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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 343 records · Page 19

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NASA NTRS1988

Supervised learning of probability distributions by neural networks

Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

Baum, Eric B.↗

NASA NTRS2004

Computer-Aided Systems Engineering for Flight Research Projects Using a Workgroup Database

An online systems engineering tool for flight research projects has been developed through the use of a workgroup database. Capabilities are implemented for typical flight research systems engineering needs in document library, configuration control, hazard analysis, hardware database, requirements management, action item tracking, project team information, and technical performance metrics. Repetitive tasks are automated to reduce workload and errors. Current data and documents are instantly available online and can be worked on collaboratively. Existing forms and conventional processes are used, rather than inventing or changing processes to fit the tool. An integrated tool set offers advantages by automatically cross-referencing data, minimizing redundant data entry, and reducing the number of programs that must be learned. With a simplified approach, significant improvements are attained over existing capabilities for minimal cost. By using a workgroup-level database platform, personnel most directly involved in the project can develop, modify, and maintain the system, thereby saving time and money. As a pilot project, the system has been used to support an in-house flight experiment. Options are proposed for developing and deploying this type of tool on a more extensive basis.

Mizukami, Masahi↗

NASA NTRS2011

MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

Albayrak, Arif↗

NASA NTRS1991

Space Communications Artificial Intelligence for Link Evaluation Terminal (SCAILET)

A software application to assis end-users of the Link Evaluation Terminal (LET) for satellite communication is being developed. This software application incorporates artificial intelligence (AI) techniques and will be deployed as an interface to LET. The high burst rate (HBR) LET provides 30 GHz transmitting/20 GHz receiving, 220/110 Mbps capability for wideband communications technology experiments with the Advanced Communications Technology Satellite (ACTS). The HBR LET and ACTS are being developed at the NASA Lewis Research Center. The HBR LET can monitor and evaluate the integrity of the HBR communications uplink and downlink to the ACTS satellite. The uplink HBR transmission is performed by bursting the bit-pattern as a modulated signal to the satellite. By comparing the transmitted bit pattern with the received bit pattern, HBR LET can determine the bit error rate BER) under various atmospheric conditions. An algorithm for power augmentation is applied to enhance the system's BER performance at reduced signal strength caused by adverse conditions. Programming scripts, defined by the design engineer, set up the HBR LET terminal by programming subsystem devices through IEEE488 interfaces. However, the scripts are difficult to use, require a steep learning curve, are cryptic, and are hard to maintain. The combination of the learning curve and the complexities involved with editing the script files may discourage end-users from utilizing the full capabilities of the HBR LET system. An intelligent assistant component of SCAILET that addresses critical end-user needs in the programming of the HBR LET system as anticipated by its developers is described. A close look is taken at the various steps involved in writing ECM software for a C&P, computer and at how the intelligent assistant improves the HBR LET system and enhances the end-user's ability to perform the experiments.

Shahidi, Anoosh↗

NASA NTRS1989

Human error and the search for blame

Human error is a frequent topic in discussions about risks in using computer systems. A rational analysis of human error leads through the consideration of mistakes to standards that designers use to avoid mistakes that lead to known breakdowns. The irrational side, however, is more interesting. It conditions people to think that breakdowns are inherently wrong and that there is ultimately someone who is responsible. This leads to a search for someone to blame which diverts attention from: learning from the mistakes; seeing the limitations of current engineering methodology; and improving the discourse of design.

Denning, Peter J.↗

NASA NTRS2001

Analysis of the Westland Data Set

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗

NASA NTRS2000

Condition Monitoring for Helicopter Data

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗

NASA NTRS2018

Connecting Satellite-Based Precipitation Estimates to Users

Beginning in 1997, the Merged Precipitation Group at NASA Goddard has distributed gridded global precipitation products built by combining satellite and surface gauge data. This started with the Global Precipitation Climatology Project (GPCP), then the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), and recently the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission (IMERG). This 20+-year (and on-going) activity has yielded an important set of insights and lessons learned for making state-of-the-art precipitation data accessible to the diverse communities of users. Merged-data products critically depend on the input sensors and the retrieval algorithms providing accurate, reliable estimates, but it is also important to provide ancillary information that helps users determine suitability for their application. We typically provide fields of estimated random error, and recently reintroduced the quality index concept at user request. Also at user request we have added a (diagnostic) field of estimated precipitation phase. Over time, increasingly more ancillary fields have been introduced for intermediate products that give expert users insight into the detailed performance of the combination algorithm, such as individual merged microwave and microwave-calibrated infrared estimates, the contributing microwave sensor types, and the relative influence of the infrared estimate.

