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In dieser Arbeit wird ein Verfahren entwickelt, das die Online-Schätzung fraktionaler Impedanzmodelle im Zeitbereich ermöglicht. Hierzu wird ein neuartiges Verfahren vorgestellt, welches die Beschreibung der Impedanz durch ein Zustandsraummodell im Zeitbereich ermöglicht. Zur Schätzung der Modellparameter werden Sigma-Punkt-Kalman-Filter eingesetzt. Die Einsetzbarkeit des Verfahrens wird durch Simulationsbeispiele belegt.

Hochtemperaturbrennstoffzellen --- Sigma-Punkt-Kalman-Filter --- nichtlineare Filterung --- lineare fraktionale Systeme

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A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.

Schätztheorie --- Kalman Filter --- estimation theory --- Sensornetze --- Verteilte SystemsData fusion --- distributed systems --- Datenfusion --- sensor networks --- Kalman filtering

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In dieser Arbeit werden zur Überwachung von Drei-Wege-Katalysatoren zwei neue modellbasierte On-Board-Diagnoseverfahren vorgestellt. Zunächst wurde ein die Alterung mit berücksichtigendes physikalisches Katalysatormodell entwickelt. Dieses bildet die Grundlage der neuen Diagnoseverfahren, bei denen ein den aktuellen Zustand des Katalysators schätzender Sigma-Punkt-Kalman-Filter zum Einsatz kommt. Das große Potential dieser neuen Diagnoseverfahren zeigen die abschließend präsentierten Ergebnisse.

Abgastemperatur --- Spektrale Verfahren --- Constrained Sigma-Punkt-Kalman-Filter --- Drei-Wege-Katalysator-Modell --- On-Board-Diagnose

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Nowadays, football is by far the most popular sport in the world. Its huge audience obviously attracts many fields, including the field of game analysis. In recent years, deep learning has brought progress to automated match analysis but a challenge remains strenuous: the ball tracking on football sequences. Indeed, the ball is often represented by few pixels on the football videos and many other object have a similar appearance. In addition, the ball can be occluded by a player or in front of a line with similar color. All these circumstances make the ball tracking a challenging objective.
In this work, an offline ball tracking system based on deep learning for soccer sequences is implemented.
The system is divided into two main parts: a ball detector followed by a ball tracking method. The detector is based on a deep convolutional neural network for image segmentation. A dataset composed of semi-synthetic images representing a synthetic ball in front of a real frame from a football video sequence is created to train the network. The objective of the detector is to identify ball candidates on each frame of the sequence.
Then the tracking method treats all the candidates of the sequence to generate candidate trajectories with the exploitation of the Kalman filter. A score is assigned to each trajectory thanks to the use of a deep neural network for image classification. The trajectories with the highest score are selected as ball trajectories. Finally, a cubic spline interpolation estimates the ball positions between the trajectories.
Evaluated on sequences of professional competitions, this ball tracking system allows an identification of the ball position on more than 80% of the images of most of the evaluated sequences, without having any false detection. Moreover, the evaluation highlights the ability of the system to estimate the ball positions during occlusion on a few frames.

Deep Learning --- Ball Tracking --- Neural Network --- Ball Detector --- Kalman Filter --- Ingénierie, informatique & technologie > Sciences informatiques

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This thesis aims at finding whether equity mutual fund managers react to changes in the macroeconomy by reallocating their funds in diverse industries. To do so, we conducted an asset allocation model, through a Kalman filter, using Fama and French industry portfolios. Among other findings, we found that equity mutual funds increase their allocation towards the consumer goods industry in times of economic uncertainty, managers do not exhibit significant selection skill and equity mutual funds adopt different behaviours depending on their defined strategy. Finally, we could
not provide evidence that equity mutual fund managers were able to time the market.

equity mutual funds --- macroeconomic indicators --- asset allocation model --- industry portfolios --- Kalman filter --- Sciences économiques & de gestion > Finance