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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors. The panel will discuss opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors.

aviation safety↗
NASA NTRS1978

Human problem solving performance in a fault diagnosis task

It is proposed that humans in automated systems will be asked to assume the role of troubleshooter or problem solver and that the problems which they will be asked to solve in such systems will not be amenable to rote solution. The design of visual displays for problem solving in such situations is considered, and the results of two experimental investigations of human problem solving performance in the diagnosis of faults in graphically displayed network problems are discussed. The effects of problem size, forced-pacing, computer aiding, and training are considered. Results indicate that human performance deviates from optimality as problem size increases. Forced-pacing appears to cause the human to adopt fairly brute force strategies, as compared to those adopted in self-paced situations. Computer aiding substantially lessens the number of mistaken diagnoses by performing the bookkeeping portions of the task.

Rouse, W. B.↗
NASA NTRSDate not supplied

Predicting Cognitive States Using Machine Learning Fusion Paradigms to Reduce Model Uncertainty

The development of a synergetic system between humans and technology is a challenge that the scientific community has been facing for many years. Our aeronautic research aims to enhance this synergy between humans and machines through predictive human performance modeling for systems to mitigate high-risk situations. By being able to predict and anticipate human states, the crew monitoring system should be able to adjust and support the pilot for aviation safety. Our work focusing on attention-related human performance-limiting states (AHPLS) that impact a pilot’s performance and introduce high-risk catastrophic situations [1]. For example, AHPLS has been cited as a causal factor in more than 50% of all loss control in flight and thus contributes significantly toward commercial aviation fatalities [1, 2]. Cognitive state and its physiological fingerprint can be valuable information for this detecting AHPLS, but human cognitive state detection is still a major limitation for these crew monitoring systems.

machine learning↗
NASA NTRSDate not supplied

Utilizing Gaps and Key Performance Parameters to Inform NASA Environmental Control and Life Support and Human Health and Performance Capability Technology Decisions

Human spaceflight is a complex endeavor requiring a multitude of capabilities for transportation, crew health, scientific goals, and safe return to Earth. The difference between spaceflight proven capabilities and those needed for a particular mission is defined as a capability gap. Capability gaps are not technology specific. Each capability gap is approachable with a wide array of technologies that have unique benefits and challenges. Determining what a capability’s relevant and distinguishing key performance parameters (KPPs) are for a mission is critical. Mass, power, and volume are always constrained and important, but defining these in a way normalized by performance is challenging. Additionally, KPP definition for reliability, dormancy, and integration needs are very important and still evolving. This paper provides the approach of the Environmental Control and Life Support – Crew Health and Performance (ECLSS-CHP) System Capability Leadership Team (SCLT) to defining gaps and KPPs in support of the NASA’s Capabilities Integration Team data call objectives. The nine ECLSS-CHP capability areas are decomposed to capabilities, gaps, and KPPs. Rather than defining very detailed gaps, ECLSS-CHP defines high-level gaps to be technology agnostic. Within a gap, detailed KPPs are defined to both compare technologies and measure progress within a technology over time. Ideally, KPPs are clearly defined, widely communicated both internally and externally, and provide a common nomenclature to describe the state of the art and the degree of improvement required for exploration missions. KPPs help define when the gap is closed and the core mission objectives can be accomplished. Further technology improvements to enhance the capability, as measured by improved KPPs, must then be weighed against investments in open capability gaps that prevent NASA from achieving its exploration missions. It is uncommon that a technology maturation to improve all the relevant KPPs simultaneously but using KPPs is a critical technology investment decision making component. In addition to traditional technology selections, KPPs are informing how investments in ground testing prior to and in parallel with ISS technology demonstrations are required to improve reliability KPPs. The collection of all major technology activities within a capability area are captured on technology roadmaps to communicate how diverse program activities are coordinated to close gaps and infuse into exploration mission needs. A selection of ECLSS-CHP gaps and KPPs and their formulation, current state, and how they inform capability roadmap planning are discussed. The paper will contain a summary of the approximately 60 gaps. Gaps are classified as to their type (architecture, knowledge, technology, developmental, or engineering) depending on the magnitude of the gap. The paper will provide brief overviews of a few major technology challenges and the technologies being considered, but will reference detailed papers for a more thorough treatment of the challenges and state of the art. Data analysis of the gaps is in work and results are not currently available for this abstract. It is anticipated the paper will include examples of select KPPs with descriptions as to why these are the relevant measures. Additionally some KPPs will be graphically presented over time to show progress to date and when performance targets need to be achieved to support exploration missions. Graphical summaries of how gaps closures with near term mission elements support follow-on mission elements will be provided.

