Search NASA⌕ Search

SEARCH · Search NASA

Results for “Task performance”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Robonaut 2 - Initial Activities On-Board the ISS

Robonaut 2, or R2, arrived on the International Space Station in February 2011 and is currently undergoing testing in preparation for it to become, initially, an Intra-Vehicular Activity (IVA) tool and then evolve into a system that can perform Extra-Vehicular Activities (EVA). After the completion of a series of system level checks to ensure that the robot traveled well on-board the Space Shuttle Atlantis, ground control personnel will remotely control the robot to perform free space tasks that will help characterize the differences between earth and zero-g control. For approximately one year, the fixed base R2 will perform a variety of experiments using a reconfigurable task board that was launched with the robot. While working side-by-side with human astronauts, Robonaut 2 will actuate switches, use standard tools, and manipulate Space Station interfaces, soft goods and cables. The results of these experiments will demonstrate the wide range of tasks a dexterous humanoid can perform in space and they will help refine the methodologies used to control dexterous robots both in space and here on earth. After the trial period that will evaluate R2 while on a fixed stanchion in the US Laboratory module, NASA plans to launch climbing legs that when attached to the current on-orbit R2 upper body will give the robot the ability to traverse through the Space Station and start assisting crew with general IVA maintenance activities. Multiple control modes will be evaluated in this extra-ordinary ISS test environment to prepare the robot for use during EVAs. Ground Controllers will remotely supervise the robot as it executes semi-autonomous scripts for climbing through the Space Station and interacting with IVA interfaces. IVA crew will locally supervise the robot using the same scripts and also teleoperate the robot to simulate scenarios with the robot working alone or as an assistant during space walks.

Diftler, M. A.↗
NASA NTRS2012

Interpersonal Biocybernetics: Connecting Through Social Psychophysiology

One embodiment of biocybernetic adaptation is a human-computer interaction system designed such that physiological signals modulate the effect that control of a task by other means, usually manual control, has on performance of the task. Such a modulation system enables a variety of human-human interactions based upon physiological self-regulation performance. These interpersonal interactions may be mixes of competition and cooperation for simulation training and/or videogame entertainment

Pope, Alan T.↗
NASA NTRS1994

The electrocortical correlates of fluctuating states of attention during vigilance tasks

This study investigated the electrocortical correlates of attention. Sixteen subjects (seven females, nine males) engaged in a forty-minute target detection vigilance task. Task-irrelevant probe tones were presented every 2-4 seconds. While performing the vigilance task, the subjects were asked to press a button if they were daydreaming (i.e. having a task unrelated thought or TUT). Continuous electroencephalograms (EEG's) and event-related potentials (ERP's) were recorded from the subjects during the entire task. The continuous EEG data were analyzed for differences in absolute power throughout the task as well as before and after the subjects indicated that they were daydreaming (TUT response). ERP's elicited by task-irrelevant probe tones were analyzed in the same manner. The results indicated performance decrements as reflected by increased RT to correct detections, and decreased number of hits. Further, as the task progressed, the number of reports of daydreaming increased. The analysis of the EEG data indicated a significant difference in the absolute power of the different frequency bands across periods. The greatest difference was observed at the posterior parietal electrode sites. In addition, when the EEG data was converted into band ratios (beta/alpha and beta/alpha+theta), the pre-TUT conditions were found to be significantly different than the post-TUT conditions in the posterior sites. The ERP components (N1, N2, and P2) were not significantly different before and after a TUT response or across periods. However, the ERP's across periods exhibited amplitudes that were similar to those found in previous studies of vigilance and ERP's.

Cunningham, Stephen G.↗
NASA NTRSDate not supplied

Performance Risk Model (PRisM) Proof-of-Concept: An Operational Decision Support Tool to Predict Crew Performance in Space from Available Performance Tests

