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At least 307 records · Page 17

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NASA NTRS2002

Human muscle sympathetic nerve activity and plasma noradrenaline kinetics in space

Astronauts returning from space have reduced red blood cell masses, hypovolaemia and orthostatic intolerance, marked by greater cardio-acceleration during standing than before spaceflight, and in some, orthostatic hypotension and presyncope. Adaptation of the sympathetic nervous system occurring during spaceflight may be responsible for these postflight alterations. We tested the hypotheses that exposure to microgravity reduces sympathetic neural outflow and impairs sympathetic neural responses to orthostatic stress. We measured heart rate, photoplethysmographic finger arterial pressure, peroneal nerve muscle sympathetic activity and plasma noradrenaline spillover and clearance, in male astronauts before, during (flight day 12 or 13) and after the 16 day Neurolab space shuttle mission. Measurements were made during supine rest and orthostatic stress, as simulated on Earth and in space by 7 min periods of 15 and 30 mmHg lower body suction. Mean (+/- S.E.M.) heart rates before lower body suction were similar pre-flight and in flight. Heart rate responses to -30 mmHg were greater in flight (from 56 +/- 4 to 72 +/- 4 beats min(-1)) than pre-flight (from 56 +/- 4 at rest to 62 +/- 4 beats min(-1), P < 0.05). Noradrenaline spillover and clearance were increased from pre-flight levels during baseline periods and during lower body suction, both in flight (n = 3) and on post-flight days 1 or 2 (n = 5, P < 0.05). In-flight baseline sympathetic nerve activity was increased above pre-flight levels (by 10-33 %) in the same three subjects in whom noradrenaline spillover and clearance were increased. The sympathetic response to 30 mmHg lower body suction was at pre-flight levels or higher in each subject (35 pre-flight vs. 40 bursts min(-1) in flight). No astronaut experienced presyncope during lower body suction in space (or during upright tilt following the Neurolab mission). We conclude that in space, baseline sympathetic neural outflow is increased moderately and sympathetic responses to lower body suction are exaggerated. Therefore, notwithstanding hypovolaemia, astronauts respond normally to simulated orthostatic stress and are able to maintain their arterial pressures at normal levels.

STS-90 Shuttle Project↗

NASA NTRS2022

Instantaneous Photosynthetically Available Radiation (IPAR) prediction models based on Neural Network for ocean waters.

Instantaneous photosynthetically available radiation (IPAR) at the ocean surface and its vertical profile below the surface play a critical role in models to calculate net primary productivity of marine phytoplankton. In this work, we report two IPAR prediction models based on neural network (NN) approach, one for open ocean and the other for coastal waters. These models are trained, validated, and tested using a large volume of synthetic datasets for open ocean and coastal waters simulated by a radiative transfer model. Our NN models are designed to predict IPAR under a large range of atmospheric and oceanic conditions. The NN models can compute subsurface IPAR profile very accurately up to euphotic zone depth. The root mean square errors associated with the diffuse attenuation coefficient of IPAR are less than 0.011 𝑚−1 and 0.036 𝑚−1 for open ocean and coastal waters respectively. The performance of the NN models is better than presently available semi analytical models, with significant superiority in coastal waters.

PACE↗

NASA NTRS1989

Computational chaos in massively parallel neural networks

A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.

Barhen, Jacob↗

NASA NTRS2022

Identification of Flux Rope Orientation via Neural Networks

Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.

Thomas Narock↗

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 NTRS1995

Solar wind/local interstellar medium interaction including charge exchange with neural hydrogen

We present results from a hydrodynamic model of the interaction of the solar wind with the local interstellar medium (LISM), self-consistently taking into account the effects of charge exchange between the plasma component and the interstellar neutrals. The simulation is fully time dependent, and is carried out in two or three dimensions, depending on whether the helio-latitudinal dependence of the solar wind speed and number density (both giving rise to three dimensional effects) are included. As a first approximation it is assumed that the neutral component of the flow can be described by a single, isotropic fluid. Clearly, this is not the actual situation, since charge exchange with the supersonic solar wind plasma in the region of the nose results in a 'second' neutral fluid propagating in the opposite direction as that of the LISM neutrals.

Pauls, H. Louis↗

NASA NTRS2004

Intelligent, Robust Control of Deteriorated Turbofan Engines via Linear Parameter Varying Quadratic Lyapunov Function Design

A method for accommodating engine deterioration via a scheduled Linear Parameter Varying Quadratic Lyapunov Function (LPVQLF)-Based controller is presented. The LPVQLF design methodology provides a means for developing unconditionally stable, robust control of Linear Parameter Varying (LPV) systems. The controller is scheduled on the Engine Deterioration Index, a function of estimated parameters that relate to engine health, and is computed using a multilayer feedforward neural network. Acceptable thrust response and tight control of exhaust gas temperature (EGT) is accomplished by adjusting the performance weights on these parameters for different levels of engine degradation. Nonlinear simulations demonstrate that the controller achieves specified performance objectives while being robust to engine deterioration as well as engine-to-engine variations.

