Compared with conventional measurements from supervisory control and data acquisition (SCADA) system, phasor measurement units (PMUs) provide time-synchronized and direct measurements of phasors. The availability of synchronized phasor measurements can significantly improve power system protection and analysis. This dissertation is specifically committed to using synchronized measurements for estimation of fault locations in transmission systems. Transmission lines are prone to various short-circuit faults. Accurate fault location is critical for rapid power recovery. Chapter 2 proposes a new fault location method based on sparse wide area measurements. One distinguishing feature of this method is its applicability to both transposed and untransposed transmission lines. In addition, the method is developed based on sparse-wide area measurement that may be taken far away from the faulted line. Shunt capacitances of transmission lines are also fully considered by the algorithm. Moreover, when synchronized measurements from multiple buses are available, an optimal estimator can be used to make the most use of measurements, and to detect and identify potential bad measurements. Most of the existing fault location literatures discuss common shunt faults, including single line-to-ground faults, line-to-line faults, line-to-line-to-ground faults, and three-phase faults. However, in addition to common shunt faults, some complex faults may also occur in power systems. Among these complex faults, evolving fault and inter-circuit fault are two typical examples. Chapter 3 extends the method developed in Chapter 2 to deal with evolving faults. The proposed wide-area fault location methods are immune to fault type evolution, and are applicable to both transposed and untransposed lines. Chapter 4 discusses location of inter-circuit faults. Inter-circuit fault is a type of simultaneous fault, and it is the most common simultaneous fault type. Inter-circuit faults between each circuit in a double-circuit line is the most common inter-circuit fault. A fault location method for inter-circuit faults on double-circuit lines are developed and evaluated in Chapter 4. Chapter 5 puts forward a fault location algorithm, which does not require line parameters information, for series-compensated transmission lines. Two-end synchronized voltage and current measurements are utilized. The proposed method is independent of source impedance and fully considers shunt capacitances of transmission lines.

Nous considérons deux problèmes. Le premier est la construction des tests d’ajustement (goodness-of-fit) pour les modèles de processus de diffusion ergodique. Nous considérons d’abord le cas où le processus sous l’hypothèse nulle appartient à une famille paramétrique. Nous étudions les tests de type Cramer-von Mises et Kolmogorov- Smirnov. Le paramètre inconnu est estimé par l’estimateur de maximum de vraisemblance ou l’estimateur de distance minimale. Nous construisons alors les tests basés sur l’estimateur du temps local de la densité invariante, et sur la fonction de répartition empirique. Nous montrons alors que les statistiques de ces deux types de test convergent tous vers des limites qui ne dépendent pas du paramètre inconnu. Par conséquent, ces tests sont appelés asymptotically parameter free. Ensuite, nous considérons l’hypothèse simple. Nous étudions donc le test du khi-deux. Nous montrons que la limite de la statistique ne dépend pas de la dérive, ainsi on dit que le test est asymptotically distribution free. Par ailleurs, nous étudions également la puissance du test du khi-deux. En outre, ces tests sont consistants. Nous traitons ensuite le deuxième problème : l’approximation des équations différentielles stochastiques rétrogrades. Supposons que l’on observe un processus de diffusion satisfaisant à une équation différentielle stochastique, où la dérive dépend du paramètre inconnu. Nous estimons premièrement le paramètre inconnu et après nous construisons un couple de processus tel que la valeur finale de l’un est une fonction de la valeur finale du processus de diffusion donné. Par la suite, nous montrons que, lorsque le coefficient de diffusion est petit, le couple de processus se rapproche de la solution d’une équations différentielles stochastiques rétrograde. A la fin, nous prouvons que cette approximation est asymptotiquement efficace. / We consider two problems in this work. The first one is the goodness of fit test for the model of ergodic diffusion process. We consider firstly the case where the process under the null hypothesis belongs to a given parametric family. We study the Cramer-von Mises type and the Kolmogorov-Smirnov type tests in different cases. The unknown parameter is estimated via the maximum likelihood estimator or the minimum distance estimator, then we construct the tests in using the local time estimator for the invariant density function, or the empirical distribution function. We show that both the Cramer-von Mises type and the Kolmogorov-Smirnov type statistics converge to some limits which do not depend on the unknown parameter, thus the tests are asymptotically parameter free. The alternatives as usual are nonparametric and we show the consistency of all these tests. Then we study the chi-square test. The basic hypothesis is now simple The chi-square test is asymptotically distribution free. Moreover, we study also power function of the chi-square test to compare with the others. The other problem is the approximation of the forward-backward stochastic differential equations. Suppose that we observe a diffusion process satisfying some stochastic differential equation, where the trend coefficient depends on some unknown parameter. We try to construct a couple of processes such that the final value of one is a function of the final value of the given diffusion process. We show that when the diffusion coefficient is small, the couple of processes approximates well the solution of a backward stochastic differential equation. Moreover, we present that this approximation is asymptotically efficient.

