No description available.

<p>Due to the advancements in computing power and the availability of high-frequency data, the analyses of the high frequency stock data and market microstructure has become more and more important in econometrics. In the high frequency data setting, volatility is a very important indicator on the movement of stock prices and measure of risk. It is a key input in pricing of assets, portfolio reallocation, and risk management. In this thesis, we use the Heterogeneous Autoregressive model of realized volatility, combined with Bayesian inference as well as Markov chain Monte Carlo method’s to estimate the innovation density of the daily realized volatility. A Dirichlet process is used as the prior in a countably infinite mixture model. The semi-parametric model provides a robust alternative to the models used in the literature. I find evidence of thick tails in the density of innovations to log-realized volatility.</p> / Master of Science (MSc)

A power grid network is an interconnected network of coupled devices that generate, transmit and distribute power to consumers. These complex and usually large-scale systems have high dimensional models that are computationally expensive to simulate especially in real time applications, stability analysis, and control design. Model order reduction (MOR) tackles this issue by approximating these high dimensional models with reduced high-fidelity representations. When the internal description of the models is not available, the reduced representations are constructed by data. In this dissertation, we investigate four problems regarding the MOR and data-driven modeling of the power networks model, particularly the swing equations. We first develop a parametric MOR approach for linearized parametric swing equations that preserves the physically-meaningful second-order structure of the swing equations dynamics. Parameters in the model correspond to variations in operating conditions. We employ a global basis approach to develop the parametric reduced model. We obtain these local bases by $mathcal{H}_2$-based interpolatory model reduction and then concatenate them to form a global basis. We develop a framework to enrich this global basis based on a residue analysis to ensure bounded $mathcal{H}_2$ and $mathcal{H}_infty$ errors over the entire parameter domain. Then, we focus on nonlinear power grid networks and develop a structure-preserving system-theoretic model reduction framework. First, to perform an intermediate model reduction step, we convert the original nonlinear system to an equivalent quadratic nonlinear model via a lifting transformation. Then, we employ the $mathcal{H}_2$-based model reduction approach, Quadratic Iterative Rational Krylov Algorithm (Q-IRKA). Using a special subspace structure of the model reduction bases resulting from Q-IRKA and the structure of the underlying power network model, we form our final reduction basis that yields a reduced model of the same second-order structure as the original model. Next, we focus on a data-driven modeling framework for power network dynamics by applying the Lift and Learn approach. Once again, with the help of the lifting transformation, we lift the snapshot data resulting from the simulation of the original nonlinear swing equations such that the resulting lifted-data corresponds to a quadratic nonlinearity. We then, project the lifted data onto a lower dimensional basis via a singular value decomposition. By employing a least-squares measure, we fit the reduced quadratic matrices to this reduced lifted data. Moreover, we investigate various regularization approaches. Finally, inspired by the second-order sparse identification of nonlinear dynamics (SINDY) method, we propose a structure-preserving data-driven system identification method for the nonlinear swing equations. Using the special structure on the right-hand-side of power systems dynamics, we choose functions in the SINDY library of terms, and enforce sparsity in the SINDY output of coefficients. Throughout the dissertation, we use various power network models to illustrate the effectiveness of our approaches. / Doctor of Philosophy / Power grid networks are interconnected networks of devices responsible for delivering electricity to consumers, e.g., houses and industries for their daily needs. There exist mathematical models representing power networks dynamics that are generally nonlinear but can also be simplified by linear dynamics. Usually, these models are complex and large-scale and therefore take a long time to simulate. Hence, obtaining models of much smaller dimension that can capture the behavior of the original systems with an acceptable accuracy is a necessity. In this dissertation, we focus on approximation of power networks model through the swing equations. First, we study the linear parametric power network model whose operating conditions depend on parameters. We develop an algorithm to replace the original model with a model of smaller dimension and the ability to perform in different operating conditions. Second, given an explicit representation of the nonlinear power network model, we approximate the original model with a model of the same structure but smaller dimension. In the cases where the mathematical models are not available but only time-domain data resulting from simulation of the model is at hand, we apply an already developed framework to infer a model of a small dimension and a specific nonlinear structure: quadratic dynamics. In addition, we develop a framework to identify the nonlinear dynamics while maintaining their original physically-meaningful structure.

