The number of horizontal wells is increasing rapidly in all over the world with the growth of new technological developments. During horizontal well drilling, much more complex problems occur when compared with vertical well drilling, such as decrease in load transfer to the bit, tubular failure, tubular fatigue and tubular lock-up. This makes selection of appropriate tubular and making the right drill string design more important. As the total compression load on the horizontal section increases, the behavior of the tubular changes from straight to sinusoidal buckling, and if the total compression load continues to increase the behavior of the tubular changes to helical buckling. Determination of critical buckling loads with finite element method (FEM) in horizontal wells is the main objective of this study. Initially, a computer program (ANSYS) that uses FEM is employed to simulate different tubular and well conditions. Four different pipe sizes, four different wellbore sizes and three different torque values are used to model the cases. Critical buckling load values corresponding to significant variables are collected from these simulated cases. The results are classified into different buckling modes according to the applied weight on bit values and the main properties of the simulated model, such as modulus of elasticity, moment of inertia of tubular cross section, weight per unit length of tubular and radial clearance between the wellbore and the tubular. Then, the boundary equations between the buckling modes are obtained. The equations developed in this thesis by simulating the cases for the specific tubular sizes are used to make a comparison between the critical buckling load values from the models in the literature and this work. It is observed that the results of this work fit with literature models as the tubular size increases. The influence of torque on critical buckling load values is investigated. It is observed that torque has a slight effect on critical buckling load values. Also the applicability of ANSYS for buckling problems was revealed by comparing the ANSYS results with the literature models&amp / #8217 / results and the experimental study in the literature.

Modern data collection methods are now frequently returning observations that should be viewed as the result of digitized recording or sampling from stochastic processes rather than vectors of finite length. In spite of great demands, only a few classification methodologies for such data have been suggested and supporting theory is quite limited. The focus of this dissertation is on discrimination and classification in this infinite dimensional setting. The methodology and theory we develop are based on the abstract canonical correlation concept of Eubank and Hsing (2005), and motivated by the fact that Fisher's discriminant analysis method is intimately tied to canonical correlation analysis. Specifically, we have developed a theoretical framework for discrimination and classification of sample paths from stochastic processes through use of the Loeve-Parzen isomorphism that connects a second order process to the reproducing kernel Hilbert space generated by its covariance kernel. This approach provides a seamless transition between the finite and infinite dimensional settings and lends itself well to computation via smoothing and regularization. In addition, we have developed a new computational procedure and illustrated it with simulated data and Canadian weather data.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 36-44). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaf 104). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaf 104). Also available on the Internet.

Thesis (Ph. D.)--University of California, Riverside, 2009. / Includes abstract. Also issued in print. Includes bibliographical references (leaves 81-84). Available via ProQuest Digital Dissertations.

Not available / text

Supervised classification methods are applied in many fields. The main problem of applying these methods is how to select the most appropriate method in particular case. The literary review was fulfilled and the advantages and disadvantages of mostly used criterion of supervised classification methods comparisons were ascertained. Then the methodology of comparisons was suggested. The analysis of SAS system procedures and macro commands was made. It was ascertained that there is not comfortable software which allows comparing the results of supervised classification methods. This work demands a lot of work, good knowledge of SAS programming language and high qualification in programming. So, the main purpose of this work is to expand the statistical data analysis system SAS possibilities in comparison of supervised classification methods and classificate various data. In this work the possibilities of SAS system are expanded by the tool which allows comparing quality of the linear, quadratic, kernel, nearest neighbor’s discriminant analysis and logistic regression analysis methods. There were used classification error estimates which were got by resubstition, cross–validation leave one out, bootstrap and Monte Carl cross–validation methods, although classification error confidence intervals which were got by non-parametric bootstrap method. The test of created tool was made with various data (different sample sizes, various classis separability, violations of assumptions... [to full text]

Impaired motor function following neurological injury may be overcome through therapies that induce neuroplastic changes in the brain. Therapeutic methods include repetitive exercises that promote use-dependent plasticity (UDP), the benefit of which may be increased by first administering peripheral nerve stimulation (PNS) to activate afferent fibers, resulting in increased cortical excitability. We speculate that PNS delivered only in response to attempted movement would induce timing-dependent plasticity (TDP), a mechanism essential to normal motor learning. Here we develop a brain-machine interface (BMI) to detect movement intent and effort in healthy volunteers (n=5) from their electroencephalogram (EEG). This could be used in the future to promote TDP by triggering PNS in response to a patient’s level of effort in a motor task. Linear classifiers were used to predict state (rest, sham, right, left) based on EEG variables in a handgrip task and to determine between three levels of force applied. Mean classification accuracy with out-of-sample data was 54% (23-73%) for tasks and 44% (21-65%) for force. There was a slight but significant correlation (p<0.001) between sample entropy and force exerted. The results indicate the feasibility of applying PNS in response to motor intent detected from the brain.

This thesis aims to study whether cash flow ratios can predict corporate financial distress in Sweden by employing multiple discriminant analysis. It was inspired by the Altman Z-score, which was adjusted for this aim. This study adopted a positivist epistemology and objectivist ontology. The research approach taken was a deductive one which employed quantitative methods in testing the hypotheses developed. The hypotheses were tested through means of accuracy and the Independent Samples Test. In order to identify financial distress a proxyratio was adopted. This proxy was the operating cash flow ratio. The sample consisted of 227 firms in total within the retail- and service industries. The time period covered 2000-2013. It was found that the proxy was unable to separate firms into distressed and nondistressedgroups, but rather classified all firms as distressed. Furthermore, the other ratios also failed to do any classification. Therefore, what the question that this study set out to answer came to the conclusion; cash flow ratios cannot predict corporate financial distress for retail and service companies in Sweden.