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Proceedings of the GCSM 2012 10th Global Conference on Sustainable Manufacturing ISBN:978-605-63463-0-9

CEOCOR 2021 Madrid

In thin-film solar cells optical scattering eects at rough layer interfaces are exploited to enhance the light absorption and make it possible to construct thin cells with inorganic materials such as a-Si or c-Si [5]. SETFOS [2] is an optical and electrical device simulator, that is widely used in the OLED and OPV community. To perform an optical simulation of an arbitrary combination of inorganic or organic layers of any thickness, it is necessary to be able to treat light propagation both as coherent or incoherent. We have therefore extended the optical model of SETFOS for the treatment of mixed systems of incoherent/coherent layers. We have broadened the scope of the simulator by including scattering eects at rough layer interfaces. 24th European Photovoltaic Solar Energy Conference, 21-25 September 2009, Hamburg, Germany; 2893-2894

Proceedings INTER International Network on Timber Engineering Research, 52

ISBN:978-3-907234-59-4

Inspire AG together with Oelhydraulik Hagenbuch AG Ebikon is involved in a project for the performance improvement of a servo hydraulic test shaker. We focus on the quality of the FRF response of the system’s acceleration. Experimental measurements showed the presence of significant harmonic components at frequencies multiple of the exciting frequency. Fig. 1 presents an example for a commanded frequency of 160 Hz and a amplitude of 40 g, Fig 2 shows the corresponding frequency spectrum. A compensation algorithm has been developed and successfully incorporated into the control loop of the shaker. The control strategy uses a Kalman filter to estimate the harmonic content of the system response during operational conditions; simultaneously the control signal is corrected by higher order harmonic terms whose magnitudes and phases are optimized in order to minimize undesired components in the response spectrum.

Proceedings of the IASS Annual Symposium 2019 – Structural Membranes 2019 Proceedings of IASS Annual Symposia, 2019 (6) ISSN:2518-6582

Graph Networks (GNs) enable the fusion of prior knowledge and relational reasoning with flexible function approximations. In this work, a general GN-based model is proposed which takes full advantage of the relational modeling capabilities of GNs and extends these to probabilistic modeling with Variational Bayes (VB). To that end, we combine complementary pre-existing approaches on VB for graph data and propose an approach that relies on graph-structured latent and conditioning variables. It is demonstrated that Neural Processes can also be viewed through the lens of the proposed model. We show applications on the problem of structured probability density modeling for simulated and real wind farm monitoring data, as well as on the meta-learning of simulated Gaussian Process data. We release the source code, along with the simulated datasets. arXiv