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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Crew Health and Performance Integrated Data Architecture (CHP-IDA) TechPort May 2024

Future exploration missions to Mars will have increased need for crew autonomy. Crew Health & Performance (CHP) related data on the ISS is currently, manually downlinked and in disparate locations, which limits crew autonomy for future missions. The CHP-IDA project is developing a backend data system platform that grants the ability to seamlessly collect, store, process, and display CHP-related data for exploration missions. This platform allows for integration of data and advanced analytics that offer crew and ground teams better insight into the crew’s health and performance. It also enables applications that can improve the crew’s ability to provide more autonomous medical care during exploration missions. Data will be collected automatically to reduce crew and ground team time and effort and will synchronize across all in-mission vehicles, habitats, and ground as communication delay permits. The Human Research Program’s (HRP) Medical Data Architecture (MDA) project focused on this backend data architecture but for medical data only. The CHP-IDA project, a joint effort between HRP’s Exploration Medical Capability (ExMC) element and the Exploration Medical Integrated Product Team (XMIPT), expands this capability to all relevant CHP-related data. The additional inputs from nutrition, environment, exercise, radiation, and any other relevant sources will give more insight into crew’s health and performance. Currently, the Human Systems Engineering and Integration Division at Johnson Space Center (JSC) is designing the system. The team completed a system requirements review (SRR) in FY22 and now the focus is on core software development, testbed buildup, and use case scenario demonstration. An end-to-end demonstration with multiple data sources across CHP domains is schedule for the end of FY24 where all three focus areas will be displayed. Following this ground demo, the software will be completed, tested, and validated for flight.

Courtney M Schkurko↗
NASA NTRS1988

Visualizing Parallel Computer System Performance

Parallel computer systems are among the most complex of man's creations, making satisfactory performance characterization difficult. Despite this complexity, there are strong, indeed, almost irresistible, incentives to quantify parallel system performance using a single metric. The fallacy lies in succumbing to such temptations. A complete performance characterization requires not only an analysis of the system's constituent levels, it also requires both static and dynamic characterizations. Static or average behavior analysis may mask transients that dramatically alter system performance. Although the human visual system is remarkedly adept at interpreting and identifying anomalies in false color data, the importance of dynamic, visual scientific data presentation has only recently been recognized Large, complex parallel system pose equally vexing performance interpretation problems. Data from hardware and software performance monitors must be presented in ways that emphasize important events while eluding irrelevant details. Design approaches and tools for performance visualization are the subject of this paper.

Malony, Allen D.↗
NASA NTRS1973

Design of experimental studies of human performance under influences of simulated artificial gravity

A ground based research program is now being undertaken to provide data concerning the effects of a rotating environment on man's ability to adequately perform gross and fine psychomotor tasks. Emphasis is being placed on establishing the levels of artificial gravity and rates and radii of rotation required in future space systems for preservation of crew performance and comfort. An experimental study utilizing a rotational facility to investigate crew mobility, cargo transfer and handling, and fine motor coordination at radii up to 24 meters and at rotational rates up to 5 rpm is reported.

Piland, W. M.↗
NASA NTRS2012

First Annual Report: NASA-ONERA Collaboration on Human Factors in Aviation Accidents and Incidents

This is the first annual report jointly prepared by NASA and ONERA on the work performed under the agreement to collaborate on a study of the human factors entailed in aviation accidents and incidents particularly focused on consequences of decreases in human performance associated with fatigue. The objective of this Agreement is to generate reliable, automated procedures that improve understanding of the levels and characteristics of flight-crew fatigue factors whose confluence will likely result in unacceptable crew performance. This study entails the analyses of numerical and textual data collected during operational flights. NASA and ONERA are collaborating on the development and assessment of automated capabilities for extracting operationally significant information from very large, diverse (textual and numerical) databases much larger than can be handled practically by human experts. This report presents the approach that is currently expected to be used in processing and analyzing the data for identifying decrements in aircraft performance and examining their relationships to decrements in crewmember performance due to fatigue. The decisions on the approach were based on samples of both the numerical and textual data that will be collected during the four studies planned under the Human Factors Monitoring Program (HFMP). Results of preliminary analyses of these sample data are presented in this report.

Srivastava, Ashok↗
NASA NTRS1990

Cooperating Expert Systems For Automated Monitoring And Diagnostics

Human operators freer to perform nonrepetitive tasks. Expert software system for monitoring multiple streams of sensory data from interacting equipment subsystems undergoing development. Organized hierarchically to monitor multiple equipment subsystems simultaneously. Each expert software subsystem responsible for one of equipment subsystems. Intended to assist human analysts in interpretation of telemetry data indicating operating statuses of subsystems of spacecraft, enables analysts to maintain desired mode(s) of operation, and helps to correct anomalies in subsystems or in overall spacecraft system. Other versions devised for industrial monitoring and analysis tasks similarly complicated.

