Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 115-117).

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also in a digital version from UMI/Dissertation Abstracts International.

Thesis (Ph. D.)--University of Florida, 1984. / Description based on print version record. Typescript. Vita. Includes bibliographical references (leaves 101-104).

Current environmental and economic trends have forced grid operators to maximize the utilization of the existing assets, which is causing systems to be operated closer to their stability limits than ever before. This requires, among other things, better knowledge and modelling of the existing power system equipment to increase the accuracy of the assessment of current stability margins.This research investigates the possibility of improving the quality of load modeling. The thesis presents a review of the traditional methods for estimation of load model parameters and proposes to use Improved Particle Swarm Optimization. Different algorithms are tested and compared in terms of accuracy, reliability and CPU requirements using computer simulations and real-data captured in a power system.Estimation of frequency and power components has also been studied in this thesis. A review of the existing methods has been provided and the use of an Unscented Kalman Filter proposed. This nonlinear recursive algorithm has been thoroughly tested and compared against selected traditional techniques in a number of experiments involving computer-generated signals as well as measurements obtained in laboratory conditions.

Variable selection refers to the problem of selecting input variables that are most predictive of a given outcome. Variable selection problems are found in all machine learning tasks, supervised or unsupervised, classification, regression, time series prediction , two - class or multi-class, posing various levels of challenges. Variables selection problems are related to the problems of input dimensionality reduction and of parameter planning. It has practical and theoretical challenges of its own. From the practical point of view, eliminating variables may reduce the cost of producing the outcome and increase its speed, while space dimensionality does not address these problems. Theoretical challenges include estimating with what confidence one can state that a variable is relevant to the concept when it is useful to the outcome and providing a theoretical understanding of the stability of selected variables subsets. As the probability cut-points increase in value, the more likely it becomes that an observation is classified as a non-event by the selected variables. The mathematical statement of the problem is not widely agreed upon and may depend on the application. One typically distinguishes: i) The problem of discovering all the variables relevant to the outcome variable and determine HOW relevant they are and how they are related to each other. ii) The problem of finding a minimum subset of variables that is useful to the outcome variable. Logistic regression is an increasingly popular statistical technique used to model the probability of discrete binary outcome. Logistic regression applies maximum likelihood estimation after transforming the outcome variable into a logit variable. In this way, logistic regression estimates the probability of a certain event. When properly applied, logistic regression analyses yield a very powerful insight in to what variables are more or less likely to predict event outcome in a population of interest. These models also show the extent to which changes in the values of the variable may increase or decrease the predicted probability of event outcome. Variable selection, in all its facets is similarly important with logistic regression. The receiver operating characteristics (ROC) curve is a graphic display that gives a measure of the predictive accuracy of a logistic regression model. It is a measure of classification performance, the area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. Another measure of predictive accuracy of a logistic regression model is a classification table. It uses the model to classifying observations as events if their estimated probability is greater or equal to a given probability cut-point, otherwise events are classified as non-events. This technique, as it appears in the literature, is also studied in this thesis. In this thesis the issue of variable selection, both for continuous and binary outcome variables, is investigated as it appears in the statistical literature. It is clear that this topic has been widely researched and still remains a feature of modern research. The last word certainly hasn’t been spoken. / Dissertation (MSc)--University of Pretoria, 2008. / Statistics / unrestricted

Conditional coding is an application of Markov Chain Monte Carlo methods for sampling from conditional distributions. It is applied here to the problem of estimating the parameters of a computer-simulated pattern of fractures in an isomorphic, homotropic material under plane strain. We investigate the theory and procedures of conditional coding and show the viability of the technique by its application.

