Search NASA⌕ Search

SEARCH · Search NASA

Results for “Transfer 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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Hyperspectral Technology Transfer to the US Department of Interior: Summary of Results of the NASA/DOI Hyperspectral Technology Transfer Project

In 1997 the Office of Biological Informatics and Outreach (OBIO), Biological Resources Division, US Geological Survey and NASA, Office of Earth Science (OES), initiated a coordinated effort for applying Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data and analysis, as a technology transfer project, to critical DOI environmental issues in four study sites throughout the United States. This work was accomplished by four US Department of the Interior (DOI) study teams with support from NASA/OES principal investigators and the Office of Earth Science programs. The studies, including personnel, objectives, background, project plans, and milestones were documented in a project website at . This report summarizes the final outcomes of the project, detailing accomplishments, lessons learned, and benefits realized to NASA, the US Geological Survey, and the participating DOI bureaus.

Root, Ralph↗
NASA NTRSDate not supplied

Trend Analysis of AI/ML Tools and Services in NASA

Usage of Machine Learning (ML) algorithms within NASA’s Science Mission Directorates have been increasing over theyears. This can be quantitatively observed in the upward trends of ML usage found by analyzing the publications andpresentations (in affiliation with NASA) available through NASA Technical Reports Server (NTRS) and PubMed Central(PMC). Identifying the problem types and class of ML algorithms used to tackle them across the divisions can presentopportunities for collaborations, interdisciplinary projects and knowledge transfer for sustainable partnerships. In thispresentation, we will present the trend analysis of ML algorithms used in different SMD divisions based on the publicationsand presentations publicly available. We identify these trends by leveraging ML algorithms which are able to search throughthe publication texts semantically; which are also highly scalable. We will also present an analysis on the available opensource tools and services in NASA leveraging AI/ML algorithms. This work will provide ample avenues for collaborativeefforts across different disciplines based on the surfaced trends.

Slesa Adhikari↗
NASA NTRS2024

Noise Reduction for Solar-Induced Fluorescence Retrievals Using Machine Learning and Principal Component Analysis: Simulations and Applications to GOME-2 Satellite Retrievals

We use a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network (NN) to substantially reduce noise and other artifacts in terrestrial chlorophyll solar-induced fluorescence (SIF) retrievals. SIF is a very small emission at red and far-red wavelengths that is difficult to measure and is highly sensitive to random errors and systematic artifacts. Our approach relies upon an assumption that a trained NN can effectively reconstruct the total SIF signal from a relatively small number of leading principal components of the satellite-observed far-red radiance spectra without using information from the trailing modes that contain most of the random errors. We test the approach with simulated reflectance spectra produced with a full atmospheric and surface radiative transfer model using different observing and geophysical parameters and various noise levels. Resulting noisy and noise-reduced retrieved SIF values are compared with true values to assess performance. We then apply our noise reduction approach to SIF derived from two different satellite spectrometers. For evaluation, since the truth in this case is unknown, we compare SIF retrievals from two independent sensors with each other. We also compare the noise-reduced SIF temporal variations with those from an independent gross primary product (GPP) product that should display similar variations. Results show that our noise reduction approach improves capture of SIF seasonal and inter-annual variability. Our approach should be applicable to many noisy data products derived from spectral measurements. Our methodology does not replace the original retrieval algorithms; rather the original noisy retrievals are needed as the target for the NN training process.

noise reduction, solar-induced fluorescence, SIF, ↗
NASA NTRS1981

ATC contingency operations in the en-route flight regime

Air traffic control (ATC) operations were examined to learn what factors of controller performance should be given consideration in the design and development of future automation systems enhancing ATC. Contingencies were of two types: those constraining airspace usage or traffic flow (i.e., weather); and those related to system and equipment usage (i.e., radar/radio status). Examination of controller response to contingencies and workload pressures showed differing effects on controller allocations of effort among the three primary function of planning, monitoring, and informaton transfer. Automation advancements oriented towards aiding the controller in performing monitoring tasks may offer the most substantial safety benefit.

