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Dissertation
Static and Dynamic Characterization of MEMS Micromirror and Microshutter arrays: Reliability and Lifetime
(2024)
Buildings are responsible for 40 % of the primary energy consumption and 36 % of the total CO2 emissions. There is a huge potential to decrease energetic consumption for lighting and heating/cooling by substituting the 85 % of inefficient glazing areas in EU’s buildings with energy efficient smart glazing windows. A micro-electro-mechanical system (MEMS) based smart glazing system comprising of millions of micro mirrors (invisible to the bare eye) is investigated here. This concept allows dynamic light steering by ...
Konferenzveröffentlichung
Fast parallel quasi-static time series simulator for active distribution grid operation with pandapower
(IET, 2021)
The increasing penetration from intermittent renewable distributed energy resources in distribution grid brings along challenges in grid operation and planning. To evaluate the impact on the grid voltage profile, grid losses, and discrete actions from assets (e.g. transformer tap changes), quasi-static simulation is an appropriate method. Quasi-static time series and Monte-Carlo simulation requires a tremendous number of power flow calculations (PFCs), which can be significantly accelerated with a parallel High-Performance ...
Dissertation
Context Awareness for Smartphone-Based Cooperative VRU Collision Avoidance
(2024)
Nach Angaben der Weltgesundheitsorganisation sterben weltweit noch immer jährlich rund 1,35 Millionen Menschen bei Verkehrsunfällen. Ungefähr 54% dieser Todesfälle ereignen sich unter ungeschützten Verkehrsteilnehmern wie Fußgängern, Radfahrern und Motorradfahrern. Fußgänger und Radfahrer machen dabei zusammen rund 26% aller Verkehrstoten aus. Im Gegensatz zu fahrzeugbasierten Kollisionsvermeidungssystemen setzen kooperative Systeme die Verwendung von mobilen Geräten wie Smartphones zur kontinuierlichen Erfassung von ...
Aufsatz
Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features
(2023-12-30)
Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable if the position of detected features is relevant for the prediction task. However, the capabilities of continuous kernels to ...
Konferenzveröffentlichung
Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features
(2023-03-13)
Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels ...