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NASA NTRSDate not supplied

Preparing for Lunar Exploration: Geology and Field Training for Astronauts

NASA astronauts will soon return to the Moon, this time to the lunar south pole. Astronauts will perform traverses, make geologic observations, and collect samples to return to Earth. Lessons from Apollo show that science returns were optimized because crews were well-trained in both spacewalk operations and field geology. In that spirit, our team of geologists introduced a revised geologic training program for in-coming NASA astronauts that includes classroom activities and fieldwork and is split over 2 years. Year 1 focuses on an introduction to geologic concepts capped by a field exercise. Year two focuses on climate change, the Moon, other planets, and additional fieldwork. Our geology curriculum supports astronaut observations from the ISS and is the foundation for future Artemis geology training, which will include lunar science and increasingly complex field training in planetary-relevant locations. A final piece of our training program provides geology and field mapping experiences for NASA engineers, flight controllers, and managers, to help them understand the principles of fieldwork and relevance to lunar exploration. The Year 1 geology training must be an effective introduction for astronauts who have little or no background in geology. We focus the training around a narrowly defined field problem and use a week of classroom training to prepare the astronauts with the specific skills and background they need to carry out a field exercise on volcanic features, structures, and landforms of the Taos Plateau, New Mexico. Classroom modules are hands-on, using satellite imagery, maps, analog models, and samples, and provide the astronauts with specific and relevant experience in how to make observations and describe what they see, recognize, and interpret patterns, infer processes from products, and analyze relationships to build a story based on evidence from a variety of data sources. At the end of each classroom day, astronauts work in teams to build a preliminary geologic map of the field area, adding new observations and interpretations as they learn about topics in the classroom. Each team’s bucket list of target areas to visit helps shape their investigations during their week in the field. On the final day in the field, each astronaut team presents a geologic map, cross section, and geologic interpretation

Geology↗

DOE OSTI2021

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗

DOE OSTI2024

An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry

Structure determination workloads in neutron diffractometry are computationally expensive and routinely require several hours to many days to determine the structure of a material from its neutron diffraction patterns. The potential for machine learning models trained on simulated neutron scattering patterns to significantly speed up these tasks have been reported recently. However, the amount of simulated data needed to train these models grows exponentially with the number of structural parameters to be predicted and poses a significant computational challenge. To overcome this challenge, we introduce a novel batch-mode active learning (AL) policy that uses uncertainty sampling to simulate training data drawn from a probability distribution that prefers labelled examples about which the model is least certain. We confirm its efficacy in training the same models with ∼ 75% less training data while improving the accuracy. We then discuss the design of an efficient stream-based training workflow that uses this AL policy and present a performance study on two heterogeneous platforms to demonstrate that, compared with a conventional training workflow, the streaming workflow delivers ∼ 20% shorter training time without any loss of accuracy.

Wang, Tianle [Brookhaven National Laboratory (BNL)↗

DOE OSTI2020

Hindsight logging for model training

In modern Machine Learning, model training is an iterative, experimental process that can consume enormous computation resources and developer time. To aid in that process, experienced model developers log and visualize program variables during training runs. Exhaustive logging of all variables is infeasible, so developers are left to choose between slowing down training via extensive conservative logging, or letting training run fast via minimalist optimistic logging that may omit key information. As a compromise, optimistic logging can be accompanied by program checkpoints; this allows developers to add log statements post-hoc, and "replay" desired log statements from checkpoint---a process we refer to as hindsight logging. Unfortunately, hindsight logging raises tricky problems in data management and software engineering. Done poorly, hindsight logging can waste resources and generate technical debt embodied in multiple variants of training code. In this paper, we present methodologies for efficient and effective logging practices for model training, with a focus on techniques for hindsight logging. Our goal is for experienced model developers to learn and adopt these practices. To make this easier, we provide an open-source suite of tools for Fast Low-Overhead Recovery (flor) that embodies our design across three tasks: (i) efficient background logging in Python, (ii) adaptive periodic checkpointing, and (iii) an instrumentation library that codifies hindsight logging for efficient and automatic record-replay of model-training. Model developers can use each flor tool separately as they see fit, or they can use flor in hands-free mode, entrusting it to instrument their code end-to-end for efficient record-replay. Our solutions leverage techniques from physiological transaction logs and recovery in database systems. Evaluations on modern ML benchmarks demonstrate that flor can produce fast checkpointing with small user-specifiable overheads (e.g. 7%), and still provide hindsight log replay times orders of magnitude faster than restarting training from scratch.