The Crew Health and Performance-Probabilistic Risk Assessment (CHP-PRA) team at NASA Glenn Research Center has developed a range of tools to evaluate astronaut health during spaceflight and to optimize the medical set required for missions. Among these, the Performance Risk Model (PRisM) represents a novel advancement, extending CHP-PRA’s focus beyond medical systems into the domain of human performance. Such tool could be pivotal in optimizing astronaut capabilities during space travel, thereby enhancing overall mission success. PRisM leverages data from well-established performance assessments conducted during previous crewed space missions and Earth analogs to strategically predict outcomes for planned tasks, even when direct performance testing has not been conducted for those specific tasks. To evaluate performance, PRisM references the various metrics outlined in NASA-STD-3001 and employs a methodology to integrate different performance scales. This framework analyzes the contribution of various human system task categories (HSTCs) to task execution and compares these contributions to the HSTCs’ involvement in other known tests. The model further employs a Monte Carlo simulation to sample performance scores from their distribution in operationally relevant tests such as those in Mulavara et al. (2018) and, by leveraging similarities in HSTC involvement, transfers this knowledge to actual mission tasks, such as those outlined in the "Human Exploration of Mars: Preliminary List of Crew Tasks”. The current PRisM proof-of-concept includes analyses of the impact of exercise and specific medications on astronauts’ performance, with provisions to incorporate additional countermeasures as data becomes available. Furthermore, the tool is customizable to include any system necessary to fully encompass the domain of human systems and can be adapted to evaluate performance for any spaceflight activities as requested by operational stakeholders. PRisM has the potential to assist the Human Research Program in exploring the capabilities trade space for optimized crew performance.

performance modeling↗
NASA NTRS2011

The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide

The Multi-Attribute Task Battery (MAT Battery). is a computer-based task designed to evaluate operator performance and workload, has been redeveloped to operate in Windows XP Service Pack 3, Windows Vista and Windows 7 operating systems.MATB-II includes essentially the same tasks as the original MAT Battery, plus new configuration options including a graphical user interface for controlling modes of operation. MATB-II can be executed either in training or testing mode, as defined by the MATB-II configuration file. The configuration file also allows set up of the default timeouts for the tasks, the flow rates of the pumps and tank levels of the Resource Management (RESMAN) task. MATB-II comes with a default event file that an experimenter can modify and adapt

Santiago-Espada, Yamira↗
NASA NTRS1997

Atomic Oxygen Task

This report details work performed by the Center for Applied Optics (CAO) at the University of Alabama in Huntsville (UAH) on the contract entitled 'Atomic Oxygen Task' for NASA's Marshall Space Flight Center (contract NAS8-38609, Delivery Order 109, modification number 1). Atomic oxygen effects on exposed materials remain a critical concern in designing spacecraft to withstand exposure in the Low Earth Orbit (LEO) environment. The basic objective of atomic oxygen research in NASA's Materials & Processes (M&P) Laboratory is to provide the solutions to material problems facing present and future space missions. The objective of this work was to provide the necessary research for the design of specialized experimental test configurations and development of techniques for evaluating in-situ space environmental effects, including the effects of atomic oxygen and electromagnetic radiation on candidate materials. Specific tasks were performed to address materials issues concerning accelerated environmental testing as well as specifically addressing materials issues of particular concern for LDEF analysis and Space Station materials selection.

Hadaway, James B.↗
NASA NTRS2009

Johnson Space Center's Risk and Reliability Analysis Group 2008 Annual Report

The Johnson Space Center (JSC) Safety & Mission Assurance (S&MA) Directorate s Risk and Reliability Analysis Group provides both mathematical and engineering analysis expertise in the areas of Probabilistic Risk Assessment (PRA), Reliability and Maintainability (R&M) analysis, and data collection and analysis. The fundamental goal of this group is to provide National Aeronautics and Space Administration (NASA) decisionmakers with the necessary information to make informed decisions when evaluating personnel, flight hardware, and public safety concerns associated with current operating systems as well as with any future systems. The Analysis Group includes a staff of statistical and reliability experts with valuable backgrounds in the statistical, reliability, and engineering fields. This group includes JSC S&MA Analysis Branch personnel as well as S&MA support services contractors, such as Science Applications International Corporation (SAIC) and SoHaR. The Analysis Group s experience base includes nuclear power (both commercial and navy), manufacturing, Department of Defense, chemical, and shipping industries, as well as significant aerospace experience specifically in the Shuttle, International Space Station (ISS), and Constellation Programs. The Analysis Group partners with project and program offices, other NASA centers, NASA contractors, and universities to provide additional resources or information to the group when performing various analysis tasks. The JSC S&MA Analysis Group is recognized as a leader in risk and reliability analysis within the NASA community. Therefore, the Analysis Group is in high demand to help the Space Shuttle Program (SSP) continue to fly safely, assist in designing the next generation spacecraft for the Constellation Program (CxP), and promote advanced analytical techniques. The Analysis Section s tasks include teaching classes and instituting personnel qualification processes to enhance the professional abilities of our analysts as well as performing major probabilistic assessments used to support flight rationale and help establish program requirements. During 2008, the Analysis Group performed more than 70 assessments. Although all these assessments were important, some were instrumental in the decisionmaking processes for the Shuttle and Constellation Programs. Two of the more significant tasks were the Space Transportation System (STS)-122 Low Level Cutoff PRA for the SSP and the Orion Pad Abort One (PA-1) PRA for the CxP. These two activities, along with the numerous other tasks the Analysis Group performed in 2008, are summarized in this report. This report also highlights several ongoing and upcoming efforts to provide crucial statistical and probabilistic assessments, such as the Extravehicular Activity (EVA) PRA for the Hubble Space Telescope service mission and the first fully integrated PRAs for the CxP's Lunar Sortie and ISS missions.

