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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 NTRS1994

A mission planning concept and mission planning system for future manned space missions

The international character of future manned space missions will compel the involvement of several international space agencies in mission planning tasks. Additionally, the community of users requires a higher degree of freedom for experiment planning. Both of these problems can be solved by a decentralized mission planning concept using the so-called 'envelope method,' by which resources are allocated to users by distributing resource profiles ('envelopes') which define resource availabilities at specified times. The users are essentially free to plan their activities independently of each other, provided that they stay within their envelopes. The new developments were aimed at refining the existing vague envelope concept into a practical method for decentralized planning. Selected critical functions were exercised by planning an example, founded on experience acquired by the MSCC during the Spacelab missions D-1 and D-2. The main activity regarding future mission planning tasks was to improve the existing MSCC mission planning system, using new techniques. An electronic interface was developed to collect all formalized user inputs more effectively, along with an 'envelope generator' for generation and manipulation of the resource envelopes. The existing scheduler and its data base were successfully replaced by an artificial intelligence scheduler. This scheduler is not only capable of handling resource envelopes, but also uses a new technology based on neuronal networks. Therefore, it is very well suited to solve the future scheduling problems more efficiently. This prototype mission planning system was used to gain new practical experience with decentralized mission planning, using the envelope method. In future steps, software tools will be optimized, and all data management planning activities will be embedded into the scheduler.

Wickler, Martin↗
NASA NTRSDate not supplied

Enhancing Neural Network Decision-Making with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. This explainability encourages people to be more inclined to justifiably trust machine decision-making.

Loc Tran↗
NASA NTRSDate not supplied

Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.

Loc Tran↗
NASA NTRS1988

Synchronization in neural nets

The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

Vidal, Jacques J.↗
NASA NTRS1995

Lyapunov exponents from CHUA's circuit time series using artificial neural networks

In this paper we present the general problem of identifying if a nonlinear dynamic system has a chaotic behavior. If the answer is positive the system will be sensitive to small perturbations in the initial conditions which will imply that there is a chaotic attractor in its state space. A particular problem would be that of identifying a chaotic oscillator. We present an example of three well known different chaotic oscillators where we have knowledge of the equations that govern the dynamical systems and from there we can obtain the corresponding time series. In a similar example we assume that we only know the time series and, finally, in another example we have to take measurements in the Chua's circuit to obtain sample points of the time series. With the knowledge about the time series the phase plane portraits are plotted and from them, by visual inspection, it is concluded whether or not the system is chaotic. This method has the problem of uncertainty and subjectivity and for that reason a different approach is needed. A quantitative approach is the computation of the Lyapunov exponents. We describe several methods for obtaining them and apply a little known method of artificial neural networks to the different examples mentioned above. We end the paper discussing the importance of the Lyapunov exponents in the interpretation of the dynamic behavior of biological neurons and biological neural networks.

Gonzalez, J. Jesus↗
NASA NTRS1995

Low Temperature Performance of High-Speed Neural Network Circuits

Artificial neural networks, derived from their biological counterparts, offer a new and enabling computing paradigm specially suitable for such tasks as image and signal processing with feature classification/object recognition, global optimization, and adaptive control. When implemented in fully parallel electronic hardware, it offers orders of magnitude speed advantage. Basic building blocks of the new architecture are the processing elements called neurons implemented as nonlinear operational amplifiers with sigmoidal transfer function, interconnected through weighted connections called synapses implemented using circuitry for weight storage and multiply functions either in an analog, digital, or hybrid scheme.

artificial neural networks image processing signal↗
NASA NTRS2003

Artificial Neural Network with Hardware Training and Hardware Refresh

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

Tuan A Duong↗
NASA NTRS1990

Is thinking computable?

Strong artificial intelligence claims that conscious thought can arise in computers containing the right algorithms even though none of the programs or components of those computers understand which is going on. As proof, it asserts that brains are finite webs of neurons, each with a definite function governed by the laws of physics; this web has a set of equations that can be solved (or simulated) by a sufficiently powerful computer. Strong AI claims the Turing test as a criterion of success. A recent debate in Scientific American concludes that the Turing test is not sufficient, but leaves intact the underlying premise that thought is a computable process. The recent book by Roger Penrose, however, offers a sharp challenge, arguing that the laws of quantum physics may govern mental processes and that these laws may not be computable. In every area of mathematics and physics, Penrose finds evidence of nonalgorithmic human activity and concludes that mental processes are inherently more powerful than computational processes.

