating from Navier-Stokes flowfield calculations are in family with reconstructed heating. The paper covers the instrumentation, BLT and reconstructed heating on both heatshields, backshell heating for Mars 2020, TPS performance, and predicted versus reconstructed heat fluxes. Opportunities for improved design approaches for future Mars missions are discussed, especially for the upcoming Mars Sample Return mission.

Mars Science Laboratory↗
NASA NTRS2004

Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

Lee, Charles↗
NASA NTRS1991

Rhesus monkey (Macaca mulatta) complex learning skills reassessed

An automated computerized testing facility is employed to study basic learning and transfer in rhesus monkeys including discrimination learning set and mediational learning. The data show higher performance levels than those predicted from other tests that involved compromised learning with analogous conditions. Advanced transfer-index ratios and positive transfer of learning are identified, and indications of mediational learning strategies are noted. It is suggested that these data are evidence of the effectiveness of the present experimental apparatus for enhancing learning in nonhuman primates.

Washburn, David A.↗
NASA NTRS1985

Heterogeneous distributed query processing: The DAVID system

The objective of the Distributed Access View Integrated Database (DAVID) project is the development of an easy to use computer system with which NASA scientists, engineers and administrators can uniformly access distributed heterogeneous databases. Basically, DAVID will be a database management system that sits alongside already existing database and file management systems. Its function is to enable users to access the data in other languages and file systems without having to learn the data manipulation languages. Given here is an outline of a talk on the DAVID project and several charts.

Jacobs, Barry E.↗
NASA NTRSDate not supplied

Distributed Lunar Data Platform with Advanced Machine Learning Capabilities in Support of Lunar Science and Exploration

The United States 2020 Space Policy directive declares that NASA, in cooperation with private industry, will “extend human economic activity into deep space by establishing a permanent human presence on the Moon”. This goal will require advanced data management, as well as analysis, modeling and representation of lunar information in order to prepare for Artemis human missions, lunar science investigations and exploration. To meet this requirement, we conceptualize and present an implementation strategy for a distributed platform for lunar data retrieval, inferencing and analysis, which will be based on federated learning and the NASA Celestial Mapping System (CMS). In addition to demonstrating the imperative of enabling lunar-borne data to remain in-situ but still accessible, this presentation will also include examples of how third parties could contribute both datasets and new functionality into this platform using an AI-based data import pipeline and a plug-in architecture respectively.

Artificial Intelligence↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS1995

Data management, archiving, visualization and analysis of space physics data

A series of programs for the visualization and analysis of space physics data has been developed at UCLA. In the course of those developments, a number of lessons have been learned regarding data management and data archiving, as well as data analysis. The issues now facing those wishing to develop such software, as well as the lessons learned, are reviewed. Modern media have eased many of the earlier problems of the physical volume required to store data, the speed of access, and the permanence of the records. However, the ultimate longevity of these media is still a question of debate. Finally, while software development has become easier, cost is still a limiting factor in developing visualization and analysis software.

Russell, C. T.↗
NASA NTRS2013

Wearable Technology

Wearable technology projects, to be useful, in the future, must be seamlessly integrated with the Flight Deck of the Future (F.F). The lab contains mockups of space vehicle cockpits, habitat living quarters, and workstations equipped with novel user interfaces. The Flight Deck of the Future is one element of the Integrated Power, Avionics, and Software (IPAS) facility, which, to a large extent, manages the F.F network and data systems. To date, integration with the Flight Deck of the Future has been limited by a lack of tools and understanding of the Flight Deck of the Future data handling systems. To remedy this problem it will be necessary to learn how data is managed in the Flight Deck of the Future and to develop tools or interfaces that enable easy integration of WEAR Lab and EV3 products into the Flight Deck of the Future mockups. This capability is critical to future prototype integration, evaluation, and demonstration. This will provide the ability for WEAR Lab products, EV3 human interface prototypes, and technologies from other JSC organizations to be evaluated and tested while in the Flight Deck of the Future. All WEAR Lab products must be integrated with the interface that will connect them to the Flight Deck of the Future. The WEAR Lab products will primarily be programmed in Arduino. Arduino will be used for the development of wearable controls and a tactile communication garment. Arduino will also be used in creating wearable methane detection and warning system.

