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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Rubaai, Ahmed↗
NASA NTRS1987

Definition of tolerance to continuous hyperoxia in man - An abstract report of Predictive Studies V

The overall goals, design, and procedures of Predictive Studies V are discussed as well as the specific elements of neural effects produced by prolonged hyperoxia. It is noted that Predictive Studies V study of oxygen poisoning in normal men during uninterrupted exposures to oxygen over the range of hyperbaric oxygen exposure most useful in diving, the treatment of gas lesion diseases, and general hyperbaric medicine. It is found that, throughout the study, the most striking observations were related to effects on visual function, on the lung, and the probable interactions of preconvulsive neural activity with effects on cardiovascular and respiratory-pulmonary functions.

Lambertsen, C. J.↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRS1989

Neural networks for data compression and invariant image recognition

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

Gardner, Sheldon↗
NASA NTRS1992

The role of under-determined approximations in engineering and science application

There is currently a great deal of interest in using response surfaces in the optimization of aircraft performance. The objective function and/or constraint equations involved in these optimization problems may come from numerous disciplines such as structures, aerodynamics, environmental engineering, etc. In each of these disciplines, the mathematical complexity of the governing equations usually dictates that numerical results be obtained from large computer programs such as a finite element method program. Thus, when performing optimization studies, response surfaces are a convenient way of transferring information from the various disciplines to the optimization algorithm as opposed to bringing all the sundry computer programs together in a massive computer code. Response surfaces offer another advantage in the optimization of aircraft structures. A characteristic of these types of optimization problems is that evaluation of the objective function and response equations (referred to as a functional evaluation) can be very expensive in a computational sense. Because of the computational expense in obtaining functional evaluations, the present study was undertaken to investigate under-determinined approximations. An under-determined approximation is one in which there are fewer training pairs (pieces of information about a function) than there are undetermined parameters (coefficients or weights) associated with the approximation. Both polynomial approximations and neural net approximations were examined. Three main example problems were investigated: (1) a function of one design variable was considered; (2) a function of two design variables was considered; and (3) a 35 bar truss with 4 design variables was considered.

Carpenter, William C.↗
NASA NTRS2005

DEVELOPMENT OF AN INFLIGHT COUNTERMEASURE TO MITIGATE POSTFLIGHT GAIT DYSFUNCTION

Following spaceflight crewmembers experience gait and postural instabilities due to inflight adaptive alterations in sensorimotor function. These changes can pose a risk to crew safety if nominal or emergency vehicle egress is required immediately following long-duration spaceflight. At present, no operational countermeasure is available to mitigate postflight locomotor disturbances. Therefore, the goal of this study is to develop an inflight training regimen that facilitates the recovery of locomotor function after long-duration spaceflight. The countermeasure we are developing is based on the concept of variable practice. During this type of training the subject gains experience producing the appropriate adaptive motor behavior under a variety of sensory conditions and response constraints. This countermeasure is built around current ISS treadmill exercise activities. Crewmembers will conduct their nominal inflight treadmill exercise while being exposed to variations in visual flow patterns. These variations will challenge the postural and locomotor systems repeatedly, thereby promoting adaptive reorganization in locomotor behavior. As a result of this training a subject learns to solve a class of motor problems, rather than a specific motor solution to one problem, Le., the subject learns response generalizability or the ability to "learn to learn" under a variety of environmental constraints. We anticipate that this training will accelerate recovery of postural and locomotor function during readaptation to gravitational environments following spaceflight facilitating neural adaptation to unit (Earth) and partial (Mars) gravity after long-duration spaceflight. The study calls for one group of subjects to perform the inflight treadmill training regimen while a control group of subjects performs only the nominal exercise procedures. Locomotor function in both groups is assessed before and after spaceflight using two tests of gait function: The Integrated Treadmill Locomotion Test (ITLT) and the Functional Mobility Test (FMT). The ITLT characterizes alterations in the integrated function of multiple sensorimotor subsystems responsible for the control of locomotion. This test calls for subjects to walk on a motorized treadmill while we assess changes in dynamic postural stability, head-trunk coordination, short-latency head stabilization responses, dynamic visual acuity, lower limb coordination strategies and gait cycle timing. To make these assessments we measure the following parameters while subjects walk on the treadmill: 1) full body 3-dimensional kinematics using a motion capture system (Motion Analysis Corp., Santa Rosa, CA); 2) the shock-wave transmitted from heel-strike to the head using triaxial accelerometers placed on the tibia and head (Entran, Fairfield, NJ); 3) vertical forces using an instrumented treadmill (Kistler Instrument Corp., Amherst, NY); 4) Dynamic visual acuity using Landolt Cs presented on a laptop computer located 4m from the eyes and 5) Gait cycle timing using foot-switches (Motion Lab Systems, Inc., Baton Rouge, LA) attached to the plantar surface of each shoe at the heel and toe. The FMT evaluates a subject's ability to perform challenging locomotor maneuvers similar to those encountered during an egress from a space vehicle. Subjects step over and duck under obstacles along with negotiating a series of pylons set up on a base of 10 cm thick medium density foam. The dependent measures for the FMT are time to complete the course and the number of obstacles touched. To date, we have collected pre and postflight locomotion data from Expeditions 5-9 who will serve as part of the control group for this study. Preliminary results comparing the recovery rates in gait control sub-systems obtained from the ITLT and FMT performance showed two recovery patterns: 1) a concordant recovery trend between gait control parameters and FMT performance indicating a restitution pattern of recovery and 2) gait controecovery that lagged recovery in FMT performance suggesting that improvement in locomotor function was attained through a pattern of substitution. These data suggest that recovery of postflight locomotor function may occur through adaptive mechanisms that lead to either restitution or substitution of function. Understanding the modes of postflight readaptation has implications for countermeasure development and testing and in astronaut postflight rehabilitation.

