La spectroscopie électronique basée sur l'effet tunnel donne accès à la densité d'états des électrons (DoS) dans les matériaux conducteurs, et renseigne ainsi en détail sur leurs propriétés électroniques. <br />Au cours de cette thèse, nous avons développé un microscope permettant d'effectuer la spectroscopie tunnel résolue spatialement (10 nm) de nanocircuits individuels, avec une résolution en énergie inégalée (10 µeV). Cet appareil combine les fonctions de Microscopie par Force Atomique (mode AFM) et de spectroscopie Tunnel locale (mode STM), et fonctionne à 30 mK. Dans le mode AFM, la topographie de l'échantillon est imagée grâce à un diapason en quartz piézoélectrique, ce qui permet de repérer les circuits. La spectroscopie tunnel peut ensuite être faite sur les zones conductrices. <br />Avec ce microscope, nous avons mesuré la DoS locale dans une structure hybride Supraconducteur-métal Normal-Supraconducteur (S-N-S). Dans un tel circuit, les propriétés électroniques de N et de S sont modifiées par l'effet de proximité supraconducteur. Notamment, pour des fils N courts, nous avons pu observer -comme prédit- la présence d'un gap dans sa DoS, indépendant de la position dans la structure : le “minigap”. De plus, en modulant la phase supraconductrice entre les deux S, nous avons mesuré la modification de ce gap, et sa disparition lorsque la différence de phase vaut π. <br />Nos résultats expérimentaux pour la DoS, ainsi que ses dépendances en phase, en position, et en longueur de N sont en accord quantitatif avec les prédictions de la théorie quasiclassique de la supraconductivité. Certaines de ces prédictions sont observées pour la première fois.

The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spaces such as the space of cumulative distribution functions and the space of cumulative hazard functions. Well-known priors on the space of cumulative distribution functions are the Dirichlet process, the two-parameter Poisson-Dirichlet process and the beta-Stacy process. On the other hand, the beta process is a popular prior on the space of cumulative hazard functions. This thesis is divided into three parts. In the first part, we tackle the problem of sampling from the above mentioned processes. Sampling from these processes plays a crucial role in many applications in Bayesian nonparametric inference. However, having exact samples from these processes is impossible. The existing algorithms are either slow or very complex and may be difficult to apply for many users. We derive new approximation techniques for simulating the above processes. These new approximations provide simple, yet efficient, procedures for simulating these important processes. We compare the efficiency of the new approximations to several other well-known approximations and demonstrate a significant improvement. In the second part, we develop explicit expressions for calculating the Kolmogorov, Levy and Cramer-von Mises distances between the Dirichlet process and its base measure. The derived expressions of each distance are used to select the concentration parameter of a Dirichlet process. We also propose a Bayesain goodness of fit test for simple and composite hypotheses for non-censored and censored observations. Illustrative examples and simulation results are included. Finally, we describe the relationship between the frequentist and Bayesian nonparametric statistics. We show that, when the concentration parameter is large, the two-parameter Poisson-Dirichlet process and its corresponding quantile process share many asymptotic pr operties with the frequentist empirical process and the frequentist quantile process. Some of these properties are the functional central limit theorem, the strong law of large numbers and the Glivenko-Cantelli theorem.

Cette étude a pour objectifs la caractérisation expérimentale des frottements saccadés dans les matériaux granulaires modèles constitués des billes de verre monodisperses en compression triaxiale drainée et l'identification des paramètres de contrôle. Cinq paramètres macroscopiques caractérisent ces frottements saccadés : la chute de déviateur et la contraction volumique, l'intermittence de déformation axiale, le module d'Young et le coefficient de Poisson. Les frottements saccadés affectent simultanément le déviateur et la déformation volumique. Le comportement macroscopique est globalement contractant tandis que le matériau tend vers un état limite critique en grandes déformations, à la manière des sables lâches. Cependant il présente localement, dans les phases de blocage qui suivent immédiatement les ruptures temporaires, le comportement dilatant des sables denses, qui obéit à une relation contrainte-dilatance linéaire et unique. Les frottements saccadés disparaissent au-delà d'une vitesse critique d'écrasement axial, qui dépend de la contrainte de confinement et de la taille des grains. Le module d'Young dynamique par propagation d'ondes varie avec la contrainte de confinement selon une loi de puissance. Le module d'Young quasi-élastique au départ des phases de blocage est constant à l'intérieur du domaine élastique, de même que le coefficient de Poisson. Le suivi par granulométrie laser et par analyse d'images des matériaux après un ou plusieurs essais triaxiaux permet de suivre l'évolution de la taille moyenne et de la forme des grains. Tandis que les instabilités par saccade disparaissent suite à un certain nombre d'essais, on observe, simultanément à une légère diminution du volume moyen, l'apparition progressive de populations d'objets non sphériques par une fusion des grains analogue au frittage. / The objectives of this study are the experimental characterisatino of the stick-slip instabilities in a model granular material and the identification of relevant control parameters. As monodisperse glass beads are subjected to drained triaxial compression tests, five macroscopic parameters characterize the stick-slip phenomenon: the deviator drop, the jump in volumetric contraction, the intermittence of the axial strain, Young’s modulus and Poisson’s ratio. The stick-slip events simultaneously affect the deviator stress and the volumetric strain. While the global material behavior is that of a loose sand, gradually contracting and hardening as it approaches its large strain critical state, its response in the “stick” phases immediately following the “slip” instabilities is similar to that of dense, dilatant sands, with a unique, linear stress-dilatancy relationship. Stick-slip events disappear beyond a critical axial strain rate, depending on the confining stress and on the grain diameter. The Young modulus associated to wave propagation varies with the confining stress according to a power law. The quasi-elastic modulus measured at the beginning of the stick phase is constant inside the elastic domain, as well as the Poisson ratio. The evolution of grain size and shape after one or several triaxial tests is monitored by laser granulometry and image analysis. The gradual vanishing of stick-slip events, on repeating the tests, is likely related to the global decreasing trend of average particle volume and to the formation of non-spherical objects, apparently by some phenomenon analogous to sintering.

The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spaces such as the space of cumulative distribution functions and the space of cumulative hazard functions. Well-known priors on the space of cumulative distribution functions are the Dirichlet process, the two-parameter Poisson-Dirichlet process and the beta-Stacy process. On the other hand, the beta process is a popular prior on the space of cumulative hazard functions. This thesis is divided into three parts. In the first part, we tackle the problem of sampling from the above mentioned processes. Sampling from these processes plays a crucial role in many applications in Bayesian nonparametric inference. However, having exact samples from these processes is impossible. The existing algorithms are either slow or very complex and may be difficult to apply for many users. We derive new approximation techniques for simulating the above processes. These new approximations provide simple, yet efficient, procedures for simulating these important processes. We compare the efficiency of the new approximations to several other well-known approximations and demonstrate a significant improvement. In the second part, we develop explicit expressions for calculating the Kolmogorov, Levy and Cramer-von Mises distances between the Dirichlet process and its base measure. The derived expressions of each distance are used to select the concentration parameter of a Dirichlet process. We also propose a Bayesain goodness of fit test for simple and composite hypotheses for non-censored and censored observations. Illustrative examples and simulation results are included. Finally, we describe the relationship between the frequentist and Bayesian nonparametric statistics. We show that, when the concentration parameter is large, the two-parameter Poisson-Dirichlet process and its corresponding quantile process share many asymptotic pr operties with the frequentist empirical process and the frequentist quantile process. Some of these properties are the functional central limit theorem, the strong law of large numbers and the Glivenko-Cantelli theorem.

The aim of this diploma thesis is to develop a linear positioning stage for Ultra High Vacuum (UHV) environment. Simple prototypes of the linear positioning stage were designed and incorporated as part of a multiaxis sample manipulator for a UHV Scanning Tunneling Microscopy / Atomic Force Microscopy (STM/AFM). Different types of position encoders and linear guideways are discussed. Implementation of the homodyne interferometer as an optimization tool for a slip-stick based linear stage is described. Scalar diffraction theory is used to model the diffraction grating optical position encoder behavior.

