Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Computer science & Thin film. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Large piezoelectric effects in charged, heterogeneous fluoropolymer electrets

Abstract: Large piezoelectric d33 coefficients around 600 pC/N are found in corona-charged non-uniform electrets consisting of elastically “soft” (microporous polytetrafluoroethylene PTFE) and “stiff” (perfluorinated cyclobutene PFCB) polymer layers. The piezoelectric activity of the two-layer fluoropolymer stack exceeds the d33 coefficient of the ferroelectric ceramic lead zirconate titanate (PZT) by more than a factor of two and that of the ferroelectric polymer polyvinylidene fluoride (PVDF) by a factor of 20. Soft piezoelectric materials may become interesting for a large number of sensor and transducer applications, in areas such as security systems, medical diagnostics, and nondestructive testing.

Model-based clustering based on sparse finite Gaussian mixtures

Abstract: In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using $$K$$K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets.

Morozov's discrepancy principle for Tikhonov-type functionals with nonlinear operators

Abstract: In this paper we deal with Morozov's discrepancy principle as an a posteriori parameter choice rule for Tikhonov regularization with general convex penalty terms Ψ for nonlinear inverse problems It is shown that a regularization parameter α fulfilling the discprepancy principle exists, whenever the operator F satisfies some basic conditions, and that for suitable penalty terms the regularized solutions converge to the true solution in the topology induced by Ψ It is illustrated that for this parameter choice rule it holds α → 0, δq/α → 0 as the noise level δ goes to 0 Finally, we establish convergence rates with respect to the generalized Bregman distance and a numerical example is presented