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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients

Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.

Korsmeyer, David↗
NASA NTRS1997

Motion video compression system with neural network having winner-take-all function

A motion video data system includes a compression system, including an image compressor, an image decompressor correlative to the image compressor having an input connected to an output of the image compressor, a feedback summing node having one input connected to an output of the image decompressor, a picture memory having an input connected to an output of the feedback summing node, apparatus for comparing an image stored in the picture memory with a received input image and deducing therefrom pixels having differences between the stored image and the received image and for retrieving from the picture memory a partial image including the pixels only and applying the partial image to another input of the feedback summing node, whereby to produce at the output of the feedback summing node an updated decompressed image, a subtraction node having one input connected to received the received image and another input connected to receive the partial image so as to generate a difference image, the image compressor having an input connected to receive the difference image whereby to produce a compressed difference image at the output of the image compressor.

Fang, Wai-Chi↗
NASA NTRS1999

Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.

Lo, Ching F.↗
NASA NTRS2007

Method for Constructing Composite Response Surfaces by Combining Neural Networks with Polynominal Interpolation or Estimation Techniques

A method and system for data modeling that incorporates the advantages of both traditional response surface methodology (RSM) and neural networks is disclosed. The invention partitions the parameters into a first set of s simple parameters, where observable data are expressible as low order polynomials, and c complex parameters that reflect more complicated variation of the observed data. Variation of the data with the simple parameters is modeled using polynomials; and variation of the data with the complex parameters at each vertex is analyzed using a neural network. Variations with the simple parameters and with the complex parameters are expressed using a first sequence of shape functions and a second sequence of neural network functions. The first and second sequences are multiplicatively combined to form a composite response surface, dependent upon the parameter values, that can be used to identify an accurate mode

Rai, Man Mohan↗
NASA NTRS1992

Recovery of vestibular function following hair cell destruction by streptomycin

Can the vestibular periphery of warm-blooded vertebrates recover functionally from severe sensory hair cell loss? Recent findings in birds suggest a mechanism for recovery but in fact no direct functional evidence has been reported. We produced vestibular hair cell lesions using the ototoxic agent streptomycin sulfate (600 mg/kg/day, 8 days, chicks, Gallus domesticus). Compound action potentials of the vestibular nerve were used as a direct measure of peripheral vestibular function. Vestibular thresholds, neural activation latencies and amplitudes were documented. Eight days of drug treatment elevated thresholds significantly (P < 0.001) and eliminated all but remnants of vestibular activity. Virtually complete physiological recovery occurred in all animals studied over a period of 70 days following treatment. Thresholds recovered within two weeks of drug treatment whereas the return of response morphologies including activation latencies and amplitudes required an additional 6-8 weeks.

Non-NASA Center↗
NASA NTRS2017

Renin-Angiotensin System (RAS) in Hematopoietic Stem/Progenitor Cells (HS/PC) Predicts Vaso-Reparative Dysfunction and Progression of Diabetic Retinopathy (DR)

