Deep learning approach to impact classification in sensorized panels using self-attention

This paper proposes a new method of impact classification for a Structural Health Monitoring system through the use of Self-Attention, the central building block of the Transformer neural network. As a topical and highly promising neural network architecture, the Transformer has the potential to greatly improve the speed and robustness of impact detection. This paper investigates the suitability of this new network, confronting the advantages and disadvantages offered by the Transformer and a well-known and established neural network for impact detection, the Convolutional Neural Network (CNN). The comparison is undertaken on performance, scalability, and computational time. The inputs to the networks were created using a data transformation technique, which transforms the raw time series data collected from the network of piezoelectric sensors, installed on a composite panel, through the use of Fourier Transform. It is demonstrated that the Transformer method reduces the computational complexity of the impact detection significantly, while achieving excellent prediction results

"Der Fremde Blick" – "ein fremdes Auge":Transmediale Inszenierung von Schrift und Bild in Herta Müllers Colagen

Der Artikel beschäftigt sich mit den in der Forschungsliteratur und der breiten Öffentlichkeit bisher kaum beachteten Bild-Text-Collagen Herta Müllers. Konkret erfolgt nach einer kurzen Einführung in Herta Müllers Werkcharakter und einer medialen und strukturellen Verortung der Bild-Text-Collagen eine Analyse der lyrischen und formalen Gestaltung des Textes, der medialen Inszenierung der Schriftelemente und der Bild-Text-Korrelationen am Beispiel der Bild-Text-Collagen des aktuellsten Collage-Bandes »Die blassen Herren mit den Mokkatassen« (2005). Die Strategie der Fremdheit, die Herta Müllers prosaische Werke durchziehen, erfährt in den Bild-Text-Collagen eine Zuspitzung und mediale Komponente

A three-dimensional boundary element model for the analysis of polycrystalline materials at the microscale

A three-dimensional multi-domain anisotropic boundary element formulation is presented for the analysis of polycrystalline microstructures. The formulation is naturally expressed in terms of intergranular displacements and tractions that play an important role in polycrystalline micromechanics, micro-damage and micro-cracking. The artificial morphology is generated by Hardcore Voronoi tessellation, which embodies the main statistical features of polycrystalline microstructures. Each crystal is modeled as an anisotropic elastic region and the integrity of the aggregate is restored by enforcing interface continuity and equilibrium between contiguous grains. The developed technique has been applied to the numerical homogenization of SiC and the obtained results agree very well with available data