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At least 1,297 records · Page 72
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NASA NTRS2017

Training Early Career Scientists in Flight Instrument Design Through Experiential Learning: NASA Goddard's Planetary Science Winter School.

The NASA Goddard Planetary Science Winter School (PSWS) is a Goddard Space Flight Center-sponsored training program, managed by Goddard's Solar System Exploration Division (SSED), for Goddard-based postdoctoral fellows and early career planetary scientists. Currently in its third year, the PSWS is an experiential training program for scientists interested in participating on future planetary science instrument teams. Inspired by the NASA Planetary Science Summer School, Goddard's PSWS is unique in that participants learn the flight instrument lifecycle by designing a planetary flight instrument under actual consideration by Goddard for proposal and development. They work alongside the instrument Principal Investigator (PI) and engineers in Goddard's Instrument Design Laboratory (IDL; idc.nasa.gov), to develop a science traceability matrix and design the instrument, culminating in a conceptual design and presentation to the PI, the IDL team and Goddard management. By shadowing and working alongside IDL discipline engineers, participants experience firsthand the science and cost constraints, trade-offs, and teamwork that are required for optimal instrument design. Each PSWS is collaboratively designed with representatives from SSED, IDL, and the instrument PI, to ensure value added for all stakeholders. The pilot PSWS was held in early 2015, with a second implementation in early 2016. Feedback from past participants was used to design the 2017 PSWS, which is underway as of the writing of this abstract.

Flight↗
NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗
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 NTRS2018

Autogenic Feedback Training Exercise: Controlling Physiological Responses to Mitigate Motion Sickness

During space travel approximately 50 of the crew experience symptoms of motion sickness that can range from mild forms of nausea or dizziness to severe malaise and vomiting1. Developing an effective treatment for these symptoms has become a priority of the National Aeronautics and Space Administration (NASA). Autogenic-Feedback Training Exercise (AFTE) is a nonpharmacological countermeasure for mitigating motion sickness. It involves training subjects to control physiological responses in high stress environments2. The primary goal of this experiment is to evaluate the effectiveness of AFTE for increasing tolerance to motion sickness in high stress environments.

motion sickness↗
NASA NTRS2018

Best Practices for Training the Structures Flight Test Engineer

The Structures Flight from 412th Test Wing and the Aerostructures Branch at NASA Armstrong Flight Research Center at Edwards Air Force Base have developed a set of best practices for training a structures flight test engineer. These practices represent the hard-won lessons learned and best practices from training generations of engineers to perform high risk envelope expansion and developmental flight test. Collectively, these organizations have tested many of the world's most advanced and innovative aircraft, including the B-2, C-17, F-22, F-35, RQ-4, X-29, F-18 High Angle of Attack Research Vehicle, X-53, G-III Adaptive Compliant Trailing Edge, and X-56.

flight tests↗
NASA NTRS2018

Best Practices for Training the Structures Flight Test Engineer

The Structures Flight from 412 Test Wing and the Aerostructures Branch at NASA Armstrong Flight Research Center at Edwards Air Force Base have developed a set of best practices for training a structures flight test engineer. These practices represent the hard-won lessons learned and best practices from training generations of engineers to perform high risk envelope expansion and developmental flight test.

structures testing↗
NASA NTRS2019

Verification of a Motion Cueing Strategy for Stall Recovery Training in a Commercial Transport Simulator

This presentation verifies a motion cueing strategy for improved pilot stall recovery training in commercial transport simulators. Eight airline transport pilots flew a high-altitude stall recovery task in the NASA B747 level-D-certified full flight simulator under three different motion configurations: no motion, baseline motion, and enhanced motion. For each motion condition, pilots performed the task with both baseline aircraft dynamics and aircraft dynamics enhanced with lateral-directional characteristics of the airplane at angle of attack approaching stall. Motion configuration significantly affected: 1) pilot opinions on the helpfulness of motion in performing the task, 2) the maximum roll angle in the stall maneuver, 3) the minimum load factor in the recovery, 4) the number of secondary stick shakers in the stall recovery, and 5) the maximum airspeed in the recovery. The two different aircraft dynamics significantly affected: 1) pilot opinions on the noticeability of the banking roll off near the stall and 2) the maximum roll angle in the stall maneuver. These results indicate that the relatively minor enhancements to the motion logic of heritage commercial transport simulators presented here can significantly improve pilot performance in simulated stall recoveries, and potentially improve stall recovery training.

