La thèse se concentre sur une recherche méthodologique sur l'optimisation structurelle catégorielle au moyen d'un apprentissage multiple. Dans cette thèse, les variables catégorielles non ordinales sont traitées comme des variables discrètes multidimensionnelles. Afin de réduire la dimensionnalité, les nombreuses techniques d'apprentissage sont introduites pour trouver la dimensionnalité intrinsèque et mapper l'espace de conception d'origine sur un espace d'ordre réduit. Les mécanismes des techniques d'apprentissage à la fois linéaires et non linéaires sont d'abord étudiés. Ensuite, des exemples numériques sont testés pour comparer les performances de nombreuses techniques d’apprentissage. Sur la base de la représentation d'ordre réduit obtenue par Isomap, les opérateurs de mutation et de croisement évolutifs basés sur les graphes sont proposés pour traiter des problèmes d'optimisation structurelle catégoriels, notamment la conception du dôme, du cadre rigide de six étages et des structures en forme de dame. Ensuite, la méthode de recherche continue consistant à déplacer des asymptotes est exécutée et fournit une solution compétitive, mais inadmissible, en quelques rares itérations. Ensuite, lors de la deuxième étape, une stratégie de recherche discrète est proposée pour rechercher de meilleures solutions basées sur la recherche de voisins. Afin de traiter le cas dans lequel les instances de conception catégorielles sont réparties sur plusieurs variétés, nous proposons une méthode d'apprentissage des variétés k-variétés basée sur l'analyse en composantes principales pondérées. / The thesis concentrates on a methodological research on categorical structural optimizationby means of manifold learning. The main difficulty of handling the categorical optimization problems lies in the description of the categorical variables: they are presented in a category and do not have any orders. Thus the treatment of the design space is a key issue. In this thesis, the non-ordinal categorical variables are treated as multi-dimensional discrete variables, thus the dimensionality of corresponding design space becomes high. In order to reduce the dimensionality, the manifold learning techniques are introduced to find the intrinsic dimensionality and map the original design space to a reduced-order space. The mechanisms of both linear and non-linear manifold learning techniques are firstly studied. Then numerical examples are tested to compare the performance of manifold learning techniques mentioned above. It is found that the PCA and MDS can only deal with linear or globally approximately linear cases. Isomap preserves the geodesic distances for non-linear manifold however, its time consuming is the most. LLE preserves the neighbour weights and can yield good results in a short time. KPCA works like a non-linear classifier and we proves why it cannot preserve distances or angles in some cases. Based on the reduced-order representation obtained by Isomap, the graph-based evolutionary crossover and mutation operators are proposed to deal with categorical structural optimization problems, including the design of dome, six-story rigid frame and dame-like structures. The results show that the proposed graph-based evolutionary approach constructed on the reduced-order space performs more efficiently than traditional methods including simplex approach or evolutionary approach without reduced-order space. In chapter 5, the LLE is applied to reduce the data dimensionality and a polynomial interpolation helps to construct the responding surface from lower dimensional representation to original data. Then the continuous search method of moving asymptotes is executed and yields a competitively good but inadmissible solution within only a few of iteration numbers. Then in the second stage, a discrete search strategy is proposed to find out better solutions based on a neighbour search. The ten-bar truss and dome structural design problems are tested to show the validity of the method. In the end, this method is compared to the Simulated Annealing algorithm and Covariance Matrix Adaptation Evolutionary Strategy, showing its better optimization efficiency. In chapter 6, in order to deal with the case in which the categorical design instances are distributed on several manifolds, we propose a k-manifolds learning method based on the Weighted Principal Component Analysis. And the obtained manifolds are integrated in the lower dimensional design space. Then the method introduced in chapter 4 is applied to solve the ten-bar truss, the dome and the dame-like structural design problems.

