Simulation and modelling of turbulent flows under influence of streamline curvature and system rotation have been considered. Direct numerical simulations have been performed for fully developed rotating turbulent channel flow using a pseudo-spectral code. The rotation numbers considered are larger than unity. For the range of rotation numbers studied, an increase in rotation number has a damping effect on the turbulence. DNS-data obtained from previous simulations are used to perform a priori tests of different pressure-strain and dissipation rate models. Furthermore, the ideal behaviour of the coefficients of different model formulations is investigated. The main part of the modelling is focused on explicit algebraic Reynolds stress models (EARSMs). An EARSM based on a pressure strain rate model including terms that are tensorially nonlinear in the mean velocity gradients is proposed. The new model is tested for a number of flows including a high-lift aeronautics application. The linear extensions are demonstrated to have a significant effect on the predictions. Representation techniques for EARSMs based on incomplete sets of basis tensors are also considered. It is shown that a least-squares approach is favourable compared to the Galerkin method. The corresponding optimality aspects are considered and it is deduced that Galerkin based EARSMs are not optimal in a more strict sense. EARSMs derived with the least-squares method are, on the other hand, optimal in the sense that the error of the underlying implicit relation is minimized. It is further demonstrated that the predictions of the least-squares EARSMs are in significantly better agreement with the corresponding complete EARSMs when tested for fully developed rotating turbulent pipe flow. / QC 20100825

L'âge a de tout temps constitué un attribut identitaire important. Nous avons développé au fil de l'évolution une aptitude innée à classer les individus en fonction de leur âge. Cette classification s'appuie en grande partie sur le visage et sur les transformations anatomiques qu'il subit au cours du temps. De plus en plus de traitements cosmétiques, dermatologiques et d'interventions chirurgicales s'attaquant à un signe ou un groupe de signes spécifiques du vieillissement sont mis en oeuvre pour annuler, ou tout au moins masquer partiellement l'effet du temps sur le visage. On peut dès lors s'interroger sur l'influence de chacun des signes sur notre capacité à prédire l'âge d'un individu en observant son visage. Afin de construire un algorithme capable de déterminer l'âge d'individus à partir de leurs photos, nous nous sommes intéressés aux signes du vieillissement et à leur impact sur l'âge apparent. Dans un premier temps, nous avons déterminé et analysé les transformations anatomiques qui altèrent le visage à partir de l'âge adulte (au-delà de 20 ans). Puis nous avons étudié les signes sur lequel on se base pour prédire l'âge d'une personne. Enfin, nous avons construit et validé un modèle prédictif de l'âge en s'appuyant sur les observations précédentes. Transformations anatomiques du visage avec l'âge : La prévalence d'un certain nombre de signes de vieillissement (rides, tâches brunes, forme du visage...) a été mesurée sur un panel représentatif de femmes volontaires âgées de 20 à 74 ans. Ces données ont permis d'établir la cinétique d'apparition de ces signes. Appréciation subjective de l'âge: Il s'agissait de déterminer les signes sur lesquels un observateur s'appuie lorsqu'il évalue l'âge d'un sujet. Pour ce faire, nous avons demandé à un panel constitué de 48 observateurs d'attribuer un âge aux volontaires sur lesquelles nous avions précédemment mesuré les signes du vieillissement. Nous avons confirmé avec ce groupe d'observateurs que la perception de l'âge est liée au sexe et à l'âge de l'observateur. De plus, à l'aide d'une régression PLS (Partial Least Square régression), nous avons établi des relations entre les signes du vieillissement et l'âge observé et démontré que selon que l'on soit jeune ou âgé, un homme ou une femme, on n'exploite pas les mêmes signes de vieillissement pour prédire l'âge.Modèle de prédiction : Enfin, nous avons proposé un modèle s'appuyant sur la régression PLS pour prédire automatiquement l'âge à partir des photos du visage. Ce modèle présente la particularité d'associer, dans une approche unifiée, les signes relatifs à la couleur, à la forme et à la texture du visage, à l'âge des sujets. A l'instar des Modèles Actifs D'apparence (AAM), le modèle construit vise à réduire fortement l'information portée par l'ensemble des pixels du visage. Toutefois, ce dernier est supervisé : Il est donc très approprié dans notre contexte puisque que l'on peut mettre en oeuvre une procédure d'apprentissage pilotée par le but. Les performances sont de fait comparables à celles des humains.

