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Full-Text Articles in Engineering
A Comparison Of Main Rotor Smoothing Adjustments Using Linear And Neural Network Algorithms, Nathan A. Miller
A Comparison Of Main Rotor Smoothing Adjustments Using Linear And Neural Network Algorithms, Nathan A. Miller
Theses and Dissertations
Helicopter main rotor smoothing is a maintenance procedure that is routinely performed to minimize airframe vibrations induced by non-uniform mass and/or aerodynamic distributions in the main rotor system. This important task is both time consuming and expensive, so improvements to the process have long been sought. Traditionally, vibrations have been minimized by calculating adjustments based on an assumed linear relationship between adjustments and vibration response. In recent years, artificial neural networks have been trained to recognize non-linear mappings between adjustments and vibration response. This research was conducted in order observe the character of the adjustment mapping of the Vibration Management …
An Integrated Architecture And Feature Selection Algorithm For Radial Basis Neural Networks, Timothy D. Flietstra
An Integrated Architecture And Feature Selection Algorithm For Radial Basis Neural Networks, Timothy D. Flietstra
Theses and Dissertations
There are two basic ways to control an Unmanned Combat Aerial Vehicle (UCAV) as it searches for targets: allow the UCAV to act autonomously or employ man-in-the-loop control. There are also two target sets of interest: fixed or mobile targets. This research focuses on UCAV-based targeting of mobile targets using man-in-the-loop control. In particular, the interest is in how levels of satellite signal latency or signal degradation affect the ability to accurately track, target, and attack mobile targets. This research establishes a weapon effectiveness model assessing targeting inaccuracies as a function of signal latency and/or signal degradation. The research involved …
Pulse Coupled Neural Networks For The Segmentation Of Magnetic Resonance Brain Images, Shane L. Abrahamson
Pulse Coupled Neural Networks For The Segmentation Of Magnetic Resonance Brain Images, Shane L. Abrahamson
Theses and Dissertations
This research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image segmentation has proven difficult, primarily due to scanning artifacts such as interscan and intrascan intensity inhomogeneities. The method developed and presented here uses a PCNN to both filter and segment MR brain images. The technique begins by preprocessing images with a PCNN filter to reduce scanning artifacts. Images are then contrast enhanced via histogram equalization. Finally, a PCNN is used to segment the images to arrive at the final result. Modifications to the original PCNN model are …
Embedology And Neural Estimation For Time Series Prediction, Robert E. Garza
Embedology And Neural Estimation For Time Series Prediction, Robert E. Garza
Theses and Dissertations
Time series prediction has widespread application, ranging from predicting the stock market to trying to predict future locations of scud missiles. Recent work by Sauer and Casdagli has developed into the embedology theorem, which sets forth the procedures for state space manipulation and reconstruction for time series prediction. This includes embedding the time series into a higher dimensional space in order to form an attractor, a structure defined by the embedded vectors. Embedology is combined with neural technologies in an effort to create a more accurate prediction algorithm. These algorithms consist of embedology, neural networks, Euclidean space nearest neighbors, and …
A Fortran Based Learning System Using Multilayer Back-Propagation Neural Network Techniques, Gregory L. Reinhart
A Fortran Based Learning System Using Multilayer Back-Propagation Neural Network Techniques, Gregory L. Reinhart
Theses and Dissertations
An interactive computer system which allows the researcher to build an optimal neural network structure quickly, is developed and validated. This system assumes a single hidden layer perceptron structure and uses the back- propagation training technique. The software enables the researcher to quickly define a neural network structure, train the neural network, interrupt training at any point to analyze the status of the current network, re-start training at the interrupted point if desired, and analyze the final network using two- dimensional graphs, three-dimensional graphs, confusion matrices and saliency metrics. A technique for training, testing, and validating various network structures and …
Etann Hardware Implementation For Radar Emitter Identification, James B. Calvin Jr.
Etann Hardware Implementation For Radar Emitter Identification, James B. Calvin Jr.
Theses and Dissertations
This study investigated classification of 30 radar emitters with 16 signal features using Intel's 80170NX chip, the Electronically Trainable Analog Neural Network (ETANN). Software tools were developed to characterize the ETANN sigmoidal transfer function for use in a custom simulator, known as Neural Graphics. Neural Graphics operates on a Silicon Graphics workstation. The Intel Neural Network Training System simulators were used in early experiments, but were found to be inefficient in training on data used in this research. Using a modified Neural Graphics simulator, single chip and multi-chip experiments were performed to provide benchmark results prior to performing chip-in-loop training. …
Face Recognition With Neural Networks, Dennis L. Krepp
Face Recognition With Neural Networks, Dennis L. Krepp
Theses and Dissertations
This study investigated neural networks for face verification and classification. The research concentrated on developing a neural network based feature extractor and/or classifier to perform authorized user verification in a realistic work environment. Recognition accuracy, system assumptions, training time, and execution time were analyzed to determine the feasibility of a neural network approach. Data was collected using a camcorder and two segmentation schemes: manual segmentation and motion-based, automatic segmentation. Data consisted of over 2000. 32x32 pixel, 8 bit gray scale images of 52 subjects; each subject had two to ten days worth of images collected. Several training and test sets …
Design Of An Artificial Neural Network Based Tactile Sensor For The Utah/Mit Dexterous Hand, Jeffery D. Nering
Design Of An Artificial Neural Network Based Tactile Sensor For The Utah/Mit Dexterous Hand, Jeffery D. Nering
Theses and Dissertations
The Neural Tactile Sensor (NTS) is a high resolution, easily manufactured tactile sensor consisting of electrodes, a thin resistive 'skin', and pattern recognition circuitry that is capable of resolving dynamic and static contact location, force, and slip throughout the continuum of the sensor's active region. The sensor operates by means of a resistive 'skin' harboring the electric field generated when a current is injected into it, and a plurality of electrodes for taking measurements of said electric field. When current flows through the resistive medium from the location of tactile contact, an electric field within the resistive medium is established, …
Radar System Classification Using Neural Networks, David M. Cameron
Radar System Classification Using Neural Networks, David M. Cameron
Theses and Dissertations
This study investigated methods of improving the accuracy of neural networks in the classification of large numbers of classes. A literature search revealed that neural networks have been successful in the radar classification problem, and that many complex problems have been solved using systems of multiple neural networks. The experiments conducted were based on 32 classes of radar system data. The neural networks were modelled using a program called the Neural Graphics Analysis System. It was found that the accuracy of the individual neural networks could be increased by controlling the number of hidden nodes, the relative numbers of training …
An Investigation Of The Application Of Artificial Neural Networks To Adaptive Optics Imaging Systems, Andrew H. Suzuki
An Investigation Of The Application Of Artificial Neural Networks To Adaptive Optics Imaging Systems, Andrew H. Suzuki
Theses and Dissertations
Recurrent and feedforward artificial neural networks are developed as wavefront reconstructors. The recurrent neural network studied is the Hopfield neural network and the feedforward neural network studied is the single layer perceptron artificial neural network. The recurrent artificial neural network input features are the wavefront sensor slope outputs and neighboring actuator feedback commands. The feedforward artificial neural network input features are just the wavefront sensor slope outputs. Both artificial neural networks use their inputs to calculate deformable mirror actuator commands. The effects of training are examined.