Zaal, Petrus M. T.↗
NASA NTRS2019

Verification of a Motion Cueing Strategy for Stall Recovery Training in a Commercial Transport Simulator

This paper verifies a motion cueing strategy for improved pilot stall recovery training in commercial transport simulators. Eight airline transport pilots flew a high-altitude stall recovery task in the NASA B747 level-D-certified full flight simulator under three different motion configurations: no motion, baseline motion, and enhanced motion. For each motion condition, pilots performed the task with both baseline aircraft dynamics and aircraft dynamics enhanced with lateral-directional characteristics of the airplane at angle of attack approaching stall. Motion configuration significantly affected: 1) pilot opinions on the helpfulness of motion in performing the task, 2) the maximum roll angle in the stall maneuver, 3) the minimum load factor in the recovery, 4) the number of secondary stick shakers in the stall recovery, and 5) the maximum airspeed in the recovery. The two different aircraft dynamics significantly affected: 1) pilot opinions on the noticeability of the banking roll off near the stall and 2) the maximum roll angle in the stall maneuver. These results indicate that the relatively minor enhancements to the motion logic of heritage commercial transport simulators presented here can significantly improve pilot performance in simulated stall recoveries, and potentially improve stall recovery training.

Pilot Performance↗
NASA NTRS2019

The NASA Space Life Sciences Training Program: Accomplishments Since 2013

The NASA Space Life Sciences Training Program (SLSTP) provides undergraduate students entering their junior or senior years with professional experience in space life science disciplines. This challenging ten-week summer program is held at NASA Ames Research Center. The primary goal of the program is to train the next generation of scientists and engineers, enabling NASA to meet future research and development challenges in the space life sciences. Students work closely with NASA scientists and engineers on cutting-edge research and technology development. In addition to conducting hands-on research and presenting their findings, SLSTP students attend technical lectures given by experts on a wide range of topics, tour NASA research facilities, participate in leadership and team building exercises, and complete a group project. For this presentation, we will highlight program processes, student recruitment, accomplishments, goals, and feedback from alumni and mentors since 2013. To date, 71 students from 53 different academic institutions, 13 staffers, and 29 mentors have participated in the program.The SLSTP is funded by Space Biology, which is part of the Space Life and Physical Sciences Research and Application division of NASA's Human Exploration and Operations Mission Directorate. The SLSTP is managed by the Space Biology Project within the Science Directorate at Ames Research Center.

Rask, Jon↗
NASA NTRS2020

Effects of Long-Duration Space Flight on Training Retention and Transfer

The space environment imposes on the astronaut crew significant physiological, psycho-social, and cognitive loads that can not be replicated on the ground. These loads likely impact crew performance. To date, no systematic data collection has taken place to understand the effects of such loads on crew members ability to retain trained knowledge and skills, and to transfer such knowledge and skills to novel situations. The research described here was originally requested by HRP management to be the first such study to systematically collect data on the effects of long duration space flight on training retention and transfer. Because current theories of retention and transfer are based on results obtained in university laboratories using undergraduate students as research participants, and because crew time in space is very expensive, this study was designed to compare the performance of 4 groups of subjects: crew members in space, crew members on the ground, crew-like subjects, and university undergraduate students. Results from the ground-phase of the study reported here demonstrate that crew members performance under cognitive load can not be predicted from the performance of university undergraduate students. It is still an open question the extent to which crew members cognitive performance in space can be predicted from the performance of crew members on the ground.

