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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 271 records · Page 15
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NASA NTRS2002

Neural Correlates of Birth: Labor Contractions Induce C-Fos Expression In Newborn Rat Brain

At birth, the newborn mammal must make rapid adaptations to the extrauterine environment to survive. We have previously shown that labor contractions augment the appearance of adaptive responses at birth, viz., postpartum breathing and the onset of suckling. Since neuronal activity has been shown to upregulate the activity of immediate early genes (IEGs) in the brain, we analyzed the neural distribution of c-Fos protein expression in newborn rats using immunohistochemistry. Previous studies have reported a burst of c-Fos mRNA expression in mouse and rat brain at birth however relationships to labor and delivery have not been examined. In the present study, we exposed near-term rat fetuses to elements of the vaginal birth process: 1) Simulated labor contractions. 2) Postpartum cooling (22 deg C). 3) Umbilical cord occlusion. and 4) Stroking to mimic postpartum licking by the dam. Cardinally delivered newborns (VG) were compared with those delivered by cesarean section following either prenatal exposure to compressions (C) [simulated labor contractions], or no compressions (NC) [no labor contractions]. Similar patterns of c-fos activation were observed throughout hypothalamic and thalamic nuclei, hippocampus and cerebral cortex in VG and C newborns that were not apparent in NC newborns. Our results indicate that labor contractions play a role in the induction of widespread neural activation in the newborn brain.

Ronca, A. E.↗
NASA NTRS2007

Locomotor Dysfunction after Spaceflight: Characterization and Countermeasure Development

Astronauts returning from space flight show disturbances in locomotor control manifested by changes in various sub-systems including head-trunk coordination, dynamic visual acuity, lower limb muscle activation patterning and kinematics (Glasauer, et al., 1995; Bloomberg, et al., 1997; McDonald, et al., 1996; 1997; Layne, et al., 1997; 1998, 2001, 2004; Newman, et al., 1997; Bloomberg and Mulavara, 2003). These post flight changes in locomotor performance, due to neural adaptation to the microgravity conditions of space flight, affect the ability of crewmembers especially after a long duration mission to egress their vehicle and perform extravehicular activities soon after landing on Earth or following a landing on the surface of the Moon or Mars. At present, no operational training intervention is available pre- or in- flight to mitigate post flight locomotor disturbances. Our laboratory is currently developing a gait adaptability training program that is designed to facilitate recovery of locomotor function following a return to a gravitational environment. The training program exploits the ability of the sensorimotor system to generalize from exposure to multiple adaptive challenges during training so that the gait control system essentially "learns to learn" and therefore can reorganize more rapidly when faced with a novel adaptive challenge. Ultimately, the functional goal of an adaptive generalization countermeasure is not necessarily to immediately return movement patterns back to "normal". Rather the training regimen should facilitate the reorganization of available sensorimotor sub-systems to achieve safe and effective locomotion as soon as possible after space flight. We have previously confirmed that subjects participating in adaptive generalization training programs, using a variety of visuomotor distortions and different motor tasks from throwing to negotiating an obstacle course as the dependent measure, can learn to enhance their ability to adapt to a novel sensorimotor environment (Roller et al., 2001; Cohen et al. 2005). Importantly, this increased adaptability is retained even one month after completion of the training period. Our laboratory is currently developing adaptive generalization training procedures and the associated flight hardware to implement such a training program, using variations of visual flow, subject loading, and treadmill speed; during regular in-flight treadmill operations.

Mulavara, A. P.↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS1988

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Olshausen, Bruno A.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: (1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e., would not need to learn everything from scratch as normally required at present; and (2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS1989

A Cerebellar-model Associative Memory as a Generalized Random-access Memory

A versatile neural-net model is explained in terms familiar to computer scientists and engineers. It is called the sparse distributed memory, and it is a random-access memory for very long words (for patterns with thousands of bits). Its potential utility is the result of several factors: (1) a large pattern representing an object or a scene or a moment can encode a large amount of information about what it represents; (2) this information can serve as an address to the memory, and it can also serve as data; (3) the memory is noise tolerant--the information need not be exact; (4) the memory can be made arbitrarily large and hence an arbitrary amount of information can be stored in it; and (5) the architecture is inherently parallel, allowing large memories to be fast. Such memories can become important components of future computers.

