@inproceedings{16219, abstract = {{Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @inproceedings{16222, author = {{Zafeiropoulos, A. and Fotopoulou, E. and Peuster, Manuel and Schneider, Stefan Balthasar and Gouvas, P. and Behnke, D. and Müller, M. and Bök, P. and Trakadas, P. and Karkazis, P. and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, title = {{{Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case}}}, year = {{2020}}, } @inproceedings{16274, author = {{Jovanovikj, Ivan and Nagaraj, Achyuth and Yigitbas, Enes and Anjorin, Anthony and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST}}, location = {{Seoul}}, pages = {{31--40}}, publisher = {{ACM}}, title = {{{Validating Test Case Migration via Mutation Analysis }}}, year = {{2020}}, } @article{16278, abstract = {{Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA.}}, author = {{Nomikos, Nikolaos and Michailidis, Emmanouel T. and Trakadas, Panagiotis and Vouyioukas, Demosthenes and Karl, Holger and Martrat, Josep and Zahariadis, Theodore and Papadopoulos, Konstantinos and Voliotis, Stamatis}}, issn = {{2214-2096}}, journal = {{Vehicular Communications}}, title = {{{A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices}}}, doi = {{10.1016/j.vehcom.2020.100250}}, year = {{2020}}, } @article{16280, abstract = {{Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields.}}, author = {{Li, Feng and Yu, Dongxiao and Yang, Huan and Yu, Jiguo and Karl, Holger and Cheng, Xiuzhen}}, issn = {{1536-1284}}, journal = {{IEEE Wireless Communications}}, pages = {{24--30}}, title = {{{Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey}}}, doi = {{10.1109/mwc.001.1900280}}, year = {{2020}}, } @article{16288, abstract = {{We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.}}, author = {{Klus, Stefan and Nüske, Feliks and Peitz, Sebastian and Niemann, Jan-Hendrik and Clementi, Cecilia and Schütte, Christof}}, issn = {{0167-2789}}, journal = {{Physica D: Nonlinear Phenomena}}, title = {{{Data-driven approximation of the Koopman generator: Model reduction, system identification, and control}}}, doi = {{10.1016/j.physd.2020.132416}}, volume = {{406}}, year = {{2020}}, } @inbook{16289, abstract = {{In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented.}}, author = {{Peitz, Sebastian and Klus, Stefan}}, booktitle = {{Lecture Notes in Control and Information Sciences}}, isbn = {{9783030357122}}, issn = {{0170-8643}}, pages = {{257--282}}, publisher = {{Springer}}, title = {{{Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator}}}, doi = {{10.1007/978-3-030-35713-9_10}}, volume = {{484}}, year = {{2020}}, } @article{16290, abstract = {{The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.}}, author = {{Bieker, Katharina and Peitz, Sebastian and Brunton, Steven L. and Kutz, J. Nathan and Dellnitz, Michael}}, issn = {{0935-4964}}, journal = {{Theoretical and Computational Fluid Dynamics}}, pages = {{577–591}}, title = {{{Deep model predictive flow control with limited sensor data and online learning}}}, doi = {{10.1007/s00162-020-00520-4}}, volume = {{34}}, year = {{2020}}, } @article{16299, author = {{Castenow, Jannik and Fischer, Matthias and Harbig, Jonas and Jung, Daniel and Meyer auf der Heide, Friedhelm}}, issn = {{0304-3975}}, journal = {{Theoretical Computer Science}}, pages = {{289--309}}, title = {{{Gathering Anonymous, Oblivious Robots on a Grid}}}, doi = {{10.1016/j.tcs.2020.02.018}}, volume = {{815}}, year = {{2020}}, } @article{16309, abstract = {{In recent years, the success of the Koopman operator in dynamical systems analysis has also fueled the development of Koopman operator-based control frameworks. In order to preserve the relatively low data requirements for an approximation via Dynamic Mode Decomposition, a quantization approach was recently proposed in [Peitz & Klus, Automatica 106, 2019]. This way, control of nonlinear dynamical systems can be realized by means of switched systems techniques, using only a finite set of autonomous Koopman operator-based reduced models. These individual systems can be approximated very efficiently from data. The main idea is to transform a control system into a set of autonomous systems for which the optimal switching sequence has to be computed. In this article, we extend these results to continuous control inputs using relaxation. This way, we combine the advantages of the data efficiency of approximating a finite set of autonomous systems with continuous controls. We show that when using the Koopman generator, this relaxation --- realized by linear interpolation between two operators --- does not introduce any error for control affine systems. This allows us to control high-dimensional nonlinear systems using bilinear, low-dimensional surrogate models. The efficiency of the proposed approach is demonstrated using several examples with increasing complexity, from the Duffing oscillator to the chaotic fluidic pinball.