Orientador: Marcelo Carvalho Minhoto Teixeira / Resumo: Nesta tese, utiliza-se um otimizador analógico não derivativo proposto por Teixeira & Żak em 1999 como principal ferramenta para os sistemas de controle dos projetos desenvolvidos. Tal otimizador é composto por blocos não lineares e pode ser classificado como um sistema neural artificial. Sistemas chaveados têm grande aplicação prática na otimização de sistemas e são caracterizados por possuírem subsistemas e uma lei de chaveamento que seleciona cada subsistema a cada momento. Deve-se definir condições para que seja possível projetar uma lei de chaveamento que atenda requisitos de projeto. O estudo de técnicas de controle extremal na solução de problemas de busca pelo rastreamento do máximo ponto de potência (do inglês: Maximum Power Point Tracking - MPPT), vem apresentando resultados interessantes na literatura e um tipo de sistema à qual essa técnica pode ser aplicada, é na geração fotovoltaica. Aplica-se o otimizador analógico citado na busca do MPPT de uma célula fotovoltaica, com o objetivo de observar o controle extremal atuando em um processo de otimização, estendendo o controle para quando existem variações de irradiação solar (cenário de uma possível passagem de nuvens). Também observa-se o comportamento do sistema quanto a manter seu correto funcionamento e estabilidade ultimate bounded. A contribuição principal desta tese foi uma nova proposta de utilização conjunta do otimizador de Teixeira & Żak no projeto de controladores ˙ chaveados baseados na minimização da d... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: On this thesis, a non-derivative analog optimizer, proposed by Teixeira & Żak in 1999, was used as the main tool for the proposed control system. Such optimizer is structured by nonlinear blocks and can be classified as an artificial neural system. Switched systems have great theoretical and practical application in systems optimization and are characterized by having subsystems, and a switching law that selects each subsystem at each moment. It is necessary to define conditions so that it is possible to design a switching law for the desired performance of the controlled system. The study of Extremum Seeking Control techniques in the solution of problems of Maximum Power Point Tracking has presented interesting results, and one type of system which this technique can be applied is in the photovoltaic generation. The analog optimizer is applied in the Maximum Power Point Tracking of a photovoltaic cell, with the objective of observing the actuation of the extremal seeking control in an optimization process, extending the control when there are solar irradiation variations (a possible clouds passage scenario). And also observe the behavior of the system and how to maintain its correct functioning and ultimate bounded stability. The main contribution of this thesis was a new procedure for using the mentioned analog optimizer in the design of switched controllers based on the minimization of the derivative of a Lyapunov function. This method allows the relaxed design of controll... (Complete abstract click electronic access below) / Doutor

This study aimed to provide evidence on knowledge of attitudes toward standard precautions (SPs) and its practice of Healthcare Workers (HCWs) in government and private hospitals in Botswana. It utilised descriptive cross-sectional methodology. A range of significant findings were revealed. Good practice of SPs was noted more amongst the HCWs in government than in private hospitals. Knowledge of SPs amongst HCWs in government hospital was significantly and positively correlated to good practice of SPs. Registered Nurses (RNs) had better knowledge of SPs than HealthcareAssistants (HCAs).There was no significant difference between RNs and HCAs practice of SPS and attitudes toward the same. No significant difference in the knowledge, attitudes and practice of SPs was noted between General Practitioners (GPs) and RNs. No significant difference in the knowledge, attitudes and practice of SPs was observed between GPs and HCAs. The study findings have implications for the application of SPs in practice / Health Studies / M.A. (Public Health)

近年，IoTに基づく「つながる工場」に関する技術開発が着目され，日本の次世代の製造業を支えるために，新しい研究開発が求められている．特に機械加工の現場では，異常検知や適応制御のために加工現象を精確かつリアルタイムにモニタできる技術が必要とされている．そこで，マシニングセンタなどの工作機械において，回転工具の加工中に多チャンネルで各種の物理量を切削点近傍にてモニタ可能な無線多機能ホルダを開発し，様々な工具，加工方法を対象にその有効性を示した． / Currently, a smart monitoring technology has been attracting particular attention in the factory automation fields regarding the Internet of things (IoT). Particularly in the machining site, the technology of monitoring the processing phenomenon in precision and real-time is required for abnormality detection or adaptive control. Then, we developed a novel tool holder equipped with a wireless communication function to monitor the tool temperature and vibrating accelerations near the cutting point during a tool rotating operation, and we showed effectiveness for various tools and processing method. / 博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

