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NASA NTRS1992

Inversion of parameters for semiarid regions by a neural network

Microwave brightness temperatures obtained from a passive radiative transfer model are inverted through use of a neural network. The model is applicable to semiarid regions and produces dual-polarized brightness temperatures for 6.6-, 10.7-, and 37-GHz frequencies. A range of temperatures is generated by varying three geophysical parameters over acceptable ranges: soil moisture, vegetation moisture, and soil temperature. A multilayered perceptron (MLP) neural network is trained with a subset of the generated temperatures, and the remaining temperatures are inverted using a backpropagation method. Several synthetic terrains are devised and inverted by the network under local constraints. All the inversions show good agreement with the original geophysical parameters, falling within 5 percent of the actual value of the parameter range.

Zurk, Lisa M.↗

NASA NTRS2012

Aircraft system modeling error and control error

A method for modeling error-driven adaptive control of an aircraft. Normal aircraft plant dynamics is modeled, using an original plant description in which a controller responds to a tracking error e(k) to drive the component to a normal reference value according to an asymptote curve. Where the system senses that (1) at least one aircraft plant component is experiencing an excursion and (2) the return of this component value toward its reference value is not proceeding according to the expected controller characteristics, neural network (NN) modeling of aircraft plant operation may be changed. However, if (1) is satisfied but the error component is returning toward its reference value according to expected controller characteristics, the NN will continue to model operation of the aircraft plant according to an original description.

Kulkarni, Nilesh V.↗

NASA NTRS1993

Neural network for processing both spatial and temporal data with time based back-propagation

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Villarreal, James A.↗

NASA NTRS2013

Real-Time Minimization of Tracking Error for Aircraft Systems

This technology presents a novel, stable, discrete-time adaptive law for flight control in a Direct adaptive control (DAC) framework. Where errors are not present, the original control design has been tuned for optimal performance. Adaptive control works towards achieving nominal performance whenever the design has modeling uncertainties/errors or when the vehicle suffers substantial flight configuration change. The baseline controller uses dynamic inversion with proportional-integral augmentation. On-line adaptation of this control law is achieved by providing a parameterized augmentation signal to a dynamic inversion block. The parameters of this augmentation signal are updated to achieve the nominal desired error dynamics. If the system senses that at least one aircraft component is experiencing an excursion and the return of this component value toward its reference value is not proceeding according to the expected controller characteristics, then the neural network (NN) modeling of aircraft operation may be changed.

Garud, Sumedha↗

NASA NTRS1999

Neural-Net Processing of Characteristic Patterns From Electronic Holograms of Vibrating Blades

Finite-element-model-trained artificial neural networks can be used to process efficiently the characteristic patterns or mode shapes from electronic holograms of vibrating blades. The models used for routine design may not yet be sufficiently accurate for this application. This document discusses the creation of characteristic patterns; compares model generated and experimental characteristic patterns; and discusses the neural networks that transform the characteristic patterns into strain or damage information. The current potential to adapt electronic holography to spin rigs, wind tunnels and engines provides an incentive to have accurate finite element models lor training neural networks.

Decker, Arthur J.↗

NASA NTRS1989

Variable-Resistivity Material For Memory Circuits

Nonvolatile memory elements packed densely. Electrically-erasable, programmable, read-only memory matrices made with newly-synthesized organic material of variable electrical resistivity. Material, polypyrrole doped with tetracyanoquinhydrone (TCNQ), changes reversibly between insulating or higher-resistivity state and conducting or low-resistivity state. Thin film of conductive polymer separates layer of row conductors from layer of column conductors. Resistivity of film at each intersection and, therefore, resistance of memory element defined by row and column, increased or decreased by application of suitable switching voltage. Matrix circuits made with this material useful for experiments in associative electronic memories based on models of neural networks.

Nagasubramanian, Ganesan↗

NASA NTRS1989

Reconstructing irregularly sampled images by neural networks

Neural-network-like models of receptor position learning and interpolation function learning are being developed as models of how the human nervous system might handle the problems of keeping track of the receptor positions and interpolating the image between receptors. These models may also be of interest to designers of image processing systems desiring the advantages of a retina-like image sampling array.

Ahumada, Albert J., Jr.↗

NASA NTRS1991

Real-time fault diagnosis for propulsion systems

Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.

Merrill, Walter C.↗

NASA NTRS1992

Neural networks in structural analysis and design - An overview

The present paper provides an overview of the state-of-the-art in the application of neural networks in problems of structural analysis and design, including a survey of published applications in structural engineering. Such applications have included, among others, the use of neural networks in modeling nonlinear analysis of structures, as a rapid reanalysis capability in optimal design, and in developing problem parameter sensitivity of optimal solutions for use in multilevel decomposition based design. While most of the applications reported in the literature have been restricted to the use of the multilayer perceptron architecture and minor variations thereof, other network architectures have also been successfully explored, including the ART network, the counterpropagation network and the Hopfield-Tank model.

Hajela, P.↗

NASA NTRS1993

Material Data Representation of Hysteresis Loops for Hastelloy X Using Artificial Neural Networks

The artificial neural network (ANN) model proposed by Rumelhart, Hinton, and Williams is applied to develop a functional approximation of material data in the form of hysteresis loops from a nickel-base superalloy, Hastelloy X. Several different ANN configurations are used to model hysteresis loops at different cycles for this alloy. The ANN models were successful in reproducing the hysteresis loops used for its training. However, because of sharp bends at the two ends of hysteresis loops, a drift occurs at the corners of the loops where loading changes to unloading and vice versa (the sharp bends occurred when the stress-strain curves were reproduced by adding stress increments to the preceding values of the stresses). Therefore, it is possible only to reproduce half of the loading path. The generalization capability of the network was tested by using additional data for two other hysteresis loops at different cycles. The results were in good agreement. Also, the use of ANN led to a data compression ratio of approximately 22:1.