Computer Science↗

DOE OSTI2021

Training Restricted Boltzmann Machines With a D-Wave Quantum Annealer

Restricted Boltzmann Machine (RBM) is an energy-based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate the exact gradient of the log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), where obtaining samples is faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results of RBM trained using quantum annealing are compared with the CD-based method. The performance of the two approaches is compared with respect to the classification accuracies, image reconstruction, and log-likelihood results. The classification accuracy results indicate comparable performances of the two methods. Image reconstruction and log-likelihood results show improved performance of the CD-based method. It is shown that the samples obtained from quantum annealer can be used to train an RBM on a 64-bit “bars and stripes” dataset with classification performance similar to an RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer could be useful as it eliminates computationally expensive MCMC steps of CD.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Reliable Determination of Pulses and Pulse-Shape Instability in Ultrashort Laser Pulse Trains Using Polarization-Gating and Transient-Grating Frequency-Resolved Optical Gating Using the RANA Approach

Devices that measure the presence of instability in the pulse shapes in trains of ultrashort laser pulses do not exist, so this task necessarily falls to pulse-measurement devices, like Frequency-Resolved Optical Gating (FROG) and its variations, which have proven to be a highly reliable class of techniques for measuring stable trains of ultrashort laser pulses. Fortunately, multi-shot versions of FROG have also been shown to sensitively distinguish trains of stable from those of unstable pulse shapes by displaying readily visible systematic discrepancies between the measured and retrieved traces in the presence of unstable pulse trains. However, the effects of pulse-shape instability and algorithm stagnation can be indistinguishable, so a never-stagnating algorithm—even when instability is present—is required and is generally important. In previous work, we demonstrated that our recently introduced Retrieved-Amplitude N-grid Algorithmic (RANA) approach produces highly reliable (100%) pulse-retrieval in the second-harmonic-generation (SHG) version of FROG for thousands of sample trains of pulses with stable pulse shapes. Further, it does so even for trains of unstable pulse shapes and thus both reliably distinguishes between the two cases and provides a rough measure of the degree of instability as well as a reasonable estimate of most typical pulse parameters. Here, we perform the analogous study for the polarization-gating (PG) and transient-grating (TG) versions of FROG, which are often used for higher-energy pulse trains. We conclude that PG and TG FROG, coupled with the RANA approach, also provide reliable indicators of pulse-shape instability. In addition, for PG and TG FROG, the RANA approach provides an even better estimate of a typical pulse in an unstable pulse train than SHG FROG does, even in cases of significant pulse-shape instability.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints

Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model-training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low-z or poorly calibrated U-band data, adjusting the amount and fidelity of SN Ia spectra, and using a model-independent framework to simulate the training data. We find that the SALT3 model surfaces are improved by having additional spectra and U-band data, and can be shifted by ~5% if host-galaxy contamination is not sufficiently removed from SN spectra. We find that resulting measurements of w are consistent to within 2.5% for all of the training variants explored in this work, with the largest shifts coming from variants that add color-dependent calibration offsets or host-galaxy contamination to the training spectra and those that remove pre-2000s low-z data. These results demonstrate that the SALT3 model-training procedure is largely robust to reasonable variations in the training data, but that additional attention must be paid to the treatment of spectroscopic data in the training process. We also find that the training procedure is sensitive to the color distributions of the input data—the resulting w measurement can be biased by ~2% if the color distribution is not sufficiently wide. Future low-z data, particularly u-band observations and high signal-to-noise ratio SN Ia spectra, will help to significantly improve SN Ia modeling in the coming years.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1975

Frequency and duration of interval training programs and changes in aerobic power

The present study was designed to ascertain whether a training frequency of 2 days/wk for a 7- and 13-wk interval training program would produce improvement in maximal aerobic power comparable to that obtained from 7- and 13-wk programs of the same intensity consisting of 4 training days/wk. After training, there was a significant increase in maximal aerobic power that was independent of both training frequency and duration. Maximal heart rate was significantly decreased following training. Submaximal aerobic power did not change with training, but submaximal heart rate decreased significantly with greater decreases the more frequent and the longer the training.

Fox, E. L.↗

NASA NTRS1984

Determining Training Device Requirements in Army Aviation Systems

A decision making methodology which applies the systems approach to the training problem is discussed. Training is viewed as a total system instead of a collection of individual devices and unrelated techniques. The core of the methodology is the use of optimization techniques such as the transportation algorithm and multiobjective goal programming with training task and training device specific data. The role of computers, especially automated data bases and computer simulation models, in the development of training programs is also discussed. The approach can provide significant training enhancement and cost savings over the more traditional, intuitive form of training development and device requirements process. While given from an aviation perspective, the methodology is equally applicable to other training development efforts.