Life Support↗
NASA NTRS1990

Investigation of automated task learning, decomposition and scheduling

The details and results of research conducted in the application of neural networks to task planning and decomposition are presented. Task planning and decomposition are operations that humans perform in a reasonably efficient manner. Without the use of good heuristics and usually much human interaction, automatic planners and decomposers generally do not perform well due to the intractable nature of the problems under consideration. The human-like performance of neural networks has shown promise for generating acceptable solutions to intractable problems such as planning and decomposition. This was the primary reasoning behind attempting the study. The basis for the work is the use of state machines to model tasks. State machine models provide a useful means for examining the structure of tasks since many formal techniques have been developed for their analysis and synthesis. It is the approach to integrate the strong algebraic foundations of state machines with the heretofore trial-and-error approach to neural network synthesis.

Livingston, David L.↗
NASA NTRS1999

Measuring Astronaut Performance in Microgravity: Loads and Modeling

Quantitative analysis of human performance in microgravity is important for both scientific investigations and spacecraft engineering design. By collecting and evaluating the kinematics and kinetics data of astronauts in space, it becomes possible to characterize human motor strategies, postural behavior in weightlessness, improve the design of orbital modules, help maintain a quiescent microgravity for acceleration-sensitive material science and life science experiments (NASA JSC, 1996), and optimize the human operative capabilities during long-duration space missions. Hence, there is a need for a precise measurement of the forces and moments exerted by the astronauts on the space station and their postures and movements.

Newman, D.↗
NASA NTRS1987

NASA's Telerobotics R & D Program - Status and future directions

NASA's telerobotics technology program is described as well as the process for the transfer of this technology to the Space Station, and some of the implications of the technology for station design and operations, including those for international cooperation. A diagram is presented of the NASREM control heirarchy with the Office of Aeronautics and Space Technology telerobot testbed architecture superimposed. In telerobotics, the following areas were identified as possible subjects for developing data to support international standards: (1) task boards, (2) system performance measures on task boards, (3) human performance measures on task boards with teleoperation, and (4) autonomous-telerobotic-teleoperated performance comparisons.

Pivirotto, Donna Shirley↗
NASA NTRS1992

Towards a Mars base - Critical steps for life support on the moon and beyond

In providing crew life support for future exploration missions, overall exploration objectives will drive the life support solutions selected. Crew size, mission tasking, and exploration strategy will determine the performance required from life support systems. Human performance requirements, for example, may be offset by the availability of robotic assistance. Once established, exploration requirements for life support will be weighed against the financial and technical risks of developing new technologies and systems. Other considerations will include the demands that a particular life support strategy will make on planetary surface site selection, and the availability of precursor mission data to support EVA and in situ resource recovery planning. As space exploration progresses, the diversity of life support solutions that are implemented is bound to increase.

Rummel, John D.↗
NASA NTRS2018

Mars Transit/Surface Habitat Commonality Studies

- Perform fundamental research into biomechanics and human performance at variable gravities using the unique capabilities of underwater simulation - Microgravity - Lunar - Mars - Create limited habitat simulation with various task simulators which can be adapted across different gravities - Tie into detailed design of variable gravity habitat by leveraging senior capstone course

David Akin↗
NASA NTRS2004

Perceptual learning through optimization of attentional weighting: human versus optimal Bayesian learner

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

Non-NASA Center↗