This study deals with a general method for the analysis of a semi-active control technique for a fast-shunt switching system. The benefit of the semi-active system is the reduction in power consumption, which is a significant disadvantage of a fully active system compared with a passive system. A semi-active system under consideration is a semi-actively shunted piezoelectric system, which converts the strain energy into electrical energy through strong electromechanical coupling achieved though the piezoelectric phenomenon. Our proposed semi-active approach combines a PZT-based energy harvesting with a fast switching system driven by a Pulse-Width Modulated (PWM) signal. The fast switching system enables continuous adaptation of vibration energy control/harvesting by varying the PWM duty cycle. This contrasts with a conventional capacitance switching system that can only change the capacitance at discrete values. The analysis of the current piezoelectric system combined with a fast-switching system poses a considerable challenge as it contains both continuous and discrete characteristics. The study proposes an enhanced averaging method for analyzing the piecewise linear system. The simulation of the averaged system is much faster than that of the time-varying system. Moreover, the analysis derives error bounds that characterize convergence in the time domain of the averaged system to the original system. The dissertation begins with the derivation of the equations governing the physics of a piezostructure combined with an electrical switching shunt network. The results of the averaging analysis and numerical simulation are presented in order to provide a basis for estimating the structural responses that range between open- and short-circuit conditions which constitutes two limiting conditions. An experimental study demonstrates that the capacitive shunt bimorph piezostructure coupled with a single switch can be adjusted continuously by varying the PWM duty cycle. And the behavior of such hybrid system can be well predicted by the averaging analysis. / Ph. D.

The Multiset Sampler [Leman et al., 2009] has previously been deployed and developed for efficient sampling from complex stochastic processes. We extend the sampler and the surrounding theory to model selection problems. In such problems efficient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new pro- posed model. In order to overcome this we propose a multiset on the model space to en- able efficient exploration of multiple model modes with almost no tuning. The Multiset Model Selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. We show that posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. We also obtain typical Bayesian model averaged estimates for the parameters from MSMS. We apply our algorithm to linear regression where it allows easy moves between parame- ter modes of different models, and in probit regression where it allows jumps between widely varying model specific covariance structures in the latent space of a hierarchical model. The Storytelling algorithm [Kumar et al., 2006] constructs stories by discovering and con- necting latent connections between documents in a network. Such automated algorithms often do not agree with user's mental map of the data. Hence systems that incorporate feedback through visual interaction from the user are of immediate importance. We pro- pose a visual analytic framework in which such interactions are naturally incorporated in to the existing Storytelling algorithm through a redefinition of the latent topic space used in the similarity measure of the network. The document network can be explored us- ing the newly learned normalized topic weights for each document. Hence our algorithm augments the limitations of human sensemaking capabilities in large document networks by providing a collaborative framework between the underlying model and the user. Our formulation of the problem is a supervised topic modeling problem where the supervi- sion is based on relationships imposed by the user as a set of inequalities derived from tolerances on edge costs from inverse shortest path problem. We show a probabilistic modeling of the relationships based on auxiliary variables and propose a Gibbs sampling based strategy. We provide detailed results from a simulated data and the Atlantic Storm data set. / Ph. D.

This dissertation presents novel vibrational control strategies for mechanical control-affine systems with high-frequency, high-amplitude inputs. Since these control systems use high-frequency, zero-mean, periodic inputs, averaging techniques are widely used in the analysis of their dynamics. By studying their time-averaged approximations, new properties of the averaged dynamics of this class of systems are revealed. Using these properties, the problem of input optimization of vibrational control systems was formulated and solved by transforming the problem to a constrained optimization one. Geometric control theory provides powerful tools for studying the control properties of control-affine systems. Using the concepts of vibrational and geometric controls and averaging tools, a closed-loop control strategy for trajectory tracking of a class of underactuated mechanical control-affine systems is developed. In the developed control law, the fact that for underactuated systems, the actuated coordinates together with the corresponding generalized velocities can be considered as generalized inputs for the unactuated dynamics plays the main role. Using the developed control method, both actuated and unactuated coordinates of the system are able to follow slowly time-varying prescribed trajectories on average. The developed control method is applied for altitude control of flapping wing micro-air vehicles by considering the sweeping (flapping) angle of the wings as the inputs. Using the feathering (pitch) angles of the wings as additional inputs, and using non-symmetric flapping, the control method is then extended for three-dimensional flight control of flapping wing micro-air vehicles. / Ph. D.

