@inproceedings{HoegenDonckerBragardetal.2021, author = {Hoegen, Anne von and Doncker, Rik W. De and Bragard, Michael and Hoegen, Svenja von}, title = {Problem-Based Learning in Automation Engineering: Performing a Remote Laboratory Session Serving Various Educational Attainments}, series = {2021 IEEE Global Engineering Education Conference (EDUCON)}, booktitle = {2021 IEEE Global Engineering Education Conference (EDUCON)}, doi = {10.1109/EDUCON46332.2021.9453925}, pages = {1605 -- 1614}, year = {2021}, language = {en} } @incollection{AltherrLeisePfetschetal.2021, author = {Altherr, Lena and Leise, Philipp and Pfetsch, Marc E. and Schmitt, Andreas}, title = {Optimal design of resilient technical systems on the example of water supply systems}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78356-3}, pages = {429 -- 433}, year = {2021}, language = {en} } @inproceedings{NikolovskiRekeElsenetal.2021, author = {Nikolovski, Gjorgji and Reke, Michael and Elsen, Ingo and Schiffer, Stefan}, title = {Machine learning based 3D object detection for navigation in unstructured environments}, series = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, booktitle = {2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)}, publisher = {IEEE}, isbn = {978-1-6654-7921-9}, doi = {10.1109/IVWorkshops54471.2021.9669218}, pages = {236 -- 242}, year = {2021}, abstract = {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.}, language = {en} } @incollection{CroonCzarnecki2021, author = {Croon, Philipp and Czarnecki, Christian}, title = {Liability for loss or damages caused by RPA}, series = {Robotic process automation : Management, technology, applications}, booktitle = {Robotic process automation : Management, technology, applications}, editor = {Czarnecki, Christian and Fettke, Peter}, publisher = {De Gruyter}, address = {Oldenbourg}, isbn = {9783110676778}, doi = {10.1515/9783110676693-202}, pages = {135 -- 151}, year = {2021}, abstract = {Intelligent autonomous software robots replacing human activities and performing administrative processes are reality in today's corporate world. This includes, for example, decisions about invoice payments, identification of customers for a marketing campaign, and answering customer complaints. What happens if such a software robot causes a damage? Due to the complete absence of human activities, the question is not trivial. It could even happen that no one is liable for a damage towards a third party, which could create an uncalculatable legal risk for business partners. Furthermore, the implementation and operation of those software robots involves various stakeholders, which result in the unsolvable endeavor of identifying the originator of a damage. Overall it is advisable to all involved parties to carefully consider the legal situation. This chapter discusses the liability of software robots from an interdisciplinary perspective. Based on different technical scenarios the legal aspects of liability are discussed.}, language = {en} } @masterthesis{Latz2021, type = {Bachelor Thesis}, author = {Latz, Annika}, title = {Konzeptentwicklung einer spielerischen Lernanwendung f{\"u}r Studierende}, school = {Fachhochschule Aachen}, pages = {XIV, 136 Seiten}, year = {2021}, abstract = {Ziel der Arbeit war es eine spielerische Lernanwendung f{\"u}r Studierende der FH-Aachen zu entwickeln, um das individuelle Lernen zu f{\"o}rdern. Dabei lag der Fokus auf der Konzeptentwicklung eines Serious Games f{\"u}r die Fachhochschule Aachen. Abgeleitet von Motivationstheorien, Game Design Frameworks und Eigenschaften von digitalen spielerischen Konzepten wurde ein Vorgehensmodell zur Konzeptentwicklung eines Serious Games erstellt. Wichtige Punkte f{\"u}r die Anwendung waren eine intensive Austauschm{\"o}glichkeiten f{\"u}r Studierende und das Integrieren dieser in die Lehrveranstaltungen der FH-Aachen. In der abschließenden Evaluation wurde positives Feedback der Studierenden eingeholt und damit das Ziel der Arbeit erreicht. Zus{\"a}tzlich wurde f{\"u}r das erarbeitete Konzept die Wirtschaftlichkeit {\"u}berpr{\"u}ft. Daf{\"u}r wurde w{\"a}hrend der Bearbeitungszeit mit einem aufgestellten Team ein Business Plan f{\"u}r das F{\"o}rderprogramm Start-Up transfer.NRW entwickelt.}, language = {de} } @inproceedings{ChajanSchulteTiggesRekeetal.2021, author = {Chajan, Eduard and Schulte-Tigges, Joschua and Reke, Michael and Ferrein, Alexander and Matheis, Dominik and Walter, Thomas}, title = {GPU based model-predictive path control for self-driving vehicles}, series = {IEEE Intelligent Vehicles Symposium (IV)}, booktitle = {IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, isbn = {978-1-7281-5394-0}, doi = {10.1109/IV48863.2021.9575619}, pages = {1243 -- 1248}, year = {2021}, abstract = {One central challenge for self-driving cars is a proper path-planning. Once a trajectory has been found, the next challenge is to accurately and safely follow the precalculated path. The model-predictive controller (MPC) is a common approach for the lateral control of autonomous vehicles. The MPC uses a vehicle dynamics model to predict the future states of the vehicle for a given prediction horizon. However, in order to achieve real-time path control, the computational load is usually large, which leads to short prediction horizons. To deal with the computational load, the control algorithm can be parallelized on the graphics processing unit (GPU). In contrast to the widely used stochastic methods, in this paper we propose a deterministic approach based on grid search. Our approach focuses on systematically discovering the search area with different levels of granularity. To achieve this, we split the optimization algorithm into multiple iterations. The best sequence of each iteration is then used as an initial solution to the next iteration. The granularity increases, resulting in smooth and predictable steering angle sequences. We present a novel GPU-based algorithm and show its accuracy and realtime abilities with a number of real-world experiments.