Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 196-200).

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 129-135).

This Thesis focuses on the study of multivariate statistical regression techniques which have been used to produce non-linear empirical models of chemical processes, and on the development of a novel approach to non-linear Projection to Latent Structures regression. Empirical modelling relies on the availability of process data and sound empirical regression techniques which can handle variable collinearities, measurement noise, unknown variable and noise distributions and high data set dimensionality. Projection based techniques, such as Principal Component Analysis (PCA) and Projection to Latent Structures (PLS), have been shown to be appropriate for handling such data sets. The multivariate statistical projection based techniques of PCA and linear PLS are described in detail, highlighting the benefits which can be gained by using these approaches. However, many chemical processes exhibit severely nonlinear behaviour and non-linear regression techniques are required to develop empirical models. The derivation of an existing quadratic PLS algorithm is described in detail. The procedure for updating the model parameters which is required by the quadratic PLS algorithms is explored and modified. A new procedure for updating the model parameters is presented and is shown to perform better the existing algorithm. The two procedures have been evaluated on the basis of the performance of the corresponding quadratic PLS algorithms in modelling data generated with a strongly non-linear mathematical function and data generated with a mechanistic model of a benchmark pH neutralisation system. Finally a novel approach to non-linear PLS modelling is then presented combining the general approximation properties of sigmoid neural networks and radial basis function networks with the new weights updating procedure within the PLS framework. These algorithms are shown to outperform existing neural network PLS algorithms and the quadratic PLS approaches. The new neural network PLS algorithms have been evaluated on the basis of their performance in modelling the same data used to compare the quadratic PLS approaches.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (p. 174-179).

Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 139-144).

This thesis analyzes the wireless propagation modeling of a 2.6 GHz band channel around the Tampa Bay area. Different empirical models are compared against measured data, and an adapted model, specific for the Tampa Bay area, is presented that builds on the accuracy of existing models. The effects of the propagation characteristics along bridges are also discussed, and a two-slope model is presented. The proposed models are based on a simple linear regression method, and statistical tests are evaluated for reliability thereof. The analysis also investigates the statistical properties of shadowing effects imposed on the wireless channel. The spatial correlation properties of shadowing effects are investigated in detail, and an extension of existing correlation models for shadowing effects is suggested where the correlation properties are studied in different distance ranges rather than the whole service coverage area.

Thesis (Ph.D.)--University of Alabama at Birmingham, 2008. / Title from first page of PDF file (viewed on June 25, 2009). Includes bibliographical references.

In the United States, the federal Empowerment Zone (EZ) program aimed to create and retain business investment in poor communities and to encourage local hiring through the use of special tax credits, relaxed regulations, social service grants, and other incentives. My dissertation explores whether the Round II Urban EZs had a beneficial impact on local communities and what factors influenced the implementation and performance of the EZs, using three modes of inquiry. First, linear regression models investigate whether the federal revitalization program had a statistically significant impact on the creation of new businesses and jobs in Round II Urban EZ communities. Second, location quotient and shift-share analysis are used to reveal the industry clusters in three EZ communities that experienced positive business and job growth. Third, qualitative analysis is employed to explore factors that influenced the implementation and performance of EZs in general, and in particular, Miami-Dade County, Florida. The results show an EZ’s presence failed to have a significant influence on local business and job growth. In communities that experienced a beneficial impact from EZs, there has been a pattern of decline in manufacturing companies and increase in service-driven firms. The case study suggests that institutional factors, such as governance structure, leadership, administrative capacity, and community participation have affected the effectiveness of the program’s implementation and performance.

In many geologic environments, fluids have compositions that are approximated by the H₂O-NaCl system. When minerals grow in the presence of such fluids, some of the solution is trapped in the growing mineral as fluid inclusions. The salinity, temperature of homogenization, and pressure of homogenization are required to predict the trapping conditions of the fluid inclusion. In the laboratory the salinity and the temperature of homogenization of the trapped fluid are easily determined however, the pressure of homogenization cannot be determined directly, and must be calculated from an equation of state. A statistical model that relates the vapor pressure of H₂O-NaCl to the fluid temperature and composition has been developed. The model consists of equations that predict the vapor pressure of H₂O-NaCl from the eutectic temperature (-21.2°C) to 1500°C and for all compositions between the pure end-members. The model calculates the vapor pressure based on the composition (wt% NaCl) and the temperature of homogenization, which can be directly obtained from laboratory studies of fluid inclusions. This information in turn can be used to construct the isochore, or line of constant volume, along which the fluid inclusion was trapped. Finally the isochore can be used to determine the temperature and pressure at which the host mineral of the fluid inclusion was trapped. / Master of Science

In modern times continuous casting is the preferred way to convert molten steel into solid forms to enable further processing. At Columbus Stainless the continuous casting machine cast slabs of constant thickness with varying width. One important aspect of the continuously cast strand that must be controlled, is the strand width. The strand width exiting from the casting machine, has a direct influence on the product yield which in turn influences the profitability of the company. In general, the strand width control on the austentic and ferritic type steels achieved is excellent with the exception of the 12% chrome non stabilised ferritic steel. This steel type exhibited different strand width changes when a sequence of different heats was cast. The strand width changes corresponded to the different heats in the sequence. Each heat has a unique chemistry and a relationship between the austenite and ferrite fraction at high temperature and the resulting strand width change was explained by Siyasiya[27]. The relationship between the heat composition and width change has in the past resulted in the development of a model that enabled the prediction of the expected width change of a specific heat before it is cast to enable preventative action to be taken. This model has been implemented as an on-line prediction model in the production environment with very encouraging results. This study was initiated because it was uncertain if the implemented model was the most accurate for this application. This study is concerned with the development of more models based on different techniques in an attempt to implement a more accurate model. The data mining techniques used include statistical regression, decision trees and fuzzy logic. The results indicated that the existing model was the most accurate and it could not be improved upon. / Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2007. / Mechanical and Aeronautical Engineering / MEng / unrestricted