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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Effects on Training Using Illumination in Virtual Environments

Camera based tasks are commonly performed during orbital operations, and orbital lighting conditions, such as high contrast shadowing and glare, are a factor in performance. Computer based training using virtual environments is a common tool used to make and keep CTW members proficient. If computer based training included some of these harsh lighting conditions, would the crew increase their proficiency? The project goal was to determine whether computer based training increases proficiency if one trains for a camera based task using computer generated virtual environments with enhanced lighting conditions such as shadows and glare rather than color shaded computer images normally used in simulators. Previous experiments were conducted using a two degree of freedom docking system. Test subjects had to align a boresight camera using a hand controller with one axis of rotation and one axis of rotation. Two sets of subjects were trained on two computer simulations using computer generated virtual environments, one with lighting, and one without. Results revealed that when subjects were constrained by time and accuracy, those who trained with simulated lighting conditions performed significantly better than those who did not. To reinforce these results for speed and accuracy, the task complexity was increased.

Maida, James C.↗
NASA NTRS2001

Effect of Exercise Training and +Gz Acceleration Training on Men

Countermeasures for reduction in work capacity (maximal oxygen uptake and strength) during spaceflight and enhanced orthostatic intolerance during re-entry, landing and egress from the return vehicle are continuing problems. The purpose for this study was to test the hypothesis that passive-acceleration training; supine, interval, exercise plus acceleration training and exercise combined with acceleration training would improve orthostatic tolerance in ambulatory men; and that addition of the aerobic exercise conditioning would not alter this improved tolerance from that of passive-acceleration training. Seven men (24-38 yr) underwent "Passive" training on the Ames human-powered centrifuge (HPC) for 30 min, "Exercise" training on the cycle ergometer with constant +Gz acceleration; and "Combined" exercise training at 40% to 90% of the HPC +Gz(max) exercise level. Maximal supine exercise loads increased significant (P<0.05) by 8.3% (Passive), 12.6% (Exercise), and by 15.4% (Combined) after training, but their post-training maximal oxygen uptakes and maximal heart rates were unchanged. Maximal time to fatigue (endurance) was unchanged with Passive was increased (P<0.05) with Exercise and Combined training. Thus, the exercise in the Exercise and Combined training Phases resulted in greater maximal loads and endurance without effect on maximal oxygen uptake or heart rate. There was a 4% to 6% increase (P<0.05) in all four quadriceps muscle volumes (right and left) after post-Combined training. Resting pre-tilt heart rate was elevated by 12.9% (P<0.05) only after Passive training suggesting that the exercise training attenuated the HR response. Plasma volume (% Delta) was uniformly decreased by 8% to 14% (P<0.05) at tilt-tolerance pre- vs. post-training indicating essentially no effect of training on the level of hypovolemia. Post-training tilt-tolerance time and heart rate were increased (P<0.05) only with Passive training by 37.8% and by 29.1%, respectively. Thus, addition of exercise training appeared to attenuate the increased Passive tilt-tolerance.

Greenleaf, John E.↗
NASA NTRS2001

The Aqua-Aura Train

This talk will focus on the afternoon constellation of EOS platforms and the scientific benefits that arise from this formation. The afternoon EOS constellation or the "A-train" will provide unprecedented information on clouds and aerosols. At 1:30 PM crossing time EOS-Aqua begins the train with the MODIS, CERES and AIRS instruments making aerosol, cloud, radiation budget , temperature and water vapor measurements. AMSR-E will also make total column water measurements. Following Aqua by one minute, Cloudsat will make active radar precipitation measurements as and PICASSOCENA will make lidar measurements of clouds and aerosols. Fourteen minutes later, EOS-Aura will pass through the same space making upper troposphere water vapor and ice profiles as well as some key trace gases associated with convective processes (MLS and HIRDLS). Additional measurements of aerosols will be made by Aura's OMI instrument.

Schoeberl, Mark↗
NASA NTRS1979

Effects of tunnel and station size on the costs and service of subway transit systems

The feasibility of less spacious, less costly underground rail mass transit system designs is studied. The major cost saving expected from alternative tunnel designs results from using precast concrete segment liners in place of steel. The saying expected for a two-foot decrease in the diameter of twin, single track tunnels is about two million dollars per route mile from 13 million dollars for precast concrete segment liners (a saving of about 16%). The cost per route-mile of a double track tunnel appears to be 15 to 25% higher than for the twin, single track tunnels. The effective cost saving expected from stations with four-car train capability instead of the usual eight-car trains is nearly 25% or seven million dollars per route mile. The saving in station costs can be obtained while improving service to the user (lower transit time and less waiting for trains) up to a capacity of 36,000 riders per hour in each direction.