<p>The inverse problem of modeling biochemical processes mathematically from measured time course data falls into the category of system identification and parameter estimation. Analyzing the time course data would provide valuable insights into the model structure and dynamics of the biochemical system. Based on the types of biochemical reactions, such as metabolic networks and genetic networks, several modeling frameworks have been proposed, developed and proved effective, including the Michaelis-Menten equation, the Biochemical System Theory (BST), etc. One bottleneck in analyzing the obtained data is the estimation of parameter values within the system model.</p> <p>As most models for molecular biological systems are nonlinear with respect to both parameters and system state variables, estimation of parameters in these models from experimental measurement data is thus a nonlinear estimation problem. In principle, all algorithms for nonlinear optimization can be used to deal with this problem, for example, the Gauss-Newton iteration method and its variants. However, these methods do not take the special structures of biological system models into account. When the number of parameters to be determined increases, it will be challenging and computationally expensive to apply these conventional methods.</p> <p>In this research, several methods are proposed for estimating parameters in two classes of widely used biological system models: the S-system model and the linear fractional model (LFM), by utilizing their structure specialties. For the S-system, two estimation methods are designed. 1) Based on the two-term structure (production and degradation) of the model, an alternating iterative least squares method is proposed. 2) A separation nonlinear least squares method is proposed to deal with the partially linear structure of the model. For the LFM, two estimation methods are provided. 1) The separation nonlinear least squares method can also be adopted to treat the partially linear structure of the LFM, and moreover a modified iterative version is included. 2) A special strategy using the separation principle and the weighted least squares method is implemented to turn the cost function into a quadratic form and thus the estimates for parameters can be analytically solved. Simulation results have demonstrated the effectiveness of the proposed methods, which have shown better performance in terms of estimation accuracy and computation time, compared with those conventional nonlinear estimation methods.</p>

Stimulus-driven changes in the gain of sensory neurons are well-documented, but relatively little is known about whether analogous gain-control can also be effected in a top-down manner. A recent psychophysical study demonstrated that sensitivity to luminance contrast can be modulated by a priori knowledge (de la Rosa et al., in press). In the present study, event-related potentials were used to resolve the stages of information processing that facilitate such knowledge-driven adjustments. Groupwise independent component analysis identified two robust spatiotemporal patterns of endogenous brain activity that captured experimental effects. The first pattern was associated with obligatory processing of contextual information, while the second pattern was associated with selective initiation of contrast gain adjustment. These data suggest that knowledge-driven contrast gain control is mediated by multiple independent electrogenic sources.

In this thesis, identification of periodically varying orders of univariate Periodic Autoregressive Moving-Average (PARMA) processes is mainly studied. The identification of the varying orders of PARMA process is carried out by generalizing the well-known Box-Jenkins techniques to a seasonwise manner. The identification of pure periodic moving-average (PMA) and pure periodic autoregressive (PAR) models are considered only. For PARMA model identification, the Periodic Autocorrelation Function (PeACF) and Periodic Partial Autocorrelation Function (PePACF), which play the same role as their ARMA counterparts, are employed. For parameter estimation, which is considered only to refine model identification, the conditional least squares estimation (LSE) method is used which is applicable to PAR models. Estimation becomes very complicated, difficult and may give unsatisfactory results when a moving-average (MA) component exists in the model. On account of overcoming this difficulty, seasons following PMA processes are tried to be modeled as PAR processes with reasonable orders in order to employ LSE. Diagnostic checking, through residuals of the fitted model, is also performed stating its reasons and methods. The last part of the study demonstrates application of identification techniques through analysis of two seasonal hydrologic time series, which consist of average monthly streamflows. For this purpose, computer programs were developed specially for PARMA model identification.

