@article{WilliePapPerkaetal.2015, author = {Willie, Bettina M. and Pap, Thomas and Perka, Carsten and Schmidt, Carsten Oliver and Eckstein, Felix and Arampatzis, Adamantios and Hege, Hans-Christian and Madry, Henning and Vortkamp, Andrea and Duda, Georg}, title = {OVERLOAD - Rolle der Gelenk{\"u}berlastung in der prim{\"a}ren Arthrose - Die Krankheitsprogression verstehen und vermeiden}, volume = {74}, journal = {Zeitschrift f{\"u}r Rheumatologie}, doi = {10.1007/s00393-014-1561-2}, pages = {618 -- 621}, year = {2015}, abstract = {Intakte Gelenke sind eine Voraussetzung f{\"u}r das Funktionieren des Skeletts und die Mobilit{\"a}t im Lebensalltag. Ein gesunder Bewegungsapparat ist die Grundlage f{\"u}r die Funktionsf{\"a}higkeit des Herz-Kreislauf-Systems wie auch der Immunabwehr. Bewegungs- und Physiotherapie sowie verschiedene Formen der Patientenaktivit{\"a}t stellen essenzielle klinische Ans{\"a}tze in der Behandlung von neurodegenerativen Erkrankungen, Schlaganfall, Diabetes und Krebs dar. Kommt es zu degenerativen Ver{\"a}nderungen von Gelenken, bedeutet dies eine wesentliche Beeintr{\"a}chtigung der Mobilit{\"a}t. N{\"a}chtliche Schmerzen und Schlafst{\"o}rungen treten in fortgeschrittenen Stadien auf und sind besonders belastend. Arthrose wird auch als degenerative Gelenkerkrankung bezeichnet. Sie geht mit Ver{\"a}nderungen in der Struktur und Zusammensetzung des Gelenkknorpels wie auch des verkalkten Knorpels, der subchondralen Kortikalis, der subchondralen Spongiosa, des Meniskus, der Gelenkkapsel und der Synovialis einher, was schließlich zur Degeneration dieser Gewebe f{\"u}hrt, aus denen sich die Synovialgelenke zusammensetzen.}, language = {de} } @misc{SieberKuhnHegeetal.2015, author = {Sieber, Moritz and Kuhn, Alexander and Hege, Hans-Christian and Paschereit, C. Oliver and Oberleithner, Kilian}, title = {A Graphical Representation of the Spectral Proper Orthogonal Decomposition}, journal = {68th Annual Meeting of the APS Division of Fluid Dynamics, Gallery of Fluid Motion, Nov 22-24, 2015, Boston, MA, USA.}, year = {2015}, abstract = {We consider the spectral proper orthogonal decomposition (SPOD) for experimental data of a turbulent swirling jet. This newly introduced method combines the advantages of spectral methods, such as Fourier decomposition or dynamic mode decomposition, with the energy-ranked proper orthogonal decomposition (POD). This poster visualizes how the modal energy spectrum transitions from the spectral purity of Fourier space to the sparsity of POD space. The transition is achieved by changing a single parameter - the width of the SPOD filter. Each dot in the 3D space corresponds to an SPOD mode pair, where the size and color indicates its spectral coherence. What we notice is that neither the Fourier nor the POD spectrum achieves a clear separation of the dynamic phenomena. Scanning through the graph from the front plane (Fourier) to the back plane (POD), we observe how three highly coherent SPOD modes emerge from the dispersed Fourier spectrum and later branch out into numerous POD modes. The spatial properties of these three individual SPOD modes are displayed in the back of the graph using line integral convolution colored by vorticity. The first two modes correspond to single-helical global instabilities that are well known for these flows. Their coexistence, however, has not been observed until now. The third mode is of double- helical shape and has not been observed so far. For this considered data set and many others, the SPOD is superior in identification of coherent structures in turbulent flows. Hopefully, it gives access to new fluid dynamic phenomena and enriches the available methods.}, language = {en} } @article{FroehlerElberfeldMoelleretal.2020, author = {Fr{\"o}hler, Bernhard and Elberfeld, Tim and M{\"o}ller, Torsten and Hege, Hans-Christian and De Beenhouwer, Jan and Sijbers, Jan and Kastner, Johann and Heinzl, Christoph}, title = {Analysis and comparison of algorithms for the tomographic reconstruction of curved fibres}, volume = {35}, journal = {Nondestructive Testing and Evaluation}, number = {3}, doi = {10.1080/10589759.2020.1774583}, pages = {328 -- 341}, year = {2020}, abstract = {We present visual methods for the analysis and comparison of the results of curved fibre reconstruction algorithms, i.e., of algorithms extracting characteristics of curved fibres from X-ray computed tomography scans. In this work, we extend previous methods for the analysis and comparison of results of different fibre reconstruction algorithms or parametrisations to the analysis of curved fibres. We propose fibre dissimilarity measures for such curved fibres and apply these to compare multiple results to a specified reference. We further propose visualisation methods to analyse differences between multiple results quantitatively and qualitatively. In two case studies, we show that the presented methods provide valuable insights for advancing and parametrising fibre reconstruction algorithms, and support in improving their results in characterising curved fibres.}, language = {en} } @misc{SagnolHegeWeiser2016, author = {Sagnol, Guillaume and Hege, Hans-Christian and Weiser, Martin}, title = {Using sparse kernels to design computer experiments with tunable precision}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-59605}, year = {2016}, abstract = {Statistical methods to design computer experiments usually rely on a Gaussian process (GP) surrogate model, and typically aim at selecting design points (combinations of algorithmic and model parameters) that minimize the average prediction variance, or maximize the prediction accuracy for the hyperparameters of the GP surrogate. In many applications, experiments have a tunable precision, in the sense that one software parameter controls the tradeoff between accuracy and computing time (e.g., mesh size in FEM simulations or number of Monte-Carlo samples). We formulate the problem of allocating a budget of computing time over a finite set of candidate points for the goals mentioned above. This is a continuous optimization problem, which is moreover convex whenever the tradeoff function accuracy vs. computing time is concave. On the other hand, using non-concave weight functions can help to identify sparse designs. In addition, using sparse kernel approximations drastically reduce the cost per iteration of the multiplicative weights updates that can be used to solve this problem.}, language = {en} }