Existing traffic models are widely used in multiple frameworks, most prominently, microscopic vehicle-scale occurring on the scale of seconds and macroscopic city-scale flow patterns that develop over the scale of hours. Research works and practical applications usually employ either one or the other framework, and there is little overlap in the respective research communities. This dissertation develops mathematical techniques to bridge the two scales. The particular case of bridging the micro and macro scales of models in the stable traffic regime has been extensively studied, however what has been often overlooked is the unstable regime. Thus, of particular importance are models that can capture dynamic instabilities and traveling traffic waves called phantom jams. Such models are particularly challenging to analyze, as many papers on PDE models explicitly exclude the unstable situation. This thesis (i) outlines the mathematical foundations of microscopic and macroscopic models of interest, (ii) establishes a principled procedure of generating macroscopic flow quantities from microscopic models in the unstable regime, (iii) presents a study addressing the averaging of scales and the understanding of macroscopic manifestations of microscopic car-following traffic waves based on a framework of systematic hierarchy of tests that isolate the car-following dynamics, (iv) explains the corresponding effective traffic state and non-equilibrium wave structures that rise in the fundamental diagram, (v) and derives and validates vehicle type specific simple fuel consumption rate models that are accurate, computationally fast, and have desirable physics-like properties. The insights gained from this study has many applications. One of them presented here is the relevance of dampening traffic waves in the presence of sparse control and in light of the energy demand of traffic at the vehicle-scale, waves-scale, and city scale. / Mathematics

Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tire-pavement interaction noise is tire tread excitation. The tire tread contributes to the tire-pavement interaction noise mainly through two mechanisms: (1) tread block impact, and (2) the compression and expansion of the air in the tread groove at the contact patch. The tread pattern is the critical part of the tire design since it can be easily modified. Hence, the main focus of this study is to quantify the tread pattern contribution in total tire-pavement interaction noise. To achieve this goal, the noise produced by the tread pattern is separated from the total tire-pavement interaction noise. Since the tread pattern excitation is periodic with tire rotation, the noise produced by the tread is assumed to be related to the tire rotation. Hence, the order domain synchronous averaging method is used in this study to separate and quantify the tread pattern contribution to the total tire-pavement interaction noise. The experiment has been carried out using an On-Board-Sound-Intensity (OBSI) system. Five tires were tested including the Standard Reference Test Tire (SRTT). Compared to the conventional OBSI system, an optical sensor was added to the system to monitor the tire rotation. The once per revolution signal provided by the optical sensor is used to identify the noise signals associate to each revolution. In addition to the averaging method using optical signals, other data processing techniques have been investigated for separating the tread-pattern noise without utilizing the once per revolution signal. These techniques are autocorrelation analysis, a frequency domain filter, principal component analysis, and independent component analysis. In the tread-pattern noise generation, the tread profile is the most important input parameter. To characterize the tread profile, the tread pattern spectral content and air volume velocity spectral content for all the five tires are computed. Then, the tread pattern spectrum and the air volume velocity spectrum are both correlated with the separated tread-pattern noise by visual inspection of the spectra shape. / Master of Science / Tire-pavement interaction noise is one of the dominant sources of vehicle noise, and one of the most significant sources of urban noise pollution. One critical generation mechanism of tirepavement interaction noise is tire tread (the part that is in contact with the ground on the surface of the tire) excitation. This type of noise is called the tread-pattern noise. This study is dedicated to separating the tread-pattern noise from the total tire-pavement interaction noise, which has not been reported in the open literature. The separation of the tread-pattern noise can provide critical criteria for the tread-pattern acoustic design, which is one of the most important factors in the tire tread pattern design. Hence, the acoustic design of the tread pattern can be evaluated directly from the tread-pattern noise measurement, thus improving the design efficiency. In addition, the standalone study on the tread-pattern noise can reveal more fundamental physical underpins how the geometry of the tread can affect the noise generated. This finding has the potential to inspire the design of the tires with higher acoustic performance over the tires being used currently.

京都大学 / 新制・課程博士 / 博士(医科学) / 甲第25204号 / 医科博第160号 / 新制||医科||10(附属図書館) / 京都大学大学院医学研究科医科学専攻 / (主査)教授 佐藤 俊哉, 教授 古川 壽亮, 教授 武藤 学 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