Um modelo paramétrico regional tridimensional da Corrente do Brasil (CB) foi construído com o objetivo de investigar se o meandramento frontal da CB domina a variabilidade subinercial na Bacia de Campos. A parametrização da CB normal à costa seguiu a formulação proposta por Schmidt et al. [2007]. Os parâmetros da CB foram estimados a partir dos perfis de velocidade quase-sinóticos durante a amostragem do cruzeiro OCEANO SUDESTE II (OCSEII) da Marinha do Brasil. A frente térmica costeira (FTC), facilmente detectado na superfície do mar por imagens de satélite, é tida como a fronteira costeira do CB com velocidades desprezíveis. A FTC padrão é, então, inferida pelo método do gradiente máximo em uma data escolhida. Com a localização da FTC, várias radiais são projetadas normalmente à frente e os perfis de velocidades da CB são ajustados a estas radiais. O próximo passo é interpolar objetivamente o campo de velocidade para obter uma CB tridimensional. A variabilidade espacial da CB neste mapa é, portanto, unicamente devido à arqueamento da velocidade/ FTC, e os padrões de velocidade são devidos apenas ao meandramento frontal. Estes foram identificados nas séries temporais das FTC e o modelo foi computado para dois eventos separados no inverno de 2007 e comparados com os dados de ADCPs da PETROBRAS montados em quatro plataformas de petróleo. A comparação foi feita usando compósitos semanais e médias para isolar movimentos de mesoescala. Isso mostra que há um acordo muito favorável entre o modelo e as medições de velocidade de mar aberto em ambas as magnitudes e direções. Este assegura que os meandros frontais, ou mais especificamente, o meandro de São Tomé domina a variabilidade subinercial ao longo do talude continental e do platô de São Paulo. A única exceção é para o ADCP montado sobre uma plataforma de petróleo em plataforma continental. O modelado e as observações in situ discordam e indicam que outros fenômenos, tais como ondas de plataforma, devem ser contabilizados na parte costeira / A regional three-dimensional parametric model of the Brazil Current (BC) was built with the goal of investigating whether or not the BC frontal meandering dominates the subinercial current variability in the Campos Basin. The BC cross-stream parametrization followed the formulation proposed by Schmidt et al. [2007]. The BC parameters were estimated from quasi-synoptic velocity profiling sampled during the Brazilian Navy cruise OCEANO SUDESTE II (OCSEII). The surface inshore thermal front (ITF),easily detected in sea surface temperature satellite images, is taken as the inshore border of the BC jet with negligible velocities. The ITF spatial pattern is then inferred by the maximum gradient method on a chosen date or period. With the ITF location, several BC cross-strem profiles are projected normally to it. The next step is to objectively interpolate the velocity field to obtain a three-dimensional BC. The spatial variability of the BC in this map is therefore solely due to the arching of the velocity/ITF front, and therefore, the velocity patterns are due to frontal meandering only. Meandering events were identified in the IFT time series, the model computed the BC frontal patterns for two separate events in the winter of 2007 and compared with PETROBRAS\' ADCP records mounted on four oil rigs. The comparison was done using weekly composites and averages to isolate mesoscale motions. It shows that there is a very favorable agreement between between the model and the open sea velocity measurements in both magnitude and direction. This reassures that the frontal meandering, or more specifically, the S~ao Tom´e Meander formation dominates the subinertial variability over the continental slope and the S~ao Paulo Plateau. The only exception is for the ADCP mounted on an oil rig on the continental shelf. The modeled and in situ observations disagree and indicate that other phenomena, such as shelf waves, should be accounted in the coastal ocean in addition to the BC meandering.

Um modelo paramétrico regional tridimensional da Corrente do Brasil (CB) foi construído com o objetivo de investigar se o meandramento frontal da CB domina a variabilidade subinercial na Bacia de Campos. A parametrização da CB normal à costa seguiu a formulação proposta por Schmidt et al. [2007]. Os parâmetros da CB foram estimados a partir dos perfis de velocidade quase-sinóticos durante a amostragem do cruzeiro OCEANO SUDESTE II (OCSEII) da Marinha do Brasil. A frente térmica costeira (FTC), facilmente detectado na superfície do mar por imagens de satélite, é tida como a fronteira costeira do CB com velocidades desprezíveis. A FTC padrão é, então, inferida pelo método do gradiente máximo em uma data escolhida. Com a localização da FTC, várias radiais são projetadas normalmente à frente e os perfis de velocidades da CB são ajustados a estas radiais. O próximo passo é interpolar objetivamente o campo de velocidade para obter uma CB tridimensional. A variabilidade espacial da CB neste mapa é, portanto, unicamente devido à arqueamento da velocidade/ FTC, e os padrões de velocidade são devidos apenas ao meandramento frontal. Estes foram identificados nas séries temporais das FTC e o modelo foi computado para dois eventos separados no inverno de 2007 e comparados com os dados de ADCPs da PETROBRAS montados em quatro plataformas de petróleo. A comparação foi feita usando compósitos semanais e médias para isolar movimentos de mesoescala. Isso mostra que há um acordo muito favorável entre o modelo e as medições de velocidade de mar aberto em ambas as magnitudes e direções. Este assegura que os meandros frontais, ou mais especificamente, o meandro de São Tomé domina a variabilidade subinercial ao longo do talude continental e do platô de São Paulo. A única exceção é para o ADCP montado sobre uma plataforma de petróleo em plataforma continental. O modelado e as observações in situ discordam e indicam que outros fenômenos, tais como ondas de plataforma, devem ser contabilizados na parte costeira / A regional three-dimensional parametric model of the Brazil Current (BC) was built with the goal of investigating whether or not the BC frontal meandering dominates the subinercial current variability in the Campos Basin. The BC cross-stream parametrization followed the formulation proposed by Schmidt et al. [2007]. The BC parameters were estimated from quasi-synoptic velocity profiling sampled during the Brazilian Navy cruise OCEANO SUDESTE II (OCSEII). The surface inshore thermal front (ITF),easily detected in sea surface temperature satellite images, is taken as the inshore border of the BC jet with negligible velocities. The ITF spatial pattern is then inferred by the maximum gradient method on a chosen date or period. With the ITF location, several BC cross-strem profiles are projected normally to it. The next step is to objectively interpolate the velocity field to obtain a three-dimensional BC. The spatial variability of the BC in this map is therefore solely due to the arching of the velocity/ITF front, and therefore, the velocity patterns are due to frontal meandering only. Meandering events were identified in the IFT time series, the model computed the BC frontal patterns for two separate events in the winter of 2007 and compared with PETROBRAS\' ADCP records mounted on four oil rigs. The comparison was done using weekly composites and averages to isolate mesoscale motions. It shows that there is a very favorable agreement between between the model and the open sea velocity measurements in both magnitude and direction. This reassures that the frontal meandering, or more specifically, the S~ao Tom´e Meander formation dominates the subinertial variability over the continental slope and the S~ao Paulo Plateau. The only exception is for the ADCP mounted on an oil rig on the continental shelf. The modeled and in situ observations disagree and indicate that other phenomena, such as shelf waves, should be accounted in the coastal ocean in addition to the BC meandering.

This thesis is dealing with optimization by using artificial intelligence of an induction machine rotor slot. The one optimized is commonly manufactured induction motor with simple deep bar rotor. Goal is to design an optimization method and achieve the highest possible value of efficiency and power factor of chosen machine. Work contains the parametric model construction in Ansys Maxwell software, the optimization algorithm assembly and its setup for desirable output, and processing of the results.

No description available.