Valentine, Mark↗
NASA NTRS1992

Fault tolerance of artificial neural networks with applications in critical systems

This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

Protzel, Peter W.↗
DOE OSTI2021

Studying visual search without an eye tracker: an assessment of artificial foveation

Abstract Eye tracking is a useful tool for studying human cognition, both in the laboratory and in real-world applications. However, there are cases in which eye tracking is not possible, such as in high-security environments where recording devices cannot be introduced. After facing this challenge in our own work, we sought to test the effectiveness of using artificial foveation as an alternative to eye tracking for studying visual search performance. Two groups of participants completed the same list comparison task, which was a computer-based task designed to mimic an inventory verification process that is commonly performed by international nuclear safeguards inspectors. We manipulated the way in which the items on the inventory list were ordered and color coded. For the eye tracking group, an eye tracker was used to assess the order in which participants viewed the items and the number of fixations per trial in each list condition. For the artificial foveation group, the items were covered with a blurry mask except when participants moused over them. We tracked the order in which participants viewed the items by moving their mouse and the number of items viewed per trial in each list condition. We observed the same overall pattern of performance for the various list display conditions, regardless of the method. However, participants were much slower to complete the task when using artificial foveation and had more variability in their accuracy. Our results indicate that the artificial foveation method can reveal the same pattern of differences across conditions as eye tracking, but it can also impact participants’ task performance.

47 OTHER INSTRUMENTATION↗
NASA NTRS2000

Analytic Guided-Search Model of Human Performance Accuracy in Target- Localization Search Tasks

Current models of human visual search have extended the traditional serial/parallel search dichotomy. Two successful models for predicting human visual search are the Guided Search model and the Signal Detection Theory model. Although these models are inherently different, it has been difficult to compare them because the Guided Search model is designed to predict response time, while Signal Detection Theory models are designed to predict performance accuracy. Moreover, current implementations of the Guided Search model require the use of Monte-Carlo simulations, a method that makes fitting the model's performance quantitatively to human data more computationally time consuming. We have extended the Guided Search model to predict human accuracy in target-localization search tasks. We have also developed analytic expressions that simplify simulation of the model to the evaluation of a small set of equations using only three free parameters. This new implementation and extension of the Guided Search model will enable direct quantitative comparisons with human performance in target-localization search experiments and with the predictions of Signal Detection Theory and other search accuracy models.

Eckstein, Miguel P.↗
NASA NTRS1977

Two measures of performance in a peg-in-hole manipulation task with force feedback

The results are described from two manipulators on a peg-in-hole task, which is part of a continued effort to develop models for human performance with remote manipulators. Task difficulty is varied by changing the diameter of the peg to be inserted in a 50 mm diameter hole. An automatic measuring system records the distance between the tool being held by the manipulator and the receptacle into which it is to be inserted. The data from repeated insertions are processed by computer to determine task times, accumulated distances, and trajectories. Experiments with both the MA-11 cable-connected master-slave manipulator common to hot cell work and the MA-23 servo-controlled manipulator (with and without force feedback) are described. Comparison of these results with previous results of the Ames Manipulator shows that force feedback provides a consistent advantage.

Hill, J. W.↗
NASA NTRS1989

Learning in tele-autonomous systems using Soar

Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. It can also learn to correct its knowledge, using its own problem solving in addition to outside guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques.

Laird, John E.↗
DOE OSTI2022

Single reader between-cases AUC estimator with nested data

The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.

