TY - CHAP AU - Sacher, Marc AU - Bauer, Anna ED - Terkowsky, Claudius ED - May, Dominik ED - Frye, Silke ED - Haertel, Tobias ED - Ortelt, Tobias ED - Heix, Sabrina ED - Lensing, Karsten ID - 24951 T2 - Labore in der Hochschullehre. Didaktik, Digitalisierung, Organisation TI - Kompetenzförderung im Laborpraktikum ER - TY - JOUR AU - Bauer, Anna AU - Sacher, Marc AU - Brassat, Katharina ID - 24952 JF - hochschullehre TI - Studentische Akzeptanz und Relevanzwahrnehmung eines disziplinspezifischen Workshops „Wissenschaftliche Vorträge in der Physik“ VL - 6 ER - TY - JOUR AU - Bauer, Anna AU - Reinhold, Peter AU - Sacher, Marc ID - 24956 JF - Phydid B, Didaktik der Physik, Beiträge zur DPG-Frühjahrstagung TI - Entwicklung eines Bewertungsmodells zur handlungsorientierten Messung experimenteller Kompetenz (Physik)Studierender ER - TY - CHAP AU - Bauer, Anna AU - Reinhold, Peter AU - Sacher, Marc ED - Habig, Sebastian ID - 24957 T2 - Naturwissenschaftliche Kompetenzen in der Gesellschaft von morgen TI - Bewertungsmodell zur experimentellen Performanz (Physik)Studierender ER - TY - JOUR AB - Die Frage, wie sich die Weiterentwicklung der Lehre an Hochschulen systematisch verankern lässt, erfährt mit dem Auslaufen von Förderprogrammen wie dem QPL erneute Aufmerksamkeit. Bislang fehlt es an einer kontextspezifischen Theorie, die lehrbezogenen Wandel an Hochschulen analysier- und gestaltbar macht. In jedem Fall sind Change-Konzepte aus dem betriebswirtschaftlichen Bereich nur sehr beschränkt auf Hochschulen übertragbar. Demgegenüber gibt neuere Forschung Hinweise darauf, welche Kernkategorien eine hochschulspezifische Change- Theorie umfassen könnte. Darauf aufbauend schlägt der Beitrag zwei Konzepte als Kernkategorien einer Theorie lehrbezogenen Wandels an Hochschulen vor. AU - Jenert, Tobias ID - 24973 IS - 4 JF - Zeitschrift für Hochschulentwicklung KW - educational development KW - change management KW - educational innovation TI - Überlegungen auf dem Weg zu einer Theorie lehrbezogenen Wandels an Hochschulen VL - 15 ER - TY - GEN AB - Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas. Modern MSGs are largely driven by power electronic converters due to their high efficiency and flexibility. Nevertheless, controlling MSGs is a challenging task due to highest requirements on energy availability, safety and voltage quality within a wide range of different MSG topologies. This results in a high demand for comprehensive testing of new control concepts during their development phase and comparisons with the state of the art in order to ensure their feasibility. This applies in particular to data-driven control approaches from the field of reinforcement learning (RL), whose stability and operating behavior can hardly be evaluated a priori. Therefore, the OpenModelica Microgrid Gym (OMG) package, an open-source software toolbox for the simulation and control optimization of MSGs, is proposed. It is capable of modeling and simulating arbitrary MSG topologies and offers a Python-based interface for plug \& play controller testing. In particular, the standardized OpenAI Gym interface allows for easy RL-based controller integration. Besides the presentation of the OMG toolbox, application examples are highlighted including safe Bayesian optimization for low-level controller tuning. AU - Bode, Henrik AU - Heid, Stefan Helmut AU - Weber, Daniel AU - Hüllermeier, Eyke AU - Wallscheid, Oliver ID - 19603 T2 - arXiv:2005.04869 TI - Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control ER - TY - CONF AB - Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions. AU - Gottschalk, Sebastian AU - Yigitbas, Enes AU - Schmidt, Eugen AU - Engels, Gregor ED - Bernhaupt, Regina ED - Ardito, Carmelo ED - Sauer, Stefan ID - 19606 KW - Product Configuration KW - Augmented Reality KW - Model-based KW - Tool Support T2 - Human-Centered Software Engineering. HCSE 2020 TI - ProConAR: A Tool Support for Model-based AR Product Configuration VL - 12481 ER - TY - CONF AB - Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x). AU - Schneider, Stefan Balthasar AU - Klenner, Lars Dietrich AU - Karl, Holger ID - 19607 KW - distributed management KW - service coordination KW - network coordination KW - nfv KW - softwarization KW - orchestration T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Every Node for Itself: Fully Distributed Service Coordination ER - TY - CONF AB - Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks. AU - Schneider, Stefan Balthasar AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 19609 KW - self-driving networks KW - self-learning KW - network coordination KW - service coordination KW - reinforcement learning KW - deep learning KW - nfv T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Self-Driving Network and Service Coordination Using Deep Reinforcement Learning ER - TY - JOUR AU - Kreusser, Lisa Maria AU - McLachlan, Robert I AU - Offen, Christian ID - 19939 IS - 5 JF - Nonlinearity SN - 0951-7715 TI - Detection of high codimensional bifurcations in variational PDEs VL - 33 ER -