Thesis (Ph. D.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves 78-85). Also available on the Internet.

Thesis (Ph. D.)--University of Chicago, Dept. of Statistics, June 2000. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 93-94). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph.D.)--City University of Hong Kong, 2009. / "Submitted to Department of Management Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 114-124)

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 106-108).

Recently, structured surfaces, consisting of deterministic features designed to produce a particular effect, have shown promise in providing superior functional performance for a range of applications including: low friction surfaces, hydrophobic surfaces and optical effects. Methods have been developed to characterise such structured surfaces. The most widely used characterisation methods are based on segmenting the surface in feature and background regions and then determining the geometrical properties of those features. However, further work is needed to refine these characterisation techniques and provide associated uncertainties. This thesis considers the effect of various segmentation control parameters such as thresholds on the final geometric parameters. The effect of varying filter size is also considered. These considerations should help in selecting a suitable characterisation method for future projects. Additionally, uncertainty in the characterisation should be estimated in order to give an indication of the accuracy of the assessment. However, no previous work has assessed uncertainty in the dimensional properties of structured surfaces. Therefore, this thesis presents two methods to characterise the uncertainty in the geometric characteristics of structured surfaces. First, the measurement reproducibility is used, which can be determined by repeated measurement of a feature. However, measurement reproducibility cannot account for all sources of uncertainty and cannot assess any bias in the measurements. Therefore, a second method based on assessment of the metrological characteristics of the instrument is considered. The metrological characteristics estimate errors produced by the instrument in a way that can easily be measured. Monte Carlo techniques are then used to propagate the effects of the metrological characteristics and their uncertainties into the final measurement uncertainty. For the example used, it was found that the results using the metrological characteristics were in good agreement with the reproducibility results. From these results, it is concluded that the choice of segmentation method, control parameters and filtering can all significantly effect the characterisation of features on a structured surface, often in unexpected ways. Therefore, care must be taken when selecting these values for a specific application. Additionally, two methods of determining the uncertainty of the structured surfaces were considered. Both methods are valid and produce similar results. Using the measurement reproducibility is simple to perform, but requires many measurements and cannot account for some uncertainty sources such as those due to the instrument amplification factors. On the other hand, the use of metrological characteristics can account for all significant sources of uncertainty in a measurement, but is mathematically more complex, requiring Monte Carlo simulations to propagate the uncertainties into the final characteristics. Additionally, other artefacts than the sample being measured are required to determine the metrological characteristics, which may be an issue in some cases.

No description available.

Uncertainty is woven into the fabric of human experience. All types of experiences involve some degree of uncertainty. Given the pervasiveness of uncertainty in daily life, individual differences how people think, behave, and feel about uncertainty matters. Some respond to uncertainty with fear and anxiety while others respond to uncertainty with curiosity and interest. The current project focused on two responses to uncertainty: intolerance of uncertainty and curiosity. The three main aims were to examine the relationship between these constructs directly, to investigate their unique contributions to outcomes of wellbeing and general psychological distress, and to examine cognitive appraisals associated with proximate outcomes of worry and interest. A large online adult sample (N = 413) completed self-report measures of IU, curiosity, personality, well-being, and general psychological distress symptoms. Participants also completed a novel vignette-based task of hypothetical future scenarios that varied in degree of uncertainty and pleasantness. IU was inversely associated with only some of the hypothesized curiosity dimensions. After accounting for personality traits, trait IU explained an additional 15% of the variance in general psychological distress, and trait curiosity explained an additional 16% of the variance in wellbeing. Each reflects a small but noteworthy additional contribution to these outcomes. For uncertain situations only, coping potential demonstrated a modest moderation effect of the appraisal of uncertainty on worry and interest, in support of project hypotheses. Limitations and implications for future research are discussed.

Particle image velocimetry (PIV) is a widely used fluid measurement technique. Three dimensional PIV data or stereo PIV is acquired using two cameras. Stereo cameras are calibrated from camera coordinates, pixels, to real world units such as millimeters using calibration models. Stereo calibration is fundamental to the accuracy of a PIV measurement. In this thesis, the accuracy of the stereo calibration is assessed. The mean error of stereo calibration was found to be 0.23%.

In particle image velocimetry (PIV) the measurement signal is contained in the recorded intensity of the particle image pattern superimposed on a variety of noise sources. The inherent amount of signal mutual information between consecutive images governs the strength of the resulting PIV cross correlation and ultimately the accuracy and uncertainty of the produced PIV measurements. Hence we posit that the correlation signal-to-noise-ratio (SNR) metrics calculated from the correlation plane can be used to quantify the quality of the correlation and the resulting uncertainty of an individual measurement. A new SNR metric termed "mutual information" (MI) which quantifies the amount of common information (particle pattern) between two consecutive images is also introduced and investigated. This measure provides a direct estimation of the apparent NIFIFO parameter of an image pair providing an alternative approach towards uncertainty estimation but also connecting the current development to one of the most fundamental principles of PIV and the previous established theory. We extend the original work by Charonko and Vlachos and present a framework for evaluating the correlation strength using a set of different metrics, which in turn are used to develop models for uncertainty estimation. Several corrections have been applied in this work. The metrics and corresponding models presented herein are expanded to be applicable to both standard and filtered correlations by applying a subtraction of the minimum correlation value to remove the effect of the background image noise. In addition, the notion of a "valid" measurement is redefined with respect to the correlation peak width in order to be consistent with uncertainty quantification principles and distinct from an "outlier" measurement. Finally the type and significance of the error distribution function is investigated. These advancements lead to robust uncertainty estimation models, which are tested against both synthetic benchmark data as well as actual experimental measurements. In this work, U68.5 uncertainties are estimated at the 68.5% confidence level while U95 uncertainties are estimated at 95% confidence level. For all cases the resulting calculated coverage factors approximate the expected theoretical confidence intervals thus demonstrating the applicability of these new models for estimation of uncertainty for individual PIV measurements. / Master of Science