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La classe des modèles à chaîne de Markov cachée (HMM, Hidden Markov Models) permet, entre autres, de modéliser des données financières. Par exemple, dans ce type de modèle, la distribution du rendement sur un actif financier est exprimée en fonction d'une variable non-observée, une chaîne de Markov, qui représente la volatilité de l'actif. Notons que les dynamiques de cette volatilité sont difficiles à reproduire, car la volatilité est très persistante dans le temps. Les HMM ont la particularité de permettre une variation de la volatilité selon les états de la chaîne de Markov. Historiquement, ces modèles ont été estimés avec un nombre faible de régimes (états), car le nombre de paramètres à estimer explose rapidement avec le nombre de régimes et l'optimisation devient vite difficile. Pour résoudre ce problème une nouvelle sous-classe de modèles à chaîne de Markov cachée, dite à haute dimension, a vu le jour grâce aux modèles dits factoriels et à de nouvelles méthodes de paramétrisation de la matrice de transition. L'objectif de cette thèse est d'étendre cette classe de modèles avec de nouvelles approches plus générales et de montrer leurs applications dans le domaine financier. Dans sa première partie, cette thèse formalise la classe des modèles factoriels à chaîne de Markov cachée et étudie les propriétés théoriques de cette classe de modèles. Dans ces modèles, la dynamique de la volatilité dépend d'une chaîne de Markov latente de haute dimension qui est construite en multipliant des chaînes de Markov de dimension plus faible, appelées composantes. Cette classe englobe les modèles factoriels à chaîne de Markov cachée précédemment proposés dont les composantes sont de dimension deux. Le modèle MDSV (Multifractal Discrete Stochastic Volatility) est introduit afin de pouvoir considérer des composantes de dimension supérieure à deux, généralisant ainsi les modèles factoriels existants. La paramétrisation particulière de ce modèle lui offre suffisamment de flexibilité pour reproduire différentes allures de décroissance de la fonction d'autocorrélation, comme celles qui sont observées sur les données financières. Un cadre est également proposé pour modéliser séparément ou simultanément les données de rendements financiers et de variances réalisées. Une analyse empirique sur 31 séries d'indices financiers montre que le modèle MDSV présente de meilleures performances en termes d'estimation et de prévision par rapport au modèle realized EGARCH. La modélisation par l'entremise des modèles factoriels à chaîne de Markov cachée nécessite qu'on définisse le nombre N de composantes à multiplier et cela suppose qu'il n'existe pas d'incertitude lié à ce nombre. La seconde partie de cette thèse propose, à travers une approche bayésienne, le modèle iFHMV (infinite Factorial Hidden Markov Volatility) qui autorise les données à déterminer le nombre de composantes nécessaires à leur modélisation. En s'inspirant du processus du buffet indien (IBP, Indian Buffet Process), un algorithme est proposé pour estimer ce modèle, sur les données de rendements financiers. Une analyse empirique sur les données de deux indices financiers et de deux actions permet de remarquer que le modèle iFHMV intègre l'incertitude liée au nombre de composantes pour les estimations et les prévisions. Cela lui permet de produire de meilleures prévisions par rapport à des modèles de référence. / Hidden Markov Models (HMMs) are popular tools to interpret, model and forecast financial data. In these models, the return dynamics on a financial asset evolve according to a non-observed variable, a Markov chain, which generally represents the volatility of the asset. This volatility is notoriously difficult to reproduce with statistical models as it is very persistent in time. HMMs allow the volatility to vary according to the states of a Markov chain. Historically, these models are estimated with a very small number of regimes (states), because the number of parameters to be estimated grows quickly with the number of regimes and the optimization becomes difficult. The objective of this thesis is to propose a general framework to construct HMMs with a richer state space and a higher level of volatility persistence. In the first part, this thesis studies a general class of high-dimensional HMMs, called factorial HMMs, and derives its theoretical properties. In these models, the volatility is linked to a high-dimensional Markov chain built by multiplying lower-dimensional Markov chains, called components. We discuss how previously proposed models based on two-dimensional components adhere to the factorial HMM framework. Furthermore, we propose a new process---the Multifractal Discrete Stochastic Volatility (MDSV) process---which generalizes existing factorial HMMs to dimensions larger than two. The particular parametrization of the MDSV model allows for enough flexibility to reproduce different decay rates of the autocorrelation function, akin to those observed on financial data. A framework is also proposed to model financial log-returns and realized variances, either separately or jointly. An empirical analysis on 31 financial indices reveals that the MDSV model outperforms the realized EGARCH model in terms of fitting and forecasting performance. Our MDSV model requires us to pre-specify the number of components and assumes that there is no uncertainty on that number. In the second part of the thesis, we propose the infinite Factorial Hidden Markov Volatility (iFHMV) model as part of a Bayesian framework to let the data drive the selection of the number of components and take into account the uncertainty related to the number of components in the fitting and forecasting procedure. We also develop an algorithm inspired by the Indian Buffet Process (IBP) to estimate the iFHMV model on financial log-returns. Empirical analyses on two financial indices and two stocks show that the iFHMV model outperforms popular benchmarks in terms of forecasting performance.