Turso, James A.↗

NASA NTRS2005

Creation and testing of an artificial neural network based carbonate detector for Mars rovers

We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

neural networks↗

NASA NTRS2005

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Spikovska, Lilly↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Jani, Yashvant↗

NASA NTRS2007

Neural Net Safety Monitor Design

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

Larson, Richard R.↗

NASA NTRS2009

Implementation of an Adaptive Controller System from Concept to Flight Test

The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a "floating limiter" (FL) concept that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly "floating" and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually "hit" the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.

Larson, Richard R.↗

NASA NTRS1993

A Novel Optical/digital Processing System for Pattern Recognition

This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network.

Boone, Bradley G.↗

NASA NTRS2005

Performance Monitoring and Assessment of Neuro-Adaptive Controllers for Aerospace Applications Using a Bayesian Approach

Modern exploration missions require modern control systems-control systems that can handle catastrophic changes in the system's behavior, compensate for slow deterioration in sustained operations, and support fast system ID. Adaptive controllers, based upon Neural Networks have these capabilities, but they can only be used safely if proper verification & validation (V&V) can be done. In this paper we present our V & V approach and simulation result within NASA's Intelligent Flight Control Systems (IFCS).

Gupta, Pramod↗

NASA NTRS2024

Machine Learning for the Prediction of Local Asteroid Damages

Risk assessment studies of local asteroid hazards traditionally simulate the physics of meteors with engineering models tailored to analyze tens-of-millions of scenarios. However, these simplified approaches still need to solve time-dependent ODEs to model the entry process and the resulting ground damage. With a computational cost of O(0.01 CPU.s) per scenario, simulating these large numbers of potential entry conditions in risk assessment studies can take several days on local computers. To improve computational efficiency, we propose in this paper an orthogonal approach based on machine learning models to predict the size of damaged areas given a list of entry parameters. We train 5 machine learning methods and compare the predictions to the outputs of the PAIR model, first only with primitive entry condition variables, and then with more advanced features. We find that complex models like neural networks are well-suited to estimate blast hazards, while simpler linear models can accurately assess thermal damage. For both types of hazards, the radii of damaged areas can be predicted with around 10% average errors and a coefficient of determination (R2) of 0.99. The CPU time is decreased by a factor O(10 3 ) compared to the PAIR model, which enables the simulation of millions of scenarios in minutes, on a local computer. We then use the same machine learning approaches for a classification task where the models are trained to predict if an asteroid will produce a given level of damage. Results show that complex models like the gradient boosting classifier and the neural network can perform this task with 98% accuracy. Beyond surrogate models, we finally incorporate the machine learning algorithms to the state-of-the-art Shapley sensitivity analysis and present a ranking of the entry parameters based on their contributions to ground damages.

SMD↗

NASA NTRS2020

Neural Network Reflectance Prediction Model for Both Open Ocean and Coastal Waters

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

radiative transfer↗

NASA NTRS1993

Identification and interpretation of patterns in rocket engine data

A prototype software system was constructed to detect anomalous Space Shuttle Main Engine (SSME) behavior in the early stages of fault development significantly earlier than the indication provided by either redline detection mechanism or human expert analysis. The major task of the research project is to analyze ground test data, to identify patterns associated with the anomalous engine behavior, and to develop a pattern identification and detection system on the basis of this analysis. A prototype expert system which was developed on both PC and Symbolics 3670 lisp machine for detecting anomalies in turbopump vibration data was checked with data from ground tests 902-473, 902-501, 902-519, and 904-097 of the Space Shuttle Main Engine. The neural networks method was also applied to supplement the statistical method utilized in the prototype system to investigate the feasibility in detecting anomalies in turbopump vibration of SSME. In most cases the anomalies detected by the expert system agree with those reported by NASA. On the neural networks approach, the results are given the successful detection rate higher than 95 percent to identify either normal or abnormal running condition based on the experimental data as well as numerical simulation.

Lo, C. F.↗

NASA NTRS1994

Neural network controller development for a magnetically suspended flywheel energy storage system

A neural network controller has been developed to accommodate disturbances and nonlinearities and improve the robustness of a magnetically suspended flywheel energy storage system. The controller is trained using the back propagation-through-time technique incorporated with a time-averaging scheme. The resulting nonlinear neural network controller improves system performance by adapting flywheel stiffness and damping based on operating speed. In addition, a hybrid multi-layered neural network controller is developed off-line which is capable of improving system performance even further. All of the research presented in this paper was implemented via a magnetic bearing computer simulation. However, careful attention was paid to developing a practical methodology which will make future application to the actual bearing system fairly straightforward.

Fittro, Roger L.↗