Watson, Amanda↗
NASA NTRS2014

NASA Tech Briefs, June 2014

Topics include: Real-Time Minimization of Tracking Error for Aircraft Systems; Detecting an Extreme Minority Class in Hyperspectral Data Using Machine Learning; KSC Spaceport Weather Data Archive; Visualizing Acquisition, Processing, and Network Statistics Through Database Queries; Simulating Data Flow via Multiple Secure Connections; Systems and Services for Near-Real-Time Web Access to NPP Data; CCSDS Telemetry Decoder VHDL Core; Thermal Response of a High-Power Switch to Short Pulses; Solar Panel and System Design to Reduce Heating and Optimize Corridors for Lower-Risk Planetary Aerobraking; Low-Cost, Very Large Diamond-Turned Metal Mirror; Very-High-Load-Capacity Air Bearing Spindle for Large Diamond Turning Machines; Elevated-Temperature, Highly Emissive Coating for Energy Dissipation of Large Surfaces; Catalyst for Treatment and Control of Post-Combustion Emissions; Thermally Activated Crack Healing Mechanism for Metallic Materials; Subsurface Imaging of Nanocomposites; Self-Healing Glass Sealants for Solid Oxide Fuel Cells and Electrolyzer Cells; Micromachined Thermopile Arrays with Novel Thermo - electric Materials; Low-Cost, High-Performance MMOD Shielding; Head-Mounted Display Latency Measurement Rig; Workspace-Safe Operation of a Force- or Impedance-Controlled Robot; Cryogenic Mixing Pump with No Moving Parts; Seal Design Feature for Redundancy Verification; Dexterous Humanoid Robot; Tethered Vehicle Control and Tracking System; Lunar Organic Waste Reformer; Digital Laser Frequency Stabilization via Cavity Locking Employing Low-Frequency Direct Modulation; Deep UV Discharge Lamps in Capillary Quartz Tubes with Light Output Coupled to an Optical Fiber; Speech Acquisition and Automatic Speech Recognition for Integrated Spacesuit Audio Systems, Version II; Advanced Sensor Technology for Algal Biotechnology; High-Speed Spectral Mapper; "Ascent - Commemorating Shuttle" - A NASA Film and Multimedia Project DVD; High-Pressure, Reduced-Kinetics Mechanism for N-Hexadecane Oxidation; Method of Error Floor Mitigation in Low-Density Parity-Check Codes; X-Ray Flaw Size Parameter for POD Studies; Large Eddy Simulation Composition Equations for Two-Phase Fully Multicomponent Turbulent Flows; Scheduling Targeted and Mapping Observations with State, Resource, and Timing Constraints;

Source record↗
NASA NTRS2015

FJET Database Project: Extract, Transform, and Load

The Data Mining & Knowledge Management team at Kennedy Space Center is providing data management services to the Frangible Joint Empirical Test (FJET) project at Langley Research Center (LARC). FJET is a project under the NASA Engineering and Safety Center (NESC). The purpose of FJET is to conduct an assessment of mild detonating fuse (MDF) frangible joints (FJs) for human spacecraft separation tasks in support of the NASA Commercial Crew Program. The Data Mining & Knowledge Management team has been tasked with creating and managing a database for the efficient storage and retrieval of FJET test data. This paper details the Extract, Transform, and Load (ETL) process as it is related to gathering FJET test data into a Microsoft SQL relational database, and making that data available to the data users. Lessons learned, procedures implemented, and programming code samples are discussed to help detail the learning experienced as the Data Mining & Knowledge Management team adapted to changing requirements and new technology while maintaining flexibility of design in various aspects of the data management project.

excel vba↗