Lyman, E. G.↗
NASA NTRS1993

Solar dynamic power system development for Space Station Freedom

The development of a solar dynamic electric power generation system as part of the Space Station Freedom Program is documented. The solar dynamic power system includes a solar concentrator, which collects sunlight; a receiver, which accepts and stores the concentrated solar energy and transfers this energy to a gas; a Brayton turbine, alternator, and compressor unit, which generates electric power; and a radiator, which rejects waste heat. Solar dynamic systems have greater efficiency and lower maintenance costs than photovoltaic systems and are being considered for future growth of Space Station Freedom. Solar dynamic development managed by the NASA Lewis Research Center from 1986 to Feb. 1991 is covered. It summarizes technology and hardware development, describes 'lessons learned', and, through an extensive bibliography, serves as a source list of documents that provide details of the design and analytic results achieved. It was prepared by the staff of the Solar Dynamic Power System Branch at the NASA Lewis Research Center in Cleveland, Ohio. The report includes results from the prime contractor as well as from in-house efforts, university grants, and other contracts. Also included are the writers' opinions on the best way to proceed technically and programmatically with solar dynamic efforts in the future, on the basis of their experiences in this program.

Source record↗
NASA NTRS2004

Environmental Remediation Technologies Derived from Space Industry Research

Beginning in the 1950s and 1960s, an abundance of effort and initiative was focused on propelling the space industry outward for planetary exploration and habitation. During these early years, the push to take space science to new levels indirectly contributed to the evolution of another science field that would not fully surface until the early 1980s, environmental remediation. This field is associated with the remediation or cleanup of environmental resources such as groundwater, soil, and sediment. Because the space-exploration initiative began prior to the establishment of the U.S. Environmental Protection Agency (EPA) in December of 1970, many NASA Centers as well as space-related support contractors allowed for the release of spent chemicals into the environment. Subsequently, these land owners have been directed by the EPA to responsibly initiate cleanup of their impacted sites. This paper will focus on the processes and lessons learned with the development, testing, and commercialization initiatives associated with four remediation technologies. The technologies include installation techniques for permeable reactive barriers (PRBs), the use of ultrasound to improve long-term performance of PRBs, emulsified zero-valent iron for product-level solvent degradation, and emulsion technologies for application to metal and polychlorinated biphenyl contaminated media. Details of the paper cover technology research, evaluation, and testing; contracts and grants; and technology transfer strategies including patenting, marketing, and licensing.

Quinn, Jacqueline↗
NASA NTRS1980

Orbit transfer vehicle engine study. Volume 3: Program costs

Budgetary and planning cost estimates are presented that were prepared for the development, production and operation and flight support phases for each of the engines proposed for OTV propulsion. The major features of each category engine are described. The development program estimates were structured to the preliminary program Work Breakdown Structures (WBS). Program costs are provided within the applicable WBS elements to Level 4 for each category engine. The production program cost estimates assume a first production lot of 50 units produced at a rate of two units per month. Cumulative average unit costs assume a 90% learning capability. The operations and flight support cost estimates are based on 15, 30 and 45 missions per year for the period 1988 through 1999. Estimated funding requirements were developed for each category and program phase. All cost estimates and funding data are presented in 1979 dollars. The assumptions and ground rules for these estimates are summarized.

Source record↗
NASA NTRS2003

Orbit Determination Support for the Microwave Anisotropy Probe (MAP)

NASA's Microwave Anisotropy Probe (MAP) was launched from the Cape Canaveral Air Force Station Complex 17 aboard a Delta II 7425-10 expendable launch vehicle on June 30, 2001. The spacecraft received a nominal direct insertion by the Delta expendable launch vehicle into a 185-km circular orbit with a 28.7deg inclination. MAP was then maneuvered into a sequence of phasing loops designed to set up a lunar swingby (gravity-assisted acceleration) of the spacecraft onto a transfer trajectory to a lissajous orbit about the Earth-Sun L2 Lagrange point, about 1.5 million km from Earth. Because of its complex orbital characteristics, the mission provided a unique challenge for orbit determination (OD) support in many orbital regimes. This paper summarizes the premission trajectory covariance error analysis, as well as actual OD results. The use and impact of the various tracking stations, systems, and measurements will be also discussed. Important lessons learned from the MAP OD support team will be presented. There will be a discussion of the challenges presented to OD support including the effects of delta-Vs at apogee as well as perigee, and the impact of the spacecraft attitude mode on the OD accuracy and covariance analysis.