Health Care Sciences & Services↗
NASA NTRS2010

Effect of Passive Horizontal Rotations and Vertical Oscillations on Dynamic Visual Acuity

Astronauts experience sensorimotor disturbances after long duration space flight. These crewmembers may need to egress the vehicle within a few minutes for safety and operational reasons in various sea state conditions following a water landing. Exposure to even low frequency motions induced by sea conditions surrounding a vessel can cause significant fine and gross motor control problems affecting critical functions. The objective of this study was to document human motor and visual performance during simulated wave motion in the 0.1 to 2.0 Hz range. We examined in 12 healthy subjects the changes in accuracy when performing a seated visual target acquisition task in which the location of target was offset vertically during horizontal full body rotation at an oscillating frequency of 0.8 Hz (peak velocity of 160 deg/s). The main finding was that the accuracy of performance degraded in 7 of 12 subjects when acquiring vertical targets at perturbing frequencies of 0.8 Hz in the horizontal plane by one step size. We also examined in a separate study on 12 healthy subjects seated dynamic visual acuity (DVA) task performance during vertical full body oscillations at perturbing frequencies of 2 Hz (peak to peak motion of 5 cm). The main finding was that DVA was significantly reduced when acquiring targets at perturbing oscillations at frequencies of 2 Hz in the vertical plane by approximately 1 chart line. Thus low frequencies of perturbations in the horizontal and vertical planes can cause decrement in visual performance.

Mulavara, Ajitkumar↗
DOE OSTI2018

Performance Tradeoffs of Spectrum Sensing and Target State Estimation and Fusion

We consider a scenario where a cognitive radio sensor performs spectrum sensing and target tracking alternately. Both tasks may impose certain performance guarantee requirements that necessitate considerations of how to schedule these tasks over time. In addition, multiple such cognitive radio sensors can perform collaborative spectrum sensing and target tracking, where a fusion center serves to collect the sensor estimates and their covariances to generate fused target state estimates. We study the performance tradeoffs of these two tasks from variable scheduling intervals. The effect of sensing errors on the target tracking performance due to induced loss from perceived channel unavailability is investigated and conclusions are drawn based on the observed performance tradeoffs.

Liu, Qiang↗
DOE OSTI2025

Synergistic learning with multi-task DeepONet for efficient PDE problem solving

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Finally, our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

42 ENGINEERING↗
DOE OSTI2023

Study of Overfitting by Machine Learning Methods Using Generalization Equations

The training error of Machine Learning (ML) methods has been extensively used for performance assessment, and its low values have been used as a main justification for complex methods such as estimator fusion and ensembles, and hyper parameter tuning. We present two practical cases where independent tests indicate that the low training error is more of a reflection of over-fitting rather than the generalization ability. We derive a generic form of the generalization equations that separates the training error terms of ML methods from their epistemic terms that correspond to approximation and learnability properties. It provides a framework to separately account for both terms to ensure an overall high generalization performance. For regression estimation tasks, we derive conditions for performance enhancements achieved by hyper parameter tuning, and fusion and ensemble methods over their constituent methods. We present experimental measurements and ML estimates that illustrate the analytical results for the throughput profile estimation of a data transport infrastructure.

Rao, Nageswara↗
NASA NTRS1987

Effect of STS space suit on astronaut dominant upper limb EVA work performance

The STS Space Suited and unsuited dominant upper limb performance was evaluated in order to quantify future EVA astronaut skeletal muscle upper limb performance expectations. Testing was performed with subjects standing in EVA STS foot restraints. Data was collected with a CYBEX Dynamometer enclosed in a waterproof container. Control data was taken in one g. During one g testing, weight of the Space Suit was relieved from the subject via an overhead crane with a special connection to the PLSS of the suit. Experimental data was acquired during simulated zero g, accomplished by neutral buoyancy in the Weightless Environment Training Facility. Unsuited subjects became neutrally buoyant via SCUBA BC vests. Actual zero g experimental data was collected during parabolic arc flights on board NASA's modified KC-135 aircraft. During all test conditions, subjects performed five EVA work tasks requiring dominant upper limb performance and ten individual joint articulation movements. Dynamometer velocities for each tested movement were 0 deg/sec, 30 or 60 deg/sec and 120 or 180 deg/sec, depending on the test, with three repetitions per test. Performance was measured in foot pounds of torque.

Greenisen, Michael C.↗