Stimulus-driven changes in the gain of sensory neurons are well-documented, but relatively little is known about whether analogous gain-control can also be effected in a top-down manner. A recent psychophysical study demonstrated that sensitivity to luminance contrast can be modulated by a priori knowledge (de la Rosa et al., in press). In the present study, event-related potentials were used to resolve the stages of information processing that facilitate such knowledge-driven adjustments. Groupwise independent component analysis identified two robust spatiotemporal patterns of endogenous brain activity that captured experimental effects. The first pattern was associated with obligatory processing of contextual information, while the second pattern was associated with selective initiation of contrast gain adjustment. These data suggest that knowledge-driven contrast gain control is mediated by multiple independent electrogenic sources.

The bulk modulus of pure hydraulic oil and its dependency on pressure and temperature has been studied extensively over the past years. A comprehensive review of some of the more common definitions of fluid bulk modulus is conducted and comments on some of the confusion over definitions and different methods of measuring the fluid bulk modulus are presented in this thesis. In practice, it is known that there is always some form of air present in hydraulic systems which substantially decreases the oil bulk modulus. The term effective bulk modulus is used to account for the effect of air and/or the compliance of transmission lines. A summary from the literature of the effective bulk modulus models for a mixture of hydraulic oil and air is presented. Based on the reviews, these models are divided into two groups: “compression only” models and “compression and dissolve” models. A comparison of various “compression only” models, where only the volumetric compression of air is considered, shows that the models do not match each other at the same operating conditions. The reason for this difference is explained and after applying some modifications to the models, a theoretical model of the “compression only” model is suggested. The “compression and dissolve” models, obtained from the literature review, include the effects of the volumetric compression of air and the volumetric reduction of air due to the dissolving of air into the oil. It is found that the existing “compression and dissolve” models have a discontinuity at some critical pressure and as a result do not match the experimental results very well. The reason for the discontinuity is discussed and a new “compression and dissolve” model is proposed by introducing some new parameters to the theoretical model. A new critical pressure (PC) definition is presented based on the saturation limit of oil. In the new definition, the air stops dissolving into the oil after this critical pressure is reached and any remaining air will be only compressed afterwards. An experimental procedure is successfully designed and fabricated to verify the new proposed models and to reproduce the operating conditions that underlie the model assumptions. The pressure range is 0 to 6.9 MPa and the temperature is kept constant at °C. Air is added to the oil in different forms and the amount of air varies from about 1 to 5%. Experiments are conducted in three different phases: baseline (without adding air to the oil), lumped air (air added as a pocket of air to the top of the oil column) and distributed air (air is distributed in the oil in the form of small air bubbles). The effect of different forms and amounts of air and various volume change rates are investigated experimentally and it is shown that the value of PC is strongly affected by the volume change rate, the form, and the amount of air. It is also shown that the new model can represent the experimental data with great accuracy. The new proposed “compression and dissolve” model can be considered as a general model of the effective bulk modulus of a mixture of oil and air where it is applicable to any form of a mixture of hydraulic oil and air. However, it is required to identify model parameters using experimental measurements. A method of identifying the model parameters is introduced and the modeling errors are evaluated. An attempt is also made to verify independently the value of some of the parameters. The new proposed model can be used in analyzing pressure variations and improving the accuracy of the simulations in low pressure hydraulic systems. The new method of modeling the air dissolving into the oil can be also used to improve the modeling of cavitation phenomena in hydraulic systems.

