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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗
NASA NTRSDate not supplied

Development of Neural Network for X-59 Air Data Probe Calibration

Two wind tunnel tests were conducted in the NASA Glenn Research Center (GRC) 8- by 6-Foot Supersonic Wind Tunnel to calibrate the nose-mounted air data probe for the X-59. The probe will be the primary instrument for the flight test of the X-59 for determining angle of attack, angle of sideslip, airspeed, and pressure altitude of the aircraft in flight. This paper documents the development of several neural net algorithms that can be used to relate air data probe port pressures from those tests to relevant X-59 flight parameters, such as angle of attack, angle of sideslip, mach number, and static and dynamic pressure. These calibration algorithms address accuracy effects for significant real-world conditions, such as initial sensor measurement uncertainties, assumed parameter constraints on NN modelling errors, and malfunctioning or plugged ports. Resultant integrated uncertainties are provided for each algorithm; these uncertainties are then used to estimate the effects on relevant X-59 mission parameters.

Kurtis Roy Long↗
NASA NTRS1989

Automatic labeling and characterization of objects using artificial neural networks

Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

Campbell, William J.↗
NASA NTRS1975

The auditory neural network in man

The principles of anatomy and physiology necessary for understanding brain wave recordings made from the scalp of normal people are briefly discussed. Brain waves evoked by sounds are described and certain of their features are related to the physical aspects of the stimulus and to the psychological state of the listener. The position is taken that data obtained through scalp probes can reveal a large amount of detail about brain functioning and that analysis of such records enable detection of the response of the nervous system to an acoustic message at the moment of its inception and to the progress of the message through the brain. Brain events responsible for distinguishing between similar signals and making decisions about them appear to generate characteristic and identifiable electrical waves. Some theoretical speculation about these data are introduced with the aim of generating a more heuristic model of the functioning brain.

Galambos, R.↗
NASA NTRS1975

The auditory neural network in man

The principles of anatomy and physiology necessary for understanding brain wave recordings made from the scalp are briefly discussed. Brain waves evoked by sounds are then described and certain of their features are related to the physical aspects of the stimulus and the psychological state of the listener. It is proposed that data obtained through probes located outside the head can reveal a large amount of detail about brain activity. It is argued that analysis of such records enables one to detect the response of the nervous system to an acoustic message at the moment of its inception at the ear, and to follow the progress of the acoustic message up through the various brain levels as progressively more complex operations are performed upon it. Even those brain events responsible for the highest level of signal processing - distinguishing between similar signals and making decisions about them - seem to generate characteristic and identifiable electrical waves.

Galambos, R.↗
NASA NTRS1988

Electronic Neural-Network Simulator

Experimental circuits faster than simulation programs run on digital computers. Serial shift register routes clock pulses C1 to neurons in sequence. Clock pulses C2 interrogate neurons. Neuron interconnection information stored in simulated synapses. Can be expanded to greater complexity.

Moopenn, Alex W.↗
NASA NTRS1988

Programmable Synaptic Arrays For Electronic Neural Networks

High resistances prevent hotspots in parallel input and output operation. Nonvolatile computer memory combines ultrahigh storage density with extremely-low power dissipation. Accommodates about 1 billion bits in square centimeter of surface area. Bit written with expenditure of less than 1 nanojoule of energy and read with even lower energy. Developed for parallel input and output operation.

Thakoor, Anilkumar P.↗
NASA NTRS1988

Integrated Circuit For Simulation Of Neural Network

Ballast resistors deposited on top of circuit structure. Cascadable, programmable binary connection matrix fabricated in VLSI form as basic building block for assembly of like units into content-addressable electronic memory matrices operating somewhat like networks of neurons. Connections formed during storage of data, and data recalled from memory by prompting matrix with approximate or partly erroneous signals. Redundancy in pattern of connections causes matrix to respond with correct stored data.

Thakoor, Anilkumar P.↗
NASA NTRS1987

Resistive synaptic interconnects for electronic neural networks

The use of the alpha-Ge(1-x):Al(x) and alpha-Ge(1-x):Cu(x) alloys and Pt/Al2O3 cermet thin films as resistive interconnects for binary synaptic memory arrays is evaluated. The fabrication of the 10-20 microns long, 10 microns wide, and 0.1 micron thick interconnects from the alloys and cermet is described. The current-voltage and switching characteristics of the as-deposited films and the patterned test structure are studied. The resistivity, uniformity, stability, and compatibility of the interconnects are examined. It is observed that alpha-Ge(1-x):Cu(x) alloys have a wide resistivity range and low temperature coefficients of resistance; however, their long-term stability is limited due to their low crystallization temperature. It is detected that the alpha-Ge(1-x):Al(x) alloys have higher crystallization temperatures and their resistivity is not greatly affected by large changes in metal content. The Pt/Al2O3 samples display excellent stability, easy fabrication, and control of resistivity with metal content.

Lamb, J. L.↗
NASA NTRS1989

Studies with spike initiators - Linearization by noise allows continuous signal modulation in neural networks

It is shown that noise can be an important element in the translation of neuronal generator potentials (summed inputs) to neuronal spike trains (outputs), creating or expanding a range of amplitudes over which the spike rate is proportional to the generator potential amplitude. Noise converts the basically nonlinear operation of a spike initiator into a nearly linear modulation process. This linearization effect of noise is examined in a simple intuitive model of a static threshold and in a more realistic computer simulation of spike initiator based on the Hodgkin-Huxley (HH) model. The results are qualitatively similar; in each case larger noise amplitude results in a larger range of nearly linear modulation. The computer simulation of the HH model with noise shows linear and nonlinear features that were earlier observed in spike data obtained from the VIIIth nerve of the bullfrog. This suggests that these features can be explained in terms of spike initiator properties, and it also suggests that the HH model may be useful for representing basic spike initiator properties in vertebrates.

Yu, Xiaolong↗