Huffman, George J.↗

NASA NTRSDate not supplied

Compression of Solar Spectroscopic Observations: a Case Study of MgII k Spectral Line Profiles Observed by NASA’s IRIS Satellite

In this study we extract the deep features and investigate the compression of the MgII k spectral line profiles observed in quiet Sun regions by NASA’s IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual MgII k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4DN average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising.

SMD↗

NASA NTRS1991

Video data compression using artificial neural network differential vector quantization

An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

Krishnamurthy, Ashok K.↗

NASA NTRS2014

Biomechanics of the Treadmill Locomotion on the International Space Station

Exercise prescriptions completed by International Space Station (ISS) crewmembers are typically based upon evidence obtained during ground-based investigations, with the assumption that the results of long-term training in weightlessness will be similar to that attained in normal gravity. Coupled with this supposition are the assumptions that exercise motions and external loading are also similar between gravitational environments. Normal control of locomotion is dependent upon learning patterns of muscular activation and requires continual monitoring of internal and external sensory input [1]. Internal sensory input includes signals that may be dependent on or independent of gravity. Bernstein hypothesized that movement strategy planning and execution must include the consideration of segmental weights and inertia [2]. Studies of arm movements in microgravity showed that individuals tend to make errors but that compensation strategies result in adaptations, suggesting that control mechanisms must include peripheral information [3-5]. To date, however, there have been no studies examining a gross motor activity such as running in weightlessness other than using microgravity analogs [6-8]. The objective of this evaluation was to collect biomechanical data from crewmembers during treadmill exercise before and during flight. The goal was to determine locomotive biomechanics similarities and differences between normal and weightless environments. The data will be used to optimize future exercise prescriptions. This project addresses the Critical Path Roadmap risks 1 (Accelerated Bone Loss and Fracture Risk) and 11 (Reduced Muscle Mass, Strength, and Endurance). Data were collected from 7 crewmembers before flight and during their ISS missions. Before launch, crewmembers performed a single data collection session at the NASA Johnson Space Center. Three-dimensional motion capture data were collected for 30 s at speeds ranging from 1.5 to 9.5 mph in 0.5 mph increments with a 12-camera system. During flight, each crewmember completed up to 6 data collection sessions spread across their missions, performing their normal exercise prescription for the test day, resulting in varying data collection protocols between sessions. Motion data were collected by a single HD video camera positioned to view the crewmembers' left side, and tape markers were placed on their feet, legs, and neck on specific landmarks. Before data collection, the crewmembers calibrated the video camera. Video data were collected during the entire exercise session at 30 Hz. Kinematic data were used to determine left leg hip, knee, and ankle range of motion and contact time, flight time, and stride time for each stride. 129 trials in weightlessness were analyzed. Mean time-normalized strides were found for each trial, and cross-correlation procedures were used to examine the strength and direction of relationships between segment movement pattern timing in each gravitational condition. Cross-correlation analyses between gravitational conditions revealed highly consistent movement patterns at each joint. Peak correlation coefficients occurred at 0% phase, indicating there were no lags in movement timing. Joint ranges of motion were similar between gravitational conditions, with some slight differences between subjects. Motion patterns in weightlessness were highly consistent at a given speed with those occurring in 1G, indicating that despite differing sensory input, subjects maintain running kinematics. The data suggest that individuals are capable of compensating for loss of limb weight when creating movement strategies. These results have important implications for creating training programs for use in weightlessness as practitioners can have greater confidence in running motions transferring across gravitational environments. Furthermore, these results have implications for use by researchers investigating motor control mechanisms and investigating hypotheses related to movement strategies when using sensory input that is dependent upon gravity.

