Results for “Collaborative Learning” — Page 14

NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗

NASA NTRS2014

Behavioral Health and Performance at NASA JSC: Recent Successes and Future Plan for BHP Research and Operations

The Behavioral Health and Performance discipline at NASA Johnson Space Center is organized into two distinct Divisions (Biomedical Research and Environmental Science Division and Space and Clinical Operations Division) but is integrated and interrelated in its day‐to‐day work. Ongoing operations supporting NASA's spaceflight goals benefit from the research portfolios that address risks to mission success. Similarly, these research portfolios are informed by operations to ensure investigations stay relevant given the dynamic environment of spaceflight. There are many success stories that can be presented where initial work begun as a BHP Research project, and funded through the Human Research Program, was fully implemented in operations or addressed an operational need. Examples include improving effectiveness of the debriefings used within Mission Control by the Mission Operations Directorate and countermeasures for fatigue management. There is also ongoing collaboration with research and operations for developing selection methods for future generation astronauts, and to enhance and inform the current family support function. The objective of this panel is to provide examples of recent success stories, describe areas where close collaboration is benefitting ongoing research and operations, and summarize how this will come together as NASA plans for the one year ISS mission - a unique opportunity for both BHP operations and research to learn more about preparing and supporting crewmembers for extended missions in space. The proposed panel will be comprised of six presentations, each describing a unique aspect of research or operations and the benefits to current and future spaceflight.

Leveton, L. B.↗

NASA NTRS2019

UAS Service Supplier Checkout: How UTM Confirmed Readiness of Flight Tests with UAS Service Suppliers

NASA collaborated with industry partners to develop and test the small Unmanned Aircraft System (sUAS) Traffic Management (UTM) research platform, a software prototype used for developing airspace integration requirements for sUAS operations. The lessons learned from these activities will help inform the Federal Aviation Administration (FAA) on what is needed to safely manage sUAS operations. A core component of the UTM platform is the UAS Service Supplier (USS), which acts as a communications bridge to meet the regulatory and operational requirements. As the UTM partners began USS flight tests, NASA found that it was difficult to get all USSs functioning at comparable quality levels to ensure successful flight tests. Also, NASA anticipated that the FAA would encounter similar challenges when they begin to register USSs for operational use. These realizations led to the development of USS Checkout, a set of processes and tools designed to increase flight test efficiency. We learned that a good USS Checkout process is balanced for simplicity versus test coverage, and is amenable to automation. We also learned that when USS Checkout is a USS prerequisite for flight tests, flight tests were more efficient and effective.

Smith, Irene Skupniewicz↗

NASA NTRSDate not supplied

The PACE-MAPP Algorithm: Simultaneous Aerosol and Ocean Products From Combined Polarimeter and Shortwave Infrared Measurements

PACE-MAPP collaborative algorithm project - Produce accurate aerosol optical and microphysical properties and ocean properties - Use a coupled atmosphere-ocean vector radiative transfer (VRT) model - Use accurate but fast Mie/SS/T-matrix LUTs - Use scientific machine learning to speed-up retrievals by 1000x (PACE-MAPP Neural Network) - PACE-MAPP is a multi-instrument polarimeter algorithm for SPEXone, HARP2, OCI shortwave infrared channels

Snorre Alfred Moen Stamnes↗

NASA NTRSDate not supplied

Wildland Fire Mitigation: The Role of Airspace Management

Wildland fire is a significant problem on six continents. As wildland fires become increasingly widespread, dangerous, and costly, advances in aerial technology will make a significant difference. Leveraging Unmanned Aircraft Systems Traffic Management (UTM) success, NASA is conducting aerial suppression research and development (R&D) and is collaborating across government, industry, and the academic community to co-develop cutting-edge technology, procedures, and best practices. Join Dr. Parimal "PK" Kopardekar, Director of the NASA Aeronautics Research Institute (NARI), and learn more about how to maximize the utilization of airspace for wildfire management and mitigation.

