Artificial Neural Networks in the Power Industry, Survey and Applications
The paper presents a survey of the development and experience with artificial neural net (ANN).
Neural Networks↗
SEARCH · Engineering Papers
Results for “NEURAL NETWORKS”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Constraint-Based Repair of Neural Network Classifiers
Explore the source record for details and available documents.
Deep Neural Networks↗
Modeling of Plant-Based Life Support Processes Using Artificial Neural Networks
Future regenerative life support systems will rely heavily on crop plants to perform several functions.
neural networks regenerative life support↗
A locally-connected neural network for fingerprint recognition
Fingerprint recognition is typically done by matching graphs or templates of extracted minutia from whole fingerprint images.
neural network pattern recognition fingerprint↗
Pattern-based fault diagnosis using neural networks
An architecture for a real-time pattern-based diagnostic expert system capable of accommodating noisy, incomplete, and possibly erroneous input data is outlined. Results from prototype systems applied to jet and rocket engine fault diagnosis are presented. The ability of a neural network-based system to be trained via the presentation of behavioral patterns associated with fault conditions is demonstrated.
Dietz, W. E.↗
Neural Networks Based Approach to Enhance Space Hardware Reliability
No abstract available
neural networks↗
Comparing Deep Neural Network Architectures using Transfer Learning to Predict Cognitive States
Abstract is attached
neural network↗
Actuator tuning for nonlinear truss structures - A neural network approach
The optimal actuator placement problem encompasses finding their best locations and best gains. The latter is referred to as tuning the actuators. This paper deals with tuning a given set of actuators in presence of known structural nonlinearities arising from gaps at some joints. For this purpose, an efficient neural network-based algorithm is proposed and illustrated using numerical simulations.
Toomarian, N.↗
A Neural Network Based CDMA Detector with Robust Near-Far Resistance
In code division multiple access (CDMA), high efficiency for spectrum usage and robust jamming resistance have been increasingly important.
Neural Networks Detector Resistance↗
Discrete Recurrent Neural Networks as Pushdown Automata
In this paper, we describe a new discrete recurrent model with discrete external stacks for learning context-free grammars (or pushdown automata).
Neural networks input strings↗
A Neural Network Approach for Identifying Particle Pitch Angle Distributions in Van Allen Probes Data
Analysis of particle pitch angle distributions (PADs) has been used as a means to comprehend a multitude of different physical mechanisms that lead to flux variations in the Van Allen belts and also to particle precipitation into the upper atmosphere. In this work we developed a neural network-based data clustering methodology that automatically identifies distinct PAD types in an unsupervised way using particle flux data. One can promptly identify and locate three well-known PAD types in both time and radial distance, namely, 90deg peaked, butterfly, and flattop distributions. In order to illustrate the applicability of our methodology, we used relativistic electron flux data from the whole month of November 2014, acquired from the Relativistic Electron-Proton Telescope instrument on board the Van Allen Probes, but it is emphasized that our approach can also be used with multiplatform spacecraft data. Our PAD classification results are in reasonably good agreement with those obtained by standard statistical fitting algorithms. The proposed methodology has a potential use for Van Allen belt's monitoring.
Souza, V. M.↗
The Hardware Results of a 64 x 64 Analog Input Array for a 3-Dimensional Neural Network Processor
Algorithms for the solution of spatio-temporal recognition and classification problems are known to require intense image data computation.
3-dimensional artificial neural network (3DANN) Co↗
A Lagrangian Formulation of Neural Networks I: Theory and Analog Dynamics
We expand the mathematicla apparatus for relaxation networks, which conventionally consists of an objective function E and a dynamics given by a system of differenctial equations along whose trajectories E is diminished.
Neural Networks Analog↗
Measuring Aqueous Humor Glucose Across Physiological Levels: NIR Raman Spectroscopy, Multivariate Analysis, Artificial Neural Networks, and Bayesian Probabilities
We have elicited a reliable Raman spectral signature for glucose in rabbit aqueous humor across mammalian physiological ranges in a rabbit model stressed by recent myocardial infarction.
NIR↗
Neural Network for Analysis of Hyperspectral Imagery
Each material has unique spectral distribution that can be viewed as its fingerprint. Advanced hyper and ultra spectral sensors will produce large amount of data. Therefore, advanced capabilities are required that can effectively deal with large data cubes, possibly in real time.
hyperspectral imagery neural network↗
Learning to Classify Map Data with Cascaded VLSI Neural Network Building Block Chips
Paper maps are an important but unwieldy data format. To increase its utility, copious amounts of map data have been scanned into a digital map knowledge base. The next task in this knowledge base is to reduce this data to its underlying feature form suitable for analysis.
VLSI Neural Network Map Data↗
Artificial Neural Networks
Electric power systems represent complex systems involving many electrical components whoseoperation has to be planned, analyzed, monitored and controlled. The time-scale of tasks in electricpower systems extends from long term planning years ahead to milliseconds in the area of control. The behavior of power systems is highly non-linear. Monitoring and control involves several hundred variables which are only partly available by measurements.
Neural Networks↗