<p>The inverse problem of modeling biochemical processes mathematically from measured time course data falls into the category of system identification and parameter estimation. Analyzing the time course data would provide valuable insights into the model structure and dynamics of the biochemical system. Based on the types of biochemical reactions, such as metabolic networks and genetic networks, several modeling frameworks have been proposed, developed and proved effective, including the Michaelis-Menten equation, the Biochemical System Theory (BST), etc. One bottleneck in analyzing the obtained data is the estimation of parameter values within the system model.</p> <p>As most models for molecular biological systems are nonlinear with respect to both parameters and system state variables, estimation of parameters in these models from experimental measurement data is thus a nonlinear estimation problem. In principle, all algorithms for nonlinear optimization can be used to deal with this problem, for example, the Gauss-Newton iteration method and its variants. However, these methods do not take the special structures of biological system models into account. When the number of parameters to be determined increases, it will be challenging and computationally expensive to apply these conventional methods.</p> <p>In this research, several methods are proposed for estimating parameters in two classes of widely used biological system models: the S-system model and the linear fractional model (LFM), by utilizing their structure specialties. For the S-system, two estimation methods are designed. 1) Based on the two-term structure (production and degradation) of the model, an alternating iterative least squares method is proposed. 2) A separation nonlinear least squares method is proposed to deal with the partially linear structure of the model. For the LFM, two estimation methods are provided. 1) The separation nonlinear least squares method can also be adopted to treat the partially linear structure of the LFM, and moreover a modified iterative version is included. 2) A special strategy using the separation principle and the weighted least squares method is implemented to turn the cost function into a quadratic form and thus the estimates for parameters can be analytically solved. Simulation results have demonstrated the effectiveness of the proposed methods, which have shown better performance in terms of estimation accuracy and computation time, compared with those conventional nonlinear estimation methods.</p>

This thesis contains two parts: the detectability of convex sets and the study on regression models In the first part of this dissertation, we investigate the problem of the detectability of an inhomogeneous convex region in a Gaussian random field. The first proposed detection method relies on checking a constructed statistic on each convex set within an nn image, which is proven to be un-applicable. We then consider using h(v)-parallelograms as the surrogate, which leads to a multiscale strategy. We prove that 2/9 is the minimum proportion of the maximally embedded h(v)-parallelogram in a convex set. Such a constant indicates the effectiveness of the above mentioned multiscale detection method. In the second part, we study the robustness, the optimality, and the computing for regression models. Firstly, for robustness, M-estimators in a regression model where the residuals are of unknown but stochastically bounded distribution are analyzed. An asymptotic minimax M-estimator (RSBN) is derived. Simulations demonstrate the robustness and advantages. Secondly, for optimality, the analysis on the least angle regressions inspired us to consider the conditions under which a vector is the solution of two optimization problems. For these two problems, one can be solved by certain stepwise algorithms, the other is the objective function in many existing subset selection criteria (including Cp, AIC, BIC, MDL, RIC, etc). The latter is proven to be NP-hard. Several conditions are derived. They tell us when a vector is the common optimizer. At last, extending the above idea about finding conditions into exhaustive subset selection in regression, we improve the widely used leaps-and-bounds algorithm (Furnival and Wilson). The proposed method further reduces the number of subsets needed to be considered in the exhaustive subset search by considering not only the residuals, but also the model matrix, and the current coefficients.

This paper uses the panel data of 11 OECD countries over a period from 1971 to 2006. Unlike the traditional cointegration model which omitted the impact of structural breaks in the analysis, this paper applies panel cointegration with structural break test proposed by Westerlund (2006), panel unit root test, and panel dynamic OLS test. The empirical results indicate that health care expenditure and economic growth (GDP per capita) are non-stationary in the series; and between the two variables, a long-term cointegration relationship exists. Moreover, a positive correlation between HCE and economic growth is found in the panel dynamic OLS model. The researcher concludes that investing in health capital improves human capital and that boosts economic growth in the sample countries, and vice versa. More importantly, allowing structural breaks in the cointegration analysis obtains reliability in the estimation and proves more detailed and specific information on the consequence of the momentous events on the two variables; and thus enables policy makers and health economists to propose more effective strategies.