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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1988

Automated orbital rendezvous considerations

The control of the rendezvous vehicle during proximity operations is considered. It is shown how fuzzy sets can be used for autonomous vehicle control to model the human capability of common sense reasoning. Such models are integrated with expert systems and engineering control systems technology to create a system that performs comparably to a manned system.

Lea, Robert N.↗
NASA NTRS2000

Corpus-Based Optimization of Language Models Derived from Unification Grammars

We describe a technique which makes it feasible to improve the performance of a language model derived from a manually constructed unification grammar, using low-quality untranscribed speech data and a minimum of human annotation. The method is on a medium-vocabulary spoken language command and control task.

Rayner, Manny↗
NASA NTRS2015

Can We Trust Computational Modeling for Medical Applications?

Operations in extreme environments such as spaceflight pose human health risks that are currently not well understood and potentially unanticipated. In addition, there are limited clinical and research data to inform development and implementation of therapeutics for these unique health risks. In this light, NASA's Human Research Program (HRP) is leveraging biomedical computational models and simulations (M&S) to help inform, predict, assess and mitigate spaceflight health and performance risks, and enhance countermeasure development. To ensure that these M&S can be applied with confidence to the space environment, it is imperative to incorporate a rigorous verification, validation and credibility assessment (VV&C) processes to ensure that the computational tools are sufficiently reliable to answer questions within their intended use domain. In this presentation, we will discuss how NASA's Integrated Medical Model (IMM) and Digital Astronaut Project (DAP) have successfully adapted NASA's Standard for Models and Simulations, NASA-STD-7009 (7009) to achieve this goal. These VV&C methods are also being leveraged by organization such as the Food and Drug Administration (FDA), National Institute of Health (NIH) and the American Society of Mechanical Engineers (ASME) to establish new M&S VV&C standards and guidelines for healthcare applications. Similarly, we hope to provide some insight to the greater aerospace medicine community on how to develop and implement M&S with sufficient confidence to augment medical research and operations.

Mulugeta, Lealem↗
NASA NTRS1989

Test and validation for robot arm control dynamics simulation

The Flight Telerobotic Servicer (FTS) program will require an ability to develop, in a cost effective manner, many simulation models for design, analysis, performance evaluation, and crew training. Computational speed and the degree of modeling fidelity associated with each simulation must be commensurate with problem objectives. To demonstrate evolving state-of-the-art general purpose multibody modeling capabilities, to validate these by laboratory testing, and to expose their modeling shortcomings, two focus problems at the opposite ends of the simulation spectrum are defined: (1) Coarse Acquisition Control Dynamics. Create a real time man-in-the-control-loop simulator. Provide animated graphical display of robot arm dynamics and tactile feedback sufficient for cueing the operator. Interface simulator software with human operated tactile feedback controller; i.e., the Kraft mini-master. (2) Fine, Precision Mode Control Dynamics. Create a high speed, high fidelity simulation model for the design, analysis, and performance evaluation of autonomous 7 degree-of-freedom (DOF) trajectory control algorithms. This model must contain detail dynamic models for all significant dynamics elements within the robot arm, such as joint drive mechanisms.

Yae, K. Harold↗
NASA NTRS2010

Space Flight Resource Management for ISS Operations

Although the astronaut training flow for the International Space Station (ISS) spans 2 years, each astronaut or cosmonaut often spends most of their training alone. Rarely is it operationally feasible for all six ISS crewmembers to train together, even more unlikely that crewmembers can practice living together before launch. Likewise, ISS Flight Controller training spans 18 months of learning to manage incredibly complex systems remotely in plug-and-play ground teams that have little to no exposure to crewmembers before a mission. How then do all of these people quickly become a team - a team that must respond flexibly yet decisively to a variety of situations? The answer implemented at NASA is Space Flight Resource Management (SFRM), the so-called "soft skills" or team performance skills. Based on Crew Resource Management, SFRM was developed first for shuttle astronauts and focused on managing human errors during time-critical events (Rogers, et al. 2002). Given the nature of life on ISS, the scope of SFRM for ISS broadened to include teamwork during prolonged and routine operations (O'Keefe, 2008). The ISS SFRM model resembles a star with one competency for each point: Communication, Cross-Culture, Teamwork, Decision Making, Team Care, Leadership/Followership, Conflict Management, and Situation Awareness. These eight competencies were developed with international participation by the Human Behavior and Performance Training Working Group. Over the last two years, these competencies have been used to build a multi-modal SFRM training flow for astronaut candidates and flight controllers that integrates team performance skills into the practice of technical skills. Preliminary results show trainee skill increases as the flow progresses; and participants find the training invaluable to performing well and staying healthy during ISS operations. Future development of SFRM training will aim to help support indirect handovers as ISS operations evolve further with the retirement of the Space Shuttle Program.