}, language = {en} } @incollection{BensbergAuthCzarnecki2021, author = {Bensberg, Frank and Auth, Gunnar and Czarnecki, Christian}, title = {Finding the perfect RPA match : a criteria-based selection method for RPA solutions}, series = {Robotic process automation : Management, technology, applications}, booktitle = {Robotic process automation : Management, technology, applications}, editor = {Czarnecki, Christian and Fettke, Peter}, publisher = {De Gruyter}, address = {Oldenbourg}, isbn = {978-3-11-067677-8}, doi = {10.1515/9783110676693-201}, pages = {47 -- 75}, year = {2021}, abstract = {The benefits of robotic process automation (RPA) are highly related to the usage of commercial off-the-shelf (COTS) software products that can be easily implemented and customized by business units. But, how to find the best fitting RPA product for a specific situation that creates the expected benefits? This question is related to the general area of software evaluation and selection. In the face of more than 75 RPA products currently on the market, guidance considering those specifics is required. Therefore, this chapter proposes a criteria-based selection method specifically for RPA. The method includes a quantitative evaluation of costs and benefits as well as a qualitative utility analysis based on functional criteria. By using the visualization of financial implications (VOFI) method, an application-oriented structure is provided that opposes the total cost of ownership to the time savings times salary (TSTS). For the utility analysis a detailed list of functional criteria for RPA is offered. The whole method is based on a multi-vocal review of scientific and non-scholarly literature including publications by business practitioners, consultants, and vendors. The application of the method is illustrated by a concrete RPA example. The illustrated structures, templates, and criteria can be directly utilized by practitioners in their real-life RPA implementations. In addition, a normative decision process for selecting RPA alternatives is proposed before the chapter closes with a discussion and outlook.}, language = {en} } @incollection{LeiseAltherr2021, author = {Leise, Philipp and Altherr, Lena}, title = {Experimental evaluation of resilience metrics in a fluid system}, series = {Mastering Uncertainty in Mechanical Engineering}, booktitle = {Mastering Uncertainty in Mechanical Engineering}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-78356-3}, pages = {442 -- 447}, year = {2021}, language = {en} } @incollection{CzarneckiHongSchmitzetal.2021, author = {Czarnecki, Christian and Hong, Chin-Gi and Schmitz, Manfred and Dietze, Christian}, title = {Enabling digital transformation through cognitive robotic process automation at Deutsche Telekom Services Europe}, series = {Digitalization Cases Vol. 2 : Mastering digital transformation for global business}, booktitle = {Digitalization Cases Vol. 2 : Mastering digital transformation for global business}, editor = {Urbach, Nils and R{\"o}glinger, Maximilian and Kautz, Karlheinz and Alias, Rose Alinda and Saunders, Carol and Wiener, Martin}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-80002-4 (Print)}, doi = {10.1007/978-3-030-80003-1}, pages = {123 -- 138}, year = {2021}, abstract = {Subject of this case is Deutsche Telekom Services Europe (DTSE), a service center for administrative processes. Due to the high volume of repetitive tasks (e.g., 100k manual uploads of offer documents into SAP per year), automation was identified as an important strategic target with a high management attention and commitment. DTSE has to work with various backend application systems without any possibility to change those systems. Furthermore, the complexity of administrative processes differed. When it comes to the transfer of unstructured data (e.g., offer documents) to structured data (e.g., MS Excel files), further cognitive technologies were needed.}, language = {en} } @incollection{SchneiderWisselinkNoelleetal.2021, author = {Schneider, Dominik and Wisselink, Frank and N{\"o}lle, Nikolai and Czarnecki, Christian}, title = {Einfluss von K{\"u}nstlicher Intelligenz auf Customer Journeys am Beispiel von intelligentem Parken}, series = {K{\"u}nstliche Intelligenz in der Anwendung : Rechtliche Aspekte, Anwendungspotenziale und Einsatzszenarien}, booktitle = {K{\"u}nstliche Intelligenz in der Anwendung : Rechtliche Aspekte, Anwendungspotenziale und Einsatzszenarien}, editor = {Barton, Thomas and M{\"u}ller, Christian}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-30935-0 (Print)}, doi = {10.1007/978-3-658-30936-7_7}, pages = {99 -- 122}, year = {2021}, abstract = {Im Konsumentenmarkt entstehen vermehrt neue Anwendungen von K{\"u}nstlicher Intelligenz (KI). Zunehmend dr{\"a}ngen auch Ger{\"a}te und Dienste in den Markt, die eigenst{\"a}ndig {\"u}ber das Internet kommunizieren. Dadurch k{\"o}nnen diese Ger{\"a}te und Dienste mit neuartigen KI-basierten Diensten verbessert werden. Solche Dienste k{\"o}nnen die Art und Weise beeinflussen, wie Kunden kommerzielle Entscheidungen treffen und somit das Kundenerlebnis maßgeblich ver{\"a}ndern. Der Einfluss von KI auf kommerzielle Interaktionen wurde bisher noch nicht umfassend untersucht. Basierend auf einem Framework, welches einen ersten {\"U}berblick {\"u}ber die Effekte von KI auf kommerzielle Interaktionen gibt, wird in diesem Kapitel der Einfluss von KI auf Customer Journeys am konkreten Anwendungsfall des intelligenten Parkens analysiert. Die daraus gewonnenen Erkenntnisse k{\"o}nnen in der Praxis als Grundlage genutzt werden, um das Potenzial von KI zu verstehen und bei der Gestaltung eigener Customer Journeys umzusetzen.}, language = {de} }