Bloomberg, J. J.↗
NASA NTRS2005

Development of an Inflight Countermeasure to Mitigate Postflight Gait Dysfunction

Following spaceflight crewmembers experience gait and postural instabilities due to inflight adaptive alterations in sensorimotor function. These changes can pose a risk to crew safety if nominal or emergency vehicle egress is required immediately following long-duration spaceflight. At present, no operational countermeasure is available to mitigate postflight locomotor disturbances. Therefore, the goal of this study is to develop an inflight training regimen that facilitates the recovery of locomotor function after long-duration spaceflight. The countermeasure we are developing is based on the concept of variable practice. During this type of training the subject gains experience producing the appropriate adaptive motor behavior under a variety of sensory conditions and response constraints. This countermeasure is built around current ISS treadmill exercise activities. Crewmembers will conduct their nominal inflight treadmill exercise while being exposed to variations in visual flow patterns. These variations will challenge the postural and locomotor systems repeatedly, thereby promoting adaptive reorganization in locomotor behavior. As a result of this training a subject learns to solve a class of motor problems, rather than a specific motor solution to one problem, Le., the subject learns response generalizability or the ability to "learn to learn" under a variety of environmental constraints. We anticipate that this training will accelerate recovery of postural and locomotor function during readaptation to gravitational environments following spaceflight facilitating neural adaptation to unit (Earth) and partial (Mars) gravity after long-duration spaceflight. The study calls for one group of subjects to perform the inflight treadmill training regimen while a control group of subjects performs only the nominal exercise procedures. Locomotor function in both groups is assessed before and after spaceflight using two tests of gait function: The Integrated Treadmill Locomotion Test (ITLT) and the Functional Mobility Test (FMT). The ITLT characterizes alterations in the integrated function of multiple sensorimotor subsystems responsible for the control of locomotion. This test calls for subjects to walk on a motorized treadmill while we assess changes in dynamic postural stability, head-trunk coordination, short-latency head stabilization responses, dynamic visual acuity, lower limb coordination strategies and gait cycle timing. To make these assessments we measure the following parameters while subjects walk on the treadmill: 1) full body 3-dimensional kinematics using a motion capture system (Motion Analysis Corp., Santa Rosa, CA); 2) the shock-wave transmitted from heel-strike to the head using triaxial accelerometers placed on the tibia and head (Entran, Fairfield, NJ); 3) vertical forces using an instumented treadmill (Kistler Instrument Corp., Amherst, NY); 4) Dynamic visual acuity using Landolt Cs presented on a laptop computer located 4m from the eyes and 5) Gait cycle timing using foot-switches (Motion Lab Systems, Inc., Baton Rouge, LA) attached to the plantar surface of each shoe at the heel and toe. The FMT evaluates s. subject's ability to perform challenging locomotor maneuvers similar to those encountered during an egress from a space vehicle. Subjects step over and duck under obstacles along with negotiating a series of pylons set up on a base of 10 cm thick medium density foam. The dependent measures for the FMT are time to complete the course and the number of obstacles touched. To date, we have collected pre and postflight locomotion data from Expeditions 5-9 who will serve as part of the control group for this study. Preliminary results comparing the recovery rates in gait control sub-systems obtained from the ITLT and FMT performance showed two recovery patterns: 1) a concordant recovery trend between gait control parameters and FMT performance indicating a restitution pattern of recovery and 2) gait controecovery that lagged recovery in FMT performance suggesting that improvement in locomotor function was attained through a pattern of substitution. These data suggest that recovery of postflight locomotor function may occur through adaptive mechanisms that lead to either restitution or substitution of function. Understanding the modes of postflight readaptation has implications for countermeasure development and testing and in astronaut postflight rehabilitation.