Schmidt, Lacey L.↗
NASA NTRS1996

Speed estimates from grating patches are not contrast-normalized

We have previously shown that the perceived speed of a moving grating depends upon its contrast, with lower-contrast patterns appearing to move more slowly than otherwise identical higher-contrast patterns. To explain this finding while remaining consistent with the findings of McKee, Silverman and Nakayama [(1986) Vision Research, 26, 609-619], we proposed that this misperception might arise from a modified version of the contrast-normalization procedure, envisaged by Adelson and Bergen [(1986) The extraction of spatio-temporal energy in human and machine vision (pp. 135-139). Charleston, S.C.: IEEE Computer Society] as a necessary second stage of motion-energy models of human motion processing. Specifically, our previous results might be explained if the two gratings to be compared interfered with each other's normalization. To test this hypothesis we performed two experiments. Experiment 1 demonstrates that the contrast effects persist even when two grating patches to be compared are presented up to 5 sec apart so that they would not be expected to bias each other's normalization. Experiment 2 shows that the contrast effects are unchanged when the two grating patches are surrounded by a range of patterns whose contrast would be expected to interfere with any normalization process. These two results allow the rejection of the contrast-normalized motion-energy hypothesis as an explanation of human speed perception. We discuss the consequences of these results on models of speed processing in the human visual system.

NASA Center ARC↗
NASA NTRS1981

Quantification of airport community noise impact in terms of noise levels, population density, and human subjective response

The Fraction Impact Method (FIM), developed by the National Research Council (NRC) for assessing the amount and physiological effect of noise, is described. Here, the number of people exposed to a given level of noise is multiplied by a weighting factor that depends on noise level. It is pointed out that the Aircraft-noise Levels and Annoyance MOdel (ALAMO), recently developed at NASA Langley Research Center, can perform the NRC fractional impact calculations for given modes of operation at any U.S. airport. The sensitivity of these calculations to errors in estimates of population, noise level, and human subjective response is discussed. It is found that a change in source noise causes a substantially smaller change in contour area than would be predicted simply on the basis of inverse square law considerations. Another finding is that the impact calculations are generally less sensitive to source noise errors than to systematic errors in population or subjective response.

Deloach, R.↗
NASA NTRS1999

Modeling Calcium Loss from Bones During Space Flight

Calcium loss from bones during space flight creates a risk for astronauts who travel into space, and may prohibit space flights to other planets. The problem of calcium loss during space flight has been studied using animal models, bed rest (as a ground-based model), and humans in-flight. In-flight studies have typically documented bone loss by comparing bone mass before and after flight. To identify changes in metabolism leading to bone loss, we have performed kinetic studies using stable isotopes of calcium. Oral (Ca-43) and intravenous (Ca-46) tracers were administered to subjects (n=3), three-times before flight, once in-flight (after 110 days), and three times post-flight (on landing day, and 9 days and 3 months after flight). Samples of blood, saliva, urine, and feces were collected for up to 5 days after isotope administration, and were analyzed for tracer enrichment. Tracer data in tissues were analyzed using a compartmental model for calcium metabolism and the WinSAAM software. The model was used to: account for carryover of tracer between studies, fit data for all studies using the minimal number of changes between studies, and calculate calcium absorption, excretion, bone calcium deposition and bone calcium resorption. Results showed that fractional absorption decreased by 50% during flight and that bone resorption and urinary excretion increased by 50%. Results were supported by changes in biochemical markers of bone metabolism. Inflight bone loss of approximately 250 mg Ca/d resulted from decreased calcium absorption combined with increased bone resorption and excretion. Further studies will assess the time course of these changes during flight, and the effectiveness of countermeasures to mitigate flight-induced bone loss. The overall goal is to enable human travel beyond low-Earth orbit, and to allow for better understanding and treatment of bone diseases on Earth.

