Informatik und Kommunikation
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Auf der Basis eines Wettbewerbs des BMBF von 1997 hat das Konsortium Virtuelle Fachhochschule (VFH) 43 Mio. DM erhalten und 1999 damit begonnen Online-Studiengänge zu entwickeln. Weitere Themenfelder dieses Bundesleitprojektes sind: Struktur und Organisation einer virtuellen Hochschule, Lehr- und Lernformen in Online-Studiengängen, etc. Beteiligt sind 10 Fachhochschulen, 2 Universitäten, sowie diverse Organisationen und Wirtschaftsunternehmen. Im Jahre 2001 startete der Bachelor-Online-Studiengang Medieninformatik (www.oncampus.de). Das Projekt läuft noch bis ins Jahr 2004. Der Autor ist Vizegesamtprojektleiter, Mitglied im Teilvorhaben Struktur und Organisation sowie Entwickler von 3 Lernmodulen zur Physik für das Medieninformatikstudium.
Am Beispiel dieses Großprojektes werden einige Besonderheiten und Erfahrungen zu den Themen: Organisationsformen, Betreuung, Lehrdeputat, Workload, Ergonomie, Evaluation, Akkreditierung und die Entwicklung der Physik-Online-Lernmodule dargestellt.
In this paper, we present a method for detecting objects of interest, including cars, humans, and fire, in aerial images captured by unmanned aerial vehicles (UAVs) usually during vegetation fires. To achieve this, we use artificial neural networks and create a dataset for supervised learning. We accomplish the assisted labeling of the dataset through the implementation of an object detection pipeline that combines classic image processing techniques with pretrained neural networks. In addition, we develop a data augmentation pipeline to augment the dataset with utomatically labeled images. Finally, we evaluate the performance of different neural networks.
Technik des Online-Studiums
(2002)
This technical report is about the architecture and integration of very small commercial UAVs (< 40 cm diagonal) in indoor Search and Rescue missions. One UAV is manually controlled by only one single human operator delivering live video streams and image series for later 3D scene modelling and inspection. In order to assist the operator who has to simultaneously observe the environment and navigate through it we use multiple deep neural networks to provide guided autonomy, automatic object detection and classification and local 3D scene modelling. Our methods help to reduce the cognitive load of the operator. We describe a framework for quick integration of new methods from the field of Deep Learning, enabling for rapid evaluation in real scenarios, including the interaction of methods.
In the realm of digital situational awareness during disaster situations, accurate digital representations,
like 3D models, play an indispensable role. To ensure the
safety of rescue teams, robotic platforms are often deployed
to generate these models. In this paper, we introduce an
innovative approach that synergizes the capabilities of compact Unmaned Arial Vehicles (UAVs), smaller than 30 cm, equipped with 360° cameras and the advances of Neural Radiance Fields (NeRFs). A NeRF, a specialized neural network, can deduce a 3D representation of any scene using 2D images and then synthesize it from various angles upon request. This method is especially tailored for urban environments which have experienced significant destruction, where the structural integrity of buildings is compromised to the point of barring entry—commonly observed post-earthquakes and after severe fires. We have tested our approach through recent post-fire scenario, underlining the efficacy of NeRFs even in challenging outdoor environments characterized by water, snow, varying light conditions, and reflective surfaces.
Renewable and sustainable energy production by many small and distributed producers is revolutionizing the energy landscape as we know it. Consumers produce energy, making them to prosumers in the smart grid. The interaction between prosumers and other entities in the grid and the optimal utilization of new smart grid components (electric cars, freezers, solar panels, etc.) are crucial for the success of the smart grid. The Power Trading Agent Competition is an open simulation platform that allows researchers to conduct low risk studies in this new energy market. In this work we present Maxon16, an autonomous energy broker and champion of the 2016's Power Trading Agent Competition. We present the strategies the broker used in the final round and evaluate the effectiveness of the strategies by analyzing the tournament's results.
Intelligenzexplosion
(2016)