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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Extracting Lessons of Human Performance and Resilience Management from a Study of Weather-Related Safety Risks and Incidents Reported to NASA's Aviation Safety Reporting System (ASRS)

We present a study of weather-related incident reports submitted to NASA’s Aviation Safety Reporting System (ASRS) by air carrier pilots and air traffic controllers in the US. Using specific examples, we examine the weather-related risks reported and the relevant aspects of human performance and resilience management exhibited during these incidents. We describe our methods of lesson extraction to maximize learning, including the use of advance algorithms, and we conclude with a review of the weather-related risks encountered and the resilient practices employed to mitigate them.

resilience↗
NASA NTRS1997

Medical, Psychophysiological, and Human Performance Problems During Extended EVA

In this session, Session JP1, the discussion focuses on the following topics: New Developments in the Assessment of the Risk of Decompression Sickness in Null Gravity During Extravehicular Activity; The Dynamic of Physiological Reactions of Cosmonauts Under the Influence of Repeated EVA Workouts, The Russian Experience; Medical Emergencies in Space; The Evolution from 'Physiological Adequacy' to 'Physiological Tuning'; Five Zones of Symmetrical and Asymmetrical Conflicting Temperatures on the Human Body, Physiological Consequences; Human Performance and Subjective Perception in Nonuniform Thermal Conditions; The Hand as a Control System, Implications for Hand-Finger Dexterity During Extended EVA; and Understanding the Skill of Extravehicular Mass Handling.

Source record↗
NASA NTRS2005

An Investigation of the Combined Effect of Stress, Fatigue and Workload on Human Performance: Position Paper

Stress, fatigue, and workload affect worker performance. NSF reported that 61% of respondents state losing concentration at work while 79% occasionally or frequently made errors as a result of being fatigued. Shift work, altered work schedules, long hours of continuous wakefulness, and sleep loss can create sleep and circadian disruptions that degrade waking fundions causing stress and fatigue. Review of the literature has proven void of information that links the combined effects of fatigue, stress, and workload to human performance. This paper will address which occupational factors within stress, fatigue, and workload were identified as occupational contributors to performance changes. The results of this research will be apglied to underlying models and algorithms that will help predict performance changes in control room operators.

Mock, Jessica↗
NASA NTRS1992

Human behavior and human performance: Psychomotor demands

The results of several experiments are presented in abstract form. These studies are critical for the interpretation and acceptance of flight based science to be conducted by the Behavior and Performance project. Some representative titles are as follow: External audio for IBM/PC compatible computers; A comparative assessment of psychomotor performance (target prediction by humans and macaques); Response path (a dependent measure for computer maze solving and other tasks); Behavioral asymmetries of psychomotor performance in Rhesus monkey (a dissociation between hand preference and skill); Testing primates with joystick based automated apparatus; and Environmental enrichment and performance assessment for ground or flight based research with primates;

Source record↗
NASA NTRS1990

Automation and robotics human performance

The scope of this report is limited to the following: (1) assessing the feasibility of the assumptions for crew productivity during the intra-vehicular activities and extra-vehicular activities; (2) estimating the appropriate level of automation and robotics to accomplish balanced man-machine, cost-effective operations in space; (3) identifying areas where conceptually different approaches to the use of people and machines can leverage the benefits of the scenarios; and (4) recommending modifications to scenarios or developing new scenarios that will improve the expected benefits. The FY89 special assessments are grouped into the five categories shown in the report. The high level system analyses for Automation & Robotics (A&R) and Human Performance (HP) were performed under the Case Studies Technology Assessment category, whereas the detailed analyses for the critical systems and high leverage development areas were performed under the appropriate operations categories (In-Space Vehicle Operations or Planetary Surface Operations). The analysis activities planned for the Science Operations technology areas were deferred to FY90 studies. The remaining activities such as analytic tool development, graphics/video demonstrations and intelligent communicating systems software architecture were performed under the Simulation & Validations category.

Mah, Robert W.↗
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 OSTI2019

Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance

Picking foraminifera from sediment samples is an essential, but repetitive and low-reward task that is well-suited for automation. The first step toward building a picking robot is the development of an automated identification system. We use machine learning techniques to train convolutional neural networks (CNNs) to identify six species of extant planktic foraminifera that are widely used by paleoceanographers, and to distinguish the six species from other taxa. We employ CNNs that were previously built and trained for image classification. Foraminiferal training and identification use reflected light microscope digital images taken at 16 different illumination angles using a light-emitting diode (LED) ring. Overall machine accuracy, as a combination of precision and recall, is better than 80% even with limited training. We compare machine performance to that of human pickers (six experts and five novices) by tasking each with the identification of 540 specimens based on images. Experts achieved comparable precision but poorer recall relative to the machine, with an average accuracy of 63%. Novices scored lower than experts on both precision and recall, for an overall accuracy of 53%. The machine achieved fairly uniform performance across the six species, while participants' scores were strongly species-dependent, commensurate with their past experience and expertise. The machine was also less sensitive to specimen orientation (umbilical versus spiral views) than the humans. Finally, these results demonstrate that our approach can provide a versatile ‘brain’ for an eventual automated robotic picking system.