Bauer, Frank↗
NASA NTRSDate not supplied

Trend Analysis of AI/ML Tools and Services in NASA

Usage of Machine Learning (ML) algorithms within NASA’s Science Mission Directorates have been increasing over the years. This can be quantitatively observed in the upward trends of ML usage found by analyzing the publications and presentations (in affiliation with NASA) available through NASA Technical Reports Server (NTRS) and PubMed Central(PMC). Identifying the problem types and class of ML algorithms used to tackle them across the divisions can present opportunities for collaborations, interdisciplinary projects and knowledge transfer for sustainable partnerships. In this presentation, we will present the trend analysis of ML algorithms used in different SMD divisions based on the publications and presentations publicly available. We identify these trends by leveraging ML algorithms which are able to search through the publication texts semantically; which are also highly scalable. We will also present an analysis on the available opensource tools and services in NASA leveraging AI/ML algorithms. This work will provide ample avenues for collaborative efforts across different disciplines based on the surfaced trends.

Slesa Adhikari↗
NASA NTRSDate not supplied

Space-Based Lidar Observations of the 3D Structure of the Earth System

Lidar provides precise measurements of the three-dimensional structure of the clouds, aerosols, ocean/land/snow/ice surfaces, as well as ocean subsurface. Lidar also provides unique information about physical propertiesof particulates in the atmosphere for both radiative transfer and air quality applications.In this talk, I will present an overview of our recent studies of aerosols, clouds, ocean and snow using space-based lidar measurements (e.g., LITE, CALIPSO and ICESat-2), such as classifications of aerosols and thermodynamics phase of clouds, cloud microphysical properties, snow depths and phytoplankton biomass. I will also introduce a new concept of 3D Earth system observations with data fusion though combined active/passive remote sensing and machine learning. The new concept aims to reveal vertical structure of the aerosols/clouds/surfaces/subsurface from passive sensors by taking advantage of lidar measurements to effectively resolve the vertical structureby unscrambling the highly convoluted multi-angle, spectral and polarization information from passive sensors and apply the knowledge to a large swath where lidar measurements are not available.

Ali Omar↗
NASA NTRS2024

Crew Health and Performance Integrated Data Architecture (CHP-IDA) Project

BACKGROUND: Future Human Exploration missions introduce a new paradigm as crews move further from the resupply and near real-time ground support typical of Low Earth Orbit missions today. Without immediate support from ground-based personnel, exploration crews will be more reliant on inflight data and technology to respond to emergencies and anomalies. A data architecture to support a new generation of technologies, employing advanced analytical and predictive modeling techniques, is needed to enable crew autonomy. OVERVIEW: The Crew Health and Performance Integrated Data Architecture (CHP-IDA) project funded by NASA’s Exploration Medical Integrated Product Team (XMIPT) is laying a foundation for future in-flight informatics by providing a back-end architecture for collecting, storing, and integrating multiple sources of data generated by and around the crew. CHP-IDA provides a platform for common data models and Application Programming Interfaces to access, integrate, process, and display CHP data (e.g., environmental, exercise, medical, sleep, performance, etc.). This will facilitate the increased situation awareness and decision support required by the crew and remote support of exploration missions. This presentation will describe the currently ongoing effort to develop and evaluate a path-to-flight concept of the CHP-IDA software and its core capabilities. Current integrations will be discussed, including analytics for Extravehicular Activity metabolic rate and data ingestion from a multi-functional integrated medical device. The presentation will also provide examples of scenarios used to demonstrate the CHP-IDA through human-in-the-loop test bed activities as well as examples of appropriate system performance metrics. DISCUSSION: Today, in-flight data is often siloed, unsynchronized, and largely inaccessible in real time. Many data sets require manual entry and/or data transfer between vehicles and the ground. These issues contribute to risks in supporting exploration medical capabilities. The CHP-IDA is a back-end data system providing core capabilities needed for timely and meaningful data insights across CHP domains to crew and remote personnel to enable increased crew autonomy. Future work includes collaboration with additional CHP domains, new technology integrations, and further demonstrations of the IDA within different vehicle and communication latency contexts. LEARNING OBJECTIVES 1. The audience will understand that the CHP-IDA is a back-end system, providing a platform to facilitate access, promote decision tools, and provide meaningful insights to crew and to remote stakeholders during exploration missions. 2. The audience will gain insight into human-centered research and activities used to discover CHP domain data needs and pain points and how this information is used to guide development of the IDA.

Exploration↗
NASA NTRS2002

My Big Wall

It was June and I was in Yosemite National Park in California, 2,000-feet off the ground. I was climbing El Capitan, a majestic 3,000-foot high, mile-wide granite monolith--one of the most sought after and spectacular rock climbs in the world. After three days of climbing on its sheer face, and having completed the most difficult part of the route, my partner and I were heading down. A thunderstorm lasting all night and into the morning had soaked our tiny perch and all our worldly possessions. We began rappelling down the vertical wall by sliding to the ends of two 50meter ropes tied together and looped through a set of fixed rings bolted into the rock. At the end of the ropes was another rappel station consisting of a set of rings, placed by previous climbers for retreating parties, which we used to anchor ourselves to the rock face. We then pulled the ropes down from the rings above, threaded the ones in front of our noses and started down another rope length. Everything we brought up for our five-day climb to the summit we had to bring back down with us: ropes, climbing gear of every sort, sleeping bags, extra clothes, food, water, and other essentials. All this we either stuffed into a haul bag (an oversized reinforced duffel bag) or slung over our shoulders. The retreat was slow and methodical, akin to a train backing down a mountain, giving me ample time to think. My situation made me think about my work, mostly, about all the projects I have managed, or been involved in managing. As a NASA project manager, I have worked on a number of successful projects. I have also been involved in a number of projects I never saw the end of. I thought about all the projects I transferred off of for other opportunities, projects that were in full stride and ran out of funding, and ones put on the shelf because they would not meet a flight date. Oh yes, I have had many success, to be sure, or I would have burned out years ago. Lessons from both the successful and not-so-successful projects have taught me valuable lessons, but it has always been the failures where I've learned the most.

Espinosa, Paul S.↗
NASA NTRS2019

Generalizability of Manual Control Skills Between Control Tasks of Varying Difficulty

This paper presents the results of an experiment that was performed at NASA Ames Research Center using 18 participants in two different groups who trained a task for ten days, with the goal of identifying how skill generalization would occur between two similar tasks of varying difficulty. A cybernetic approach was used. The first group was trained in a simple one-dimensional tracking task and transferred to a difficult two-dimensional tracking task. For the second group, this was reversed. Training with a simple task before transferring to the difficult task resulted in a slower convergence to final performance. However, it did allow participants to start with a better initial performance in the difficult task. Furthermore, after training with a simple task, participants controlled with a higher gain and generated lower lead time constants. However, possibly due to the number of participants, this experiment did not find any statistical evidence to support the conclusion that training with a simple task version helps in learning a more complex task.

skill generalizability↗
NASA NTRS2019

Generalizability of Manual Control Skills between Control Tasks of Varying Difficulty

This paper presents the results of an experiment that was performed at NASA Ames Research Center using 18 participants in two different groups who trained a task for ten days, with the goal of identifying how skill generalization would occur between two similar tasks of varying difficulty. A cybernetic approach was used. The first group was trained in a simple one-dimensional tracking task and transferred to a difficult two-dimensional tracking task. For the second group, this was reversed. Training with a simple task before transferring to the difficult task resulted in a slower convergence to final performance. However, it did allow participants to start with a better initial performance in the difficult task. Furthermore, after training with a simple task, participants controlled with a higher gain and generated lower lead time constants. However, possibly due to the number of participants, this experiment did not find any statistical evidence to support the conclusion that training with a simple task version helps in learning a more complex task.

