A project report submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for 'the degree of Master of Science in Engineering. Johannesburg 1991. / The report describes the design of an algorithm which can be used for automatic controller tuning purposes. It uses an on-line parameter estimator and a pole assignrnent design method. The resulting control law is formulated to approximate a proportional-integral (PI) industrial controller. The development ofthe algorithm is based on the delta-operator, Some implementation aspects such as covariance resetting, dead zone, and signal conditioning are also discussed. Robust stability and performance are two issues that govern the design approach. Additionally transient and steady state system response criteria are utilized from the time and frequency domains. The design work is substantiated with the use of simulation and real plant tests. / AC2017

The overall theme of this thesis focuses on the joint modeling of longitudinal covariates and a censored survival outcome, where a survival outcome is modeled using a conditional quantile regression. In traditional joint modeling approaches, a survival outcome is usually parametrically modeled as a Cox regression. Censored quantile regressions can model a survival outcome without pre-specifying a parametric likelihood function or assuming a proportional hazard ratio. Existing censored quantile methods are mostly limited to fixed cross-sectional covariates, while in many longitudinal studies, researchers wish to investigate the associations between longitudinal covariates and a survival outcome. The first part considers the problem of joint modeling with a survival outcome under a mixture of censoring: left censoring, interval censoring or right censoring. We pose a linear mixed effect model for a longitudinal covariate and a conditional quantile regression for a censored survival outcome, assuming that a longitudinal covariate and a survival outcome are conditional independent on individual level random effects. We propose a Gibbs sampling approach as an extension of a censored quantile based data augmentation algorithm, to allow for a longitudinal covariate process. We also propose an iterative algorithm that alternately updates individual level random effects and model parameters, where a censored survival outcome is treated in the way of re-weighting. Both of our methods are illustrated by the application to the LEGACY Girls cohort Study to understand the influence of individual genetic profiles on the pubertal development (i.e., the onset of breast development) while adjusting for BMI growth trajectories. The second part considers the problem of joint modelling with a random right censoring survival outcome. We pose a linear mixed effect model for a longitudinal covariate and a conditional quantile regression for a censored survival outcome, assuming that a longitudinal covariate and a survival outcome are conditional independent on individual level random effects. We propose a Gibbs sampling approach as an extension of a censored quantile based data augmentation algorithm, to allow for a longitudinal covariate process. Theoretical properties for the resulting parameter estimates are established. We also propose an iterative algorithm that alternately updates individual level random effects and model parameters, where a censored survival outcome is treated in the way of re-weighting. Both of our methods are illustrated by the application to Mayo Clinic Primary Biliary Cholangitis Data to assess the effect of drug D-penicilamine on risk of liver transplantation or death, while controlling for age at registration and serBilir marker.

Using intersectionality as a guiding framework, the current study examined how gendered and racialized perceptions of Black women as "strong Black women" may be shaped by colorism. This experimental study sampled 314 Black and White participants from the community. Participants were presented with a vignette that described a Black woman coping with workplace stress in one of two ways, one congruent with strong Black womanhood (emotional restriction) and one incongruent with strong Black womanhood (emotional vulnerability), alongside the image of a light skin or dark skin Black woman. Then, participants were asked to rate how "strong" they perceived the woman in the vignette to be. A factorial ANCOVA was conducted to test how perceptions of the woman in the vignette varied based on her emotional response to workplace stress and skin tone, while controlling for perceptions of likability and competence. As hypothesized, we observed that participants perceived the woman responding to workplace stress with emotional restriction as stronger than the women who responded with emotional vulnerability. However, skin tone, nor the interaction between emotional response and skin tone had a bearing on participants' perceptions. There were also no differences in perceptions based on participant race. Through its intersectional framing, this study challenges scholars and practitioners to consider how the interplay between racism, sexism, and colorism shapes how Black women are seen by others and, in turn, how they may see themselves as strong Black women. Implications of the findings, limitations, and future directions are discussed.