Poumade, M. L.↗

NASA NTRS1984

Preparing for the unexpected - A psychologist's case for improved training

In the procedures designed to minimize human errors that lead to aircraft incidents, the improved human factor engineering and automation approaches must be supplemented by new training methods. Changes are suggested in the preprogrammed training principles which are currently based almost exclusively on the procedures-oriented environment, with insufficient training for cognitive processing and awareness. Use of the Line-Oriented Flight Training procedure, in which a training simulator is supplemented by a highly structured script or scenario to simulate the total line operational environment for the purpose of simultaneously training the entire flight crew, offers one way of providing pilots and other crewmembers with the experience of dealing with unexpected or stressful events. Of primary importance is maximal coordination between the aircraft captain and other crewmembers during the flight, which puts emphasis on the importance of teamwork and personal relations among all other crewmembers. The current FARs governing training and proficiency will have to be modified to accommodate new training appoaches.

Foushee, H. C.↗

NASA NTRS1989

Effect of swim exercise training on human muscle fiber function

The effect of swim exercise training on the human muscle fiber function was investigated in swimmers trained in a typical collegiate swim-training program followed by an intensified 10-day training period. The measured parameters included the peak tension (P0), negative log molar Ca(2+) concentration (pCa)-force, and maximal shortening speed (Vmax) of the slow-twitch type I and fast-twitch type II fibers obtained by biopsy from the deltoid muscle. The P0 values were found to be not altered after either the training or the 10-day intensive program. The type I fibers from the trained swimmers showed pCa-force curves shifted to the right, such that higher free Ca(2+) levels were required to elicit a given percent of P0. The training program significantly increased the Vmax in the type I fibers and decreased that of the type II fibers, and the 10-day intensive training produced a further significant decrease of the type II fibers.

Fitts, R. H.↗

NASA NTRS1991

Intelligent Computer-Aided Training (ICAT)

The Software Technology Branch has developed and demonstrated a number of ICAT System for a variety of complex procedural tasks in the NASA operational environment. A general ICAT architecture was developed and shown to be adaptable across this spectrum of tasks. Currently underway is the assembly of a suite of software tools that will permit the training community to rapidly develop and deploy ICAT systems for a variety of Space Station training tasks. The use of ICAT technology for selected training applications within the Space Station Freedom program can significantly reduce the costs of training system development. Once developed ICAT systems can be more readily and efficiently evolved and maintained than many conventional training systems. ICAT systems can be delivered for both ground based and on-orbit training. The availability of sophisticated on-orbit training will serve to reduce EVA time and can be especially useful in preparing crew for the performance on infrequent, mission critical tasks. ICAT systems can deliver uniform but individualized training to large numbers of personnel in a workstation environment.

Savely, Robert T.↗

NASA NTRS1992

Carotid-cardiac baroreflex response and LBNP tolerance following resistance training

The purpose of this study was to examine the effect of lower body resistance training on cardiovascular control mechanisms and blood pressure maintenance during an orthostatic challenge. Lower body negative pressure (LBNP) tolerance, carotid-cardiac baroreflex function (using neck chamber pressure), and calf compliance were measured in eight healthy males before and after 19 wk of knee extension and leg press training. Resistance training sessions consisted of four or five sets of 6-12 repetitions of each exercise, performed two times per week. Training increased strength 25 +/- 3 (SE) percent (P = 0.0003) and 31 +/- 6 percent (P = 0.0004), respectively, for the leg press and knee extension exercises. Average fiber size in biopsy samples of m. vastus lateralis increased 21 +/- 5 percent (P = 0.0014). Resistance training had no significant effect on LBNP tolerance. However, calf compliance decreased in five of the seven subjects measured, with the group average changing from 4.4 +/- 0.6 ml.mm Hg-1 to 3.9 +/- 0.3 ml.mm Hg-1 (P = 0.3826). The stimulus-response relationship of the carotid-cardiac baroreflex response shifted to the left on the carotid pressure axis as indicated by a reduction of 6 mm Hg in baseline systolic blood pressure (P = 0.0471). In addition, maximum slope increased from 5.4 +/- 1.3 ms.mm Hg-1 before training to 6.6 +/- 1.6 ms.mm Hg-1 after training (P = 0.0141). Our results suggest the possibility that high resistance, lower extremity exercise training can cause a chronic increase in sensitivity and resetting of the carotid-cardiac baroreflex.