Wastney, Meryl E.↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
DOE OSTI2020

Integrating Predictions for Improving Defect Classification Accuracy in NDT-based Assessment of Concrete - 20229

There is an increasing need to create predictive models for defect classification in concrete using the output of non-destructive testing (NDT) techniques. Recent advancement of machine-learning algorithms has offered several techniques for developing classification models for different types of data sets. However, the performance of these algorithms is very uncertain, mainly when applied to small and noisy datasets. For example, when human access is limited (e.g., nuclear facility), robot-based NDT is preferred. But compared to manual tests with humans present on site, the data sets are small, and more noise can exist. Therefore, it is imperative to develop new approaches to ensure a consistently high classification accuracy for inadequate data sets. This study explores the classification performance on NDT dataset using classifiers from different machine-learning algorithms, namely k-Nearest Neighbor (kNN), Decision Tree, Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). The authors further integrated the predictions from these classifiers using proposed methods. The integration strategy combines the output of the classifiers based on two different measures, accuracy, and performance (ACC and PERF), using equations such as sum, average, and square-root-of-sums-of-squares (SRSS). Our results reveal varying classification accuracies across individual classifiers with different misclassifications across the test data set. The integration strategy provided significant improvement in the classification accuracy compared to the individual classifiers. Furthermore, the results indicate minimal variation across the integration methods as compared to the variation across the individual classifiers. To conclude, prediction integration offers a unique approach for combining the output of multiple classifiers to create redundancies with the potential of achieving high classification performance and improved reliability in predictive models for defect detection in concrete. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1979

Analysis of visual estimation of system state from arbitrary displays

A method is presented for implementing the state estimator of the manual control model when the system output is a visual display of arbitrary form; that is, the display may be pictorial, including real world, or made up of dials and pointers. The method is used to provide error criteria for a look-point controller that appears to be capable of modeling human scanning behavior. This model, if combined with a model of the control process, should be useful in predicting effects of changes in displays on performance of flight tasks.

Gainer, P. A.↗
NASA NTRS1989

Predictive performance models and multiple task performance

Five models that predict how performance of multiple tasks will interact in complex task scenarios are discussed. The models are shown in terms of the assumptions they make about human operator divided attention. The different assumptions about attention are then empirically validated in a multitask helicopter flight simulation. It is concluded from this simulation that the most important assumption relates to the coding of demand level of different component tasks.

Wickens, Christopher D.↗
NASA NTRS2008

Rescue Shuttle Flight Re-Entry: Controlling Astronaut Thermal Exposure

A rescue mission for the STS-125 Hubble Telescope Repair Mission requires reentry from space with 11 crew members aboard, exceeding past cabin thermal load experience and risking crew thermal stress potentially causing cognitive performance and physiological decrements. The space shuttle crew cabin air revitalization system (ARS) was designed to support a nominal crew complement of 4 to 7 crew and 10 persons in emergencies, all in a shirt-sleeve environment. Subsequent to the addition of full pressure suits with individual cooling units, the ARS cannot maintain a stable temperature in the crew cabin during reentry thermal loads. Bulk cabin thermal models, used for rescue mission planning and analysis of crew cabin air, were unable to accurately represent crew workstation values of air flow, carbon dioxide, and heat content for the middeck. Crew temperature models suggested significantly elevated core temperatures. Planning for an STS-400 potential rescue of seven stranded crew utilized computational fluid dynamics (CFD) models to demonstrate inhomogeneous cabin thermal properties and improve analysis compared to bulk models. In the absence of monitoring of crew temperature, heart rate, metabolic rate and incomplete engineering data on the performance of the integrated cooling garment/cooling unit (ICG/CU) at cabin temperatures above 75 degrees F, related systems & models were reevaluated and tests conducted with humans in the loop. Changes to the cabin ventilation, ICU placement, crew reentry suit-donning procedures, Orbiter Program wave-off policy and post-landing power down and crew extraction were adopted. A second CFD and core temperature model incorporated the proposed changes and confirmed satisfactory cabin temperature, improved air distribution, and estimated core temperatures within safe limits. CONCLUSIONS: These changes in equipment, in-flight and post-landing procedures, and policy were implemented for the STS-400 rescue shuttle & will be implemented in any future rescue flights from the International Space Station of stranded shuttle crews.

Gillis, David B.↗