Wildland fire↗

NASA NTRSDate not supplied

BurstCube: Behind the Scenes of a Do-No-Harm I&T Production

BurstCube is one of the most recent 6-U CubeSats built and developed by NASA Goddard Spaceflight Center (GSFC). As an astrophysics mission, BurstCube will be a rapid detection alert end-to-end mission system for short astrophysical gamma-ray bursts with the aim of increasing the chance of coincident detection of gamma-ray bursts. In addition, the mission is intended to augment the current fleet of gamma-ray astronomy satellites. The payload instrument includes 4 scintillator heads read out by arrays of silicon photomultipliers which will detect short astrophysical gamma-ray bursts. BurstCube provides a high field of view previously unavailable to larger missions and is intended to provide rapid alerts for follow up observations with other assets, increasing the chance of a coincident detection of an event. From the design to the integration and test phases, the project aimed to provide realistic test plans and stimulus to help verify and validate reachable areas of this innovative payload/instrument system and even spacecraft performance. Typical robust integration and test phases for space missions are unaligned with the budget and risk postures of small satellite or CubeSat missions, often designated as “Do No Harm” projects where the primary requirement is not harming the host platform or other payloads. Despite this status, CubeSats are complex missions that mix new and prior technologies. Integration and test for these missions requires responsible engineering, creative collaboration, and careful observation to deliver a reliable mission. This paper will provide an overview of the payload instrument and mission system, areas of injected automation (current and future), environmental testing results, and lessons learned during the integration and test phase.

CubeSats↗

NASA NTRS2003

Working and Learning with Knowledge in the Lobes of a Humanoid's Mind

Humanoid class robots must have sufficient dexterity to assist people and work in an environment designed for human comfort and productivity. This dexterity, in particular the ability to use tools, requires a cognitive understanding of self and the world that exceeds contemporary robotics. Our hypothesis is that the sense-think-act paradigm that has proven so successful for autonomous robots is missing one or more key elements that will be needed for humanoids to meet their full potential as autonomous human assistants. This key ingredient is knowledge. The presented work includes experiments conducted on the Robonaut system, a NASA and the Defense Advanced research Projects Agency (DARPA) joint project, and includes collaborative efforts with a DARPA Mobile Autonomous Robot Software technical program team of researchers at NASA, MIT, USC, NRL, UMass and Vanderbilt. The paper reports on results in the areas of human-robot interaction (human tracking, gesture recognition, natural language, supervised control), perception (stereo vision, object identification, object pose estimation), autonomous grasping (tactile sensing, grasp reflex, grasp stability) and learning (human instruction, task level sequences, and sensorimotor association).

Ambrose, Robert↗

NASA NTRSDate not supplied

The SSRI Knowledge Base Tool: Current Tool Functionality Review and Planned Future Enhancements

NASA’s Small Satellite Reliability Initiative (SSRI), in conjunction with NASA’s Small Spacecraft Systems Virtual Institute (S3VI), has developed the SSRI Knowledge Base to improve mission confidence for small spacecraft. The SSRI Knowledge Base provides vetted, high-quality sources of information on elements that are key to successful small spacecraft missions. These resources include SSRI working group generated documents and presentations in addition to existing guides, publications, standards, software tools, websites, and books. The Knowledge Base is fully searchable, offers downloadable content when possible, and otherwise links to or references content directly from within the tool. All 58 of the planned topic pages that comprise the SSRI Knowledge Base have been recently completed and include over 450 unique resources that are now available for review. Over the past several months significant enhancements to the tool’s capabilities have been developed and implemented. These enhancements consist of the completed baseline content; development of an Application Programming Interface (API); improved user interfaces; scalable and searchable Best Practices and Lessons Learned (BPLL) lists with ratings; and custom website analytics. The SSRI Knowledge Base is a comprehensive and searchable online tool that consolidates and organizes resources, best practices, and lessons learned from previous small spacecraft missions sponsored by NASA, other government agencies, and academia. This free, publicly available tool is available to the entire SmallSat community at: NASA SSRI Knowledge Base | Explore. This presentation will discuss the motivation for and development of the SSRI Knowledge Base, demonstrate the existing tool, share how the smallsat community can get involved and outline plans for further enhancement development. The SSRI is a collaborative activity with broad participation from civil, Department of Defense, and both national and international commercial space systems providers and stakeholders. The S3VI is jointly sponsored by NASA’s Space Technology Mission Directorate and Science Mission Directorate.

Small Spacecraft↗

NASA NTRS1992

Neurocontrol and neurobiology - New developments and connections

At McDonnell-Douglas, controllers which combine adaptive critic networks with the use of backpropagation in real time have solved difficult control problems crucial to the feasibility of building the National Aerospace Plane (NASP) able to reach earth orbit. As details emerged, parallels to neurobiology have grown stronger and have begun to lead to empirical possibilities of importance to neuroscience. This has led to thoughts of institutional collaboration facilitating what could become a Newtonian revolution in neuroscience, with cognitive implications as well. The authors elaborate on each of these points. The topics discussed are recent progress in neurocontrol; progress in optimization and reinforcement learning; implications for neurobiology and science policy; and a new view of the brain.

