Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Pies with EYEs: the limits of hierarchical pie menus in gaze control

Abstract: Pie menus offer several features which are advantageous especially for gaze control. Although the optimal number of slices per pie and of depth layers has already been established for manual control, these values may differ in gaze control due to differences in spatial accuracy and congitive processing. Therefore, we investigated the layout limits for hierarchical pie menu in gaze control. Our user study indicates that providing six slices in multiple depth layers guarantees fast and accurate selections. Moreover, we compared two different methods of selecting a slice. Novices performed well with both, but selecting via selection borders produced better performance for experts than the standard dwell time selection.

A simple circular cell method for multilevel finite element analysis

Abstract: A simple multiscale analysis framework for heterogeneous solids based on a computational homogenization technique is presented. The macroscopic strain is linked kinematically to the boundary displacement of a circular or spherical representative volume which contains the microscopic information of the material. The macroscopic stress is obtained from the energy principle between the macroscopic scale and the microscopic scale. This new method is applied to several standard examples to show its accuracy and consistency of the method proposed.

TDR measurements and simulations in high lossy bentonite materials

Abstract: The measurement of moisture distribution in bentonite barriers in salt mines is absolutely essential in order to monitor fluid ingress and record data for long-term security analyses The aim of this investigation was to apply a TDR measurement system using flexible flat band cable sensors in the harsh environment of a salt mine The measurement of the dielectric material properties of sodium-bentonite sand mixtures moistened with tap water and concentrated NaCl solutions was the basis for the development of the measurement system If the sensor was surrounded by a material mixture with strong losses it was found that both the pulse travel time and the complex wave impedance depended on the real part of permittivity and the loss factor The step responses of the TDR sensors recorded in experiments and in electromagnetic field simulations were used to show the pulse travel time versus moisture content using tap water and concentrated NaCl solution

Faster Discovery of Faster System Configurations with Spectral Learning

Abstract: Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT's innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors - less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2 to 10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.