Bloomberg, J. J.↗
NASA NTRS1994

Preconditioning electromyographic data for an upper extremity model using neural networks

A back propagation neural network has been employed to precondition the electromyographic signal (EMG) that drives a computational model of the human upper extremity. This model is used to determine the complex relationship between EMG and muscle activation, and generates an optimal muscle activation scheme that simulates the actual activation. While the experimental and model predicted results of the ballistic muscle movement are very similar, the activation function between the start and the finish is not. This neural network preconditions the signal in an attempt to more closely model the actual activation function over the entire course of the muscle movement.

Roberson, D. J.↗
NASA NTRS1996

Soft Computing Methods in Design of Superalloys

Soft computing techniques of neural networks and genetic algorithms are used in the design of superalloys. The cyclic oxidation attack parameter K(sub a), generated from tests at NASA Lewis Research Center, is modelled as a function of the superalloy chemistry and test temperature using a neural network. This model is then used in conjunction with a genetic algorithm to obtain an optimized superalloy composition resulting in low K(sub a) values.

Cios, K. J.↗
NASA NTRS1995

Soft computing methods in design of superalloys

Soft computing techniques of neural networks and genetic algorithms are used in the design of superalloys. The cyclic oxidation attack parameter K(sub a), generated from tests at NASA Lewis Research Center, is modeled as a function of the superalloy chemistry and test temperature using a neural network. This model is then used in conjunction with a genetic algorithm to obtain an optimized superalloy composition resulting in low K(sub a) values.

Cios, K. J.↗
NASA NTRS1992

Application of neural networks to autonomous rendezvous and docking of space vehicles

NASA-Marshall has investigated the feasibility of numerous autonomous rendezvous and docking (ARD) candidate techniques. Neural networks have been studied as a viable basis for such systems' implementation, due to their intrinsic representation of such nonlinear functions as those for which analytical solutions are either difficult or nonexistent. Neural networks are also able to recognize and adapt to changes in their dynamic environment, thereby enhancing redundancy and fault tolerance. Outstanding performance has been obtained from ARD azimuth, elevation, and roll networks of this type.

Dabney, Richard W.↗
NASA NTRS2012

Nature's Autonomous Oscillators

Nonlinearity is required to produce autonomous oscillations without external time dependent source, and an example is the pendulum clock. The escapement mechanism of the clock imparts an impulse for each swing direction, which keeps the pendulum oscillating at the resonance frequency. Among nature's observed autonomous oscillators, examples are the quasi-biennial oscillation and bimonthly oscillation of the Earth atmosphere, and the 22-year solar oscillation. The oscillations have been simulated in numerical models without external time dependent source, and in Section 2 we summarize the results. Specifically, we shall discuss the nonlinearities that are involved in generating the oscillations, and the processes that produce the periodicities. In biology, insects have flight muscles, which function autonomously with wing frequencies that far exceed the animals' neural capacity; Stretch-activation of muscle contraction is the mechanism that produces the high frequency oscillation of insect flight, discussed in Section 3. The same mechanism is also invoked to explain the functioning of the cardiac muscle. In Section 4, we present a tutorial review of the cardio-vascular system, heart anatomy, and muscle cell physiology, leading up to Starling's Law of the Heart, which supports our notion that the human heart is also a nonlinear oscillator. In Section 5, we offer a broad perspective of the tenuous links between the fluid dynamical oscillators and the human heart physiology.

