TY - JOUR A1 - Bung, Daniel Bernhard A1 - Erpicum, Sébastien A1 - Tullis, Blanke P. T1 - Advances in hydraulic structures engineering JF - Journal of Hydraulic Engineering Y1 - 2020 U6 - https://doi.org/10.1061/(ASCE)HY.1943-7900.0001851 SN - 0733-9429 (Druckausgabe) SN - 1943-7900 (Online-Ausgabe) VL - 147 IS - 1 PB - ASCE CY - Reston, Va. ER - TY - CHAP A1 - Eggert, Mathias A1 - Stanke, Max-Alexander T1 - Adoption of Integrated Voice Assistants in Health Care– Requirements and Design Guidelines N2 - Integrated voice assistants (IVA) receive more and more attention and are widespread for entertainment use cases, such as radio hearing or web searches. At the same time, the health care segment suffers in process inefficiency and missing staff, whereas the usage of IVA has the potential to improve caring processes and patient satisfaction. By applying a design science approach and based on a qualitative study, we identify IVA requirements, barriers and design guidelines for the health care sector. The results reveal three important IVA functions: the ability to set appointments with care service staff, the documentation of health history and the communication with service staff. Integration, system stability and volume control are the most important nonfunctional requirements. Based on the interview results and project experiences, six design and implementation guidelines are derived. Y1 - 2020 U6 - https://doi.org/10.30844/wi_2020_k2-eggert N1 - 15. Internationale Tagung zur Wirtschaftsinformatik, 09. – 11.03.2020, Potsdam PB - GITO CY - Berlin ER - TY - JOUR A1 - Ketelhut, Maike A1 - Brügge, G. M. A1 - Göll, Fabian A1 - Braunstein, Bjoern A1 - Albracht, Kirsten A1 - Abel, Dirk T1 - Adaptive iterative learning control of an industrial robot during neuromuscular training JF - IFAC PapersOnLine N2 - To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur. KW - Iterative learning control KW - Robotic rehabilitation KW - Adaptive control Y1 - 2020 U6 - https://doi.org/10.1016/j.ifacol.2020.12.741 SN - 2405-8963 VL - 53 IS - 2 SP - 16468 EP - 16475 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Eltester, Niklas Sebastian A1 - Ferrein, Alexander A1 - Schiffer, Stefan T1 - A smart factory setup based on the RoboCup logistics league T2 - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS) N2 - In this paper we present SMART-FACTORY, a setup for a research and teaching facility in industrial robotics that is based on the RoboCup Logistics League. It is driven by the need for developing and applying solutions for digital production. Digitization receives constantly increasing attention in many areas, especially in industry. The common theme is to make things smart by using intelligent computer technology. Especially in the last decade there have been many attempts to improve existing processes in factories, for example, in production logistics, also with deploying cyber-physical systems. An initiative that explores challenges and opportunities for robots in such a setting is the RoboCup Logistics League. Since its foundation in 2012 it is an international effort for research and education in an intra-warehouse logistics scenario. During seven years of competition a lot of knowledge and experience regarding autonomous robots was gained. This knowledge and experience shall provide the basis for further research in challenges of future production. The focus of our SMART-FACTORY is to create a stimulating environment for research on logistics robotics, for teaching activities in computer science and electrical engineering programmes as well as for industrial users to study and explore the feasibility of future technologies. Building on a very successful history in the RoboCup Logistics League we aim to provide stakeholders with a dedicated facility oriented at their individual needs. Y1 - 2020 U6 - https://doi.org/10.1109/ICPS48405.2020.9274766 N1 - 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 10-12 June 2020, Tampere, Finland. SP - 297 EP - 302 PB - IEEE CY - New York, NY ER - TY - CHAP A1 - Reke, Michael A1 - Peter, Daniel A1 - Schulte-Tigges, Joschua A1 - Schiffer, Stefan A1 - Ferrein, Alexander A1 - Walter, Thomas A1 - Matheis, Dominik T1 - A Self-Driving Car Architecture in ROS2 T2 - 2020 International SAUPEC/RobMech/PRASA Conference, Cape Town, South Africa N2 - In this paper we report on an architecture for a self-driving car that is based on ROS2. Self-driving cars have to take decisions based on their sensory input in real-time, providing high reliability with a strong demand in functional safety. In principle, self-driving cars are robots. However, typical robot software, in general, and the previous version of the Robot Operating System (ROS), in particular, does not always meet these requirements. With the successor ROS2 the situation has changed and it might be considered as a solution for automated and autonomous driving. Existing robotic software based on ROS was not ready for safety critical applications like self-driving cars. We propose an architecture for using ROS2 for a self-driving car that enables safe and reliable real-time behaviour, but keeping the advantages of ROS such as a distributed architecture and standardised message types. First experiments with an automated real passenger car at lower and higher speed-levels show that our approach seems feasible for autonomous driving under the necessary real-time conditions. Y1 - 2020 SN - 978-1-7281-4162-6 U6 - https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041020 N1 - 2020 