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Cette thèse est dédiée à l’étude de deux familles de problèmes de partition de graphe. Nous considérons tout d’abord le problème de sommet-partitionner un graphe en sous-graphesconnexes. Plus précisément, étant donnés p entiers positifs n1; n2; :::; np dont la somme vautl’ordre d’un graphe G, peut-on partitionner V (G) en p parts V1; V2; :::; Vp de sorte que chaque Vi induise un sous-graphe connexe d’ordre ni ? Nous nous intéressons ensuite à des questions plus fortes. Que peut-on dire si l’on souhaite que G soit partitionnable de cette manière quels que soient p et n1; n2; :::; np ? Si l’on souhaite que des sommets particuliers de G appartiennent à des sous-graphes particuliers de la partition ? Et si l’on souhaite que les sous-graphes induits soient plus que connexes ? Nous considérons toutes ces questions à la fois du point de vue structurel (sous quelles conditions structurelles une partition particulière existe-t-elle nécessairement ?) et algorithmique (est-il difficile de trouver une partition particulière ?).Nous nous intéressons ensuite à la 1-2-3 Conjecture, qui demande si tout graphe G admet une 3-pondération voisin-somme-distinguante de ses arêtes, i.e. une 3-pondération par laquelle chaque sommet de G peut être distingué de ses voisins en comparant uniquement leur somme de poids incidents. Afin d’étudier la 1-2-3 Conjecture, nous introduisons notamment la notionde coloration localement irrégulière d’arêtes, qui est une coloration d’arêtes dont chaque classe de couleur induit un sous-graphe dans lequel les sommets adjacents sont de degrés différents.L’intérêt principal de cette coloration est que, dans certaines situations, une pondération d’arêtes voisin-somme-distinguante peut être déduite d’une coloration d’arêtes localement irrégulière. Nospréoccupations dans ce contexte sont principalement algorithmiques (est-il facile de trouver une pondération d’arêtes voisin-somme-distinguante ou une coloration d’arêtes localement irrégulière utilisant le plus petit nombre possible de poids ou couleurs ?) et structurelles (quel est le plus petit nombre de couleurs d’une coloration d’arêtes localement irrégulière ?). Nous considérons également ces questions dans le contexte des graphes orientés. / This thesis is dedicated to the study of two families of graph partition problems.First, we consider the problem of vertex-partitioning a graph into connected subgraphs.Namely, given p positive integers n1; n2; :::; np summing up to the order of some graph G, canwe partition V (G) into p parts V1; V2; :::; Vp so that each Vi induces a connected subgraph withorder ni? We then consider stronger questions. Namely, what if we want G to be partitionablewhatever are p and n1; n2; :::; np? What if we also want specific vertices of G to belong to somespecific subgraphs induced by the vertex-partition? What if we want the subgraphs induced bythe vertex-partition to be more than connected? We consider all these questions regarding boththe structural (are there structural properties ensuring that a specific vertex-partition necessarilyexists?) and algorithmic (is it hard to deduce a specific vertex-partition?) points of view.Then, we focus on the so-called 1-2-3 Conjecture, which asks whether every graph G admitsa neighbour-sum-distinguishing 3-edge-weighting, i.e. a 3-edge-weighting by which all adjacentvertices of G get distinguished by their sums of incident weights. As a tool to deal with the1-2-3 Conjecture, we notably introduce the notion of locally irregular edge-colouring, which isan edge-colouring in which every colour class induces a subgraph whose adjacent vertices havedistinct degrees. The main point is that, in particular situations, a neighbour-sum-distinguishingedge-weighting of G can be deduced from a locally irregular edge-colouring of it. Our concernsin this context are mostly algorithmic (can we easily find a neighbour-sum-distinguishing edgeweightingor locally irregular edge-colouring using the least number of weights or colours?) andstructural (what is the least number of colours in a locally irregular edge-colouring?). We alsoconsider similar matters in the context of oriented graphs.