Dayman, B., Jr.↗
NASA NTRS1992

Space life sciences perspectives for Space Station Freedom

It is now generally acknowledged that the life science discipline will be the primary beneficiary of Space Station Freedom. The unique facility will permit advances in understanding the consequences of long duration exposure to weightlessness and evaluation of the effectiveness of countermeasures. It will also provide an unprecedented opportunity for basic gravitational biology, on plants and animals as well as human subjects. The major advantages of SSF are the long duration exposure and the availability of sufficient crew to serve as subjects and operators. In order to fully benefit from the SSF, life sciences will need both sufficient crew time and communication abilities. Unlike many physical science experiments, the life science investigations are largely exploratory, and frequently bring unexpected results and opportunities for study of newly discovered phenomena. They are typically crew-time intensive, and require a high degree of specialized training to be able to react in real time to various unexpected problems or potentially exciting findings. Because of the long duration tours and the large number of experiments, it will be more difficult than with Spacelab to maintain astronaut proficiency on all experiments. This places more of a burden on adequate communication and data links to the ground, and suggests the use of AI expert system technology to assist in astronaut management of the experiment. Typical life science experiments, including those flown on Spacelab Life Sciences 1, will be described from the point of view of the demands on the astronaut. A new expert system, 'PI in a Box,' will be introduced for SLS-2, and its applicability to other SSF experiments discussed. (This paper consists on an abstract and ten viewgraphs.)

Young, Laurence R.↗
NASA NTRS1992

A teleoperation training simulator with visual and kinesthetic force virtual reality

A force-reflecting teleoperation training simulator with a high-fidelity real-time graphics display has been developed for operator training. A novel feature of this simulator is that it enables the operator to feel contact forces and torques through a force-reflecting controller during the execution of the simulated peg-in-hole task, providing the operator with the feel of visual and kinesthetic force virtual reality. A peg-in-hole task is used in our simulated teleoperation trainer as a generic teleoperation task. A quasi-static analysis of a two-dimensional peg-in-hole task model has been extended to a three-dimensional model analysis to compute contact forces and torques for a virtual realization of kinesthetic force feedback. The simulator allows the user to specify force reflection gains and stiffness (compliance) values of the manipulator hand for both the three translational and the three rotational axes in Cartesian space. Three viewing modes are provided for graphics display: single view, two split views, and stereoscopic view.

Kim, Won S.↗
NASA NTRS2004

Using an Extended Kalman Filter Learning Algorithm for Feed-Forward Neural Networks to Describe Tracer Correlations

In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

Lary, David J.↗
NASA NTRS1981

Eastern Airlines LOFT program

Beginning with scenario design and development issues, Eastern Airlines committed itself to the full four-hour LOFT training format without the additional time for specific maneuvers. Abnormals and emergency conditions, pacing, and quiet periods are included in the scenarios which are written for the instructor to follow verbatim. Simulator capabilities, performance assessment; training vs. checking; crew composition and scheduling; satisfactory completion; the use of video performance printouts; the number of instructors; instructor training and standardization; and initial, transition, and upgrade training are discussed.

Beach, B. E.↗
NASA NTRS2003

Cascade Back-Propagation Learning in Neural Networks

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Duong, Tuan A.↗
NASA NTRS2007