Barshi, Immanuel↗
NASA NTRS2019

Using Computational Flow Imaging to Optimize Filtered Rayleigh Scattering Measurements of an Isolator Shock Train

Filtered Rayleigh scattering (FRS) is a laser diagnostic where the intensity of elastically scattered light is measured after it passes through a molecular absorption filter. The filter removes background interference that overwhelms the relatively weak scattering from the gas molecules. However, with a filter, the measured light intensity depends on many of the scattering gas properties, including pressure, density, temperature, and velocity. In this work, CFD simulations of an isolator shock train flow field are input into a physics-based model to predict the values of FRS intensity measurements in a proposed experiment. The goal is to evaluate if a simplistic FRS setup (that utilizes one camera, laser, and absorption filter) can be used to accurately quantify number density despite the fact that the scattered light intensity also depends on other gas properties. It is found that the experimental setup can be optimized such that a linear relationship describes the number density with an average prediction error of 2%. The vast majority of the flow exhibits prediction errors of less than 3%, but small regions of the flow reach up to 11% error. A sensitivity analysis shows that the prediction error increases with the central wavelength of the incident laser light and decreases with the angle between the camera and laser propagation directions. The optimal experimental parameters are chosen based on a compromise between the prediction error, spatial resolution, and the amount of unfiltered light. In the future, the proposed FRS setup will be implemented to acquire new and valuable information on an isolator shock train, a flow field that has been traditionally studied using wall static pressure measurements and path-integrated visualization techniques, such as schlieren and shadowgraphy.

Hunt, Robin L.↗
NASA NTRS2020

Designing and Training for Appropriate Trust in Increasingly Autonomous Advanced Air Mobility Operations: A Mental Model Approach: Version 1

To enable effective human-autonomy teaming (HAT) in Advanced Air Mobility (AAM) operations, the current paper presents a theoretical framework to design and train for appropriate trust in automation. The novel contribution of this work resides in connecting the construct of trust to mental models and showing how this method could be used to enable emerging HAT concepts such as Adaptive Trust Calibration. To contextualize this framework, in section 2 we discuss simplified vehicle operations (SVO) and remote vehicle operations (RVO), which are leading operational concepts within AAM. In section 3 we describe our perspective on automation and increasingly autonomous systems and present a brief discussion on human-automation interaction and human-autonomy teaming. In section 4 we provide a detailed discussion on the construct of trust in automation. In section 5 we present a framework that associates mental models with trust through principles of transparent design. Finally, in section 6 we present three descriptive models for designing and training for appropriate trust in increasingly autonomous systems.

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated Tracks

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this work indicate that it is feasible to classify sensor collected trajectories using a classifier trained on flight controller data.

Henry Holbrook↗
NASA NTRS2022

DataSet: Aerial Object Trajectory Classification by Training on Flight Controller Data and Testing on RADAR Generated

Onboard collision avoidance is needed to enable safe, autonomous flight operations for NASA projects such as Advanced Air Mobility (AAM), as well as many commercial applications. Real-time aerial object classification will improve onboard collision avoidance algorithm decision making and may reduce unnecessary activation of avoidance systems. This work trains an aircraft trajectory classifier using trajectories from flight controller logs and tests the classifier using RADAR collected trajectories during air to air experiments and ground to air experiments. In contrast to RADAR data, these flight controller logs are relatively abundant, which makes the possibility of substituting flight data for RADAR data an attractive, cost-effective option. The SVM model developed in this work achieved a 79.7% classification accuracy on the first second of radar trajectories of GA, multirotor sUAS, and fixed wing sUAS. Findings from this e that it is feasible to classify sensor collected trajectories using a classifier trained flight controller data.

