Bøger udgivet af Universitätsverlag Ilmenau

Die 20. ASIM-Fachtagung "Simulation in Produktion und Logistik", Ilmenau, 13.-15. September 2023, steht unter dem Motto der ¿Nachhaltigkeit in Produktion und Logistik¿. Sie soll Anregungen und Denkanstöße geben und über bereits erfolgreiche Projekte und Neuerungen berichten. Der vorliegende Tagungsband präsentiert neben aktuellen Beiträgen aus der klassischen Simulationsforschung und -anwendung, die z.B. den Digitalen Zwilling thematisieren, auch hochinteressante und einschlägige Beiträge zu Fragen der Abbildung energie- und nachhaltigkeitsbezogener Einflussfaktoren in der Simulation.

Autonomous self-driving cars need a very precise perception system of their environment, working for every conceivable scenario. Therefore, different kinds of sensor types, such as lidar scanners, are in use. This thesis contributes highly efficient algorithms for 3D object recognition to the scientific community. It provides a Deep Neural Network with specific layers and a novel loss to safely localize and estimate the orientation of objects from point clouds originating from lidar sensors. First, a single-shot 3D object detector is developed that outputs dense predictions in only one forward pass. Next, this detector is refined by fusing complementary semantic features from cameras and joint probabilistic tracking to stabilize predictions and filter outliers. The last part presents an evaluation of data from automotive-grade lidar scanners. A Generative Adversarial Network is also being developed as an alternative for target-specific artificial data generation.

The challenges of stable operation in the electrical power system are increasing with the infrastructure shifting of the power grid from the centralized energy supply with fossil fuels towards sustainable energy generation. The predominantly RES plants, due to the non-linear electronic switch, have brought harmonic oscillations into the power grid. These changes lead to difficulties in stable operation, reduction of outages and management of variations in electric power systems. The emergence of the Digital Twin in the power system brings the opportunity to overcome these challenges. Digital Twin is a digital information model that accurately represents the state of every asset in a physical system. It can be used not only to monitor the operation states with actionable insights of physical components to drive optimized operation but also to generate abundant data by simulation according to the guidance on design limits of physical systems. The work addresses the topic of the origin of the Digital Twin concept and how it can be utilized in the optimization of power grid operation.