Electric power systems are characterized by their immense complexity. The assessment of their security on-line has always been a challenging task. Many possibilities were investigated in the past in an attempt to characterize the secure operating region of a power system. Pattern recognition is thus far the only tool that can take various degrees of network complexity into consideration. / In the present study, an efficient algorithm which learns adaptively the secure operating region is proposed. At each iteration, training operating points are generated sequentially on a piecewise linearly approximated separation surface computed by the one-nearest-neighbor (1-NN) rule. The separation surface so estimated approaches the true one as the number of training points increases. The algorithm not only provides a consistent technique in learning an unknown region, it generates a highly efficient training set. It is found to be effective in reducing the size of the training set without adverse effect to the classifier. / Once the secure region of a power system is available, the task of on-line security monitoring reduces to one of determining whether the current operating point resides in the secure region. As demonstrated in the thesis, both the security status and the security margin of the operating point can be assessed very efficiently. By using the piecewise linearly approximated secure region, the thesis proceeds to give efficient ways of moving an insecure operating point into the secure region. This comprises the problem of security enhancement. / The regionwise methodology via the Voronoi diagram developed in the thesis is also applied to a wide range of problems, such as network planning, coordinating tuning of machine parameters and automatic contingency selection. The major merit is that the dynamics and the nonlinearity of the system no longer present a limitation to solving these problems.

The Fourier-Mellin transform was implemented on a digital computer and applied towards the recognition and differentiation of images of plant leaves regardless of translation, rotation or scale. Translated, rotated and scaled leaf images from seven species of plants were compared: avocado ( Persea americana), trembling aspen (Populus tremuloides), lamb's-quarter (Chenopodium album), linden (Tilla americana), silver maple (Acer saccharinum), plantain (Plantago major) and sumac leaflets (Rhus typhina ). The rate of recognition was high among translated and rotated leaf images for all plant species. The rates of recognition and differentiation were poor, however, among scaled leaf images and between leaves of different species. Improvements to increase the effectiveness of the algorithm are suggested.

Traditional Automatic Target Recognition (ATR) Systems often fail when faced with complex recognition tasks involving noise, clutter, and complexity. This work is concerned with implementing a real time, vision based ATR system using an Artificial Neural Network (ANN) to overcome some of the shortcomings of traditional ATR systems. The key issues of this work are vision, pattern recognition and artificial neural networks. The ANN presented in this thesis is inspired by Prof. Stephen Grossberg's work in Adaptive Resonance Theory (ART) and neurophysiological data on the primate brain. An ANN known as Selective Attention Adaptive Resonance Theory (SAART) (Lozo, 1995, 1997) forms the basis of this work. SAART, which is based on Grossberg's ART, models the higher levels of visual processing in the primate brain to provide an ATR system capable of learning and recognising targets in cluttered and complex backgrounds. This thesis contributes an extension to the SAART model to allow a degree of tolerance to imperfections including distortion, changes in size, orientation, or position. In addition to this extension, it is also demonstrated how modulated neural layers can be used for image filtering. A possible extension of the architecture for multi-sensory environments is proposed as a foundation for future research. / Thesis (MEng)--University of South Australia, 2000

Includes bibliographical references (leaves 225-241). / xxviii, 241 leaves : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Proposes an architectural framework based on neural networks for visual scene analysis with attentional mechanisms. / Thesis (Ph.D.)--Adelaide University, Dept. of Electrical and Electronic Engineering, 2001

Traditional Automatic Target Recognition (ATR) Systems often fail when faced with complex recognition tasks involving noise, clutter, and complexity. This work is concerned with implementing a real time, vision based ATR system using an Artificial Neural Network (ANN) to overcome some of the shortcomings of traditional ATR systems. The key issues of this work are vision, pattern recognition and artificial neural networks. The ANN presented in this thesis is inspired by Prof. Stephen Grossberg's work in Adaptive Resonance Theory (ART) and neurophysiological data on the primate brain. An ANN known as Selective Attention Adaptive Resonance Theory (SAART) (Lozo, 1995, 1997) forms the basis of this work. SAART, which is based on Grossberg's ART, models the higher levels of visual processing in the primate brain to provide an ATR system capable of learning and recognising targets in cluttered and complex backgrounds. This thesis contributes an extension to the SAART model to allow a degree of tolerance to imperfections including distortion, changes in size, orientation, or position. In addition to this extension, it is also demonstrated how modulated neural layers can be used for image filtering. A possible extension of the architecture for multi-sensory environments is proposed as a foundation for future research. / Thesis (MEng)--University of South Australia, 2000

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 4, 2008) Vita. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Computer Science." Includes bibliographical references (p. 183-192).

Thesis (M.S.)--Ohio University, November, 1999. / Title from PDF t.p.

Thesis (Ph.D.)--Murdoch University, 2003. / Thesis submitted to the Division of Arts. Bibliography: leaves 156-165.