Denning, Peter J.↗
NASA NTRS1994

Empirical modeling for intelligent, real-time manufacture control

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Xu, Xiaoshu↗
NASA NTRS1993

Communications and control for electric power systems: Power flow classification for static security assessment

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Niebur, D.↗
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 NTRS1992

Fault tolerance of artificial neural networks with applications in critical systems

This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

Protzel, Peter W.↗
NASA NTRS2005

Journal of Gravitational Physiology, Volume 12, Number 1

The following topics were covered: System Specificity in Responsiveness to Intermittent -Gx Gravitation during Simulated Microgravity in Rats; A Brief Overview of Animal Hypergravity Studies; Neurovestibular Adaptation to Short Radius Centrifugation; Effect of Artificial Gravity with Exercise Load by Using Short-Arm Centrifuge with Bicycle Ergometer as a Countermeasure Against Disused Osteoporosis; Perception of Body Vertical in Microgravity during Parabolic Flight; Virtual Environment a Behavioral and Countermeasure Tool for Assisted Gesture in Weightlessness: Experiments during Parabolic Flight; Artificial Gravity: Physiological Perspectives for Long-Term Space Exploration; Comparison of the Effects of DL-threo-Beta-Benzyloxyaspartate on the Glutamate Release from Synaptosomes before and after Exposure of Rats to Artificial Gravity; Do Perception and Postrotatory Vestibulo-Ocular Reflex Share the Same Gravity Reference?; Vestibular Adaptation to Changing Gravity Levels and the Orientation of Listing's Plane; Compound Mechanism Hypothesis on +Gz - Induced Brain Injury and Dysfunction of Learning and Memory; Environmental Challenge Impairs Prefrontal Brain Functions; Effect of 6-Days of Support Withdrawal on Characteristics of Balance Function; Hypergravity-Induced Changes of Neuronal Activities in CA1 Region of Rat Hippocampus; Audiological Findings in Antiorthostatic Position Modelling Microgravitation; Investigating Human Cognitive Performance during Spaceflight; The Relevance of the Minimization of Torque Exchange with the Environment in Weightlessness is Confirmed by Asimulation Study; Characteristics of the Eyes Pursuit Function during Readaptation to Terrestrial Gravity after Prolonged Flights Aboard the International Space Station; Comparison of Cognitive Performance Tests for Promethazine Pharmacodynamics in Human Subjects; Structural Reappraisal of Dendritic Tree of Cerebellar Purkinje Cell for Novel Functional Modeling of Elementary Sensorimotor Adaptive Processes; Orpheus 0 G or Ear in Microgravity to Establish Symptoms Concomittant of Inner and Middle Ear and Osteoporosis in Microgravity; Understanding Visual Perception in the Perspective of Gravity; Cortical Regions Associated with Orthostatic Stress in Conscious Humans; Restoration of Central Blood Volume: Application of a Simple Concept and Simple Device to Counteract Cardiovascular Instability in Syncope and Hemorrhage; WISE-2005: Integrative Cardiovascular Responses with LBNP during 60-Day Bed Rest in Women; Intracranial Pressure Increases during Weightlessness. A Parabolic Flights Study; Lower Limb & Portal Veins Echography for Predicting Risk of Thrombosis during a 90-D Bed Rest; Calf Tissue Liquid Stowage and Muscular and Deep Vein Distension in Orthostatic Tests after a 90-Day Head Down Bed Rest; Morphology of Brain Vessels in the Tail Suspended Rats Exposed to Intermittent 2 G; Alterations in Vasoreactivity of Femoral Artery Induced by Hindlimb Unweighting are Related to the Changes of Contractile Protein in Rats; and Respiratory Sinus Arrhythmia: A Marker of Decreased Parasympathetic Modulation after Short Duration.