Sensitivity analysis is essential for the hydrologic models to help gain insight into model’s behavior, and assess the model structure and conceptualization. Parameter estimation in the distributed hydrologic models is difficult due to the high-dimensional parameter spaces. Sensitivity analysis identified the influential and non-influential parameters in the modeling process, thus it will benefit the calibration process. This study identified, applied and evaluated two sensitivity analysis methods for the APEX model. The screening methods, the Morris method, and LH-OAT method, were implemented in the experimental site in North Carolina for modeling runoff, sediment loss, TP and DP losses. At the beginning of the application, the run number evaluation was conducted for the Morris method. The result suggested that 2760 runs were sufficient for 45 input parameters to get reliable sensitivity result. Sensitivity result for the five management scenarios in the study site indicated that the Morris method and LH-OAT method provided similar results on the sensitivity of the input parameters, except the difference on the importance of PARM2, PARM8, PARM12, PARM15, PARM20, PARM49, PARM76, PARM81, PARM84, and PARM85. The results for the five management scenarios indicated the very influential parameters were consistent in most cases, such as PARM23, PARM34, and PARM84. The “sensitive” parameters had good overlaps between different scenarios. In addition, little variation was observed in the importance of the sensitive parameters in the different scenarios, such as PARM26. The optimization process with the most influential parameters from sensitivity analysis showed great improvement on the APEX modeling performance in all scenarios by the objective functions, PI1, NSE, and GLUE.

Inlet distortion research has become increasingly important over the past several years as demands for aircraft flight efficiency and performance has increased. To accommodate these demands, research progression has shifted the emphasis onto airframe-engine integration and improved understanding of engine operability in less than ideal conditions. Swirl distortion, which is considered a type of non-uniform inflow inlet distortion, is characterized by the presence of swirling flow in an inlet. The presence of swirling flow entering an engine can affect the compression systems performance and operability, therefore it is an area of current research. A swirl distortion generation device created by Virginia Tech, identified as the StreamVane, has the ability to produce various swirl distortion flow profiles. In its current state, the StreamVane methodology generates a design swirl distortion at the trailing edge of the device. However, in many applications the plane at which the researcher wants a desired distortion is downstream of the StreamVane trailing edge. After the distortion is discharged from the StreamVane it develops as it moves downstream. Therefore, to more accurately replicate a desired swirl distortion at a given downstream plane, distortion development downstream of the StreamVane must be considered. Currently Virginia Tech utilizes a numerical modeling design tool, designated StreamFlow, that generates predictions of how a StreamVane-generated distortion propagates downstream. However, due to the non-linear physics of the flow problem, StreamFlow cannot directly calculate an accurate inverse solution that can predict upstream conditions from a downstream boundary, as needed to design a StreamVane. To solve this problem, in this research, an efficient estimation process has been created, combining the use of the StreamFlow model with a Markov Chain Monte Carlo (MCMC) parameter estimation tool to estimate upstream flow profiles that will produce the desired downstream profiles. The process is designated the StreamFlow-MC2 Estimation Process. The process was tested on four fundamental types of swirl distortions. The desired downstream distortion was input into the estimation process to predict an upstream profile that would create the desired downstream distortion. Using the estimated design profiles, 6-inch diameter StreamVanes were designed then wind tunnel tested to verify the distortion downstream. Analysis and experimental results show that using this method, the upstream distortion needed to create the desired distortion was estimated with excellent accuracy. Based on those results, the StreamFlow-MC2 Estimation Process was validated. / Master of Science

Biological systems, processes, and applications present modeling challenges in the form of system complexity, limited steady-state availability, and limited measurements. One primary issue is the lack of well-estimated parameters. This thesis presents two contributions in the area of modeling and parameter estimation for these kinds of biological processes. The primary contribution is the development of an adaptive parameter estimation process that includes parameter selection, evaluation, and estimation, applied along with modeling of cell growth in culture. The second contribution shows the importance of parameter estimation for evaluation of experiment and process design. / Thesis / Master of Applied Science (MASc)

The collection of biological data has been limited by instrumentation, the complexity of the systems themselves, and even the ability of graduate students to stay awake and record the data. However, increasing measurement capabilities and decreasing costs may soon enable the collection of reasonably sampled time course data characterizing biological systems, though in general only a subset of the system's species would be measured. This increase in data volume requires a corresponding increase in the use and interpretation of such data, specifically in the development of system identification techniques to identify parameter sets in proposed models. In this paper, we present the results of identifiability analysis on a small test system, including the identifiability of parameters with respect to different measurements (proteins and mRNA), and propose a working definition for "biologically meaningful estimation". We also analyze the correlations between parameters, and use this analysis to consider effective approaches to determining parameters with biological meaning. In addition, we look at other methods for determining relationships between parameters and their possible significance. Finally, we present potential biologically meaningful parameter groupings from the test system and present the results of our attempt to estimate the value of select groupings. / Master of Science