Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.

Many-body theory of laser-induced ultrafast demagnetization and angular momentum transfer in ferromagnetic transition metals

An electronic theory is developed, which describes the ultrafast demagnetization in itinerant ferromagnets following the absorption of a femtosecond laser pulse. The present work intends to elucidate the microscopic physics of this ultrafast phenomenon by identifying its fundamental mechanisms. In particular, it aims to reveal the nature of the involved spin excitations and angular-momentum transfer between spin and lattice, which are still subjects of intensive debate. In the first preliminary part of the thesis the initial stage of the laser-induced demagnetization process is considered. In this stage the electronic system is highly excited by spin-conserving elementary excitations involved in the laser-pulse absorption, while the spin or magnon degrees of freedom remain very weakly excited. The role of electron-hole excitations on the stability of the magnetic order of one- and two-dimensional 3d transition metals (TMs) is investigated by using ab initio density-functional theory. The results show that the local magnetic moments are remarkably stable even at very high levels of local energy density and, therefore, indicate that these moments preserve their identity throughout the entire demagnetization process. In the second main part of the thesis a many-body theory is proposed, which takes into account these local magnetic moments and the local character of the involved spin excitations such as spin fluctuations from the very beginning. In this approach the relevant valence 3d and 4p electrons are described in terms of a multiband model Hamiltonian which includes Coulomb interactions, interatomic hybridizations, spin-orbit interactions, as well as the coupling to the time-dependent laser field on the same footing. An exact numerical time evolution is performed for small ferromagnetic TM clusters. The dynamical simulations show that after ultra-short laser pulse absorption the magnetization of these clusters decreases on a time scale of hundred femtoseconds. In particular, the results reproduce the experimentally observed laser-induced demagnetization in ferromagnets and demonstrate that this effect can be explained in terms of the following purely electronic non-adiabatic mechanism: First, on a time scale of 10–100 fs after laser excitation the spin-orbit coupling yields local angular-momentum transfer between the spins and the electron orbits, while subsequently the orbital angular momentum is very rapidly quenched in the lattice on the time scale of one femtosecond due to interatomic electron hoppings. In combination, these two processes result in a demagnetization within hundred or a few hundred femtoseconds after laser-pulse absorption.

The Human in the Loop: User Participation in Self-Adaptive Software

Self-adaptive software provides a profound solution for adapting applications to changing contexts in dynamic and heterogeneous environments. Having emerged from Autonomic Computing, it incorporates fully autonomous decision making based on predefined structural and behavioural models. The most common approach for architectural runtime adaptation is the MAPE-K adaptation loop implementing an external adaptation manager without manual user control. However, it has turned out that adaptation behaviour lacks acceptance if it does not correspond to a user’s expectations – particularly for Ubiquitous Computing scenarios with user interaction. Adaptations can be irritating and distracting if they are not appropriate for a certain situation. In general, uncertainty during development and at run-time causes problems with users being outside the adaptation loop. In a literature study, we analyse publications about self-adaptive software research. The results show a discrepancy between the motivated application domains, the maturity of examples, and the quality of evaluations on the one hand and the provided solutions on the other hand. Only few publications analysed the impact of their work on the user, but many employ user-oriented examples for motivation and demonstration. To incorporate the user within the adaptation loop and to deal with uncertainty, our proposed solutions enable user participation for interactive selfadaptive software while at the same time maintaining the benefits of intelligent autonomous behaviour. We define three dimensions of user participation, namely temporal, behavioural, and structural user participation. This dissertation contributes solutions for user participation in the temporal and behavioural dimension. The temporal dimension addresses the moment of adaptation which is classically determined by the self-adaptive system. We provide mechanisms allowing users to influence or to define the moment of adaptation. With our solution, users can have full control over the moment of adaptation or the self-adaptive software considers the user’s situation more appropriately. The behavioural dimension addresses the actual adaptation logic and the resulting run-time behaviour. Application behaviour is established during development and does not necessarily match the run-time expectations. Our contributions are three distinct solutions which allow users to make changes to the application’s runtime behaviour: dynamic utility functions, fuzzy-based reasoning, and learning-based reasoning. The foundation of our work is a notification and feedback solution that improves intelligibility and controllability of self-adaptive applications by implementing a bi-directional communication between self-adaptive software and the user. The different mechanisms from the temporal and behavioural participation dimension require the notification and feedback solution to inform users on adaptation actions and to provide a mechanism to influence adaptations. Case studies show the feasibility of the developed solutions. Moreover, an extensive user study with 62 participants was conducted to evaluate the impact of notifications before and after adaptations. Although the study revealed that there is no preference for a particular notification design, participants clearly appreciated intelligibility and controllability over autonomous adaptations.