Le contexte hospitalier actuel exhorte les établissements de santé à une gestion plus rigoureuse et objective des ressources dans le but de maitriser leur performance. Le patrimoine immobilier des établissements publics de santé contribue à la qualité des soins demandée, à la qualité d'accueil des patients et des conditions de travail du personnel. Aujourd'hui, les hôpitaux demeurent conçus et réalisés sans aucune anticipation des coûts futurs d'exploitation-maintenance. Ces coûts qui représentent indéniablement une valeur importante du budget global de fonctionnement, sont pourtant méconnus. Afin de palier à la faiblesse des moyens de financement public, l'administration a comme recours la mise en place d'une nouvelle procédure de contrats de Partenariat Public Privé. Or celle-ci ne dispose pas d'un minimum d'informations en terme de connaissances sur les dépenses d'exploitation-maintenance actuelles de son patrimoine. Dans ce contexte, le coût global constitue une approche innovante dans la gestion des projets immobiliers.La problématique développée dans ce travail de thèse est donc d'approfondir les connaissances en amont des coûts d'exploitation-maintenance des bâtiments hospitaliers. Elle apporte un éclairage sur la relation entre les coûts d'exploitation-maintenance et l'activité hospitalière. Elle propose ainsi une réponse plus adaptée et plus opérationnelle à la question de l'estimation de ces coûts d'exploitation-maintenance en aidant le maître d'ouvrage à prendre en compte l'activité de soin et les choix architecturaux dans son projet. Cela revient à proposer une méthode d'aide à la décision pour construire un bâtiment hospitalier qui minimise les dépenses futures de fonctionnement. Cette méthode s'appuie sur un modèle d'estimation et sur un observatoire des coûts d'exploitation-maintenance réel de 37 établissements publics de santé. / The current situations of hospitals require more efficient management in order to control their performance. The real estates of public hospitals contribute to the quality of required healthcare, the quality of patient care and working conditions of healthcare staff.Today, hospitals are designed and built without anticipating of future evolution of operation and maintenance costs. These costs that are an important contribution to the value of the overall operating budget are underestimated and far unknown.To overcome the lack of public finance, the government has so used the Public Private Partnership Contracts as a new procedure. Yet the government does not have a minimum of information about operating and maintenance expenses of its real estates. In this context, the Whole Life Cycle Costing is an innovative approach in managing public healthcare real estate projects.The problem developed in this thesis is to expend is to expend the knowledge of operation and maintenance costs of healthcare real estates. This thesis highlights the relationship between operating and maintenance costs and hospital activity. It proposes a more efficient response in the issue of estimating these costs by helping the director to take into account the healthcare activity and the architectural choices. It provides a decision making tool for designing and building a new hospitals for minimizing future costs of operation.This method is based on a model for estimating costs and on an observatory of real operation and maintenance costs of 37 public hospitals.