Development of Training Programs to Optimize Planetary Ambulation

Astronauts experience disturbances in functional mobility following their return to Earth due to adaptive responses that occur during exposure to the microgravity conditions of space flight. Despite significant time spent performing in-flight exercise routines, these training programs have not been able to mitigate postflight alterations in postural and locomotor function. Therefore, the goal of our two inter-related projects (NSBRI-ground based and ISS flight study, "Mobility") is to develop and test gait training programs that will serve to optimize functional mobility during the adaptation period immediately following space flight, thereby improving the safety and efficiency of planetary ambulation. The gait training program entails manipulating the sensory conditions of treadmill exercise to systematically challenge the balance and gait control system. This enhances the overall adaptability of locomotor function enabling rapid reorganization of gait control to respond to ambulation in different gravitational environments. To develop the training program, we are conducting a series of ground-based studies evaluating the training efficacy associated with variation in visual flow, body loading, and support surface stability during treadmill walking. We will also determine the optimal method to present training stimuli within and across training sessions to maximize both the efficacy and efficiency of the training procedure. Results indicate that variations in both visual flow and body unloading during treadmill walking leads to modification in locomotor control and can be used as effective training modalities. Additionally, the composition and timing of sensory challenges experienced during each training session has significant impact on the ability to rapidly reorganize locomotor function when exposed to a novel sensory environment. We have developed the capability of producing support surface variation during gait training by mounting a treadmill on a six-degree-of-freedom motion device. This hardware development will allow us to evaluate the efficacy of this type of training in conjunction with variation in visual flow and body unloading.

Bloomberg, J. J.↗
NASA NTRS2005

Simulating Descent and Landing of a Spacecraft

The Dynamics Simulator for Entry, Descent, and Surface landing (DSENDS) software performs high-fidelity simulation of the Entry, Descent, and Landing (EDL) of a spacecraft into the atmosphere and onto the surface of a planet or a smaller body. DSENDS is an extension of the DShell and DARTS programs, which afford capabilities for mathematical modeling of the dynamics of a spacecraft as a whole and of its instruments, actuators, and other subsystems. DSENDS enables the modeling (including real-time simulation) of flight-train elements and all spacecraft responses during various phases of EDL. DSENDS provides high-fidelity models of the aerodynamics of entry bodies and parachutes plus supporting models of atmospheres. Terrain and real-time responses of terrain-imaging radar and lidar instruments can also be modeled. The program includes modules for simulation of guidance, navigation, hypersonic steering, and powered descent. Automated state-machine-driven model switching is used to represent spacecraft separations and reconfigurations. Models for computing landing contact and impact forces are expected to be added. DSENDS can be used as a stand-alone program or incorporated into a larger program that simulates operations in real time.

Balaram, J.↗
NASA NTRS2007

Exploring and Visualizing A-Train Instrument Data

The succession of US and international satellites that follow each other in close succession, known as the A-Train, affords an opportunity to atmospheric researchers that no single platform could provide: Increasing the number of observations at any given geographic location.. . a more complete "virtual science platform". However, vertically and horizontally, co-registering and regridding datasets from independently developed missions, Aqua, Calipso, Cloudsat, Parasol, and Aura, so that they can be inter-compared can be daunting to some, and may be repeated by many. Scientists will individually spend much of their time and resources acquiring A-Train datasets of interest residing at various locations, developing algorithms to match up and graph datasets along the A-Train track, and search through large amounts of data for areas and/or phenomena of interest. The aggregate amount of effort that can be expended on repeating pre-science tasks could climb into the tens of millions of dollars. The goal of the A-Train Data Depot (ATDD) is to enable free movement of remotely located A-Train data so that they are combined to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. The innovative approach of analyzing and visualizing atmospheric profiles along the platforms track (i.e., time) is accomplished by through the ATDDs Giovanni data analysis and visualization tool. Giovanni brings together data from Aqua (MODIS, AIRS, AMSR-E), Cloudsat (cloud profiling radar) and Calipso (CALIOP, IIR), as well as the Aura (OMI, MLS, HIRDLS, TES) to create a consolidated vertical view of the Earth's Atmosphere along the A-Train tracks. This easy to learn and use exploration tool will allow users to create vertical profiles of any desired A-Train dataset, for any given time of choice. This presentation shows the power of Giovanni by describing and illustrating how this tool facilitates and aids A-Train science and research. A web based display system Giovanni provides users with the capability of creating co-located profile images of temperature and humidity data from the MODIS, MLS and AIRS instruments for a user specified time and spatial area. In addition, Cloud and Aerosol profiles may also be displayed for the Cloudsat and Caliop instruments. The ability to modify horizontal and vertical axis range, data range and dynamic color range is also provided. Two dimensional strip plots of MODIS, AIRS, OM1 and POLDER parameters, co-located along the Cloudsat reference track, can also be plotted along with the Cloudsat cloud profiling data. Center swath pixels for the same parameters can also be shown as line plots overlaying the Cloudsat or Calipso profile images. Images and subsetted data produced in each analysis run may be downloaded. Users truly can explore and discover data specific to their needs prior to ever transferring data to their analysis tools.