Nous étudions le problème de suivi de cible dans une séquence vidéo sans aucune connaissance préalable autre qu'une référence annotée dans la première image. Pour résoudre ce problème, nous proposons une nouvelle méthode de suivi temps-réel se basant sur à la fois une représentation originale de l’objet à suivre (descripteur) et sur un algorithme adaptatif capable de suivre la cible même dans les conditions les plus difficiles comme le cas où la cible disparaît et réapparait dans le scène (ré-identification). Tout d'abord, pour la représentation d’une région de l’image à suivre dans le temps, nous proposons des améliorations au descripteur de covariance. Ce nouveau descripteur est capable d’extraire des caractéristiques spécifiques à la cible, tout en ayant la capacité à s’adapter aux variations de l’apparence de la cible. Ensuite, l’étape algorithmique consiste à mettre en cascade des modèles génératifs et des modèles discriminatoires afin d’exploiter conjointement leurs capacités à distinguer la cible des autres objets présents dans la scène. Les modèles génératifs sont déployés dans les premières couches afin d’éliminer les candidats les plus faciles alors que les modèles discriminatoires sont déployés dans les couches suivantes afin de distinguer la cibles des autres objets qui lui sont très similaires. L’analyse discriminante des moindres carrés partiels (AD-MCP) est employée pour la construction des modèles discriminatoires. Enfin, un nouvel algorithme d'apprentissage en ligne AD-MCP a été proposé pour la mise à jour incrémentale des modèles discriminatoires / We study the challenging problem of tracking an arbitrary object in video sequences with no prior knowledge other than a template annotated in the first frame. To tackle this problem, we build a robust tracking system consisting of the following components. First, for image region representation, we propose some improvements to the region covariance descriptor. Characteristics of a specific object are taken into consideration, before constructing the covariance descriptor. Second, for building the object appearance model, we propose to combine the merits of both generative models and discriminative models by organizing them in a detection cascade. Specifically, generative models are deployed in the early layers for eliminating most easy candidates whereas discriminative models are in the later layers for distinguishing the object from a few similar "distracters". The Partial Least Squares Discriminant Analysis (PLS-DA) is employed for building the discriminative object appearance models. Third, for updating the generative models, we propose a weakly-supervised model updating method, which is based on cluster analysis using the mean-shift gradient density estimation procedure. Fourth, a novel online PLS-DA learning algorithm is developed for incrementally updating the discriminative models. The final tracking system that integrates all these building blocks exhibits good robustness for most challenges in visual tracking. Comparing results conducted in challenging video sequences showed that the proposed tracking system performs favorably with respect to a number of state-of-the-art methods

La vidéosurveillance représente l’un des domaines de recherche privilégiés en vision par ordinateur. Le défi scientifique dans ce domaine comprend la mise en œuvre de systèmes automatiques pour obtenir des informations détaillées sur le comportement des individus et des groupes. En particulier, la détection de mouvements anormaux de groupes d’individus nécessite une analyse fine des frames du flux vidéo. Dans le cadre de cette thèse, la détection de mouvements anormaux est basée sur la conception d’un descripteur d’image efficace ainsi que des méthodes de classification non linéaires. Nous proposons trois caractéristiques pour construire le descripteur de mouvement : (i) le flux optique global, (ii) les histogrammes de l’orientation du flux optique (HOFO) et (iii) le descripteur de covariance (COV) fusionnant le flux optique et d’autres caractéristiques spatiales de l’image. Sur la base de ces descripteurs, des algorithmes de machine learning (machines à vecteurs de support (SVM)) mono-classe sont utilisés pour détecter des événements anormaux. Deux stratégies en ligne de SVM mono-classe sont proposées : la première est basée sur le SVDD (online SVDD) et la deuxième est basée sur une version « moindres carrés » des algorithmes SVM (online LS-OC-SVM) / One of the major research areas in computer vision is visual surveillance. The scientific challenge in this area includes the implementation of automatic systems for obtaining detailed information about the behavior of individuals and groups. Particularly, detection of abnormal individual movements requires sophisticated image analysis. This thesis focuses on the problem of the abnormal events detection, including feature descriptor design characterizing the movement information and one-class kernel-based classification methods. In this thesis, three different image features have been proposed: (i) global optical flow features, (ii) histograms of optical flow orientations (HOFO) descriptor and (iii) covariance matrix (COV) descriptor. Based on these proposed descriptors, one-class support vector machines (SVM) are proposed in order to detect abnormal events. Two online strategies of one-class SVM are proposed: The first strategy is based on support vector description (online SVDD) and the second strategy is based on online least squares one-class support vector machines (online LS-OC-SVM)

Thesis (Elec.E. and S.M. in Electrical Engineering)--Joint Program in Applied Ocean Physics and Engineering (Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; and the Woods Hole Oceanographic Institution), 2003. / "February 2003." / Includes bibliographical references (leaves 139-141). / by Andrea L. Kraay. / Elec.E.and S.M.in Electrical Engineering

While variation analysis methods for compliant assemblies are not new, little has been done to include the effects of multi-step, fixtured assembly processes. This thesis introduces a new method to statistically analyze compliant part assembly processes using fixtures. This method, consistent with the FASTA method developed at BYU, yields both a mean and a variant solution. The method, called Piecewise-Linear Elastic Analysis, or PLEA, is developed for predicting the residual stress, deformation and springback variation in compliant assemblies. A comprehensive, step-by-step analysis map is provided. PLEA is validated on a simple, laboratory assembly and a more complex, production assembly. Significant modeling findings are reported as well as the comparison of the analytical to physical results.