Alam, Javed↗

NASA NTRS1991

Watch what you say, your computer might be listening: A review of automated speech recognition

Spoken language is the most convenient and natural means by which people interact with each other and is, therefore, a promising candidate for human-machine interactions. Speech also offers an additional channel for hands-busy applications, complementing the use of motor output channels for control. Current speech recognition systems vary considerably across a number of important characteristics, including vocabulary size, speaking mode, training requirements for new speakers, robustness to acoustic environments, and accuracy. Algorithmically, these systems range from rule-based techniques through more probabilistic or self-learning approaches such as hidden Markov modeling and neural networks. This tutorial begins with a brief summary of the relevant features of current speech recognition systems and the strengths and weaknesses of the various algorithmic approaches.

Degennaro, Stephen V.↗

NASA NTRS1996

Advanced Intelligent System Application to Load Forecasting and Control for Hybrid Electric Bus

The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.

Momoh, James↗

NASA NTRS2000

Reliability Modeling of Microelectromechanical Systems Using Neural Networks

Microelectromechanical systems (MEMS) are a broad and rapidly expanding field that is currently receiving a great deal of attention because of the potential to significantly improve the ability to sense, analyze, and control a variety of processes, such as heating and ventilation systems, automobiles, medicine, aeronautical flight, military surveillance, weather forecasting, and space exploration. MEMS are very small and are a blend of electrical and mechanical components, with electrical and mechanical systems on one chip. This research establishes reliability estimation and prediction for MEMS devices at the conceptual design phase using neural networks. At the conceptual design phase, before devices are built and tested, traditional methods of quantifying reliability are inadequate because the device is not in existence and cannot be tested to establish the reliability distributions. A novel approach using neural networks is created to predict the overall reliability of a MEMS device based on its components and each component's attributes. The methodology begins with collecting attribute data (fabrication process, physical specifications, operating environment, property characteristics, packaging, etc.) and reliability data for many types of microengines. The data are partitioned into training data (the majority) and validation data (the remainder). A neural network is applied to the training data (both attribute and reliability); the attributes become the system inputs and reliability data (cycles to failure), the system output. After the neural network is trained with sufficient data. the validation data are used to verify the neural networks provided accurate reliability estimates. Now, the reliability of a new proposed MEMS device can be estimated by using the appropriate trained neural networks developed in this work.

Perera. J. Sebastian↗

NASA NTRS2001

A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks

An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.

Rai, Man Mohan↗

NASA NTRS2002

Using Support Vector Machines to Automatically Extract Open Water Signatures from POLDER Multi-Angle Data Over Boreal Regions

This study used Support Vector Machines to classify multiangle POLDER data. Boreal wetland ecosystems cover an estimated 90 x 10(exp 6) ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 4 degrees N latitude. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches, neural networks, and active microwave, examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

Pierce, J.↗

NASA NTRS2007

Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

Jafri, Madiha J.↗

NASA NTRS2018

Advantages of Measuring the Q Stokes Parameter in Addition to the Total Radiance I in the Detection of Absorbing Aerosols: a Proof of Concept Using Vector Radiative Transfer, Neural Networks, and Optimal Estimation

A simple but novel study was conducted to investigate whether an imager-type spectroradiometer instrument like MODIS, currently flying on board the Aqua and Terra satellites, or MERIS, which flew on board Envisat, could detect absorbing aerosols if they could measure the Q Stokes parameter in addition to the total radiance I, that is if they could also measure the linear polarization of the light. Accurate radiative transfer calculations were used to train a fast neural network forward model, which together with a simple statistical optimal estimation scheme was used to retrieve three aerosol parameters: aerosol optical depth, fraction of absorbing aerosols, and aerosol location. The aerosols were assumed to be located either between 0 and 2 km or between 2 and 4 km in the Earth’s atmosphere. From simulated data it was found that by itself the total radiance I was generally insufficient to retrieve all three aerosol parameters, but that together with the Q Stokes component it was possible to reliably retrieve values of aerosol optical depth, fraction, and location.

Stamnes, Snorre↗

NASA NTRSDate not supplied

Expanding NeMO-Net Machine Learning Capabilities for Citizen Science

NASA NeMO-Net, the neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive citizen science video game, released this April, for desktop and iOS devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using diver photomosaic imagery, the UAV enabled NASA FluidCam instrument, and satellite datasets. To date, the application has had over 40,000 downloads and over 60,000 unique coral reef classifications, each filtered through a user-based rating and expert evaluation system. We also present results from NeMO-Net’s convolutional neural network (CNN) models used to semantically segment 2D satellite imagery as well as projections of 3D coral reconstructions using user input data as training datasets. Fusing datasets using machine learning from multiple remote sensing platforms presents novel methodologies for assessing the health of coral ecosystems, which are critically endangered by a changing climate. In partnering with Mission Blue, the National Oceanic and Atmospheric Administration (NOAA), and the Living Oceans Foundation (LOF), NeMO-Net leverages an international consortium of subject matter experts to provide both proper training for citizen scientists and the generation of a labeled datasets to ingest into machine learning algorithms for global coral reef identification.

NeMO-Net↗