Schwuttke, Ursula M.↗
NASA NTRS2005

Step 1: C3 Flight Demo Data Analysis Plan

The Data Analysis Plan (DAP) describes the data analysis that the C3 Work Package (WP) will perform in support of the Access 5 Step 1 C3 flight demonstration objectives as well as the processes that will be used by the Flight IPT to gather and distribute the data collected to satisfy those objectives. In addition to C3 requirements, this document will encompass some Human Systems Interface (HSI) requirements in performing the C3 flight demonstrations. The C3 DAP will be used as the primary interface requirements document between the C3 Work Package and Flight Test organizations (Flight IPT and Non-Access 5 Flight Programs). In addition to providing data requirements for Access 5 flight test (piggyback technology demonstration flights, dedicated C3 technology demonstration flights, and Airspace Operations Demonstration flights), the C3 DAP will be used to request flight data from Non- Access 5 flight programs for C3 related data products

Source record↗
NASA NTRS1991

Real Time Data System (RTDS)

Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

Muratore, John F.↗
DOE OSTI2019

Analyzing machine learning models to accelerate generation of fundamental materials insights

Abstract Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding. A primary role of scientists is extraction of fundamental knowledge from data, and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction tool. Convolutional neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces, such as that mapped by a combinatorial materials science experiment. Measuring a performance metric in a given materials space provides direct information about (locally) optimal materials but not the underlying materials science that gives rise to the variation in performance. By building a model that predicts performance (in this case photoelectrochemical power generation of a solar fuels photoanode) from materials parameters (in this case composition and Raman signal), subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical analyses. Human interpretation of these key relationships produces the desired fundamental understanding, demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human scientist. We also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space, such as the addition of specific alloying elements, that may increase performance by moving beyond the confines of existing data.

36 MATERIALS SCIENCE↗
NASA NTRS1992

A principled approach to the measurement of situation awareness in commercial aviation

The issue of how to support situation awareness among crews of modern commercial aircraft is becoming especially important with the introduction of automation in the form of sophisticated flight management computers and expert systems designed to assist the crew. In this paper, cognitive theories are discussed that have relevance for the definition and measurement of situation awareness. These theories suggest that comprehension of the flow of events is an active process that is limited by the modularity of attention and memory constraints, but can be enhanced by expert knowledge and strategies. Three implications of this perspective for assessing and improving situation awareness are considered: (1) Scenario variations are proposed that tax awareness by placing demands on attention; (2) Experimental tasks and probes are described for assessing the cognitive processes that underlie situation awareness; and (3) The use of computer-based human performance models to augment the measures of situation awareness derived from performance data is explored. Finally, two potential example applications of the proposed assessment techniques are described, one concerning spatial awareness using wide field of view displays and the other emphasizing fault management in aircraft systems.

Tenney, Yvette J.↗
NASA NTRS1992

Effect of contrast on human speed perception

This study is part of an ongoing collaborative research effort between the Life Science and Human Factors Divisions at NASA ARC to measure the accuracy of human motion perception in order to predict potential errors in human perception/performance and to facilitate the design of display systems that minimize the effects of such deficits. The study describes how contrast manipulations can produce significant errors in human speed perception. Specifically, when two simultaneously presented parallel gratings are moving at the same speed within stationary windows, the lower-contrast grating appears to move more slowly. This contrast-induced misperception of relative speed is evident across a wide range of contrasts (2.5-50 percent) and does not appear to saturate (e.g., a 50 percent contrast grating appears slower than a 70 percent contrast grating moving at the same speed). The misperception is large: a 70 percent contrast grating must, on average, be slowed by 35 percent to match a 10 percent contrast grating moving at 2 deg/sec (N = 6). Furthermore, it is largely independent of the absolute contrast level and is a quasilinear function of log contrast ratio. A preliminary parametric study shows that, although spatial frequency has little effect, the relative orientation of the two gratings is important. Finally, the effect depends on the temporal presentation of the stimuli: the effects of contrast on perceived speed appears lessened when the stimuli to be matched are presented sequentially. These data constrain both physiological models of visual cortex and models of human performance. We conclude that viewing conditions that effect contrast, such as fog, may cause significant errors in speed judgments.

Stone, Leland S.↗
NASA NTRS2000

Multi-Agent Diagnosis and Control of an Air Revitalization System for Life Support in Space

An architecture of interoperating agents has been developed to provide control and fault management for advanced life support systems in space. In this adjustable autonomy architecture, software agents coordinate with human agents and provide support in novel fault management situations. This architecture combines the Livingstone model-based mode identification and reconfiguration (MIR) system with the 3T architecture for autonomous flexible command and control. The MIR software agent performs model-based state identification and diagnosis. MIR identifies novel recovery configurations and the set of commands required for the recovery. The AZT procedural executive and the human operator use the diagnoses and recovery recommendations, and provide command sequencing. User interface extensions have been developed to support human monitoring of both AZT and MIR data and activities. This architecture has been demonstrated performing control and fault management for an oxygen production system for air revitalization in space. The software operates in a dynamic simulation testbed.

