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Moderne als Geschichte
(2015)
The proposed approach applies current unsupervised clustering approaches in a different dynamic manner. Instead of taking all the data as input and finding clusters among them, the given approach clusters Holter ECG data (long-term electrocardiography data from a holter monitor) on a given interval which enables a dynamic clustering approach (DCA). Therefore advanced clustering techniques based on the well known Dynamic Time Warping algorithm are used. Having clusters e.g. on a daily basis, clusters can be compared by defining cluster shape properties. Doing this gives a measure for variation in unsupervised cluster shapes and may reveal unknown changes in healthiness. Embedding this approach into wearable devices offers advantages over the current techniques. On the one hand users get feedback if their ECG data characteristic changes unforeseeable over time which makes early detection possible. On the other hand cluster properties like biggest or smallest cluster may help a doctor in making diagnoses or observing several patients. Further, on found clusters known processing techniques like stress detection or arrhythmia classification may be applied.
This chapter contains three advanced topics in model order reduction (MOR): nonlinear MOR, MOR for multi-terminals (or multi-ports) and finally an application in deriving a nonlinear macromodel covering phase shift when coupling oscillators. The sections are offered in a preferred order for reading, but can be read independently.
The detection of differences between images of a printed reference and a reprinted wood decor often requires an initial image registration step. Depending on the digitalization method, the reprint will be displaced and rotated with respect to the reference. The aim of registration is to match the images as precisely as possible. In our approach, images are first matched globally by extracting feature points from both images and finding corresponding point pairs using the RANSAC algorithm. From these correspondences, we compute a global projective transformation between both images. In order to get a pixel-wise registration, we train a learning machine on the point correspondences found by RANSAC. The learning algorithm (in our case Gaussian process regression) is used to nonlinearly interpolate between the feature points which results in a high precision image registration method on wood decors.
Brennstoffzellen
(2015)
Kapitel 9 beginnt mit einer kurzen historischen Einführung und beschreibt dann nach einer ersten Klassifizierung das Funktionsprinzip und die thermodynamischen Grundlagen der direkten Umwandlung chemischer in elektrische Energie. Es folgen die verschiedenen Wirkungsgraddefinitionen und Verlustursachen und das sich daraus ergebende typische Betriebsverhalten einer Brennstoffzelle mit höherem Wirkungsgrad im Teillastbereich. Es werden die Anwendungsgebiete, die notwendigen Grundkomponenten und der Stand der Entwicklung der verschiedenen Typen AFC, PEFC, PAFC, MCFC und SOFC besprochen, Vor- und Nachteile diskutiert und einige ausgeführte Anlagen vorgestellt.