by Wong Siu Fun. / Bibliography: leaves 55-59 / Thesis (M.Ph.)--Chinese University of Hong Kong, 1986

by Kwok-Fai Mo. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 74-79). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Statistical Modeling --- p.5 / Chapter 2.1 --- Poisson Regression --- p.5 / Chapter 2.2 --- Overdispersion and Autorrelation --- p.7 / Chapter 2.3 --- Generalized Estimating Equation --- p.9 / Chapter 2.4 --- Zeger's Mehthod --- p.12 / Chapter 2.5 --- Multicollinearity --- p.18 / Chapter 2.5.1 --- The Modified Generalized Estimating Equation --- p.18 / Chapter 2.6 --- Bootstrapping Method --- p.21 / Chapter 2.7 --- The Bootstrap Choice of Ridge Parameter --- p.23 / Chapter 3 --- The Robustness of Zeger's Approach to the Specification of ηt - Simulation Study --- p.26 / Chapter 3.1 --- Introduction --- p.26 / Chapter 3.2 --- Zeger's Algorithm with Varoious Time Series Data --- p.27 / Chapter 3.2.1 --- Data without Multicollinearity --- p.27 / Chapter 3.2.1 --- Data with Multicollinearity --- p.34 / Chapter 3.3 --- Modified Generalized Estimating Equation Approach --- p.40 / Chapter 3.3 --- The Choice of Ridge Paramter in Bootstrap --- p.42 / Chapter 4 --- Real Example --- p.46 / Chapter 4.1 --- Data Structure --- p.46 / Chapter 4.2 --- Model Building --- p.49 / Chapter 4.3 --- Single Pollutant Model --- p.57 / Chapter 4.4 --- Multiple Pollutant Model --- p.62 / Chapter 5 --- Conclusion and Discussion --- p.64 / Appendix --- p.69 / References --- p.74

Solute transport studies frequently rely on numerical solutions of the classical advection-diffusion equation. Unfortunately, solutions obtained with traditional finite difference and finite element techniques typically exhibit excessive numerical diffusion or spurious oscillation when advection dominates, especially when velocity field is highly variable. One recently developed technique, the finite analytic method, offers an attractive alternative. Finite analytic methods utilize local analytic solutions in discrete elements to obtain the algebraic representations of the governing partial differential equations, thus eliminating the truncation error in the finite difference and the use of approximating functions in the finite element method. The finite analytic solutions have been shown to be stable and numerically robust for advection-dominated transport in heterogeneous velocity fields. However, the existing finite analytic methods for solute transport in multiple dimensions have the following disadvantages. First, the method is computationally inefficient when applied to heterogeneous media due to the complexity of the formulation. Second, the evaluation of finite analytic coefficients is when the Peclet number is large. Third, the method introduces significant numerical diffusion due to inadequate temporal approximation when applied to transient problems. This thesis develops improved finite analytic methods for two-dimensional steady as well as unsteady solute transports in steady velocity fields. For steady transport, the new method exploits the advantages of the existing finite analytic and finite difference methods. The analytically difficult diffusion terms are approximated by finite difference and numerically difficult advection and reaction terms are treated analytically in a local element in deriving the numerical schemes. The new finite analytic method is extended to unsteady transport through application of Laplace transformation. Laplace transformation converts the transient equation to a steady-state expression that can be solved with the steady version of the improved finite analytic method. Numerical inversion of the transformed variables is used to recover solute concentration in the physical space-time domain. The effectiveness and accuracy of the new finite analytic method is demonstrated through stringent test examples of two dimensional steady-state transport in highly variable velocity fields. The results clearly demonstrated that the improved finite analytic methods are efficient, robust and accurate.

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

Not available / text

published_or_final_version / abstract / toc / Mechanical Engineering / Master / Master of Philosophy

One of the important factors in developing an emission control strategy for a coal fired steam generator is the characterization of the insitu variability of the coal being used in the furnace. Development of a model to correctly capture the insitu variability of the coal is thus fundamental to the analyses of emission control strategies. A simulation model of a portion of the Upper Freeport coal seam in Western Pennsylvania was developed using the recently developed technique called conditional simulation. This model was constructed so that it has the same mean, variance, and distribution of values as the real deposit, and most importantly, has the same spatial correlations as the real deposit. Validation of the model confirmed that the statistical characteristics of the model closely matched the characteristics of the real deposit. A second validation of the model showed that when the model is "mined" according to an actual daily mining sequence, the resulting daily variability corresponded extremely well to what was observed during the actual mining. This second verification served not only to validate the model but also served as a practical demonstration that the model can be successfully used to predict day by day variation in the quality of run of mine coal. One potential use of conditional simulation to "test" how well a mine plan works in actual mining was illustrated by an example where four mine plans were tested on their ability to correctly estimate coal production and sulfur content on a yearly basis. In each case, the simulated deposit was mined out according to the mine plan. The resulting comparison of "actual" production and estimated production clearly shows the adequacy or inadequacy of each one.

No description available.

The temporal and spatial changes in chemical and biological properties of Lake Mead have been investigated, thereby indicating the sources of water pollution and the time of highest pollution potential. Planktonic organisms have been shown to indicate the presence of water problems. Macro- and micro-nutrient analyses have shown that primary productivity is not inhibited by limiting concentrations. A mathematical model has been developed, tested with one set of independent data, and shown worthy of management utility. Although the model works very well for the Lake Mead area, the physical reality of the MLR equation should be tested on independent data.

This research develops an algorithm for solving a class of multiple objective decision problems. These problems are characterized by continuous policy variables, nonlinear constraints, and nonlinear criterion functions. Our underlying philosophy is that of the Gestalt psychologists-- we cannot separate the problem and its solution from the environment in which the problem is placed. The decision maker is necessarily a part of this environment, thus implying that he, as an individual, must be part of the solution of the problem. Another central assumption in this research is that there is not an "optimal" answer to the problem, only "satisfactory" solutions. The reasons for this are based partly on the insensitivities of the body to minute changes and to the insensitivity of our preferences within certain ranges of acceptance. In addition, we assure that the individual is capable of solving decision situations involving a maximum of about 10 goals and that he operates upon them in some sort of serial manner as he searches for a satisfactory alternative. The serial manner is a reflection of his current ranking of the goals. Based on these assumptions we have developed a cyclical interactive algorithm in which the decision maker guides a search mechanism in attempting to find a satisfactory alternative. Each cycle in the search consists of an optimization phase and an evaluation phase, after which the decision maker can define a new direction of search or terminate the algorithm. The optimization phase is based on a linearization technique which has been quite effective in terms of the problems we have attempted to solve. It is capable of solving general nonlinear programming problems with a large number of nonlinear constraints. Although the constraint set must be convex in order to guarantee the location of a global optimum, we can use the method on concave sets recognizing that we may find only a local optimum. An extensive synthetic case study of a water pollution decision problem with 6 conflicting goals is provided to demonstrate the feasibility of the algorithm. Finally, the limitations of the research are discussed. We tentatively conclude that we have developed a method applicable to our research problem and that the method can be applied to "real world" decision situations.