Kanerva, Pentti↗
NASA NTRS2016

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗
NASA NTRS1999

Electric organ discharges and electric images during electrolocation

Weakly electric fish use active electrolocation - the generation and detection of electric currents - to explore their surroundings. Although electrosensory systems include some of the most extensively understood circuits in the vertebrate central nervous system, relatively little is known quantitatively about how fish electrolocate objects. We believe a prerequisite to understanding electrolocation and its underlying neural substrates is to quantify and visualize the peripheral electrosensory information measured by the electroreceptors. We have therefore focused on reconstructing both the electric organ discharges (EODs) and the electric images resulting from nearby objects and the fish's exploratory behaviors. Here, we review results from a combination of techniques, including field measurements, numerical and semi-analytical simulations, and video imaging of behaviors. EOD maps are presented and interpreted for six gymnotiform species. They reveal diverse electric field patterns that have significant implications for both the electrosensory and electromotor systems. Our simulations generated predictions of the electric images from nearby objects as well as sequences of electric images during exploratory behaviors. These methods are leading to the identification of image features and computational algorithms that could reliably encode electrosensory information and may help guide electrophysiological experiments exploring the neural basis of electrolocation.

Review, Tutorial↗
NASA NTRS1996

Verification and Validation of KBS with Neural Network Components

Artificial Neural Network (ANN) play an important role in developing robust Knowledge Based Systems (KBS). The ANN based components used in these systems learn to give appropriate predictions through training with correct input-output data patterns. Unlike traditional KBS that depends on a rule database and a production engine, the ANN based system mimics the decisions of an expert without specifically formulating the if-than type of rules. In fact, the ANNs demonstrate their superiority when such if-then type of rules are hard to generate by human expert. Verification of traditional knowledge based system is based on the proof of consistency and completeness of the rule knowledge base and correctness of the production engine.These techniques, however, can not be directly applied to ANN based components.In this position paper, we propose a verification and validation procedure for KBS with ANN based components. The essence of the procedure is to obtain an accurate system specification through incremental modification of the specifications using an ANN rule extraction algorithm.

Wen, Wu↗
NASA NTRS2004

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

Patnaik, Surya N.↗
NASA NTRS2006

Structure and Function of the Snail Statocyst System after a 16-Day Flight on Foton-M-2

In terrestrial gastropod snail Helix lucorum L. we studied the changes after a 16-day exposure to microgravity in: behavior, neural responses to adequate motion stimulation, intersensory interactions between the photosensory pathways and the statocyst receptors, and in expression of the HPeP gene in the statocyst receptors. In behavioral experiments it was found that the latency of body position change to sudden orientation change (flip from horizontal to downwards position) was significantly reduced in the postflight snails. Extracellularly recorded neural responses of the statocyst nerve to adequate motion stimulation in the postflight snails were independent of the motion direction while in the control animals an orientation selectivity was observed. Significant differences in the HPeP gene mRNA expression pattern in the statocyst receptor neurons were observed in postflight (30h) and control snails. Obtained results confirm the possibility to elucidate the influence of microgravity exposure on mechanisms and function of gravireceptors using this simple model animal.

Balaban, P. M.↗
NASA NTRS1993

Parametric motion control of robotic arms: A biologically based approach using neural networks

A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

Bock, O.↗
NASA NTRS1990

Quantized-"Gray-Scale" Electronic Synapses

Proposed array of programmable synaptic connections for electronic neural network applications offers multiple quantized levels of connection strength using only simple, two-terminal, binary microswitch devices. Subgrids in fine grid of programmable resistive connections connected externally in parallel to form coarser synaptic grid. By selection of pattern of connections in each subgrid, connection strength of synaptic node represented by that subgrid set at quantized "gray level". Device structures promise implementations of quantized-"gray-scale" synaptic arrays with very high density.

