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Thesis (M.S.)--Ohio University, March, 1992. / Title from PDF t.p.

Thesis (M.S.)--University of Nevada, Reno, 2006. / "December 2006." Includes bibliographical references (leaves 42-44). Online version available on the World Wide Web.

Thesis (M.S.)--University of Nevada, Reno, 2007. / "December, 2007." Includes bibliographical references (leaves 88-94). Online version available on the World Wide Web.

Predictive maintenance or condition-based maintenance offers significant advantages over the traditional methods of preventive or breakdown maintenance of electromechanical systems. Despite its benefits, predictive maintenance is difficult to implement. This has led to the development of various techniques which allow the early detection of many common fault conditions, through analysis of quantities such as spectral components of line currents, magnetic fields and frame vibrations. Associating the observed signal patterns with the condition of the machine depends to a great extent on the experience and knowledge of experts. The fact that human operators are very successful at these monitoring tasks suggests that one possible method for designing computer-based monitoring systems is to model the learning and decision-making abilities of a human operator. The philosophy pursued in this research is therefore to design a system that should be able to emulate as closely as possible the learning, pattern recognition and the sensor fusion abilities of human operators. This thesis is, therefore, concerned with the application of artificial neural networks to condition monitoring of electrical drives, with particular reference to induction motors. The neural networks studied were the multi-layered perceptron (MLP) and the Kohonen self-organising feature map (KFM). The learning paradigm of the former is supervised, while the latter is unsupervised. A comprehensive theoretical basis is provided for both neural networks employed, and their effectiveness is verified by suitable experiments. The ability of the neural networks to predict the condition of the machine for varying fault severity as well as the transferrability of a trained network to monitor other machines of similar characteristics were also of interest. The suitability of the neural network fault diagnosis system for inverter-fed machines was also studied.

Thesis (M.S.)--Florida State University, 2005. / Advisor: Mark H. Weatherspoon, Florida State University, College of Engineering, Dept. of Electrical and Computer Engineering. Title and description from dissertation home page (viewed Jan. 26, 2006). Document formatted into pages; contains x, 107 pages. Includes bibliographical references.

Thesis (M.App.Sc.) - Carleton University, 2005. / Includes bibliographical references (p. 172-178). Also available in electronic format on the Internet.

Thesis (Ph. D.) -- University of Texas Southwestern Medical Center at Dallas, 2000. / Vita. Bibliography: 174-182.

Environmental gamma-ray spectroscopy provides a powerful tool that can be used in environmental monitoring given that it offers a compromise between measurement time and accuracy allowing for large areas to be surveyed quickly and relatively inexpensively. Depending on monitoring objectives, spectral information can then be analysed in real-time or post survey to characterise contamination and identify potential anomalies. Smaller volume detectors are of particular worth to environmental surveys as they can be operated in the most demanding environments. However, difficulties are encountered in the selection of an appropriate detector that is robust enough for environmental surveying yet still provides a high quality signal. Furthermore, shortcomings remain with methods employed for robust spectral processing since a number of complexities need to be overcome including: the non-linearity in detector response with source burial depth, large counting uncertainties, accounting for the heterogeneity in the natural background and unreliable methods for detector calibration. This thesis aimed to investigate the application of machine learning algorithms to environmental gamma-ray spectroscopy data to identify changes in spectral shape within large Monte Carlo calibration libraries to estimate source characteristics for unseen field results. Additionally, a 71 × 71 mm lanthanum bromide detector was tested alongside a conventional 71 × 71 mm sodium iodide to assess whether its higher energy efficiency and resolution could make it more reliable in handheld surveys. The research presented in this thesis demonstrates that machine learning algorithms could be successfully applied to noisy spectra to produce valuable source estimates. Of note, were the novel characterisation estimates made on borehole and handheld detector measurements taken from land historically contaminated with 226Ra. Through a novel combination of noise suppression and neural networks the burial depth, activity and source extent of contamination was estimated and mapped. Furthermore, it was demonstrated that Machine Learning techniques could be operated in real-time to identify hazardous 226Ra containing hot particles with much greater confidence than current deterministic approaches such as the gross counting algorithm. It was concluded that remediation of 226Ra contaminated legacy sites could be greatly improved using the methods described in this thesis. Finally, Neural Networks were also applied to estimate the activity distribution of 137Cs, derived from the nuclear industry, in an estuarine environment. Findings demonstrated the method to be theoretically sound, but practically inconclusive, given that much of the contamination at the site was buried beyond the detection limits of the method. It was generally concluded that the noise posed by intrinsic counts in the 71 × 71 mm lanthanum bromide was too substantial to make any significant improvements over a comparable sodium iodide in contamination characterisation using 1 second counts.

The thesis deals with design, implementation and optimization of a model based on artificial intelligence and neural networks, which is able to predict future time series prices on a stock market. Main goal is to create an object oriented application for successful future trend prediction of financial derivatives with the use of cooperating methods such as Hurst exponent evaluation and automated market simulation.