Kempler, S.↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRS1992

Psychological training of NASA astronauts for extended missions

The success of operational teams working in remote and hostile environments rests in large part on adequate preparation of those teams prior to emplacement in field settings. Psychological training, directed at the maintenance of crew health and performance becomes increasingly important as space missions grow in duration and complexity. Methods: Topics to be discussed include: the conceptual framework of psychological training; needs analysis; content and delivery options; methods of assessing training efficacy; use of testbeds and analogies and the relationship of training to crew selection and real-time support activities. Results and Conclusions: This paper will discuss the psychological training approach being developed at the NASA/JSC Behavior and Performance Laboratory. This approach will be compared and contrasted with those underway in the U.S. Department of Defense and in other space agencies.

Holland, A. W.↗
NASA NTRS2003

Computer Program Recognizes Patterns in Time-Series Data

A computer program recognizes selected patterns in time-series data like digitized samples of seismic or electrophysiological signals. The program implements an artificial neural network (ANN) and a set of N clocks for the purpose of determining whether N or more instances of a certain waveform, W, occur within a given time interval, T. The ANN must be trained to recognize W in the incoming stream of data. The first time the ANN recognizes W, it sets clock 1 to count down from T to zero; the second time it recognizes W, it sets clock 2 to count down from T to zero, and so forth through the Nth instance. On the N + 1st instance, the cycle is repeated, starting with clock 1. If any clock has not reached zero when it is reset, then N instances of W have been detected within time T, and the program so indicates. The program can readily be encoded in a field-programmable gate array or an application-specific integrated circuit that could be used, for example, to detect electroencephalographic or electrocardiographic waveforms indicative of epileptic seizures or heart attacks, respectively.

Hand, Charles↗
NASA NTRS1991

Intelligent computer-aided training and tutoring

Specific autonomous training systems based on artificial intelligence technology for use by NASA astronauts, flight controllers, and ground-based support personnel that demonstrate an alternative to current training systems are described. In addition to these specific systems, the evolution of a general architecture for autonomous intelligent training systems that integrates many of the features of traditional training programs with artificial intelligence techniques is presented. These Intelligent Computer-Aided Training (ICAT) systems would provide, for the trainee, much of the same experience that could be gained from the best on-the-job training. By integrating domain expertise with a knowledge of appropriate training methods, an ICAT session should duplicate, as closely as possible, the trainee undergoing on-the-job training in the task environment, benefitting from the full attention of a task expert who is also an expert trainer. Thus, the philosophy of the ICAT system is to emulate the behavior of an experienced individual devoting his full time and attention to the training of a novice - proposing challenging training scenarios, monitoring and evaluating the actions of the trainee, providing meaningful comments in response to trainee errors, responding to trainee requests for information, giving hints (if appropriate), and remembering the strengths and weaknesses displayed by the trainee so that appropriate future exercises can be designed.

Loftin, R. Bowen↗
NASA NTRS2012

Command Center Training Tool (C2T2)

This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.

Jones, Phillip↗
NASA NTRS2010

Gait Adaptability Training Improves Both Postural Stability and Dual-Tasking Ability

After spaceflight, the process of readapting to Earth's gravity commonly presents crewmembers with a variety of locomotor challenges. Our recent work has shown that the ability to adapt to a novel discordant sensorimotor environment can be increased through preflight training, so one focus of our laboratory has been the development of a gait training countermeasure to expedite the return of normal locomotor function after spaceflight. We used a training system comprising a treadmill mounted on a motion base facing a virtual visual scene that provided a variety of sensory challenges. As part of their participation in a larger retention study, 10 healthy adults completed 3 training sessions during which they walked on a treadmill at 1.1 m/s while receiving discordant support-surface and visual manipulations. After a single training session, subjects stride frequencies improved, and after 2 training sessions their auditory reaction times improved, where improvement was indicated by a return toward baseline values. Interestingly, improvements in reaction time came after stride frequency improvements plateaued. This finding suggests that postural stability was given a higher priority than a competing cognitive task. Further, it demonstrates that improvement in both postural stability and dual-tasking can be achieved with multiple training exposures. We conclude that, with training, individuals become more proficient at walking in discordant sensorimotor conditions and are able to devote more attention to competing tasks.

Brady, Rachel A.↗