This thesis addresses three topics in the area of statistics and probability, with applications in risk management. First, for the testing problems in the high-dimensional (HD) data analysis, we present a novel method to formulate empirical likelihood tests and jackknife empirical likelihood tests by splitting the sample into subgroups. New tests are constructed to test the equality of two HD means, the coefficient in the HD linear models and the HD covariance matrices. Second, we propose jackknife empirical likelihood methods to formulate interval estimations for important quantities in actuarial science and risk management, such as the risk-distortion measures, Spearman's rho and parametric copulas. Lastly, we introduce the theory of completely mixable (CM) distributions. We give properties of the CM distributions, show that a few classes of distributions are CM and use the new technique to find the bounds for the sum of individual risks with given marginal distributions but unspecific dependence structure. The result partially solves a problem that had been a challenge for decades, and directly leads to the bounds on quantities of interest in risk management, such as the variance, the stop-loss premium, the price of the European options and the Value-at-Risk associated with a joint portfolio.

Thesis (PhD)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: When estimating the covariance matrices of two or more populations, the covariance matrices are often assumed to be either equal or completely unrelated. The common principal components (CPC) model provides an alternative which is situated between these two extreme assumptions: The assumption is made that the population covariance matrices share the same set of eigenvectors, but have di erent sets of eigenvalues. An important question in the application of the CPC model is to determine whether it is appropriate for the data under consideration. Flury (1988) proposed two methods, based on likelihood estimation, to address this question. However, the assumption of multivariate normality is untenable for many real data sets, making the application of these parametric methods questionable. A number of non-parametric methods, based on bootstrap replications of eigenvectors, is proposed to select an appropriate common eigenvector model for two population covariance matrices. Using simulation experiments, it is shown that the proposed selection methods outperform the existing parametric selection methods. If appropriate, the CPC model can provide covariance matrix estimators that are less biased than when assuming equality of the covariance matrices, and of which the elements have smaller standard errors than the elements of the ordinary unbiased covariance matrix estimators. A regularised covariance matrix estimator under the CPC model is proposed, and Monte Carlo simulation results show that it provides more accurate estimates of the population covariance matrices than the competing covariance matrix estimators. Covariance matrix estimation forms an integral part of many multivariate statistical methods. Applications of the CPC model in discriminant analysis, biplots and regression analysis are investigated. It is shown that, in cases where the CPC model is appropriate, CPC discriminant analysis provides signi cantly smaller misclassi cation error rates than both ordinary quadratic discriminant analysis and linear discriminant analysis. A framework for the comparison of di erent types of biplots for data with distinct groups is developed, and CPC biplots constructed from common eigenvectors are compared to other types of principal component biplots using this framework. A subset of data from the Vermont Oxford Network (VON), of infants admitted to participating neonatal intensive care units in South Africa and Namibia during 2009, is analysed using the CPC model. It is shown that the proposed non-parametric methodology o ers an improvement over the known parametric methods in the analysis of this data set which originated from a non-normally distributed multivariate population. CPC regression is compared to principal component regression and partial least squares regression in the tting of models to predict neonatal mortality and length of stay for infants in the VON data set. The tted regression models, using readily available day-of-admission data, can be used by medical sta and hospital administrators to counsel parents and improve the allocation of medical care resources. Predicted values from these models can also be used in benchmarking exercises to assess the performance of neonatal intensive care units in the Southern African context, as part of larger quality improvement programmes. / AFRIKAANSE OPSOMMING: Wanneer die kovariansiematrikse van twee of meer populasies beraam word, word dikwels aanvaar dat die kovariansiematrikse of gelyk, of heeltemal onverwant is. Die gemeenskaplike hoofkomponente (GHK) model verskaf 'n alternatief wat tussen hierdie twee ekstreme aannames gele e is: Die aanname word gemaak dat die populasie kovariansiematrikse dieselfde versameling eievektore deel, maar verskillende versamelings eiewaardes het. 'n Belangrike vraag in die toepassing van die GHK model is om te bepaal of dit geskik is vir die data wat beskou word. Flury (1988) het twee metodes, gebaseer op aanneemlikheidsberaming, voorgestel om hierdie vraag aan te spreek. Die aanname van meerveranderlike normaliteit is egter ongeldig vir baie werklike datastelle, wat die toepassing van hierdie metodes bevraagteken. 'n Aantal nie-parametriese metodes, gebaseer op skoenlus-herhalings van eievektore, word voorgestel om 'n geskikte gemeenskaplike eievektor model te kies vir twee populasie kovariansiematrikse. Met die gebruik van simulasie eksperimente word aangetoon dat die voorgestelde seleksiemetodes beter vaar as die bestaande parametriese seleksiemetodes. Indien toepaslik, kan die GHK model kovariansiematriks beramers verskaf wat minder sydig is as wanneer aanvaar word dat die kovariansiematrikse gelyk is, en waarvan die elemente kleiner standaardfoute het as die elemente van die gewone onsydige kovariansiematriks beramers. 'n Geregulariseerde kovariansiematriks beramer onder die GHK model word voorgestel, en Monte Carlo simulasie resultate toon dat dit meer akkurate beramings van die populasie kovariansiematrikse verskaf as ander mededingende kovariansiematriks beramers. Kovariansiematriks beraming vorm 'n integrale deel van baie meerveranderlike statistiese metodes. Toepassings van die GHK model in diskriminantanalise, bi-stippings en regressie-analise word ondersoek. Daar word aangetoon dat, in gevalle waar die GHK model toepaslik is, GHK diskriminantanalise betekenisvol kleiner misklassi kasie foutkoerse lewer as beide gewone kwadratiese diskriminantanalise en line^ere diskriminantanalise. 'n Raamwerk vir die vergelyking van verskillende tipes bi-stippings vir data met verskeie groepe word ontwikkel, en word gebruik om GHK bi-stippings gekonstrueer vanaf gemeenskaplike eievektore met ander tipe hoofkomponent bi-stippings te vergelyk. 'n Deelversameling van data vanaf die Vermont Oxford Network (VON), van babas opgeneem in deelnemende neonatale intensiewe sorg eenhede in Suid-Afrika en Namibi e gedurende 2009, word met behulp van die GHK model ontleed. Daar word getoon dat die voorgestelde nie-parametriese metodiek 'n verbetering op die bekende parametriese metodes bied in die ontleding van hierdie datastel wat afkomstig is uit 'n nie-normaal verdeelde meerveranderlike populasie. GHK regressie word vergelyk met hoofkomponent regressie en parsi ele kleinste kwadrate regressie in die passing van modelle om neonatale mortaliteit en lengte van verblyf te voorspel vir babas in die VON datastel. Die gepasde regressiemodelle, wat maklik bekombare dag-van-toelating data gebruik, kan deur mediese personeel en hospitaaladministrateurs gebruik word om ouers te adviseer en die toewysing van mediese sorg hulpbronne te verbeter. Voorspelde waardes vanaf hierdie modelle kan ook gebruik word in normwaarde oefeninge om die prestasie van neonatale intensiewe sorg eenhede in die Suider-Afrikaanse konteks, as deel van groter gehalteverbeteringprogramme, te evalueer.