This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. People use emotional information from facial expressions to switch between conversation topics and to determine attitudes of individuals. Missing emotional information from facial expressions and head gestures makes the visually impaired extremely difficult to interact with others in social events. To enhance the visually impaired’s social interactive ability, in this thesis we have been working on the scientific topic of ‘expressing human emotions through vibrotactile patterns’. It is quite challenging to deliver human emotions through touch since our touch channel is very limited. We first investigated how to render emotions through a vibrator. We developed a real time “lipless” tracking system to extract dynamic emotions from the mouth and employed mobile phones as a platform for the visually impaired to perceive primary emotion types. Later on, we extended the system to render more general dynamic media signals: for example, render live football games through vibration in the mobile for improving mobile user communication and entertainment experience. To display more natural emotions (i.e. emotion type plus emotion intensity), we developed the technology to enable the visually impaired to directly interpret human emotions. This was achieved by use of machine vision techniques and vibrotactile display. The display is comprised of a ‘vibration actuators matrix’ mounted on the back of a chair and the actuators are sequentially activated to provide dynamic emotional information. The research focus has been on finding a global, analytical, and semantic representation for facial expressions to replace state of the art facial action coding systems (FACS) approach. We proposed to use the manifold of facial expressions to characterize dynamic emotions. The basic emotional expressions with increasing intensity become curves on the manifold extended from the center. The blends of emotions lie between those curves, which could be defined analytically by the positions of the main curves. The manifold is the “Braille Code” of emotions. The developed methodology and technology has been extended for building assistive wheelchair systems to aid a specific group of disabled people, cerebral palsy or stroke patients (i.e. lacking fine motor control skills), who don’t have ability to access and control the wheelchair with conventional means, such as joystick or chin stick. The solution is to extract the manifold of the head or the tongue gestures for controlling the wheelchair. The manifold is rendered by a 2D vibration array to provide user of the wheelchair with action information from gestures and system status information, which is very important in enhancing usability of such an assistive system. Current research work not only provides a foundation stone for vibrotactile rendering system based on object localization but also a concrete step to a new dimension of human-machine interaction. / Taktil Video

A main thrust of this thesis is to develop and explore linearization-based numeric-analytic integration techniques in the context of stochastically driven nonlinear oscillators of relevance in structural dynamics. Unfortunately, unlike the case of deterministic oscillators, available numerical or numeric-analytic integration schemes for stochastically driven oscillators, often modelled through stochastic differential equations (SDE-s), have significantly poorer numerical accuracy. These schemes are generally derived through stochastic Taylor expansions and the limited accuracy results from difficulties in evaluating the multiple stochastic integrals. We propose a few higher-order methods based on the stochastic version of transversal linearization and another method of linearizing the nonlinear drift field based on a Girsanov change of measures. When these schemes are implemented within a Monte Carlo framework for computing the response statistics, one typically needs repeated simulations over a large ensemble. The statistical error due to the finiteness of the ensemble (of size N, say)is of order 1/√N, which implies a rather slow convergence as N→∞. Given the prohibitively large computational cost as N increases, a variance reduction strategy that enables computing accurate response statistics for small N is considered useful. This leads us to propose a weak variance reduction strategy. Finally, we use the explicit derivative-free linearization techniques for state and parameter estimations for structural systems using the extended Kalman filter (EKF). A two-stage version of the EKF (2-EKF) is also proposed so as to account for errors due to linearization and unmodelled dynamics. In Chapter 2, we develop higher order locally transversal linearization (LTL) techniques for strong and weak solutions of stochastically driven nonlinear oscillators. For developing the higher-order methods, we expand the non-linear drift and multiplicative diffusion fields based on backward Euler and Newmark expansions while simultaneously satisfying the original vector field at the forward time instant where we intend to find the discretized solution. Since the non-linear vector fields are conditioned on the solution we wish to determine, the methods are implicit. We also report explicit versions of such linearization schemes via simple modifications. Local error estimates are provided for weak solutions. Weak linearized solutions enable faster computation vis-à-vis their strong counterparts. In Chapter 3, we propose another weak linearization method for