@inproceedings{SteinbauerFerrein2019, author = {Steinbauer, Gerald and Ferrein, Alexander}, title = {CogRob 2018 : Cognitive Robotics Workshop. Proceedings of the 11th Cognitive Robotics Workshop 2018 co-located with 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018). Tempe, AZ, USA, October 27th, 2018.}, series = {CEUR workshop proceedings}, booktitle = {CEUR workshop proceedings}, number = {Vol-2325}, issn = {1613-0073}, pages = {46 Seiten}, year = {2019}, language = {en} } @inproceedings{DonnerRabelScholletal.2019, author = {Donner, Ralf and Rabel, Matthias and Scholl, Ingrid and Ferrein, Alexander and Donner, Marc and Geier, Andreas and John, Andr{\´e} and K{\"o}hler, Christian and Varga, Sebastian}, title = {Die Extraktion bergbaulich relevanter Merkmale aus 3D-Punktwolken eines untertagetauglichen mobilen Multisensorsystems}, series = {Tagungsband Geomonitoring}, booktitle = {Tagungsband Geomonitoring}, doi = {10.15488/4515}, pages = {91 -- 110}, year = {2019}, language = {de} } @inproceedings{FerreinBharatheeshaSchifferetal.2019, author = {Ferrein, Alexander and Bharatheesha, Mukunda and Schiffer, Stefan and Corbato, Carlos Hernandez}, title = {TRROS 2018 : Teaching Robotics with ROS Workshop at ERF 2018; Proceedings of the Workshop on Teaching Robotics with ROS (held at ERF 2018), co-located with European Robotics Forum 2018 (ERF 2018), Tampere, Finland, March 15th, 2018}, series = {CEUR Workshop Proceedings}, booktitle = {CEUR Workshop Proceedings}, number = {Vol-2329}, issn = {1613-0073}, pages = {68 Seiten}, year = {2019}, language = {en} } @inproceedings{FerreinSchollNeumannetal.2019, author = {Ferrein, Alexander and Scholl, Ingrid and Neumann, Tobias and Kr{\"u}ckel, Kai and Schiffer, Stefan}, title = {A system for continuous underground site mapping and exploration}, doi = {10.5772/intechopen.85859}, pages = {16 Seiten}, year = {2019}, language = {en} } @inproceedings{SchollBartellaMoluluoetal.2019, author = {Scholl, Ingrid and Bartella, Alexander K. and Moluluo, Cem and Ertural, Berat and Laing, Frederic and Suder, Sebastian}, title = {MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality}, series = {Bildverarbeitung f{\"u}r die Medizin 2019 : Algorithmen - Systeme - Anwendungen}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019 : Algorithmen - Systeme - Anwendungen}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-25326-4}, doi = {10.1007/978-3-658-25326-4_32}, pages = {152 -- 157}, year = {2019}, language = {en} } @inproceedings{NeesStengelMeisteretal.2020, author = {Nees, Franz and Stengel, Ingo and Meister, Vera G. and Barton, Thomas and Herrmann, Frank and M{\"u}ller, Christian and Wolf, Martin R.}, title = {Angewandte Forschung in der Wirtschaftsinformatik 2020 : Tagungsband zur 33. AKWI-Jahrestagung am 14.09.2020, ausgerichtet von der Hochschule Karlsruhe - Wirtschaft und Technik / hrsg. von Franz Nees, Ingo Stengel, Vera G. Meister, Thomas Barton, Frank Herrmann, Christian M{\"u}ller, Martin R. Wolf}, publisher = {mana-Buch}, address = {Heide}, isbn = {978-3-944330-66-2}, doi = {10.15771/978-3-944330-66-2}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-13840}, pages = {147 Seiten}, year = {2020}, abstract = {Tagungsbeitr{\"a}ge aus den Bereichen KI, Prozessorganisation und Plattformen f{\"u}r Gesch{\"a}ftsprozesse, Sicherheit und Datenschutz sowie Prototypen und Modelle.}, language = {de} } @inproceedings{KirschMatareFerreinetal.2020, author = {Kirsch, Maximilian and Matar{\´e}, Victor and Ferrein, Alexander and Schiffer, Stefan}, title = {Integrating golog++ and ROS for Practical and Portable High-level Control}, series = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2}, booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2}, publisher = {SciTePress}, address = {Set{\´u}bal, Portugal}, doi = {10.5220/0008984406920699}, pages = {692 -- 699}, year = {2020}, abstract = {The field of Cognitive Robotics aims at intelligent decision making of autonomous robots. It has matured over the last 25 or so years quite a bit. That is, a number of high-level control languages and architectures have emerged from the field. One concern in this regard is the action language GOLOG. GOLOG has been used in a rather large number of applications as a high-level control language ranging from intelligent service robots to soccer robots. For the lower level robot software, the Robot Operating System (ROS) has been around for more than a decade now and it has developed into the