Fuller, Charles A.↗
NASA NTRS1997

Effect of Artificial Gravity: Central Nervous System Neurochemical Studies

The major objective of this project was to assess chemical and morphological modifications occurring in muscle receptors and the central nervous system of animals subjected to altered gravity (2 x Earth gravity produced by centrifugation and simulated micro gravity produced by hindlimb suspension). The underlying hypothesis for the studies was that afferent (sensory) information sent to the central nervous system by muscle receptors would be changed in conditions of altered gravity and that these changes, in turn, would instigate a process of adaptation involving altered chemical activity of neurons and glial cells of the projection areas of the cerebral cortex that are related to inputs from those muscle receptors (e.g., cells in the limb projection areas). The central objective of this research was to expand understanding of how chronic exposure to altered gravity, through effects on the vestibular system, influences neuromuscular systems that control posture and gait. The project used an approach in which molecular changes in the neuromuscular system were related to the development of effective motor control by characterizing neurochemical changes in sensory and motor systems and relating those changes to motor behavior as animals adapted to altered gravity. Thus, the objective was to identify changes in central and peripheral neuromuscular mechanisms that are associated with the re-establishment of motor control which is disrupted by chronic exposure to altered gravity.

Fox, Robert A.↗
NASA NTRS2023

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗
NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗
NASA NTRS1996

Interactions of anesthetics with the membrane-water interface

Although the potency of conventional anesthetics correlates with lipophilicity, an affinity to water also is essential. It was recently found that compounds with very low affinities to water do not produce anesthesia regardless of their lipophilicity. This finding implies that clinical anesthesia might arise because of interactions at molecular sites near the interface of neuronal membranes with the aqueous environment and, therefore, might require increased concentrations of anesthetic molecules at membrane interfaces. As an initial test of this hypothesis, we calculated in molecular dynamics simulations the free energy profiles for the transfer of anesthetic 1,1,2-trifluoroethane and nonanesthetic perfluoroethane across water-membrane and water-hexane interfaces. Consistent with the hypothesis, it was found that trifluoroethane, but not perfluoroethane, exhibits a free energy minimum and, therefore, increased concentrations at both interfaces. The transfer of trifluoroethane from water to the nonpolar hexane or interior of the membrane is accompanied by a considerable, solvent-induced shift in the conformational equilibrium around the C-C bond.

NASA Center ARC↗
NASA NTRS1996

Shape deformation of the organ of Corti associated with length changes of outer hair cell

Cochlear outer hair cells (OHC) are commonly assumed to function as mechanical effectors as well as sensory receptors in the organ of Corti (OC) of the inner ear. OHC in vitro and in organ explants exhibit mechanical responses to electrical, chemical or mechanical stimulation which may represent an aspect of their effector process that is expected in vivo. A detailed description, however, of an OHC effector operation in situ is still missing. Specifically, little is known as to how OHC movements influence the geometry of the OC in situ. Previous work has demonstrated that the motility of isolated OHCs in response to electrical stimulation and to K(+)-gluconate is probably under voltage control and causes depolarisation (shortening) and hyperpolarization (elongation). This work was undertaken to investigate if the movements that were observed in isolated OHC, and which are induced by ionic stimulation, could change the geometry of the OC. A synchronized depolarization of OHC was induced in guinea pig cochleae by exposing the entire OC to artificial endolymph (K+). Subsequent morphometry of mid-modiolar sections from these cochleae revealed that the distance between the basilar membrane (BM) and the reticular lamina (RL) had decreased considerably. Furthermore, in the three upper turns OHC had significantly shortened in all rows. The results suggest that OHC can change their length in the organ of Corti (OC) thus deforming the geometry of the OC. The experiments reveal a tonic force generation within the OC that may change the position of RL and/or BM, contribute to damping, modulate the BM-RL-distance and control the operating points of RL and sensory hair bundles. Thus, the results suggest active self-adjustments of cochlear mechanics by slow OHC length changes. Such mechanical adjustments have recently been postulated to correspond to timing elements of animal communication, speech or music.

Non-NASA Center↗