DeWitt, John↗

NASA NTRS2023

Toward Physics-informed Neural Networks for 3D Multi-layer Cloud Mask Reconstruction

Three-dimensional (3D) cloud retrievals are critical for understanding their impact on climate and other applications such as aviation safety, weather prediction, and remote sensing. However, obtaining high-resolution and accurate vertical representation of clouds remains unsolved due to the limitations imposed by satellite instrumentation, viewing conditions, and the complexity of cloud dynamics. Cloud masks are essential for comprehending various cloud vertical properties, but deriving accurate 3D cloud masks from 2D satellite imagery data is a challenging task. To tackle these challenges, we introduce a physics-informed loss function for training deep learning models that can extend 2D cloud images into 3D cloud masks. The proposed loss, called CloudMask Loss, is composed of two domain knowledge-informed loss terms: one for evaluating cloud position and thickness, and the other for measuring the number of layers. By combining these loss terms, we improve the trainability of the deep learning models for more accurate and meaningful results. We apply the proposed loss function to different neural networks and demonstrate significant improvements in multi-layer cloud mask reconstruction. Utilizing the same neural network architecture, our proposed loss outperforms standard binary crossentropy loss in terms of multi-layer cloud classification accuracy, number of layers accuracy, and thickness mean absolute error (MAE). The proposed loss function can be readily integrated into various neural network architectures, resulting in substantial performance gains in 3D cloud mask generation.

multi-layer clouds↗

NASA NTRS2017

Semi-Supervised Learning of Lift Optimization of Multi-Element Three-Segment Variable Camber Airfoil

This chapter describes a new intelligent platform for learning optimal designs of morphing wings based on Variable Camber Continuous Trailing Edge Flaps (VCCTEF) in conjunction with a leading edge flap called the Variable Camber Krueger (VCK). The new platform consists of a Computational Fluid Dynamics (CFD) methodology coupled with a semi-supervised learning methodology. The CFD component of the intelligent platform comprises of a full Navier-Stokes solution capability (NASA OVERFLOW solver with Spalart-Allmaras turbulence model) that computes flow over a tri-element inboard NASA Generic Transport Model (GTM) wing section. Various VCCTEF/VCK settings and configurations were considered to explore optimal design for high-lift flight during take-off and landing. To determine globally optimal design of such a system, an extremely large set of CFD simulations is needed. This is not feasible to achieve in practice. To alleviate this problem, a recourse was taken to a semi-supervised learning (SSL) methodology, which is based on manifold regularization techniques. A reasonable space of CFD solutions was populated and then the SSL methodology was used to fit this manifold in its entirety, including the gaps in the manifold where there were no CFD solutions available. The SSL methodology in conjunction with an elastodynamic solver (FiDDLE) was demonstrated in an earlier study involving structural health monitoring. These CFD-SSL methodologies define the new intelligent platform that forms the basis for our search for optimal design of wings. Although the present platform can be used in various other design and operational problems in engineering, this chapter focuses on the high-lift study of the VCK-VCCTEF system. Top few candidate design configurations were identified by solving the CFD problem in a small subset of the design space. The SSL component was trained on the design space, and was then used in a predictive mode to populate a selected set of test points outside of the given design space. The new design test space thus populated was evaluated by using the CFD component by determining the error between the SSL predictions and the true (CFD) solutions, which was found to be small. This demonstrates the proposed CFD-SSL methodologies for isolating the best design of the VCK-VCCTEF system, and it holds promise for quantitatively identifying best designs of flight systems, in general.

Intelligent Systems↗

NASA NTRS1982

OSTA-1 lessons learned and recommendations for future payloads

Five remote sensing experiments, together with one air pollution and one bioengineering experiment, were incorporated into the first Space Shuttle scientific payload of the NASA Office of Space and Terrestrial Applications (OSTA-1). The instrument package, experimental procedures, and specialized personnel teams all performed satisfactorily. Attention is presently given to the lessons learned during the OSTA-1-incorporating mission, STS-2 of November 12, 1981. Payload test and integration teams should never take unfamiliar facilities for granted. Training, especially if it employs hands-on experience with full-scale mockups and mission simulations, is found to be highly effective. Ephemeris data is too important to trust to table-top calculators, since errors propagate in repeated calculations. Terminal access to an off-line computer is required.

