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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Evolvable synthetic neural system

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

Curtis, Steven A.↗
NASA NTRS1993

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular Neural Ring (MNR).

Lure, Y. M. Fleming↗
NASA NTRS1993

Attitude control of spacecraft using neural networks

This paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.

Vadali, Srinivas R.↗
NASA NTRS1990

Neural networks for function approximation in nonlinear control

Two neural network architectures are compared with a classical spline interpolation technique for the approximation of functions useful in a nonlinear control system. A standard back-propagation feedforward neural network and a cerebellar model articulation controller (CMAC) neural network are presented, and their results are compared with a B-spline interpolation procedure that is updated using recursive least-squares parameter identification. Each method is able to accurately represent a one-dimensional test function. Tradeoffs between size requirements, speed of operation, and speed of learning indicate that neural networks may be practical for identification and adaptation in a nonlinear control environment.

Linse, Dennis J.↗
NASA NTRS1992

A radial basis function neurocomputer implemented with analog VLSI circuits

An electronic neurocomputer which implements a radial basis function neural network (RBFNN) is described. The RBFNN is a network that utilizes a radial basis function as the transfer function. The key advantages of RBFNNs over existing neural network architectures include reduced learning time and the ease of VLSI implementation. This neurocomputer is based on an analog/digital hybrid design and has been constructed with both custom analog VLSI circuits and a commercially available digital signal processor. The hybrid architecture is selected because it offers high computational performance while compensating for analog inaccuracies, and it features the ability to model large problems.

Watkins, Steven S.↗
NASA NTRS2005

Neural network post-processing of grayscale optical correlator

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

grayscale optical correlator↗
NASA NTRS2017

Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations

Surface Lambertian-equivalent reflectivity (LER) is important for trace gas retrievals in the direct calculation of cloud fractions and indirect calculation of the air mass factor. Current trace gas retrievals use climatological surface LER's. Surface properties that impact the bidirectional reflectance distribution function (BRDF) as well as varying satellite viewing geometry can be important for retrieval of trace gases. Geometry Dependent LER (GLER) captures these effects with its calculation of sun normalized radiances (I/F) and can be used in current LER algorithms (Vasilkov et al. 2016). Pixel by pixel radiative transfer calculations are computationally expensive for large datasets. Modern satellite missions such as the Tropospheric Monitoring Instrument (TROPOMI) produce very large datasets as they take measurements at much higher spatial and spectral resolutions. Look up table (LUT) interpolation improves the speed of radiative transfer calculations but complexity increases for non-linear functions. Neural networks perform fast calculations and can accurately predict both non-linear and linear functions with little effort.

Geometry Dependent LER (GLER) capture↗
NASA NTRS1992

Fault detection and diagnosis using neural network approaches

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Kramer, Mark A.↗
NASA NTRS2005

Medial vestibular connections with the hypocretin (orexin) system

The mammalian medial vestibular nucleus (MVe) receives input from all vestibular endorgans and provides extensive projections to the central nervous system. Recent studies have demonstrated projections from the MVe to the circadian rhythm system. In addition, there are known projections from the MVe to regions considered to be involved in sleep and arousal. In this study, afferent and efferent subcortical connectivity of the medial vestibular nucleus of the golden hamster (Mesocricetus auratus) was evaluated using cholera toxin subunit-B (retrograde), Phaseolus vulgaris leucoagglutinin (anterograde), and pseudorabies virus (transneuronal retrograde) tract-tracing techniques. The results demonstrate MVe connections with regions mediating visuomotor and postural control, as previously observed in other mammals. The data also identify extensive projections from the MVe to regions mediating arousal and sleep-related functions, most of which receive immunohistochemically identified projections from the lateral hypothalamic hypocretin (orexin) neurons. These include the locus coeruleus, dorsal and pedunculopontine tegmental nuclei, dorsal raphe, and lateral preoptic area. The MVe itself receives a projection from hypocretin cells. CTB tracing demonstrated reciprocal connections between the MVe and most brain areas receiving MVe efferents. Virus tracing confirmed and extended the MVe afferent connections identified with CTB and additionally demonstrated transneuronal connectivity with the suprachiasmatic nucleus and the medial habenular nucleus. These anatomical data indicate that the vestibular system has access to a broad array of neural functions not typically associated with visuomotor, balance, or equilibrium, and that the MVe is likely to receive information from many of the same regions to which it projects.