Pieters, Marc A.↗
NASA NTRS2010

Field Testing of a Pneumatic Regolith Feed System During a 2010 ISRU Field Campaign on Mauna Kea, Hawaii

Lunar In Situ Resource Utilization (ISRU) consists of a number of tasks starting with mining of lunar regolith, followed by the transfer of regolith to an oxygen extraction reactor and finally processing the regolith and storing of extracted oxygen. The transfer of regolith from the regolith hopper at the ground level to an oxygen extraction reactor many feet above the surface could be accomplished in different ways, including using a mechanical auger, bucket ladder system or a pneumatic system. The latter system is commonly used on earth when moving granular materials since it offers high reliability and simplicity of operation. In this paper, we describe a pneumatic regolith feed system, delivering feedstock to a Carbothermal reactor and lessons learned from deploying the system during the 2010 ISRU field campaign on the Mauna Kea, Hawaii.

Craft, Jack↗
NASA NTRS2016

MoonRIDERS: NASA and Hawaiis Innovative Lunar Surface Flight Experiment for Landing in Late 2017

Recently, NASA Kennedy Space Center, Hawaii's state aerospace agency PISCES, and two Hawaii high schools Iolani and Kealakehe have come together in a unique collaboration called MoonRIDERS. This strategic partnership will allow Hawaii students to participate directly in sending a science experiment to the surface of the moon. The MoonRIDERS project started in the spring of 2014, with each institution responsible for its own project costs and activities. PISCES, given its legislative direction in advancing planetary surface systems, saw this collaboration as an important opportunity to inspire a young generation and encourage STEM (Science, Technology, Engineering, and Mathematics) learning. Under the guidance of PISCES and NASA, the students will be involved hands-on from start to finish in the engineering, testing, and validation of a space technology called the Electrodynamic Dust Shield (EDS). Dust is a critical issue for space exploration, as evidenced by the Apollo lunar missions and Mars rovers and landers. Dust creates a number of problems for humans and hardware, including inhalation, mechanical interference, wear and tear on spacesuits, inhibition of heat transfer on radiators, and reduced efficiency of solar panels. To address this, the EDS is designed to work on a variety of materials, and functions by generatingelectrodynamic fields to clear away the dust. The Google Lunar XPRIZE (GLXP), a space competition "designed to inspire pioneers to do robotic space transport on a budget," serves as a likely method for the MoonRIDERS to get their project to the moon. The EDS would potentially be flown as a hosted payload on a competitor's lander (still to be chosen). This briefing will provide an overview of the technology, the unique partnership, progress update and testing leading to this flight opportunity.

Payload experiment↗
NASA NTRSDate not supplied

Electrified Aircraft Propulsion Integration Concepts: Primary Fuel Cells and Cryogenic Hydrogen Storage

This presentation leverages lessons learned from the multiple activities funded by the National Aeronautics and Space Agency (NASA) to convey development paths for implementing an Electrified Aircraft Propulsion (EAP) system powered by primary fuel cells systems and fueled by cryogenically stored hydrogen. To reduce greenhouse gas emissions associated with all aspects of hydrogen-fueled aviation, this work minimizes required energy and time per payload distance at take-off so as to reduce emissions associated with ground operations and the production, liquefaction, transfer, and storage of hydrogen. The approach targets efficient system integration, maximizing hydrogen utilization efficiency, maximizing power system efficiency, and viable operational concepts for delivered payload distances. The discussion outlines the performance and compliance challenges revealed by analyses of current fuel cell and cryogenic management technologies. The conclusion offers potential mitigation strategies to the identified the performance and compliance challenges.

Fuel Cell↗
NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