Le sujet de la présente étude est la violence scolaire, phénomène complexe et polysémique qui préoccupe légitimement le monde de l’éducation depuis plus de trente ans. À partir des analyses factorielles exploratoires, analyses de variance factorielle et finalement analyses multivariées de covariance, cette recherche vise plus précisément à dégager la relation entre le climat scolaire, le contexte scolaire et les différents rôles adoptés par les élèves du niveau secondaire lors d’une situation de violence scolaire. Les données de la présente étude ont été collectées par Michel Janosz et son équipe pendant l’année 2010, dans quatre établissements éducatifs provenant d’une commission scolaire de la grande région de Montréal. L’échantillon de départ est composé de 1750 élèves qui fréquentent des classes ordinaires et spéciales du premier et deuxième cycle du secondaire âgés entre 10 et 18 ans. Pour fins d’analyse, deux petites écoles ainsi que les classes spéciales ont été retirées. Il demeure donc 1551 élèves dans l’échantillon initial analysé. Les résultats des analyses permettent de constater d’une part, la relation significative existante entre les dimensions du climat scolaire et l’adoption des différents rôles lors d’une situation de violence scolaire, les climats d’appartenance et de sécurité étant les plus importants, et d’autre part d’observer des différences dans les perceptions que les élèves ont de la violence scolaire selon le niveau et selon l’école. / The present study pertains to a complex and polysemic phenomenon that has preoccupied people working in the field of education since at least thirty years, that is, school violence. Using factor analysis, analysis of variance and multivariate analysis of covariance, this research aims at exploring specifically the relationship between school climate, school context and the various roles adopted by students at the high school level when they face a situation in which school violence in present. Data for this study were collected by Michel Janosz and his team in 2010, within four schools, all in the same school board of the Montreal region. The original sample comprises 1750 students who attend both standard special classes, between 10 and 18 years of age. The analyses presented are based on a reduced sample where the special classes and the two small schools have been withdrawn. Therefore, the answers from 1551 student s are used. The results show that first, there is a significant relationship between the various dimensions of school climate and the different roles adopted by students facing a situation in which violence is present, perceptions of belonging and of security being the most important. Second, all things being equal, there are significant difference between schools, and school levels.