La théorie statistique univariée des valeurs extrêmes se généralise au cas multivarié mais l'absence d'un cadre paramétrique naturel complique l'inférence de la loi jointe des extrêmes. Les marges d'erreur associée aux estimateurs non paramétriques de la structure de dépendance sont difficilement accessibles à partir de la dimension trois. Cependant, quantifier l'incertitude est d'autant plus important pour les applications que le problème de la rareté des données extrêmes est récurrent, en particulier en hydrologie. L'objet de cette thèse est de développer des modèles de dépendance entre extrêmes, dans un cadre bayésien permettant de représenter l'incertitude. Le chapitre 2 explore les propriétés des modèles obtenus en combinant des modèles paramétriques existants, par mélange bayésien (Bayesian Model Averaging BMA). Un modèle semi-paramétrique de mélange de Dirichlet est étudié au chapitre suivant : une nouvelle paramétrisation est introduite afin de s'affranchir d'une contrainte de moments caractéristique de la structure de dépendance et de faciliter l'échantillonnage de la loi à posteriori. Le chapitre 4 est motivé par une application hydrologique : il s'agit d'estimer la structure de dépendance spatiale des crues extrêmes dans la région cévenole des Gardons en utilisant des données historiques enregistrées en quatre points. Les données anciennes augmentent la taille de l'échantillon mais beaucoup de ces données sont censurées. Une méthode d'augmentation de données est introduite, dans le cadre du mélange de Dirichlet, palliant l'absence d'expression explicite de la vraisemblance censurée. Les conclusions et perspectives sont discutées au chapitre 5 / Uni-variate extreme value theory extends to the multivariate case but the absence of a natural parametric framework for the joint distribution of extremes complexifies inferential matters. Available non parametric estimators of the dependence structure do not come with tractable uncertainty intervals for problems of dimension greater than three. However, uncertainty estimation is all the more important for applied purposes that data scarcity is a recurrent issue, particularly in the field of hydrology. The purpose of this thesis is to develop modeling tools for the dependence structure between extremes, in a Bayesian framework that allows uncertainty assessment. Chapter 2 explores the properties of the model obtained by combining existing ones, in a Bayesian Model Averaging framework. A semi-parametric Dirichlet mixture model is studied next : a new parametrization is introduced, in order to relax a moments constraint which characterizes the dependence structure. The re-parametrization significantly improves convergence and mixing properties of the reversible-jump algorithm used to sample the posterior. The last chapter is motivated by an hydrological application, which consists in estimating the dependence structure of floods recorded at four neighboring stations, in the ‘Gardons’ region, southern France, using historical data. The latter increase the sample size but most of them are censored. The lack of explicit expression for the likelihood in the Dirichlet mixture model is handled by using a data augmentation framework

Submitted by Franciele Moreira (francielemoreyra@gmail.com) on 2018-03-28T11:58:56Z No. of bitstreams: 2 Dissertação - Jeferson Arley Poveda Contreras - 2018.pdf: 763599 bytes, checksum: 6800571168e0aa9de85d151e4c912725 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-03-29T11:29:24Z (GMT) No. of bitstreams: 2 Dissertação - Jeferson Arley Poveda Contreras - 2018.pdf: 763599 bytes, checksum: 6800571168e0aa9de85d151e4c912725 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-03-29T11:29:24Z (GMT). No. of bitstreams: 2 Dissertação - Jeferson Arley Poveda Contreras - 2018.pdf: 763599 bytes, checksum: 6800571168e0aa9de85d151e4c912725 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-03-07 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / The goal of this work is study limit cycles of piecewise smooth vector fields. First, we present the basic theory, passing through the areas of analysis, qualitative theory of differential equations and algebra. We also present basic concepts of Filippov fields, which are indispensable for the study of piecewise smooth fields. In chapter one, was the main topic, a general method for finding limit cycles will be described; in the second chapter limit cycles are found in a piecewise smooth vector field with non-degenerate center being perturbed by a piecewise polynomial vector field. In the fourth chapter, we study limit cycles in piecewise smooth Hamiltonian fields. / O objetivo deste trabalho é estudar ciclos limite de campos de vetores suaves por parte. Primeiro apresentaremos a teoria básica, passando pelas áreas de análise, teoria qualitativa das equações diferenciais e álgebra. Apresentamos também conceitos básicos de campos de Filippov, os quais são imprescindíveis para o estudo dos campos suaves por partes. No capítulo dos, como tópico principal, será descrito um método geral para encontrar ciclos limite; no segundo três são encontrados ciclos limites em um campo de vetores suave por partes com um centro não degenerado sendo perturbado por um polinômio. No quarto capitulo estudaremos os ciclos limites de campos de vetores Hamiltonianos por parte.

