TY - CHAP A1 - Niemüller, Tim A1 - Lakemeyer, Gerhard A1 - Ferrein, Alexander ED - Boots, Byron T1 - Incremental task-level reasoning in a competitive factory automation scenario T2 - Designing intelligent robots : reintegrating AI II ; papers from the AAAI spring symposium ; [held March 25 - 27, 2013 in Palo Alto, California, USA, on the campus of Stanford University]. (Technical Report / Association for the Advancement of Artificial Intelligence ; 2013,4) Y1 - 2013 SN - 9781577356011 SP - 43 EP - 48 ER - TY - CHAP A1 - Niemüller, Tim A1 - Lakemeyer, Gerhard A1 - Ferrein, Alexander T1 - Aspects of integrating diverse software into robotic systems extended abstract T2 - ICRA 2013 - 8th Workshop on Software Development and Integration in Robotics (SDIR), Karlsruhe, Germany Y1 - 2013 SP - 1 EP - 2 ER - TY - JOUR A1 - Niemüller, Tim A1 - Lakemeyer, Gerhard A1 - Ferrein, Alexander A1 - Reuter, S. A1 - Ewert, D. A1 - Jeschke, S. A1 - Pensky, D. A1 - Karras, Ulrich T1 - Proposal for advancements to the LLSF in 2014 and beyond Y1 - 2013 SP - Publ. online ER - TY - CHAP A1 - Niendorf, Thoralf A1 - Winter, Lukas A1 - Frauenrath, Tobias ED - Millis, Richard T1 - Electrocardiogram in an MRI environment: Clinical needs, practical considerations, safety implications, technical solutions and fFuture directions T2 - Advances in Electrocardiograms - Methods and Analysis Y1 - 2012 SN - 978-953-307-923-3 (print) SN - 978-953-51-6762-4 (eBook) U6 - https://doi.org/10.5772/24340 SP - 309 EP - 324 PB - IntechOpen CY - London ER - TY - CHAP A1 - Nierle, Elisabeth A1 - Pieper, Martin T1 - Measuring social impacts in engineering education to improve sustainability skills T2 - European Society for Engineering Education (SEFI) N2 - In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars. In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany. The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison. KW - Social impact measurement KW - Key competences KW - Sustainable engineering education KW - Future skills Y1 - 2023 U6 - https://doi.org/10.21427/QPR4-0T22 N1 - 51st Annual Conference of the European Society for Engineering Education, Technological University Dublin, 10th-14th September, 2023 N1 - Corresponding Author: Elisabeth Nierle ER - TY - CHAP A1 - Nikolovski, Gjorgji A1 - Limpert, Nicolas A1 - Nessau, Hendrik A1 - Reke, Michael A1 - Ferrein, Alexander T1 - Model-predictive control with parallelised optimisation for the navigation of autonomous mining vehicles T2 - 2023 IEEE Intelligent Vehicles Symposium (IV) N2 - The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment. KW - Mpc KW - Control KW - Path-following KW - Navigation KW - Automation Y1 - 2023 SN - 979-8-3503-4691-6 (Online) SN - 979-8-3503-4692-3 (Print) U6 - https://doi.org/10.1109/IV55152.2023.10186806 N1 - IEEE Symposium on Intelligent Vehicle, 4.-7. June 2023, Anchorage, AK, USA. PB - IEEE ER - 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 - https://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 - BOOK A1 - Nissen, Holger T1 - [Skripte] Y1 - 2008 N1 - Im Downloadbereich [Anmeldung erforderlich]: ER - TY - CHAP A1 - Nix, Yvonne A1 - Frotscher, Ralf A1 - Staat, Manfred ED - Eberhardsteiner, J. T1 - Implementation of the edge-based smoothed extended finite element method T2 - Proceedings 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012) Vienna, Austria, September 10-14, 2012 Y1 - 2012 ER - TY - JOUR A1 - Nobis, Moritz A1 - Schmitt, Carlo A1 - Schemm, Ralf A1 - Schnettler, Armin T1 - Pan-European CVAR-constrained stochastic unit commitment in day-ahead and intraday electricity markets JF - Energies N2 - The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources. Y1 - 2020 U6 - https://doi.org/10.3390/en13092339 SN - 1996-1073 N1 - Special Issue Uncertainties and Risk Management in Competitive Energy Markets VL - 13 IS - Art. 2339 SP - 1 EP - 35 PB - MDPI CY - Basel ER -