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Full-Text Articles in Computer Engineering
Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar
Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar
Electrical & Computer Engineering Faculty Research
The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder.
A Simplistic Plasma Dust Removal Model Employing Radiation Pressure, Robert A. Schill Jr.
A Simplistic Plasma Dust Removal Model Employing Radiation Pressure, Robert A. Schill Jr.
Electrical & Computer Engineering Faculty Research
A simple heuristic model is developed to examine the feasibility of using radiation pressure as a means to transport plasma dust out of the path of the forthcoming electron or photon beam. A slow electromagnetic surface wave coupled to a planar target or substrate exerts the required pressure in the removal process. The model is examined using data and parameters from single-shot radiography experiments. Optimal source requirements are identified for a typical radiography experiment. Source energies and powers are a minimum over an optimum band of frequencies where both conduction and plasma oscillation effects are mutually significant. Above the band …