La vidéo sur IP a connu un essor rapide ces dernières années allant de la diffusion télévisuelle en haute qualité via des réseaux dédiés à la diffusion sur internet de contenus vidéo grand public. L’optimisation de l’encodage vidéo H.264/AVC permet aux différents acteurs du marché de se différencier en proposant des solutions pour réduire le débit nécessaire à la représentation d’un flux vidéo ainsi que pour améliorer la qualité perçue par les utilisateurs. C’est dans ce contexte de vidéo professionnelle en haute qualité que s’inscrivent ces travaux de thèse CIFRE réalisés au sein de l’entreprise Digigram, proposant des encodeurs vidéo temps réel pour des diffusions professionnelles en direct. Nous proposons deux solutions de prétraitement pour répondre aux problématiques du secteur de la distribution vidéo. Les deux solutions considèrent les caractéristiques du système visuel humain en exploitant un modèle de JND (Just Noticeable Distortion) définissant des seuils de perception en fonction d’une analyse du contenu des séquences vidéo à encoder. La première solution utilise un préfiltre adaptatif indépendant de l’encodeur, contrôlé par un modèle JND afin d'éliminer le contenu perceptuellement non pertinent et ainsi réduire le débit sans altérer la qualité ressentie. Une analyse approfondie de plusieurs filtres de la littérature, dont le filtre AWA (Adaptive Weighted Averaging) et le filtre bilatéral, nous a également amené à définir deux nouveaux filtres à support étendu qui permettent d’exploiter au mieux les corrélations dans les images haute définition. A l’aide de tests subjectifs, nous montrons que les préfiltres perceptuels proposés permettent en moyenne de diminuer le débit en sortie du codeur d'environ 20% pour une qualité constante en encodage VBR (débit variable) Intra et Inter-image. Finalement, une deuxième solution s’attache à améliorer la qualité perçue dans un contexte d’encodage CBR (débit constant) en intégrant un modèle JND dans l’une des implémentations de la norme H.264/AVC la plus reconnue, le codec x264. Une quantification adaptative perceptuelle est ainsi proposée permettant d’améliorer les performances du codec x264 en améliorant le codage de l’information de contour à moyen et bas débits en encodage intra et inter-image. / The use of digital video over IP has increased exponentially over the last years, due to the development of high-speed networks dedicated to high quality TV transmission as well as the wide development of the nonprofessional video webcast. Optimization of the H.264/AVC encoding process allows manufacturers to offer differentiating encoding solutions, by reducing the bandwidth necessary for transmitting a video sequence at a given quality level, or improving the quality perceived by final users at a fixed bit rate. This thesis was carried out at the company Digigram in a context of professional high quality video. We propose two solutions of preprocessing which consider the characteristics of the human visual system by exploiting a JND profile (Just Noticeable Distortion). A JND model defines perceptual thresholds, below which a distortion cannot be seen, according to the video content. The first solution proposes an adaptive pre-filter independent to the encoder, controlled by a JND profile to reduce the perceptually non-relevant content and so reduce the bitrate while maintaining the perceived quality. By analyzing the state-of-the-art literature, the AWA (Adaptive Weighted Averaging) and Bilateral filters have been selected. Then we define two new filters using a large convolution mask, which enable to better exploit correlations in high-definition video contents. Through subjective tests, we show that the proposed perceptual prefilters give an average bitrate reduction of 20% for the same visual quality in VBR (Variable Bitrate) H.264/AVC Intra and Inter encoding. Finally, the second solution enables to improve the perceived quality in CBR (Constant Bitrate) encoding, by integrating the JND profile into the x264 codec, one of the best implementation of the H.264/AVC standard. Thus, we propose a perceptual adaptive quantization which enhances the x264 performance by improving edge information coding in low and middle bitrate applications.

In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots. This methodology involves subjective human judgment, is time consuming, and can result in suboptimal results. The newly developed automated construction algorithm solves these difficulties by performing model selection based on an objective model selection criterion. Partial residual plots are only utilized after the best model is found to gain insight into the relationships between inputs and the target. Models are organized in a search tree with a greedy search procedure that identifies good models in a relatively short time. The automated construction algorithm, implemented in the powerful SAS® language, is nontrivial, effective, and comparable to other model selection methodologies found in the literature. This implementation, which is called AutoGANN, has a simple, intuitive, and user-friendly interface. The AutoGANN system is further extended with an approximation to Bayesian Model Averaging. This technique accounts for uncertainty about the variables that must be included in the model and uncertainty about the model structure. Model averaging utilizes in-sample model selection criteria and creates a combined model with better predictive ability than using any single model. In the field of Credit Scoring, the standard theory of scorecard building is not tampered with, but a pre-processing step is introduced to arrive at a more accurate scorecard that discriminates better between good and bad applicants. The pre-processing step exploits GANN models to achieve significant reductions in marginal and cumulative bad rates. The time it takes to develop a scorecard may be reduced by utilizing the automated construction algorithm. / Thesis (Ph.D. (Computer Science))--North-West University, Potchefstroom Campus, 2006.