Non-NASA Center↗
NASA NTRS2001

Effects of exposure to 56Fe particles on the acquisition of a conditioned place preference in rats

Exposure to low doses of 56Fe particles produces changes in neural function and behavior. The present experiments were designed to examine the effects of irradiation on the acquisition of a dopamine-mediated conditioned place preference (CPP). In the CPP procedure, rats are given an injection of the dopamine agonist amphetamine in one distinctive compartment and a saline injection in a different compartment of a three-compartment apparatus. Control rats develop a preference for the amphetamine-paired compartment. In contrast, rats exposed to 1 Gy of 56Fe particles fail to develop a similar preference. The results of the experiment indicate that exposure to low doses of heavy particles can disrupt the neural mechanisms that mediate the reinforcement of behavior.

Non-NASA Center↗
DOE OSTI2024

Controlling reversible phase transitions in rare-earth nickelates for novel memory devices

Resistive switching in correlated complex oxides is lucrative for emerging applications in neuromorphic computing, and densely scaled non-volatile memory. Electrical conductance of such complex oxides can be controllable switched across multiple orders of magnitude by either (a) electroforming a conduction channel (e.g., in tungsten oxide), or (b) inducing Mott-Hubbard transition (e.g., in rare-earth nickelates)– both via controlled migration of defects (such as oxygen vacancies) under applied bias. Nevertheless, the promise of such defect-driven electronic transitions are far from realized due to a lack of fundamental understanding of the atomic-scale processes that underlie migration and spatiotemporal evolution of oxygen vacancies over nano-to-mesoscopic length/timescales under applied electric field. In this project, we employ a synergistic integration of density functional theory (DFT) calculations, ab initio/classical molecular dynamics (AIMD/CMD) simulations, machine learning (ML), precision synthesis, and multi-modal X-ray imaging experiments to address this knowledge gap. Such an integrated approach offers to elucidate the correlations between subtle structural distortion and oxidation states; treat localized charge carriers; describe defect/ion transport in the presence of electric field; and, in turn, greatly advance the current understanding of microstructural evolution in complex oxides under applied bias. The fundamental knowledge gained from this work will enable precise control over hierarchical defect structures and unravel new routes to manipulate resistance states in complex oxides. This, in turn, will accelerate design of novel devices with desired set of neural functionalities, and high-speed densely-scaled resistive random access memory technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Neuromorphic ionic computing in droplet interface synapses

Ionic devices with memory capabilities can emulate neural functionality, enabling neuromorphic computing and biomedical applications. In this study, we report an ionic spiking synapse based on aqueous droplet interface bilayer assembly. Under stepwise triangular voltages, the device displays coupled memcapacitive-memristive behavior, showing noncrossing pinched hysteretic I-V loops. This hysteretic ion dynamics can be regulated by modifying bilayer components, reconstituting protein channels, or adjusting droplet assembly configuration. Droplet interface synapses (DIS) exhibit fundamental neuromorphic behaviors such as paired-pulse facilitation/depression, spike rate–dependent plasticity, Hebbian learning, and short-term associative learning under classical conditioning. We also used reservoir computing with DIS to implement two learning algorithms: a classification algorithm that recognizes handwritten digits and a reinforcement learning algorithm that learns to play a board game of tic-tac-toe.

Li, Zhongwu [Lawrence Livermore National Laborator↗
NASA NTRS1990

Neuronal plasticity in relation to long-duration spaceflight

Exposure to microgravity leads to a marked reduction in sensory-motor stimuli to the vestibular, proprioceptive and somatosensory systems. Long-duration missions, such as those proposed for a trip to Mars, may lead to significant changes in neural function. This paper presents results based on studies of sensory deafferentation of specific brain regions and detailed changes which occur in neuronal architecture. Data from these studies emphasize the need for further research related to sensory system deprivation and the development of new unique countermeasures for long-duration space flight.

Hillman, Dean E.↗
NASA NTRS1999

Some Behavioral Effects of Exposure to Low Doses of Fe-56 Particles

Future missions in space (such as a mission to Mars) will involve long-term travel beyond the magnetic field of the Earth. As a result, astronauts will be exposed to radiation qualities and doses that differ from those experienced in low earth orbit, including exposure to heavy particles, such as Fe-56, which are a component of cosmic rays. Although the hazards of exposure to heavy particles are often minimized, they can affect neural functioning, and as a consequence, behavior. Unless the effects of exposure to cosmic rays can somehow be reduced, their effects on the brain throughout long duration flights could be disastrous. In the extreme case, it is possible that the effects of cosmic rays on space travelers could result in symptomatology resembling that of Alzheimer's or Parkinson's diseases or of advancing age, including significant cognitive and/or motor impairments. Because successful operations in space depend in part on the performance capabilities of astronauts, such impairments could jeopardize their ability to satisfy mission requirements, as well as have long-term consequences on the health of astronauts. As such, understanding the nature and extent of this risk may be vital to the effective performance and possibly the survival of astronauts during future missions in space.