Werbos, Paul J.↗

NASA NTRS2005

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes data management of NASA remote sensing data for Northern Eurasia Earth Science Partnership Initiative (NEESPI). Many types of ground and integrative (e.g., satellite, GIs) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of Northern Eurasia cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential. The proposed project targets integration of remote sensing data from AVHRR, MODIS, and other NASA instruments on board US- satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEPNCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

NASA NTRS2006

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes the recently awarded ACCESS project to provide data management of NASA remote sensing data for the Northern Eurasia Earth Science Partnership Initiative (NEESPI). The project targets integration of remote sensing data from MODIS, and other NASA instruments on board US-satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEP/NCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗

NASA NTRS2016

Results of the Test Program for Replacement of AK-225G Solvent for Cleaning NASA Propulsion Oxygen Systems

Since the 1990's, when the Class I Ozone Depleting Substance chlorofluorocarbon-113 was banned, NASA's propulsion test facilities at Marshall Space Flight Center and Stennis Space Center have relied upon the solvent Asahiklin AK-225 (hydrochlorofluorocarbon-225ca/cb or HCFC-225ca/cb) and, more recently AK-225G (the single isomer form, HCFC-225cb) to safely clean and verify the cleanliness of large scale propulsion oxygen systems. Effective January 1, 2015, the production, import, export, and new use of Class II Ozone Depleting Substances, including AK-225G, was prohibited in the United States by the Clean Air Act. In 2012 through 2014, NASA test labs at MSFC, SSC, and Johnson Space Center's White Sands Test Facility collaborated to seek out, test, and qualify a solvent replacement for AK-225G that is both an effective cleaner and safe for use with oxygen systems. This paper summarizes the tests performed, results, and lessons learned.

Lowrey, Nikki M.↗

NASA NTRS2016

Results of the Test Program for Replacement of AK-225G Solvent for Cleaning NASA Propulsion Oxygen Systems

Since the 1990's, when the Class I Ozone Depleting Substance (ODS) chlorofluorocarbon-113 (CFC-113) was banned, NASA's propulsion test facilities at Marshall Space Flight Center (MSFC) and Stennis Space Center (SSC) have relied upon the solvent AsahiKlin AK-225 (hydrochlorofluorocarbon-225ca/cb or HCFC-225ca/cb) and, more recently AK-225G (the single isomer form, HCFC-225cb) to safely clean and verify the cleanliness of large scale propulsion oxygen systems. Effective January 1, 2015, the production, import, export, and new use of Class II Ozone Depleting Substances, including AK-225G, was prohibited in the United States by the Clean Air Act. In 2012 through 2014, NASA test labs at MSFC, SSC, and Johnson Space Center's White Sands Test Facility (WSTF) collaborated to seek out, test, and qualify a solvent replacement for AK-225G that is both an effective cleaner and safe for use with oxygen systems. This paper summarizes the tests performed, results, and lessons learned.

Lowrey, Nikki M.↗

NASA NTRS2016

Nanotechnology: From Science Fiction to Reality

Nanotechnology promises unconventional solutions to challenging problems because of expectations that matter can be manipulated at the atomic scale to yield properties that exceed those predicted for bulk materials. The excitement at this possibility has been fueled by significant investments in this technology area. This talk will focus on three examples of where advances are being made to exploit unique properties made possible by nanoscale features for aerospace applications. The first two topics will involve the development of carbon nanotubes for (a) lightweight structural applications and (b) net shape fabricated multifunctional components. The third topic will highlight lessons learned from the demonstration of the effect of nanoengineered surfaces on insect residue adhesion. In all three cases, the approaches used to mature these emerging technologies are based on the acceleration of technology development through multidisciplinary collaborations.

Siochi, Mia↗

NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics dataand collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretationof the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLabhave begunand will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Getachew Gebre↗

NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data, and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Gebre↗

NASA NTRSDate not supplied

WEBINAR, May 6: New Discoveries Using GeneLab

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetry data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Sylvain V. Costes↗

NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 200 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

GeneLab↗