non-linear oscillators under stochastic excitations based on Girsanov transformation of measures. Here, the non-linear drift vector is appropriately linearized such that the resulting SDE is analytically solvable. In order to account for the error in replacing of non-linear drift terms, the linearized solutions are multiplied by scalar weighting function. The weighting function is the solution of a scalar SDE(i.e.,Radon-Nikodym derivative). Apart from numerically illustrating the method through applications to non-linear oscillators, we also use the Girsanov transformation of measures to correct the truncation errors in lower order discretizations. In order to achieve efficiency in the computation of response statistics via Monte Carlo simulation, we propose in Chapter 4 a weak variance reduction strategy such that the ensemble size is significantly reduced without seriously affecting the accuracy of the predicted expectations of any smooth function of the response vector. The basis of the variance reduction strategy is to appropriately augment the governing system equations and then weakly replace the associated stochastic forcing functions through variance-reduced functions. In the process, the additional computational cost due to system augmentation is generally far less besides the accrued advantages due to a drastically reduced ensemble size. The variance reduction scheme is illustrated through applications to several non-linear oscillators, including a 3-DOF system. Finally, in Chapter 5, we exploit the explicit forms of the LTL techniques for state and parameters estimations of non-linear oscillators of engineering interest using a novel derivative-free EKF and a 2-EKF. In the derivative-free EKF, we use one-term, Euler and Newmark replacements for linearizations of the non-linear drift terms. In the 2-EKF, we use bias terms to account for errors due to lower order linearization and unmodelled dynamics in the mathematical model. Numerical studies establish the relative advantages of EKF-DLL as well as 2-EKF over the conventional forms of EKF. The thesis is concluded in Chapter 6 with an overall summary of the contributions made and suggestions for future research.

Development of dynamic state estimation techniques and their applications in problems of identification in structural engineering have been taken up. The thrust of the study has been the identification of structural systems that exhibit nonlinear behavior, mainly in the form of constitutive and geometric nonlinearities. Methods encompassing both linearization based strategies and those involving nonlinear filtering have been explored. The applications of derivative-free locally transversal linearization (LTL) and multi-step transversal linearization (MTrL) schemes for developing newer forms of the extended Kalman filter (EKF) algorithm have been explored. Apart from the inherent advantages of these methods in avoiding gradient calculations, the study also demonstrates their superior numerical accuracy and considerably less sensitivity to the choice of step sizes. The range of numerical illustrations covers SDOF as well as MDOF oscillators with time-invariant parameters and those with discontinuous temporal variations. A new form of the sequential importance sampling (SIS) filter is developed which explores the scope of the existing SIS filters to cover nonlinear measurement equations and more general forms of noise involving multiplicative and (or) Gaussian/ non-Gaussian noises. The formulation of this method involves Ito-Taylor’s expansions of the nonlinear functions in the measurement equation and the development of the ideal ispdf while accounting for the non-Gaussian terms appearing in the governing equation. Numerical illustrations on parameter identification of a few nonlinear oscillators and a geometrically nonlinear Euler–Bernoulli beam reveal a remarkably improved performance of the proposed methods over one of the best known algorithms, i.e. the unscented particle filter. The study demonstrates the applicability of diverse range of mathematical tools including Magnus’ functional expansions, theory of SDE-s, Ito-Taylor’s expansions and simulation and characterization of the non-Gaussian random variables to the problem of nonlinear structural system identification.

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This thesis deals with the analysis of brain function in Magnetic Resonance Imaging (MRI) using two sequences: BOLD functional MRI (fMRI) and Arterial Spin Labelling (ASL). In this context, group statistical analyses are of great importance in order to understand the general mechanisms underlying a pathology, but there is also an increasing interest towards patient-specific analyses that draw conclusions at the patient level. Both group and patient-specific analyses are studied in this thesis. We first introduce a group analysis in BOLD fMRI for the study of specific language impairment, a pathology that was very little investigated in neuroimaging. We outline atypical patterns of functional activity and lateralisation in language regions. Then, we move forward to patient-specific analysis. We propose the use of robust estimators to compute cerebral blood flow maps in ASL. Then, we analyse the validity of the assumptions underlying standard statistical analyses in the context of ASL. Finally, we propose a new locally multivariate statistical method based on an a contrario approach and apply it to the detection of atypical patterns of perfusion in ASL and to activation detection in BOLD functional MRI.