Purpose: We tested the hypothesis that loss of angiotensin converting enzyme 2 (ACE2) within diabetic HS/PCs (Hematopoietic Stem/Progenitor Cells) would be detrimental to HS/PC reparative function, and alter their ability to contribute to vascular remodeling in human subjects and rodent models of DR (Diabetic Retinopathy). Methods: Subjects (n52) were recruited as controls (n13) or diabetics (n39) with either no DR, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR or proliferative DR (PDR). Fluorescein angiograms were analyzed using Vessel Generation Analysis (VESGEN) software in a cohort of subjects. CD34+ HS/PCs were isolated from peripheral blood. RAS (Renin-Angiotensin System) gene expression and migration was measured. Diabetic ACE2 knockout (KO)C57BL6-Ins2 (Akita) mice at 3, 6 and 9 months of diabetes were compared to age-matched controls. Bone marrow HS/PC populations were analyzed by flow cytometry and migration and proliferation studies performed. Results: ACE2 gene expression in human CD34+ cells from diabetics without DR was increased compared to controls (p0.0437). Mas receptor mRNA was also increased in diabetics without DR, but reduced with the onset of NPDR (p0.0002), suggesting a loss of compensation. DR was associated with CD34+ cell migratory dysfunction. By VESGEN analysis, vessel density measured by several confirming parameters in early NPDR (n3) was greater than in normal retina (n6) in both arteries and veins, which suggests active retinal remodeling. ACE2KO-Akita and Akita cohorts showed reduced retinal thickness by OCT (Optical Coherence Tomography) at 9 months of diabetes. Absence of ACE2 in 9-month Akita mice led to an accelerated increase in acellular capillaries compared to diabetic alone. Electroretinogram (ERG) in ACE2KO-Akita mice resulted in persistent deterioration of the neural retina. Reparative function studies showed that ACE2KO exacerbated diabetes-induced impairment of LK (Low Potassium) cell migration and proliferative functions as early as 3-month of diabetes (p0.0019). Conclusions: Retinopathy and adverse vascular remodeling in subjects with diabetes was associated with a loss of the protective arm of RAS in HS/PCs. Loss of ACE2 exacerbated vascular dysfunction in diabetic mice.

retina↗
NASA NTRS2004

Low-Cutoff, High-Pass Digital Filtering of Neural Signals

The figure depicts the major functional blocks of a system, now undergoing development, for conditioning neural signals acquired by electrodes implanted in a brain. The overall functions to be performed by this system can be summarized as preamplification, multiplexing, digitization, and high-pass filtering. Other systems under development for recording neural signals typically contain resistor-capacitor analog low-pass filters characterized by cutoff frequencies in the vicinity of 100 Hz. In the application for which this system is being developed, there is a requirement for a cutoff frequency of 5 Hz. Because the resistors needed to obtain such a low cutoff frequency would be impractically large, it was decided to perform low-pass filtering by use of digital rather than analog circuitry. In addition, it was decided to timemultiplex the digitized signals from the multiple input channels into a single stream of data in a single output channel. The signal in each input channel is first processed by a preamplifier having a voltage gain of approximately 50. Embedded in each preamplifier is a low-pass anti-aliasing filter having a cutoff frequency of approximately 10 kHz. The anti-aliasing filters make it possible to couple the outputs of the preamplifiers to the input ports of a multiplexer. The output of the multiplexer is a single stream of time-multiplexed samples of analog signals. This stream is processed by a main differential amplifier, the output of which is sent to an analog-to-digital converter (ADC). The output of the ADC is sent to a digital signal processor (DSP).

Mojarradi,Mohammad↗
NASA NTRS2005

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗
NASA NTRS2007

Hybrid Neural Network and Support Vector Machine Method for Optimization

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Rai, Man Mohan↗
NASA NTRS1993

Photodetector Array For Neural Network

Integrated circuit performs 1,024 threshold operations in parallel. Designed for use in postprocessing output of optical correlator in optoelectronic neural network. Overall function of device to put out digital signals indicative of location(s) of bright spot(s) on detector plane. Response time less than commercial charge-coupled-device (CCD) imaging detector operating at standard television frame rate.

Langenbacher, Harry↗
NASA NTRS1997

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

NASA Discipline Regulatory Physiology↗
NASA NTRS1991

Depth-size tradeoffs for neural computation

The tradeoffs between the depth (i.e., the time for parallel computation) and the size (i.e., the number of threshold gates) in neural networks are studied. The authors focus the study on the neural computations of symmetric Boolean functions and some arithmetic functions. It is shown that a significant reduction in the size is possible for symmetric functions and some arithmetic functions, at the expense of a small constant increase in depth. In the process, several neural networks which have the minimum size among all the known constructions have been developed. Results on implementing symmetric functions can be used to improve results about arbitrary Boolean functions. In particular, It is shown that any Boolean function can be computed in a depth-3 neural network with O(2n/2) threshold gates; it is also proven that a minimum number of threshold gates are required.

Siu, Kai-Yeung↗
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 NTRS2011

System and method for determining stability of a neural system

Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

Curtis, Steven A.↗
NASA NTRS2011

Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

pattern identification↗