Spelling suggestions: "subject:"eomputer bcience. algorithms"" "subject:"eomputer bcience. flgorithms""
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Merging of XML documents /Wei, Wanxia, January 1900 (has links)
Thesis (M.C.S.)--Carleton University, 2004. / Includes bibliographical references (p. 84-89). Also available in electronic format on the Internet.
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Automated defect detection for textile fabrics using Gabor wavelet networksPeng, Pai, January 2006 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.
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A Neural network approach to the virtual dressing room /Muttulingam, Thanasiri, January 1900 (has links)
Thesis (M.App.Sc.) - Carleton University, 2007. / Includes bibliographical references (p. 85-87). Also available in electronic format on the Internet.
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VLSI implementation of a Montgomery modular multiplier /Wang, Xin. January 1900 (has links)
Thesis (M.App.Sc.) - Carleton University, 2006. / Includes bibliographical references (p. 80-82). Also available in electronic format on the Internet.
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Parallele dynamische Adaption hybrider Netze für effizientes verteiltes Rechnen / Parallel dynamic adaptation of hybrid grids for efficient distributed computingAlrutz, Thomas 17 September 2008 (has links)
No description available.
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AI and Machine Learning for SNM detection and Solution of PDEs with Interface ConditionsPola Lydia Lagari (11950184) 11 July 2022 (has links)
<p>Nuclear engineering hosts diverse domains including, but not limited to, power plant automation, human-machine interfacing, detection and identification of special nuclear materials, modeling of reactor kinetics and dynamics that most frequently are described by systems of differential equations (DEs), either ordinary (ODEs) or partial ones (PDEs). In this work we study multiple problems related to safety and Special Nuclear Material detection, and numerical solutions for partial differential equations using neural networks. More specifically, this work is divided in six chapters. Chapter 1 is the introduction, in Chapter</p>
<p>2 we discuss the development of a gamma-ray radionuclide library for the characterization</p>
<p>of gamma-spectra. In Chapter 3, we present a new approach, the ”Variance Counterbalancing”, for stochastic</p>
<p>large-scale learning. In Chapter 4, we introduce a systematic approach for constructing proper trial solutions to partial differential equations (PDEs) of up to second order, using neural forms that satisfy prescribed initial, boundary and interface conditions. Chapter 5 is about an alternative, less imposing development of neural-form trial solutions for PDEs, inside rectangular and non-rectangular convex boundaries. Chapter 6 presents an ensemble method that avoids the multicollinearity issue and provides</p>
<p>enhanced generalization performance that could be suitable for handling ”few-shots”- problems frequently appearing in nuclear engineering.</p>
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