58 GEOSCIENCES↗
NASA NTRS1997

Center of Excellence in Model-Based Human Performance

The Center of Excellence (COE) was created in 1984 to facilitate active collaboration between the scientists at Ames Research Center and the Stanford Psychology Department. As this document will review, over that period of time, the COE served its function well. Funds from the Center supported a large number of projects over the last ten years. Many of the people who were supported by the Center Have gone on to distinguished research careers in government, industry and university. In fact, several of the people currently working at NASA Ames were initially funded by the Center mechanism, which served as a useful vehicle for attracting top quality candidates and supporting their research efforts. We are grateful for NASA's support over the years. As we reviewed in the reports for each year, the COE budget generally provided a portion of the true costs of the individual research project. Hence, the funds from the COE were leveraged with funds from industry and other government agencies. In this way, we feel that all parties benefitted greatly from the collaborative spirit and interactive aspects of the COE. The portion of the support from NASA was particularly important in helping members of the COE to set aside the time to publish papers and communicate advances in our understanding of human performance in NASA-related missions.

Wandell, Brian A.↗
NASA NTRS1997

Center of Excellence in Model-Based Human Performance

The Center of Excellence (COE) was created in 1984 to facilitate active collaboration between the scientists at Ames Research Center and the Stanford Psychology Department. As this document will review, over that period of time, the COE served its function well. Funds from the Center supported a large number of projects over the last ten years. Many of the people who were supported by the Center have gone on to distinguished research careers in government, industry and university. In fact, several of the people currently working at NASA Ames were initially funded by the Center mechanism, which served as a useful vehicle for attracting top quality candidates and supporting their research efforts. We are grateful for NASA's support over the years. As we reviewed in the reports for each year, the COE budget generally provided a portion of the true costs of the individual research projects. Hence, the funds from the COE were leveraged with funds from industry and other government agencies. In this way, we feel that all parties benefitted greatly from the collaborative spirit and interactive aspects of the COE. The portion of the support from NASA was particularly important in helping members of the COE to set aside the time to publish papers and communicate advances in our understanding of human performance in NASA-related missions.

Wandell, Brian A.↗
DOE OSTI2021

Human Performance Improvement Task Group Task 21-1 Best Practice: Using virtual capabilities or options for HPI application (to reduce errors, strengthening defenses, strengthening the organization, and/or increasing capacity)

In 2020 the world was impacted by COVID-19. How work is done was dramatically changed for a large portion of the workforce. Many have made the abrupt shift to working from their home while others have adapted to COVID protocols (such as social distancing and wearing facemasks). Fundamentally, Human and Organizational Performance Improvement concepts and science did not change – only the delivery method changed (in some cases). The primary effect has been to accelerate dynamics already present in society – from e-commerce to online education to remote healthcare. Change is never easy for humans. However, remote work should be viewed as an opportunity; not a fix to a problem. In this Best Practice paper, you’ll get practical suggestions to help you reduce errors and improve Human Performance in remote, socially-distanced “work-from-home” environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1995

A Multiple Agent Model of Human Performance in Automated Air Traffic Control and Flight Management Operations

A predictive model of human operator performance (flight crew and air traffic control (ATC)) has been developed and applied in order to evaluate the impact of automation developments in flight management and air traffic control. The model is used to predict the performance of a two person flight crew and the ATC operators generating and responding to clearances aided by the Center TRACON Automation System (CTAS). The purpose of the modeling is to support evaluation and design of automated aids for flight management and airspace management and to predict required changes in procedure both air and ground in response to advancing automation in both domains. Additional information is contained in the original extended abstract.

Corker, Kevin↗
NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗
NASA NTRS1995

Haptic interfaces: Hardware, software and human performance

Virtual environments are computer-generated synthetic environments with which a human user can interact to perform a wide variety of perceptual and motor tasks. At present, most of the virtual environment systems engage only the visual and auditory senses, and not the haptic sensorimotor system that conveys the sense of touch and feel of objects in the environment. Computer keyboards, mice, and trackballs constitute relatively simple haptic interfaces. Gloves and exoskeletons that track hand postures have more interaction capabilities and are available in the market. Although desktop and wearable force-reflecting devices have been built and implemented in research laboratories, the current capabilities of such devices are quite limited. To realize the full promise of virtual environments and teleoperation of remote systems, further developments of haptic interfaces are critical. In this paper, the status and research needs in human haptics, technology development and interactions between the two are described. In particular, the excellent performance characteristics of Phantom, a haptic interface recently developed at MIT, are highlighted. Realistic sensations of single point of contact interactions with objects of variable geometry (e.g., smooth, textured, polyhedral) and material properties (e.g., friction, impedance) in the context of a variety of tasks (e.g., needle biopsy, switch panels) achieved through this device are described and the associated issues in haptic rendering are discussed.

Srinivasan, Mandayam A.↗