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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Electronic Handbooks Simplify Process Management

Getting a multitude of people to work together to manage processes across many organizations for example, flight projects, research, technologies, or data centers and others is not an easy task. Just ask Dr. Barry E. Jacobs, a research computer scientist at Goddard Space Flight Center. He helped NASA develop a process management solution that provided documenting tools for process developers and participants to help them quickly learn, adapt, test, and teach their views. Some of these tools included editable files for subprocess descriptions, document descriptions, role guidelines, manager worksheets, and references. First utilized for NASA's Headquarters Directives Management process, the approach led to the invention of a concept called the Electronic Handbook (EHB). This EHB concept was successfully applied to NASA's Small Business Innovation Research (SBIR) and Small Business Technology Transfer (STTR) programs, among other NASA programs. Several Federal agencies showed interest in the concept, so Jacobs and his team visited these agencies to show them how their specific processes could be managed by the methodology, as well as to create mockup versions of the EHBs.

Source record↗
NASA NTRS2021

Locating the Isolator Shock-Train Leading Edge with Limited Pressure Information

Real-time detection and control of the isolator shock-train leading edge (STLE) is important to the performance of high-speed air-breathing engines, such as dual-mode scramjets. Typically, the STLE location is determined using wall static-pressure measurements, but there are often restrictions on the placement and overall number of the pressure transducers, reducing the viability and accuracy of such approaches. To address these issues, we introduce the adaptive pressure profile (APP) method for estimating the STLE location. This method does not require extensive prior characterization of the isolator or engine model. Instead, it uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile and subsequently uses this deduced profile to estimate the STLE location in a data-driven manner. The APP method works well in situations with sparse transducer placement. It produces accurate estimates when the STLE location is 1) not bounded by two or more transducers or 2) between two transducers that are several isolator duct heights apart. We demonstrate the efficacy of the APP method using simulations and experimental data from direct-connect isolator models. This validation shows that the APP method is accurate and robust for different flow regimes, transducer configurations, and model geometries.

Gregory J. Hunt↗
NASA NTRSDate not supplied

Deploying a Self-Supervised Learning Based Model to Search Events Across Space and Time

Motivation - Scientific Study of natural events, phenomena, or disasters require examples which span across time and space. - Machine Learning adaptation is on the rise, but there’s a lack of labeled training datasets that could be used to train or validate the models. - Best case scenario: - There’s an event database that tracks events available through time and space. - Provides all data associated with the events. - Real life scenario: - Some events are better tracked than others. - Scientists need to spend significant time identifying and gathering examples of events from different sources.

Iksha Gurung↗
NASA NTRS1979

System parameter adaptation via a learning procedure

The paper considers the problem of designing a learning control system (LCS) that is capable of meeting design requirements over many possible operating conditions of the plant by adjusting, in a prescribed manner, the feedforward and feedback gains of the plant. The approach utilizes the best features of two methods, gain scheduling and adaptive control. The LCS was implemented using two models, one representing the longitudinal dynamics and the other the lateral dynamics of the simulated plant. A block diagram illustrating the functional organization of an LCS is presented.

Mekel, R.↗
NASA NTRS1991

Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

Lee, Chuen-Chien↗
NASA NTRS2004

Learning to behave: adaptive behavior for planetary surface rovers

Robotic missions to planetary surfaces are becoming more ambitious and of longer duration. The nominal mission timeline for the MER called Spirit currently on the Martian surface is 90 days, with extensions to 180 days depending on rover health. The upcoming 2009 MSL mission is planned to be 300-500 days and will possibly involve traverses on the order of a kilometer or more.

autonomous robots↗
NASA NTRSDate not supplied

Simultaneous Trajectory Tracking Control and Online Mass Estimation for a Regolith Excavating Robot via Integral Concurrent Learning

An adaptive controller is developed for a regolith excavation robot to determine the mass of excavated material and to account for the effects of gravity and friction while on the surface of other celestial bodies. A data-based integral concurrent learning (ICL) parameter update law accounts for and estimates the unknown mass, gravity, and friction parameters. A Lyapunov-based analysis proves that the trajectory tracking error and the parameter estimate errors exponentially converge to zero. An estimation of the mass of regolith excavated by the robot is calculated from the estimated parameters. A simulation study is performed to show the performance of the developed technique. Simulation results show that for 3 kilograms of excavated material, the mass estimate has an error of 4.9 grams.

