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Full-Text Articles in Computational Engineering

Improving Predictive Capabilities Of Classical Cascade Theory For Nonproliferation Analysis, David Allen Vermillion May 2017

Improving Predictive Capabilities Of Classical Cascade Theory For Nonproliferation Analysis, David Allen Vermillion

Doctoral Dissertations

Uranium enrichment finds a direct and indispensable function in both peaceful and nonpeaceful nuclear applications. Today, over 99% of enriched uranium is produced by gas centrifuge technology. With the international dissemination of the Zippe archetypal design in 1960 followed by the widespread illicit centrifuge trafficking efforts of the A.Q. Khan network, traditional barriers to enrichment technologies are no longer as effective as they once were. Consequently, gas centrifuge technology is now regarded as a high-priority nuclear proliferation threat, and the international nonproliferation community seeks new avenues to effectively and efficiently respond to this emergent threat.

Effective response first requires ...


Lattice Boltzmann Methods For Wind Energy Analysis, Stephen Lloyd Wood Aug 2016

Lattice Boltzmann Methods For Wind Energy Analysis, Stephen Lloyd Wood

Doctoral Dissertations

An estimate of the United States wind potential conducted in 2011 found that the energy available at an altitude of 80 meters is approximately triple the wind energy available 50 meters above ground. In 2012, 43% of all new electricity generation installed in the U.S. (13.1 GW) came from wind power. The majority of this power, 79%, comes from large utility scale turbines that are being manufactured at unprecedented sizes. Existing wind plants operate with a capacity factor of only approximately 30%. Measurements have shown that the turbulent wake of a turbine persists for many rotor diameters, inducing ...


Bayesian Dictionary Learning For Single And Coupled Feature Spaces, Li He Dec 2013

Bayesian Dictionary Learning For Single And Coupled Feature Spaces, Li He

Doctoral Dissertations

Over-complete bases offer the flexibility to represent much wider range of signals with more elementary basis atoms than signal dimension. The use of over-complete dictionaries for sparse representation has been a new trend recently and has increasingly become recognized as providing high performance for applications such as denoise, image super-resolution, inpaiting, compression, blind source separation and linear unmixing. This dissertation studies the dictionary learning for single or coupled feature spaces and its application in image restoration tasks. A Bayesian strategy using a beta process prior is applied to solve both problems.

Firstly, we illustrate how to generalize the existing beta ...