TY - CHAP A1 - Nikolovski, Gjorgji A1 - Reke, Michael A1 - Elsen, Ingo A1 - Schiffer, Stefan T1 - Machine learning based 3D object detection for navigation in unstructured environments T2 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) N2 - In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine. KW - 3D object detection KW - LiDAR KW - autonomous driving KW - Deep learning KW - Three-dimensional displays Y1 - 2021 SN - 978-1-6654-7921-9 U6 - http://dx.doi.org/10.1109/IVWorkshops54471.2021.9669218 N1 - 2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops), 11-17 July 2021, Nagoya, Japan. SP - 236 EP - 242 PB - IEEE ER - TY - CHAP A1 - Pfetsch, Marc E. A1 - Abele, Eberhard A1 - Altherr, Lena A1 - Bölling, Christian A1 - Brötz, Nicolas A1 - Dietrich, Ingo A1 - Gally, Tristan A1 - Geßner, Felix A1 - Groche, Peter A1 - Hoppe, Florian A1 - Kirchner, Eckhard A1 - Kloberdanz, Hermann A1 - Knoll, Maximilian A1 - Kolvenbach, Philip A1 - Kuttich-Meinlschmidt, Anja A1 - Leise, Philipp A1 - Lorenz, Ulf A1 - Matei, Alexander A1 - Molitor, Dirk A. A1 - Niessen, Pia A1 - Pelz, Peter F. A1 - Rexer, Manuel A1 - Schmitt, Andreas A1 - Schmitt, Johann M. A1 - Schulte, Fiona A1 - Ulbrich, Stefan A1 - Weigold, Matthias T1 - Strategies for mastering uncertainty T2 - Mastering uncertainty in mechanical engineering N2 - This chapter describes three general strategies to master uncertainty in technical systems: robustness, flexibility and resilience. It builds on the previous chapters about methods to analyse and identify uncertainty and may rely on the availability of technologies for particular systems, such as active components. Robustness aims for the design of technical systems that are insensitive to anticipated uncertainties. Flexibility increases the ability of a system to work under different situations. Resilience extends this characteristic by requiring a given minimal functional performance, even after disturbances or failure of system components, and it may incorporate recovery. The three strategies are described and discussed in turn. Moreover, they are demonstrated on specific technical systems. Y1 - 2021 SN - 978-3-030-78353-2 U6 - http://dx.doi.org/10.1007/978-3-030-78354-9_6 N1 - Part of the Springer Tracts in Mechanical Engineering book series (STME) SP - 365 EP - 456 PB - Springer CY - Cham ER - TY - CHAP A1 - Ritschel, Konstantin A1 - Stenzel, Adina A1 - Czarnecki, Christian A1 - Hong, Chin-Gi ED - Gesellschaft für Informatik e.V. (GI), T1 - Realizing robotic process automation potentials: an architectural perspective on a real-life implementation case T2 - GI Edition Proceedings Band 314 "INFORMATIK 2021" Computer Science & Sustainability N2 - The initial idea of Robotic Process Automation (RPA) is the automation of business processes through a simple emulation of user input and output by software robots. Hence, it can be assumed that no changes of the used software systems and existing Enterprise Architecture (EA) is required. In this short, practical paper we discuss this assumption based on a real-life implementation project. We show that a successful RPA implementation might require architectural work during analysis, implementation, and migration. As practical paper we focus on exemplary lessons-learned and new questions related to RPA and EA. KW - Robotic Process Automation KW - Enterprise Architecture KW - Implementation Case Y1 - 2021 SN - 9783885797081 SN - 1617-5468 N1 - INFORMATIK 2021 – 51. Jahrestagung der Gesellschaft für Informatik, 27. September – 01. Oktober 2021 / Virtuell, Berlin. https://informatik2021.gi.de/ SP - 1303 EP - 1311 PB - Köllen CY - Bonn ER - TY - JOUR A1 - Serror, Martin A1 - Hack, Sacha A1 - Henze, Martin A1 - Schuba, Marko A1 - Wehrle, Klaus T1 - Challenges and Opportunities in Securing the Industrial Internet of Things JF - IEEE Transactions on Industrial Informatics Y1 - 2021 U6 - http://dx.doi.org/10.1109/TII.2020.3023507 SN - 1941-0050 VL - 17 IS - 5 SP - 2985 EP - 2996 PB - IEEE CY - New York ER -