La segmentation est le principal problème de l'analyse d'image qui concerne l'extraction de l'information quantitative de l'image. La segmentation d'image partitionne une image en un nombre de régions separées du fond qui pourraient correspondre aux objets dans l'image. La technique la plus simple de segmentation est le seuillage, en considérant par exemple un seuil au dessous duquel les pixels/voxels sont considérés comme du fond. Le problème du seuillage est de trouver un seuil global; si le seuil est très bas, les objets se touchent et cela nécessite un post traitement, en revanche pour un seuil très haut, les objets ayant des intensités faibles seront supprimés. L'information qualitative peut être extraite directement sur l'image segmentée. Or, afin de donner plus de sens aux objets, les objets détéctés peuvent être assignés à des classes ou clusters d'objets prédefinis. Dans cette thèse, je présente une nouvelle contribution dans le domaine de l'informatique appliquée à la biologie. La contribution « informatique » c'est la nouvelle technique d'apprentisage supervisé (machine leaning) afin d'obtenir une nouvelle segmentation et classification sans paramètres. La contribution « biologique » c'est cette nouvelle technique appliquée à la segmentation et la classification de noyaux de differents embryons. Dans cette thèse, je présente une méthode automatique de segmentation et classification appliquée à l'étude de cycle cellulaire de noyaux dans l'embryon pour des images de microscopie 3D/4D. Ce qui permet aux biologistes d'étudier comment les cellules s'organisent spatialement et temporellement à l'intérieur de l'embryon, et de quantifier l'effet des perturbations génétiques et des médicaments. Dans cette thèse, deux nouvelles techniques de segmentation supervisée se basant sur l'apprentissage d'objects prédéfinis sont présentées. La première technique supervisée de segmentation dévéloppée est la composition de machine learning et de seuillage iteratif (seuillage montant). Pour chaque seuil, les objets détéctés passent par la classification. À la fin du seuillage, afin de trouver le meilleur seuil pour chaque objet, le seuil qui donne la plus haute probablité d' appartenance dans la classe stabilisée est pris. Cette technique a donné des résultats relativement bons sur 3 modèles différents d'image malgré la présence de variations d'intensité temporelle et spatiale. Dans la même prespective, une autre technique se basant sur une croissance de region (watershes descendant) a été développée pour surmonter les cas où noyaux de cellule se touchent et présentent des intensités inhomogènes. La technique est basée sur la croissance des région à partir des maximum locaux. Une fois que les régions se réunissent, des combinaisons de régions sont créées et la combinaision qui à la plus haute probablité d' appartenance aux classes d'objets prédéfinis. L'originalité de cette thèse est ; 1- la combinaision de segmentation et classification dans un processus unique. 2- la généricité du modèle de segmentation et classification étant applicable à des images de modèles biologiques différents. 3- l' fait de ne pas de necessité de réglage de paramètres ( Parameter-free ). / Image segmentation, being the main challenge in image analysis that deals with extraction of quantitative information. Segmentation partitions an image into a number of separate regions which might correspond to objects in the image. The simplest technique is thresholding, by considering a threshold below which pixels/voxels are assumed as background. Finding optimal threshold is critical; if the threshold is very low, the observed nuclei in fluorescent image are touching and requires a post-processing, on the other hand, with very high threshold, nuclei with low intensities will be deleted. Afterwards, qualitative information can be extracted directly from segmented image. However, in order to give more meaning to detected objects, these objects can be assigned to predefined classes. This challenge is carried out in this thesis through an automatic method of segmentation and classification which was applied to the study of cell cycle of nuclei in 3D/4D embryo microscopy images. Our method ensures optimal threshold for each object. In this thesis, we present two new segmentation techniques which are based on supervised learning of predefined classes of objects. The first technique of supervised segmentation is realized by combining machine learning and iterative thresholding (bottom-up thresholding). For each threshold, the detected objects will be classified. At the end of thresholding, to find optimal threshold for each object, the threshold that gives the highest probability of belonging in the stabilized class is taken. This technique was tested on three different datasets and gave good results despite the presence of temporal and spatial variations of intensity. In the same perspective, another technique based on a region-growing (top-down thresholding) approach was developed to overcome overlapping and inhomogeneous cell nuclei problems. This technique is based on region-growth from the local maximum. Once the regions meet, combinations of regions are created and combination that gives the highest membership probability to predefined classes of object is retained. The originality of this work is that segmen- tation and classification are performed simultaneously. The program is also generic and applicable to wide biological datasets, without any parameter (parameter-free).

Soft robots have the potential to transform the way robots interact with their environment. This is due to their low inertia and inherent ability to more safely interact with the world without damaging themselves or the people around them. However, existing sensing for soft robots has at least partially limited their ability to control interactions with their environment. Tactile sensors could enable soft robots to sense interaction, but most tactile sensors are made from rigid substrates and are not well suited to applications for soft robots that can deform. In addition, the benefit of being able to cheaply manufacture soft robots may be lost if the tactile sensors that cover them are expensive and their resolution does not scale well for manufacturability. Soft robots not only need to know their interaction forces due to contact with their environment, they also need to know where they are in Cartesian space. Because soft robots lack a rigid structure, traditional methods of joint estimation found in rigid robots cannot be employed on soft robotic platforms. This requires a different approach to soft robot pose estimation. This thesis will discuss both tactile force sensing and pose estimation methods for soft-robots. A method to make affordable, high-resolution, tactile sensor arrays (manufactured in rows and columns) that can be used for sensorizing soft robots and other soft bodies isReserved developed. However, the construction results in a sensor array that exhibits significant amounts of cross-talk when two taxels in the same row are compressed. Using the same fabric-based tactile sensor array construction design, two different methods for cross-talk compensation are presented. The first uses a mathematical model to calculate a change in resistance of each taxel directly. The second method introduces additional simple circuit components that enable us to isolate each taxel electrically and relate voltage to force directly. This thesis also discusses various approaches in soft robot pose estimation along with a method for characterizing sensors using machine learning. Particular emphasis is placed on the effectiveness of parameter-based learning versus parameter-free learning, in order to determine which method of machine learning is more appropriate and accurate for soft robot pose estimation. Various machine learning architectures, such as recursive neural networks and convolutional neural networks, are also tested to demonstrate the most effective architecture to use for characterizing soft-robot sensors.

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