Lamb, James L.↗
NASA NTRSDate not supplied

Guiding Integration of Formal Verification in Assurance Cases

Assurance cases are being increasingly acknowledged as away to build trust in complex systems with autonomous capabilities. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for a specific mission and operating environment. Formal verification is often reserved for the most critical components of such systems. However, formal verification tools are often complex, and their usage is subject to many constraints and contextual dependencies. This can raise challenges both for performing the verification as well as reflecting the verification results appropriately in the assurance case, especially for non-expert users of the verification tool. To address these challenges, we present a tool-supported methodology for integrating formal verification results in an assurance case by capturing key verification method information in a rigorously constructed assurance case. In particular, we capture the tool specification in terms of its inputs, outputs, and assurance constraints as assumptions over inputs and guarantees provided over its outputs. The tool specification is parametrized over the inputs and outputs to both guide the intended application of the tool, as well as to check that the tool has been applied following the stated assumptions and that the guarantees hold. We define a generic tool assurance argument pattern that enables integration of the verification results in the assurance case by allowing custom refinement and automated instantiation for each tool use. We demonstrate our methodology on two formal verification tools and their applications to the verification of neural network properties for the aircraft domain.

Assurance Cases↗
NASA NTRS2021

Dynamic fire and smoke detection and classification for flashover prediction

Flashover is a dangerous phenomenon caused by near-simultaneous ignition of exposed materials. It is one of the major causes of firefighter fatalities. Research has been done using CMOS vision cameras combined with thermal sensors to perform remote detection and dynamic classification of fire and smoke patterns. Tests and experiments have been done to detect fire and smoke remotely. The inexpensive visible and infrared sensors used in the tests corroborate and closely follow the detailed trends recorded by the more expensive (and less mobile) radiometers and thermocouples. Deep neural networks (DNN) have been used to detect, classify and track fire and smoke areas. Real-time segmentation is utilized to measure the fire and smoke boundaries. The segmentations are used to dynamically monitor fluctuations in temperature, fire size and smoke progression in the monitored areas. A fire and smoke progression curve has been drawn to predict the flashover point. In the paper, data analysis and preliminary results will be shown. Keywords: Flashover, fire, smoke, deep learning, visible and infrared vision

Chow, Edward↗
NASA NTRS2023

EVA Task and 3D Pose Recognition from Video

Extravehicular Activity (EVA) has been known to involve potential risks of biomechanical stresses and injuries to crewmembers. Gathering of EVA motion patterns is necessary for risk analysis and mitigation. However, many existing techniques, such as motion capture systems, are not only cost-prohibitive but are impractical for retrospective analysis of past missions. In this work, a software tool was developed, which can estimate the 3D poses of a spacesuit from photographs or videos, without using special sensors or equipment. The tool is based on the state-of-the-art artificial intelligence and machine learning (AI/ML) system, which was trained by studying and capturing motion patterns of past and current spacesuit test data. The AI/ML tool was further enhanced using synthetically generated data, in which the suit postures, backgrounds, camera angles and illumination conditions were parametrically adjusted and rendered for training. The tool, incorporated the methodologies of Convolutional Neural Network (CNN), was trained, and tested in the cloud computing environment. The trained model was then applied on new imagery and video to extract estimated joint positions and suit outlines. The joint positions were further processed to capture activity (“digging”), pose labels (“bending”), and other useful downstream information. The model performance on new imagery and video was successfully assessed for accuracy and reliability. This AI/ML based posture recognition tool thus allows for the quantification of injury risk and task performance characterization for both current and past missions and training, which can immensely help to improve EVA task and suit design.

Kyung Han Kim↗
NASA NTRS1988

Integrated Circuit For Simulation Of Neural Network

Ballast resistors deposited on top of circuit structure. Cascadable, programmable binary connection matrix fabricated in VLSI form as basic building block for assembly of like units into content-addressable electronic memory matrices operating somewhat like networks of neurons. Connections formed during storage of data, and data recalled from memory by prompting matrix with approximate or partly erroneous signals. Redundancy in pattern of connections causes matrix to respond with correct stored data.

Thakoor, Anilkumar P.↗