Tatro, D. L.↗

NASA NTRS1999

STS-93 Crew Training

Live footage of the STS-93 crewmembers shows Commander Eileen M. Collins, Pilot Jeffrey S. Ashby, Mission Specialists Steven A. Hawley, Catherine G. Coleman, and Michel Tognini going through various training activities. These activities include Bail Out Training NBL, Emergency Egress Training, Earth Observations Classroom Training, Simulator Training, T-38 Departure from Ellington Field, Chandra Deploy Training, SAREX Shuttle Amateur Radio Experiment, CCT Bail Out Crew Compartment Training, and Southwest Research Ultraviolet Imaging System (SWUIS) Training.

Source record↗

NASA NTRS2000

STS-92 Crew Training

Footage shows the crew of STS-92, Commander Brian Duffy, Pilot Pamela A. Melroy, and Mission Specialists Koichi Wakata, Leroy Chiao, Peter J.K. Wisoff, Michael E. Lopez-Alegria, and William S. McArthur during various parts of their training. Clips are seen of the Shuttle bailout training, Shuttle arm and extravehicular activity (EVA) training at the Virtual Reality Lab, EVA training at the Neutral Buoyancy Lab, Shuttle operations training, EVA prep and post training in the Full Fuselage Trainer, ascent and post insertion training in the Guidance Navigation Simulator, and Mission Specialist Wakata in the Shuttle Engineering Dome and training on the Manipulator Development Facility.

Source record↗

NASA NTRS2000

The Dynamical Evolution of A Tubular Leonid Persistent Train

The dynamical evolution of the persistent train of a bright Leonid meteor was examined for evidence of the source of the luminosity and the physical conditions in the meteor path. The train consisted of two parallel somewhat diffuse luminous tracks, interpreted as the walls of a tube. A general lack of wind shear along the trail allowed these structures to remain intact for nearly 200 s, from which it was possible to determine that the tubular structure expanded at a near constant 10.5 m/s, independent of altitude between 86 and 97 km. An initial fast decrease of train intensity below 90 km was followed by an increase in intensity and then a gradual decrease at longer times, whereas at high attitudes the integrated intensity was nearly constant with time. These results are compared to a model that describes the dynamical evolution of the train by diffusion, following an initial rapid expansion of the hot gaseous trail behind the meteoroid. The train luminosity is produced by O ((sup 1)S) emission at 557 nm, driven by elevated atomic O levels produced by the meteor impact, as well as chemiluminescent reactions of the ablated metals Na and Fe with O3. Ozone is rapidly removed within the train, both by thermal decomposition and catalytic destruction by the metallic species. Hence, the brightest emission occurs at the edge of the train between outwardly diffusing metallic species and inwardly diffusing O3. Although the model is able to account plausibly for a number of characteristic features of the train evolution, significant discrepancies remain that cannot easily be resolved.

Jenniskens, Peter↗

NASA NTRS2001

STS-105 Crew Training Clip

The STS-105 crewmembers, Commander Scott Horowitz, Pilot Frederick Sturckow, and Mission Specialists Daniel Barry and Patrick Forrester, are seeing during various stages of their training. Footage includes Post-Insertion Training, Virtual Reality Laboratory Remote Manipulator System Training, Emergency Egress Training in the CCT, Neutral Buoyancy Laboratory Training, Post Egress Landing Training at FFT, Long Rendezvous Training in the GNS (Navigation Simulator), and Post Insertion Operations Training at FFT.

Source record↗

NASA NTRS2001

Effects of Above Real Time Training (ARTT) On Individual Skills and Contributions to Crew/Team Performance

Above Real Time Training (ARTT) is the training acquired on a real time simulator when it is modified to present events at a faster pace than normal. The experiments on training of pilots performed by NASA engineers and others have indicated that real time training (RTT) reinforced with ARTT would offer an effective training strategy for such tasks which require significant effort at time and workload management. A study was conducted to find how ARTT and RTT complement each other for training of novice pilot-navigator teams to fly on a required route. In the experiment, each of the participating pilot-navigator teams was required to conduct simulator flights on a prescribed two-legged ground track while maintaining required air speed and altitude. At any instant in a flight, the distance between the actual spatial point location of the airplane and the required spatial point was used as a measure of deviation from the required route. A smaller deviation represented better performance. Over a segment of flight or over complete flight, an average value of the deviation represented consolidated performance. The deviations were computed from the information on latitude, longitude, and altitude. In the combined ARTT and RTT program, ARTT at intermediate training intervals was beneficial in improving the real time performance of the trainees. It was observed that the team interaction between pilot and navigator resulted in maintaining high motivation and active participation throughout the training program.

Ali, Syed Firasat↗