standard middleware for robot applications. ROS provides a large number of packages for standard tasks in robotics like localisation, navigation, and object recognition. Interestingly enough, only little work within ROS has gone into the high-level control of robots. In this paper, we describe our approach to marry the GOLOG action language with ROS. In particular, we present our architecture on inte grating golog++, which is based on the GOLOG dialect Readylog, with the Robot Operating System. With an example application on the Pepper service robot, we show how primitive actions can be easily mapped to the ROS ActionLib framework and present our control architecture in detail.}, language = {en} } @inproceedings{DinghoferHartung2020, author = {Dinghofer, Kai and Hartung, Frank}, title = {Analysis of Criteria for the Selection of Machine Learning Frameworks}, series = {2020 International Conference on Computing, Networking and Communications (ICNC)}, booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)}, publisher = {IEEE}, address = {New York, NY}, doi = {10.1109/ICNC47757.2020.9049650}, pages = {373 -- 377}, year = {2020}, abstract = {With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.}, language = {en} } @inproceedings{LeiseBreuerAltherretal.2020, author = {Leise, Philipp and Breuer, Tim and Altherr, Lena and Pelz, Peter F.}, title = {Development, validation and assessment of a resilient pumping system}, series = {Proceedings of the Joint International Resilience Conference, JIRC2020}, booktitle = {Proceedings of the Joint International Resilience Conference, JIRC2020}, isbn = {978-90-365-5095-6}, pages = {97 -- 100}, year = {2020}, abstract = {The development of resilient technical systems is a challenging task, as the system should adapt automatically to unknown disturbances and component failures. To evaluate different approaches for deriving resilient technical system designs, we developed a modular test rig that is based on a pumping system. On the basis of this example system, we present metrics to quantify resilience and an algorithmic approach to improve resilience. This approach enables the pumping system to automatically react on unknown disturbances and to reduce the impact of component failures. In this case, the system is able to automatically adapt its topology by activating additional valves. This enables the system to still reach a minimum performance, even in case of failures. Furthermore, timedependent disturbances are evaluated continuously, deviations from the original state are automatically detected and anticipated in the future. This allows to reduce the impact of future disturbances and leads to a more resilient system behaviour.}, language = {en} } @inproceedings{LorenzAltherrPelz2020, author = {Lorenz, Imke-Sophie and Altherr, Lena and Pelz, Peter F.}, title = {Resilience enhancement of critical infrastructure - graph-theoretical resilience analysis of the water distribution system in the German city of Darmstadt}, series = {14th WCEAM Proceedings}, booktitle = {14th WCEAM Proceedings}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-64228-0}, doi = {10.1007/978-3-030-64228-0_13}, pages = {137 -- 149}, year = {2020}, abstract = {Water suppliers are faced with the great challenge of achieving high-quality and, at the same time, low-cost water supply. Since climatic and demographic influences will pose further challenges in the future, the resilience enhancement of water distribution systems (WDS), i.e. the enhancement of their capability to withstand and recover from disturbances, has been in particular focus recently. To assess the resilience of WDS, graph-theoretical metrics have been proposed. In this study, a promising approach is first physically derived analytically and then applied to assess the resilience of the WDS for a district in a major German City. The topology based resilience index computed for every consumer node takes into consideration the resistance of the best supply path as well as alternative supply paths. This resistance of a supply path is derived to be the dimensionless pressure loss in the pipes making up the path. The conducted analysis of a present WDS provides insight into the process of actively influencing the resilience of WDS locally and globally by adding pipes. The study shows that especially pipes added close to the reservoirs and main branching points in the WDS result in a high resilience enhancement of the overall WDS.}, language = {en} }