Rabin, Bernard M.↗
NASA NTRS2004

The effects of proton exposure on neurochemistry and behavior

Future space missions will involve long-term travel beyond the magnetic field of the Earth, where astronauts will be exposed to radiation hazards such as those that arise from galactic cosmic rays. Galactic cosmic rays are composed of protons, alpha particles, and particles of high energy and charge (HZE particles). Research by our group has shown that exposure to HZE particles, primarily 600 MeV/n and 1 GeV/n 56Fe, can produce significant alterations in brain neurochemistry and behavior. However, given that protons can make up a significant portion of the radiation spectrum, it is important to study their effects on neural functioning and on related performance. Therefore, these studies examined the effects of exposure to proton irradiation on neurochemical and behavioral endpoints, including dopaminergic functioning, amphetamine-induced conditioned taste aversion learning, and spatial learning and memory as measured by the Morris water maze. Male Sprague-Dawley rats received a dose of 0, 1.5, 3.0 or 4.0 Gy of 250 MeV protons at Loma Linda University and were tested in the different behavioral tests at various times following exposure. Results showed that there was no effect of proton irradiation at any dose on any of the endpoints measured. Therefore, there is a contrast between the insignificant effects of high dose proton exposure and the dramatic effectiveness of low dose (<0.1 Gy) exposures to 56Fe particles on both neurochemical and behavioral endpoints. Published by Elsevier Ltd on behalf of COSPAR.

Non-NASA Center↗
NASA NTRS2005

Coping with Trial-to-Trial Variability of Event Related Signals: A Bayesian Inference Approach

In electro-neurophysiology, single-trial brain responses to a sensory stimulus or a motor act are commonly assumed to result from the linear superposition of a stereotypic event-related signal (e.g. the event-related potential or ERP) that is invariant across trials and some ongoing brain activity often referred to as noise. To extract the signal, one performs an ensemble average of the brain responses over many identical trials to attenuate the noise. To date, h s simple signal-plus-noise (SPN) model has been the dominant approach in cognitive neuroscience. Mounting empirical evidence has shown that the assumptions underlying this model may be overly simplistic. More realistic models have been proposed that account for the trial-to-trial variability of the event-related signal as well as the possibility of multiple differentially varying components within a given ERP waveform. The variable-signal-plus-noise (VSPN) model, which has been demonstrated to provide the foundation for separation and characterization of multiple differentially varying components, has the potential to provide a rich source of information for questions related to neural functions that complement the SPN model. Thus, being able to estimate the amplitude and latency of each ERP component on a trial-by-trial basis provides a critical link between the perceived benefits of the VSPN model and its many concrete applications. In this paper we describe a Bayesian approach to deal with this issue and the resulting strategy is referred to as the differentially Variable Component Analysis (dVCA). We compare the performance of dVCA on simulated data with Independent Component Analysis (ICA) and analyze neurobiological recordings from monkeys performing cognitive tasks.

Ding, Mingzhou↗
DOE OSTI2025

Data-driven analysis of dipole strength functions using artificial neural networks

Here, we present a data-driven analysis of dipole strength functions across the nuclear chart, employing an artificial neural network to model nuclear dipole responses. We train the network on a dataset of experimentally measured dipole strength functions for 216 different nuclei. To assess its predictive capability, we test the trained model on an additional set of 10 new nuclei, where experimental data exist. We demonstrate that the artificial neural network not only accurately reproduces known data but also identifies potential inconsistencies in experimental datasets, indicating which results may warrant further review or possible rejection. For nuclei where experimental data are sparse or unavailable, the network confirms theoretical calculations, reinforcing its utility as a predictive tool in nuclear physics. Finally, utilizing the predicted electric dipole polarizability, we extract the value of the symmetry energy at saturation density and find it consistent with results from the literature.

artificial neural networks↗
NASA NTRS1990

Neural computation of arithmetic functions

An area of application of neural networks is considered. A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n-bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions.

Siu, Kai-Yeung↗