Baldwin, R. R.↗

NASA NTRS2000

A Flight Training Simulator for Instructing the Helicopter Autorotation Maneuver (Enhanced Version)

Autorotation is a maneuver that permits a safe helicopter landing when the engine loses power. A catastrophe may occur if the pilot's control inputs are incorrect, insufficient, excessive, or poorly timed. Due to the danger involved, full-touchdown autorotations are very rarely practiced. Because in-flight autorotation training is risky, time-consuming, and expensive, the objective of the project was to develop the first helicopter flight simulator expressly designed to train students in this critical maneuver. A central feature of the project was the inclusion of an enhanced version of the Pilot-Rotorcraft Intelligent Symbology Management Simulator (PRISMS), a virtual-reality system developed by Anacapa Sciences and Thought Wave. A task analysis was performed to identify the procedural steps in the autorotation, to inventory the information needed to support student task performance, to identify typical errors, and to structure the simulator's practice environment. The system provides immediate knowledge of results, extensive practice of perceptual-motor skills, part-task training, and augmented cueing in a realistic cockpit environment. Additional work, described in this report, extended the capabilities of the simulator in three areas: 1. Incorporation of visual training aids to assist the student in learning the proper appearance of the visual scene when the maneuver is being properly performed; 2. Introduction of the requirement to land at a particular spot, as opposed to the wide, flat open field initially used, and development of appropriate metrics of success; and 3. Inclusion of wind speed and wind direction settings (and random variability settings) to add a more realistic challenge in "hitting the spot."

Rogers, Steven P.↗

NASA NTRS2021

Mass Inferencing Model Creation And Deployment To Lunar Excavation Robot, RASSOR

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. This research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. Radio wave propagation time to the Moon and back is ~2.56 seconds. Though teleoperation is possible with this delay, autonomous capability that enables RASSOR to plan and execute excavation missions intelligently and efficiently is preferred. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation (e.g. knowledge of whether drums are full informs the task of highest priority, whether it be continuing to dig, or returning to a processing plant to offload regolith). A configurable data reduction and analysis pipeline was created to allow for straightforward incorporation of new data, such as that from lunar excavation, to improve model performance in new environments. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All four models take in system states and output a mass prediction for each set of the robot’s bucket drums. Initial results from deployment to RASSOR and testing in a simulated lunar environment show that the models have <10% mean error during robot operation. Future work includes refinement of a model that estimates regolith mass in real-time during excavation as well as further testing of the developed models on the hardware.

ROS↗

NASA NTRS1989

The effects of practice on tracking and subjective workload

Six college-age male subjects performed one hundred, two-minute trials on a second-order tracking task. After each trial, subjects estimated perceived workload using both the NASA TLX and SWAT workload assessment procedures. Results confirmed an expected performance improvement on the tracking task which followed traditional learning curves within the performance of each individual. Perceived workload also decreased for both scales across trials. While performance variability significantly decreased across trials, workload variability remained constant. One month later, the same subjects returned to complete the second experiment in the sequence which was a retention replication of the first experiment. Results replicated those for the first experiment except that both performance error and workload were at reduced overall levels. Results in general affirm a parallel workload reduction with performance improvement, an observation consistent with a resource-based view of automaticity.

Hancock, P. A.↗

NASA NTRS2008

Cassini Attitude Control Fault Protection Design: Launch to End of Prime Mission Performance

The Cassini Attitude and Articulation Control Subsystem (AACS) Fault Protection (FP) has been successfully supporting operations for over 10 years from launch through the end of the prime mission. Cassini's AACS FP is complex, containing hundreds of error monitors and thousands of tunable parameters. Since launch there have been environmental, hardware, personnel and mission event driven changes which have required AACS FP to adapt and be robust to a variety of scenarios. This paper will discuss the process of monitoring, maintaining and updating the AACS FP during Cassini's lengthy prime mission as well as provide some insight into lessons learned during tour operations.

Meakin, Peter C.↗