Max L. Greene↗
NASA NTRS1989

Computational neural learning formalisms for manipulator inverse kinematics

An efficient, adaptive neural learning paradigm for addressing the inverse kinematics of redundant manipulators is presented. The proposed methodology exploits the infinite local stability of terminal attractors - a new class of mathematical constructs which provide unique information processing capabilities to artificial neural systems. For robotic applications, synaptic elements of such networks can rapidly acquire the kinematic invariances embedded within the presented samples. Subsequently, joint-space configurations, required to follow arbitrary end-effector trajectories, can readily be computed. In a significant departure from prior neuromorphic learning algorithms, this methodology provides mechanisms for incorporating an in-training skew to handle kinematics and environmental constraints.

Gulati, Sandeep↗
NASA NTRS1972

An estimation algorithm with learning feature for an adaptive bit synchronizer.

Description of an on-line optimal (in the sense of linear minimum mean square error) sequential bit synchronizer assuming minimal a priori knowledge of the incoming data. The adaptive synchronization algorithms, implemented on a small computer, are capable of accurately estimating pulse transition time, pulse amplitude, and pulse width by invoking the powerful Gauss-Markov theorem. Numerous simulations indicate that the performance of the synchronizer is satisfactory from the point of view of the amount of data required and the speed of convergence.

Chow, L. R.↗
NASA NTRS1995

Dynamic neural networks based on-line identification and control of high performance motor drives

In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

Rubaai, Ahmed↗
NASA NTRS1991

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Ross, Muriel D.↗
NASA NTRS1990

Introduction to Fuzzy Set Theory

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Kosko, Bart↗
NASA NTRS2014

Customizing Countermeasure Prescriptions using Predictive Measures of Sensorimotor Adaptability

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the readapation phase following a return to a gravitational environment. These alterations may lead to disruption in the ability to perform mission critical functional tasks during and after these gravitational transitions. Astronauts show significant inter-subject variation in adaptive capability following gravitational transitions. The ability to predict the manner and degree to which each individual astronaut will be affected would improve the effectiveness of a countermeasure comprised of a training program designed to enhance sensorimotor adaptability. Due to this inherent individual variability we need to develop predictive measures of sensorimotor adaptability that will allow us to predict, before actual space flight, which crewmember will experience challenges in adaptive capacity. Thus, obtaining this information will allow us to design and implement better sensorimotor adaptability training countermeasures that will be customized for each crewmember's unique adaptive capabilities. Therefore the goals of this project are to: 1) develop a set of predictive measures capable of identifying individual differences in sensorimotor adaptability, and 2) use this information to design sensorimotor adaptability training countermeasures that are customized for each crewmember's individual sensorimotor adaptive characteristics. To achieve these goals we are currently pursuing the following specific aims: Aim 1: Determine whether behavioral metrics of individual sensory bias predict sensorimotor adaptability. For this aim, subjects perform tests that delineate individual sensory biases in tests of visual, vestibular, and proprioceptive function. Aim 2: Determine if individual capability for strategic and plastic-adaptive responses predicts sensorimotor adaptability. For this aim, each subject's strategic and plastic-adaptive motor learning abilities are assessed using a test of locomotor function designed specifically to delineate both mechanisms. Aim 3: Develop predictors of sensorimotor adaptability using brain structural and functional metrics. We will measure individual differences in regional brain volumes (structural MRI), white matter integrity (diffusion tensor imaging, or DTI), functional network integrity (resting state functional connectivity MRI), and sensorimotor adaptation task-related functional brain activation (functional MRI). We decided to complete the data collection for Specific Aims 1, 2 and 3 simultaneously on the same subjects to increase data capture. By having the same subjects perform all three specific aims we can enhance our ability to detect how a wider range of factors can predict adaptability in a specific individual. This provides a much richer database and potentially a better understanding of the predictive power of the selected factors. In this presentation I will discuss preliminary data obtained to date.

Bloomberg, J. J.↗