Ce mémoire porte sur l’étude des équations d’évolution sur des variétés à coubure non nulle, plus particulièrement l’équation des ondes sur les espaces symétriques riemanniens de type non compact.Des propriétés de dispersion des solutions du problème de Cauchy homogène sont démontrées. Ces propriétés sont ensuite utilisées pour établir des estimations dites estimations de Strichartz. L’examen de ces estimées permet de déduire que le problème de Cauchy non linéaire avec des non-linéarités de type puissance est globalement bien posé pour des données initiales petites et localement bien posé pour des données arbitraires.Après un chapitre introductif dédié aux définitions, propriétés algébriques et géométriques des espaces symétriques et à quelques aspects élémentaires d’analyse harmonique sphérique sur ces espaces, un article est présenté : Wave equation on Riemannian symmetric spaces. Cet article contient nos résultats principaux. Dans le dernier chapitre nous présentons en détail deux problèmes ouverts qui prolongent nos travaux. Il s’agit respectivement d’établir le lien entre le comportement asymptotique des estimées et les orbites nilpotentes, et l’étude de l’équation des ondes pour les formes différentielles sur les espaces symétriques. / In this memoir we study evolution equations on curved manifolds. In particular we are interested in the wave equation on Riemannian symmetric spaces of the noncompact type.Dispersive properties of solutions of homogeneous Cauchy problem are proved. These properties are then used to establish Strichartz-type estimates. A closer study of these estimates shows that the nonlinear Cauchy problem with power-like nonlinearities is globally well posed for small initial data and locally well posed for arbitrary initial data.The first chapter is devoted to definitions, algebraic and geometric properties of symmetric spaces and to few elementary aspects of spherical analysis on these spaces. Then our main results are represented in an article : Wave equation on Riemannian symmetric spaces. In the last chapter we present in detail two open problems for future work. One issue is to establish a link between the asymptotic behavior of the estimates and nilpotent orbits, while another issue is the study of wave equation for differential forms on symmetric spaces.

Submitted by ANA KARLA PEREIRA RODRIGUES (anakarla_@hotmail.com) on 2017-08-17T14:00:23Z No. of bitstreams: 1 arquivototal.pdf: 1661057 bytes, checksum: 913a7f671e2e028b60d14a02274f932a (MD5) / Made available in DSpace on 2017-08-17T14:00:23Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1661057 bytes, checksum: 913a7f671e2e028b60d14a02274f932a (MD5) Previous issue date: 2015-02-27 / In this work we investigate the concept of topological vector spaces and their properties. In the rst chapter we present two sections of basic results and in the other sections we present a more general study of such spaces. In the second chapter we restrict ourselves to the real scalar eld and we study, in the context of locally convex spaces, the Hahn-Banach and Banach-Alaoglu theorems. We also build the weak, weak-star, of bounded convergence and of pointwise convergence topologies. Finally we investigate the Theorem of Banach-Steinhauss, the Open Mapping Theorem and the Closed Graph Theorem. / Neste trabalho, estudamos o conceito de espa cos vetoriais topol ogicos e suas propriedades. No primeiro cap tulo, apresentamos duas se c~oes de resultados b asicos e, nas demais se c~oes, apresentamos um estudo sobre tais espa cos de forma mais ampla. No segundo cap tulo, restringimo-nos ao corpo dos reais e fazemos um estudo sobre os espa cos localmente convexos, o Teorema de Hahn-Banach, o Teorema de Banach- Alaoglu, constru mos as topologias fraca, fraca-estrela, da converg^encia limitada e da converg^encia pontual. Por ultimo, estudamos o Teorema da Limita c~ao Uniforme, o Teorema do Gr a co Fechado e o da Aplica c~ao Aberta no contexto mais geral dos espa cos de Fr echet.