International SAUPEC/RobMech/PRASA Conference, 29-31 Jan. 2020, Cape Town, South Africa SP - 1 EP - 6 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Asante-Asamani, E.O. A1 - Kleefeld, Andreas A1 - Wade, B.A. T1 - A second-order exponential time differencing scheme for non-linear reaction-diffusion systems with dimensional splitting JF - Journal of Computational Physics N2 - A second-order L-stable exponential time-differencing (ETD) method is developed by combining an ETD scheme with approximating the matrix exponentials by rational functions having real distinct poles (RDP), together with a dimensional splitting integrating factor technique. A variety of non-linear reaction-diffusion equations in two and three dimensions with either Dirichlet, Neumann, or periodic boundary conditions are solved with this scheme and shown to outperform a variety of other second-order implicit-explicit schemes. An additional performance boost is gained through further use of basic parallelization techniques. KW - Exponential time differencing KW - Real distinct pole KW - Dimensional splitting KW - Reaction-diffusion systems KW - Matrix exponential Y1 - 2020 U6 - https://doi.org/10.1016/j.jcp.2020.109490 SN - 0021-9991 N1 - Corresponding author: Andreas Kleefeld VL - 415 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Philipp, Brauner A1 - Brillowski, Florian Sascha A1 - Dammers, Hannah A1 - Königs, Peter A1 - Kordtomeikel, Frauke Carole A1 - Petruck, Henning A1 - Schaar, Anne Kathrin A1 - Schmitz, Seth A1 - Steuer-Dankert, Linda A1 - Mertens, Alexander A1 - Gries, Thomas A1 - Leicht-Scholten, Carmen A1 - Nagel, Saskia K. A1 - Nitsch, Verena A1 - Schuh, Günther A1 - Ziefle, Martina ED - Mrugalska, Beata ED - Trzcielinski, Stefan ED - Karwowski, Waldemar ED - Nicolantonio, Massimo Di ED - Roossi, Emilio T1 - A research framework for human aspects in the internet of production: an intra-company perspective T2 - Proceedings of the AHFE 2020 N2 - Digitalization in the production sector aims at transferring concepts and methods from the Internet of Things (IoT) to the industry and is, as a result, currently reshaping the production area. Besides technological progress, changes in work processes and organization are relevant for a successful implementation of the “Internet of Production” (IoP). Focusing on the labor organization and organizational procedures emphasizes to consider intra-company factors such as (user) acceptance, ethical issues, and ergonomics in the context of IoP approaches. In the scope of this paper, a research approach is presented that considers these aspects from an intra-company perspective by conducting studies on the shop floor, control level and management level of companies in the production area. Focused on four central dimensions—governance, organization, capabilities, and interfaces—this contribution presents a research framework that is focused on a systematic integration and consideration of human aspects in the realization of the IoP. KW - Human factors KW - Digitalization KW - Acceptance KW - Ethics KW - Human-robot collaboration Y1 - 2020 SN - 978-3-030-51980-3 U6 - https://doi.org/10.1007/978-3-030-51981-0_1 N1 - AHFE 2020 Virtual Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, July 16–20, 2020, USA SP - 3 EP - 17 PB - Springer CY - Cham ER - TY - JOUR A1 - Roepke, Rene A1 - Köhler, Klemens A1 - Drury, Vincent A1 - Schroeder, Ulrik A1 - Wolf, Martin R. A1 - Meyer, Ulrike T1 - A pond full of phishing games - analysis of learning games for anti-phishing education JF - Model-driven Simulation and Training Environments for Cybersecurity. MSTEC 2020 N2 - Game-based learning is a promising approach to anti-phishing education, as it fosters motivation and can help reduce the perceived difficulty of the educational material. Over the years, several prototypes for game-based applications have been proposed, that follow different approaches in content selection, presentation, and game mechanics. In this paper, a literature and product review of existing learning games is presented. Based on research papers and accessible applications, an in-depth analysis was conducted, encompassing target groups, educational contexts, learning goals based on Bloom’s Revised Taxonomy, and learning content. As a result of this review, we created the publications on games (POG) data set for the domain of anti-phishing education. While there are games that can convey factual and conceptual knowledge, we find that most games are either unavailable, fail to convey procedural knowledge or lack technical depth. Thus, we identify potential areas of improvement for games suitable for end-users in informal learning contexts. Y1 - 2020 SN - 978-3-030-62433-0 U6 - https://doi.org/10.1007/978-3-030-62433-0_32020 N1 - Lecture Notes in Computer Science, vol 12512 SP - 41 EP - 60 PB - Springer CY - Cham ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalil, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 SN - 2169-3536 U6 - https://doi.org/10.1109/ACCESS.2020.2999898 SP - 1 EP - 12 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Khayyam, Hamid A1 - Jamali, Ali A1 - Bab-Hadiashar, Alireza A1 - Esch, Thomas A1 - Ramakrishna, Seeram A1 - Jalili, Mahdi A1 - Naebe, Minoo T1 - A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0 JF - IEEE Access N2 - To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements. Y1 - 2020 U6 - https://doi.org/10.1109/ACCESS.2020.2999898 SN - 2169-3536 VL - 8 IS - Art. 9108222 SP - 111381 EP - 111393 PB - IEEE CY - New York, NY ER -