La géolocalisation indoor en temps réel a largement été étudiée ces dernières années, et de nombreuses applications y sont associées. Une estimation en temps différé de la trajectoire présente également un certain intérêt. La géolocalisation indoor en temps différé permet par exemple de développer des approches de type crowdsourcing qui tirent profit d'un grand nombre d'utilisateurs afin de récolter un grand nombre de mesures : la connaissance du trajet d'un utilisateur muni d'un smartphone permet par exemple d'alimenter une carte de fréquentation du bâtiment. Estimer la trajectoire de cet utilisateur ne nécessite pas de traitement en temps réel et peut s'effectuer en temps différé ce qui offre deux avantages. D'abord, l'approche temps réel estime une position courante uniquement avec les mesures présentes et passées, alors que l'approche temps différé permet d'avoir accès à l'ensemble des mesures et permet d'obtenir une trajectoire estimée plus régulière et plus précise qu'en temps réel. Par ailleurs, cette estimation peut se faire sur un serveur et n'a pas besoin d'être portée par un smartphone comme c'est le cas en temps réel, ce qui permet d'utiliser une puissance de calcul et un volume mémoire plus importants. L'objet de ces travaux de thèse est de proposer une estimation de la trajectoire d'un individu se déplaçant avec un smartphone recevant des mesures de puissance wifi ou bluetooth (RSS) et enregistrant des mesures inertielles (IMU). En premier lieu, sans la connaissance de la position des murs de la carte, un modèle paramétrique est proposé, basé sur un modèle de propagation d'onde adaptatif pour les mesures RSS ainsi que sur une modélisation par morceaux de la trajectoire inertielle, issue des mesures IMU. Les résultats obtenus en temps différé ont une moyenne d'erreur de 6.2m contre 12.5men temps réel. En second lieu, l'information des contraintes de déplacement induites par la présence des murs du bâtiment est ajoutée et permet d'affiner l'estimation de la trajectoire avec une technique particulaire, comme il est couramment utilisé dans la littérature. Cette seconde approche a permis de développer un lisseur particulaire ainsi qu'un estimateur du maximum a posteriori par l'algorithme de Viterbi. D'autres heuristiques numériques ont été présentées. Une première heuristique ajuste le modèle d'état de l'utilisateur, qui est initialement uniquement basé sur les mesures IMU, à partir du modèle paramétrique développé sans les murs. Une seconde heuristique met en œuvre plusieurs réalisations d'un filtre particulaire et définit deux scores basés sur les mesures RSS et sur la continuité de la trajectoire. Les scores permettent de sélectionner la meilleure réalisation du filtre. Un algorithme global, regroupant l'ensemble de ces approche permet d'obtenir une erreur moyenne de 3.6m contre 5.8m en temps réel. Enfin, un modèle d'apprentissage statistique basé sur des forêts aléatoires a permis de distinguer les trajectoires qui ont été correctement estimées en fonction d'un faible nombre de variables, en prévision d'une application au crowdsourcing. / Real time indoor geolocalization has recently been widely studied, and has many applications. Off-line (post-processing) trajectory estimation also presents some interest. Off-line indoor geolocalization makes it possible for instance to develop crowdsourcing approaches that take advantage of a large number of users to collect a large number of measurements: knowing the trajectory of a smartphone user makes it possible for instance to feed an attendance map. Estimating this trajectory does not need to be performed in real-time and can be performed off-line, two main benefits. Firstly, the real-time approach estimates a current position using present and past measurements only, when the off-line approach has access to the whole measurements, and makes it possible to obtain an estimated trajectory that is smoother and more accurate than with a real-time approach. Secondly, this estimation can be done on a server and does not need to be implemented in the smartphone as it is the case in the real-time approach, with the consequence that more computing power and size memory are available. The objective of this PhD is to provide an off-line estimation of the trajectory of a smartphone user receiving signal strength (RSS) of wifi or bluetooth measurements and collecting inertial measurements (IMU). In the beginning, without the floorplan of the building, a parametric model is proposed, based on an adaptive pathloss model for RSS measurements and on a piecewise parametrization for the inertial trajectory, obtained with IMU measurements. Results are an average error of 6.2mfor the off-line estimation against 12.5m for the real-time estimation. Then, information on displacement constraints induced by the walls is considered, that makes it possible to adjust the estimated trajectory by using a particle technique as often done in the state-of-the-art. With this second approach we developped a particle smoother and a maximum a posteriori estimator using the Viterbi algorithm. Other numerical heuristics have been introduced. A first heuristic makes use of the parametric model developed without the floorplan to adjust the state model of the user which was originally based on IMUalone. A second heuristic proposes to performseveral realization of a particle filter and to define two score functions based on RSS and on the continuity of the estimated trajectory. The scores are then used to select the best realization of the particle filter as the estimated trajectory. A global algorithm, which uses all of the aforementioned approaches, leads to an error of 3.6m against 5.8m in real-time. Lastly, a statistical machine learning model produced with random forests makes it possible to distinguish the correct estimated trajectories by only using few variables to be used in a crowdsourcing framework.

The ICRP recommends a radiation weighting factor of one for all low-LET radiation. However, many experimental studies find inconsistencies between low-LET RBE and the ICRP's current radiation weighting factor. Generally, there is evidence that dependence exists between radiation energy and radiation RBE where lower energy radiations tend to have a greater biological effect than higher energy radiation. Specifically, the radiations of tritium and carbon K-shell x-rays have been studied in numerous experiments and the biological effects of both of these radiations are consistently greater than that of Co-60. In this work, the relationship between radiation energy and radiation effect has been investigated with the use of a newly developed double strand break (DSB) yield estimation algorithm. This algorithm makes use of a detailed solenoidal 30 nm DNA chromatin model to describe the radiation-sensitive biological target. In addition to the DNA model, NOREC, an event by event Monte Carlo code, was used in this algorithm to characterize the electron track. As an alternative to the conventional approach of computationally simulating DNA damage by spatial overlay of an electron track on DNA, this algorithm instead focuses on quantifying the distance between ionizations in an electron track and next determining the likelihood that any given ionization pair forms a DSB. The first step of the algorithm involves electron characterization while the second step relies on DNA molecule characterization. By assuming a DSB biological endpoint and determining the DSB yield as a function of electron energy, energy dependent RBE values were estimated for monoenergetic electrons from 10 eV to 1 MeV. Photon RBE values, x-ray RBE values and radionuclide RBE values were also calculated and reported in this work in addition to electron RBE values. Photon RBE values were estimated based upon the electron RBE calculation. Photon RBE values were reported from 1 eV to 10 MeV. In turn, x-ray RBE values were calculated based upon photon values for several tube voltage and filter combinations. Finally, RBE values for over 1000 radionuclides were estimated and reported.