Chester V Dolph↗
NASA NTRSDate not supplied

GeneLab for High Schools – Bioinformatic Training For Students And Educators

Modern biological sciences are increasingly based on high-throughput molecular techniques, including genomics, transcriptomics, and proteomics. NASA’s GeneLab program has collected extensive data from ‘omics’ studies, curated them into an accessible platform and provided data analysis/visualization tools to facilitate the generation of new hypotheses and research directions. GeneLab for High Schools (GL4HS), launched in 2017, has endeavored to utilize this database and provide tools for students to understand and analyze omics datasets whilst also learning about spaceflight research. The GL4HS program ran in person at Ames from 2017-2019 and has run virtually since 2020. Each year fifteen high school students are trained to analyze and interpret GeneLab transcriptomic data. Additionally, in the last several years we have expanded our “teacher training program” to include 10 teachers total in an effort to enable this program to be utilized in classrooms across the USA. Teachers also join the NASA GeneLab Education Working Group (EWG) enabling support as they implement custom GL4HS modules into their classrooms. The GL4HS program consists of three main components – (1) core learning modules, (2) networking and teamwork, and (3) an independent learning project. Students are also taught critical networking and science communication skills facilitating their ability to ‘sell their science’ in innovative and creative ways. This program has enabled students to learn about biology in space and to have a glimpse into the world of research for the first time. Many of the students in this program shared that the course was transformative to their perception about biological sciences and how it linked to other areas of STEM. The ultimate and long-term goal of GL4HS is to expand the program to multiple locations thereby facilitating the reach of NASA Space Biology beyond NASA-centric regions.

GeneLab↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS2024

Crew State and Risk Model Development to Predict Hydration Status During Extravehicular Activity Training Events

Introduction: Hydration is critical for optimal human health and performance and dehydration can lead to impaired cardiovascular function, thermal dysregulation, decreased blood plasma volume, and cognitive impacts, particularly during physical activity. Prolonged and repeated extravehicular activities (EVA) without sufficiently available drinking water may increase risk for dehydration, which could impair crew health and impact mission success. Understanding hydration needs and potential effects on health and performance are necessary to optimize crew well-being and enable successful EVA objectives. This study aims to develop a model of hydration status during EVA using water balance techniques. Methods: Water balance measures were collected on 15 healthy astronauts who performed ≈6-hour simulated microgravity extravehicular activity (EVA) training in the NASA Neutral Buoyancy Laboratory (NBL). Data collected included pre-and post-EVA nude body weight (BW), maximum absorption garment (MAG) weight, Disposable In-suit Drink Bag (DIDB) weight, urine specific gravity (USG), and extra pre-EVA intake (W). Variables were combined to create the water balance model as pre-EVA (Hn)= BWn+ MAGn+ DIDBn+ Wnand post EVA (Hn+1) = BWn+1+ MAGn+1+ DIDBn+1. Urine specific gravity values were used to refine water balance measures into hydration categories: Hydrated, Marginally Hydrated, and Dehydrated. Results: Pre-EVA modeling indicated53% of crew were hydrated, 20% were marginally hydrated, and 27% were dehydrated. Alternately, Hn+1 showed 13% of crew remained hydrated, 47% were marginally hydrated, and 40% were dehydrated at the end of the EVA. Furthermore, 75% of the crewmembers who started sufficiently hydrated finished the run marginally hydrated or dehydrated. According to USG indices presented by Casa and Lawrence, et al. (2000), only 25% of the crew started and remained hydrated throughout the EVA, and those who were dehydrated at the outset stayed dehydrated. Conclusion: Model outcomes assessing hydration status during 6-hour simulated microgravity EVAs demonstrate the necessity to further address hydration requirements for optimal human performance during spaceflight and EVA. This study enables additional baseline development of the Crew State and Risk Model Hydration, Nutrition, and Waste Management component that aims to provide individualized crew state and risk predictions during EVAs. Reference: Casa, D. J., Armstrong, L. E., et al. (2000). National Athletic Trainers’ Association Position Statement: Fluid Replacement for Athletes. Journal of Athletic Training, 35:212-224.

L Cooper↗