Mayr, H. G.↗
NASA NTRS2016

Neural Mechanisms to the Space Environment

Highly conserved neural systems have evolved to sense the inertial forces due to head translation and head tilt relative to gravitational vertical. These structures consist of ciliated mechanosensitive receptor cells inserted into a neuroepithelium surmounted by biomineral grains of calcium carbonate (CaCO3) called oto- (vertebrates) or stato-conia (invertebrates). Detection of these forces by receptor cells relies on the CaCO3 mass being weighted in Earths 1G. A change in gravity or orientation with respect to gravity has a profound effect on how an organism interacts with its environment, and it is evident that the nervous system responds to the new gravity state. This response might involve the peripheral receptors, the CaCO3 mass, the brain or any combination of these mechanisms based on the intensity and duration of the gravity change. Here, we examine the arguments supporting the different mechanisms of adaptation to the space environment. First, a pre- or post-synaptic alteration in the strength of synaptic transmission between the receptor cell and nerve afferent can adjust the system output. The number of synaptic ribbons in certain type II hair cells in rodent is labile, increasing following exposure to microgravity. An increase in number of synaptic ribbons in toadfish otolith hair cells following exposure to microgravity could potentially explain the observed afferent hypersensitivity to acceleration postflight. The physiological findings in the isolated statocyst in snails are in line with the vertebrate data, and conform to the proposition that G exposure leads to changes in gravireceptor function. At the same time this similarity in neural response to G exposure between the vertebrates and invertebrates is intriguing: the increased neural sensitivity in the vertebrate was detected in the nerve afferents, one synapse away from the receptor cell, whereas the increased neural sensitivity observed in the snail was detected directly at the receptor level. Second, the CaCO3 mass provides mechanical loading of receptor cell cilia, and their density alters sensitivity. A widely considered mechanism by which the animal responds to a chronic change in amplitude of gravity is a change in weight-lending CaCO3 mass. In G, it is argued, the organism counters the loss of gravity by increasing CaCO3 production, thereby increasing its mass, as a means to increase system gain. In hypergravity, the converse is argued. Earlier evidence in mollusks and recent results in mice suggest a remodeling might occur, especially after long-term space exposure. Lastly, we have to distinguish at least two kinds of neural feedbacks. One is connected with local mechanisms of self-regulation and specific for initial period of organ development when the neural connections are still absent. And the other feedback is related to neural self-regulation and specific for later stages of the organ development, and includes an efferent vestibular feedback. Complexity of the problem is enhanced by incompleteness of experiments, and consequently the experimental results have not led to a clear interpretation despite the numerous studies.

Boyle, Richard D.↗
NASA NTRS1992

A neuro-fuzzy architecture for real-time applications

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

Ramamoorthy, P. A.↗
NASA NTRS1990

3-D components of a biological neural network visualized in computer generated imagery. I - Macular receptive field organization

Computer-assisted, 3-dimensional reconstructions of macular receptive fields and of their linkages into a neural network have revealed new information about macular functional organization. Both type I and type II hair cells are included in the receptive fields. The fields are rounded, oblong, or elongated, but gradations between categories are common. Cell polarizations are divergent. Morphologically, each calyx of oblong and elongated fields appears to be an information processing site. Intrinsic modulation of information processing is extensive and varies with the kind of field. Each reconstructed field differs in detail from every other, suggesting that an element of randomness is introduced developmentally and contributes to endorgan adaptability.

Ross, Muriel D.↗
NASA NTRS1992

Opportunities and questions for the fundamental biological sciences in space

The nature of biological issues which can be addressed during long-term space missions is briefly discussed. These issues include structure, from cell to organ to organism; function, the regulation of systems such as immunology, neural sciences, and behavior; and reproduction and development.

Sharp, Joseph C.↗
NASA NTRS1994

Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

Tawel, Raoul↗