Malin, Jane T.↗
NASA NTRS2020

End-to-End Assessment of Artemis-1 Development Flight Instrumentation

As the space industry continues to strive for more efficient launch vehicles they must rely on increasingly accurate predictive models. Verification of models typically requires physical testing. Flight data measurements offer the most real and therefore the most accurate data for model correlation. As NASA prepares for the inaugural launch of their new Space Launch System (SLS), Artemis-1, they must rely heavily on predictive system models to ensure flight safety. Artemis-1 will be an unmanned scientific mission with the intent of blazing a trail for future manned missions. NASA has implemented a system of Development Flight Instrumentation (DFI) in the hopes of recovering useful flight data during liftoff and ascent to aid in correlating their predictive models to ensure human safety in future missions. An end-to-end assessment of the DFI system was performed to verify data acquired during Artemis-1 would be adequate for the targeted flight test objectives (FTOs). This was accomplished using a computational simulation of all sensors and Data Acquisition (DAQ) parameters to investigate any potential problem areas in the current architecture. Input nominal signals were approximated and injected into the system model. Synthesized acquired signals were recovered to verify FTO success.

Flight measurement↗
DOE OSTI2020

Deep transfer learning for star cluster classification: I. application to the PHANGS– HST survey

ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.

Wei, Wei↗
NASA NTRS1987

Tracking 3-D body motion for docking and robot control

An advanced method of tracking three-dimensional motion of bodies has been developed. This system has the potential to dynamically characterize machine and other structural motion, even in the presence of structural flexibility, thus facilitating closed loop structural motion control. The system's operation is based on the concept that the intersection of three planes defines a point. Three rotating planes of laser light, fixed and moving photovoltaic diode targets, and a pipe-lined architecture of analog and digital electronics are used to locate multiple targets whose number is only limited by available computer memory. Data collection rates are a function of the laser scan rotation speed and are currently selectable up to 480 Hz. The tested performance on a preliminary prototype designed for 0.1 in accuracy (for tracking human motion) at a 480 Hz data rate includes a worst case resolution of 0.8 mm (0.03 inches), a repeatability of plus or minus 0.635 mm (plus or minus 0.025 inches), and an absolute accuracy of plus or minus 2.0 mm (plus or minus 0.08 inches) within an eight cubic meter volume with all results applicable at the 95 percent level of confidence along each coordinate region. The full six degrees of freedom of a body can be computed by attaching three or more target detectors to the body of interest.

Donath, M.↗
NASA NTRS1992

Methodology issues concerning the accuracy of kinematic data collection and analysis using the ariel performance analysis system

Kinematics, the study of motion exclusive of the influences of mass and force, is one of the primary methods used for the analysis of human biomechanical systems as well as other types of mechanical systems. The Anthropometry and Biomechanics Laboratory (ABL) in the Crew Interface Analysis section of the Man-Systems Division performs both human body kinematics as well as mechanical system kinematics using the Ariel Performance Analysis System (APAS). The APAS supports both analysis of analog signals (e.g. force plate data collection) as well as digitization and analysis of video data. The current evaluations address several methodology issues concerning the accuracy of the kinematic data collection and analysis used in the ABL. This document describes a series of evaluations performed to gain quantitative data pertaining to position and constant angular velocity movements under several operating conditions. Two-dimensional as well as three-dimensional data collection and analyses were completed in a controlled laboratory environment using typical hardware setups. In addition, an evaluation was performed to evaluate the accuracy impact due to a single axis camera offset. Segment length and positional data exhibited errors within 3 percent when using three-dimensional analysis and yielded errors within 8 percent through two-dimensional analysis (Direct Linear Software). Peak angular velocities displayed errors within 6 percent through three-dimensional analyses and exhibited errors of 12 percent when using two-dimensional analysis (Direct Linear Software). The specific results from this series of evaluations and their impacts on the methodology issues of kinematic data collection and analyses are presented in detail. The accuracy levels observed in these evaluations are also presented.

Wilmington, R. P.↗
DOE OSTI2023

Efforts to enhance reproducibility in a human performance research project

Background: Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. Methods: We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Results: Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. Conclusions: In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A data-centric weak supervised learning for highway traffic incident detection

Using the data from loop detector sensors for near-real-time detection of traffic incidents on highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leveraging human-labeled incident data, the false alarm rate is often too high to be used in practice. Specifically, the inconsistency in the human labeling of the incidents significantly affects the performance of supervised learning models. To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways. We develop a weak supervised learning workflow to generate high-quality training labels for the incident data without the ground truth labels, and we use those generated labels in the supervised learning setup for final detection. This approach comprises three stages. First, we introduce a data preprocessing and curation pipeline that processes traffic sensor data to generate high-quality training data through leveraging labeling functions, which can be domain knowledge-related or simple heuristic rules. Second, we evaluate the training data generated by weak supervision using three supervised learning models-random forest, k-nearest neighbors, and a support vector machine ensemble-and long short-term memory classifiers. The results show that the accuracy of all of the models improves significantly after using the training data generated by weak supervision. Third, we develop an online real-time incident detection approach that leverages the model ensemble and the uncertainty quantification while detecting incidents. Finally, we show that our proposed weak supervised learning workflow achieves a high incident detection rate (0.90) and low false alarm rate (0.08).

97 MATHEMATICS AND COMPUTING↗