}}, author = {{Peitz, Sebastian and Otto, Samuel E. and Rowley, Clarence W.}}, journal = {{SIAM Journal on Applied Dynamical Systems}}, number = {{3}}, pages = {{2162--2193}}, title = {{{Data-Driven Model Predictive Control using Interpolated Koopman Generators}}}, doi = {{10.1137/20M1325678}}, volume = {{19}}, year = {{2020}}, } @inproceedings{16363, author = {{Hansmeier, Tim and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{GECCO '20: Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-7127-8}}, location = {{Cancún, Mexico}}, pages = {{125--126}}, publisher = {{Association for Computing Machinery (ACM)}}, title = {{{Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold}}}, doi = {{10.1145/3377929.3389968}}, year = {{2020}}, } @inproceedings{16400, abstract = {{Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration.}}, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft) Demo Track}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Cloud-Native Threat Detection and Containment for Smart Manufacturing}}}, year = {{2020}}, } @inproceedings{13868, author = {{Pukrop, Simon and Mäcker, Alexander and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM)}}, title = {{{Approximating Weighted Completion Time for Order Scheduling with Setup Times}}}, year = {{2020}}, } @inbook{13108, abstract = {{Diagrammatisches Schlie{\ss}en wird im Zusammenhang mit dem Lernen von Mathmematik und ihrer Symbolsprache als wesentliche Theorie der Wissenskonstruktion diskutiert. Dabei wird h{\"{a}}ufig davon ausgegangen, dass die Wissenskonstruktion im Sinne diagrammatischen Schlie{\ss}ens erfolgt. Deskriptive Rekonstruktionen diagrammatischen Schlie{\ss}ens bei Lernenden stellen jedoch ein Desiderat der mathematikdidaktischen Forschung dar. Der vorliegende Beitrag befasst sich mit der Fragestellung, wie sich diagrammatisches Schlie{\ss}en bei Lernenden rekonstruieren l{\"{a}}sst. Als m{\"{o}}gliche Werkzeuge f{\"{u}}r eine solche Rekonstruktion werden Toulmins Argumentationsschema und Vergnauds Schema-Begriff exemplarisch angewandt, um das diagrammatische Schlie{\ss}en eines Sch{\"{u}}lerpaars beim Einstieg in die Subtraktion negativer Zahlen zu rekonstruieren. Abschlie{\ss}end wird die tats{\"{a}}chliche Eignung der beiden Ans{\"{a}}tze zur Rekonstruktion diagrammatischen Schlie{\ss}ens diskutiert.}}, author = {{Schumacher, Jan and Rezat, Sebastian}}, booktitle = {{Zeichen und Sprache im Mathematikunterricht}}, editor = {{Kadunz, Gert}}, publisher = {{Springer}}, title = {{{Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge}}}, doi = {{10.1007/978-3-662-61194-4_5}}, year = {{2020}}, } @article{10330, author = {{Kiesel, Dora and Riehmann, Patrick and Wachsmuth, Henning and Stein, Benno and Fröhlich, Bernd}}, journal = {{IEEE Transactions of Visualization & Computer Graphics}}, number = {{2}}, pages = {{1139--1148}}, title = {{{Visual Analysis of Argumentation in Essays}}}, volume = {{27}}, year = {{2020}}, } @article{21252, author = {{Traue, Arne and Book, Gerrit and Kirchgässner, Wilhelm and Wallscheid, Oliver}}, issn = {{2162-237X}}, journal = {{IEEE Transactions on Neural Networks and Learning Systems}}, pages = {{1--10}}, title = {{{Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control}}}, doi = {{10.1109/tnnls.2020.3029573}}, year = {{2020}}, } @inproceedings{29884, author = {{Böcker, Joachim}}, booktitle = {{2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)}}, publisher = {{IEEE}}, title = {{{Analysis of the Magnetic Skin Effekt in Motors and Inductors}}}, doi = {{10.1109/speedam48782.2020.9161895}}, year = {{2020}}, } @inproceedings{19422, author = {{Sprenger, Alexander and Sadeghi-Kohan, Somayeh and Reimer, Jan Dennis and Hellebrand, Sybille}}, booktitle = {{IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020}}, title = {{{Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study}}}, year = {{2020}}, } @article{21250, author = {{Kirchgässner, Wilhelm and Wallscheid, Oliver and Böcker, Joachim}}, issn = {{0885-8993}}, journal = {{IEEE Transactions on Power Electronics}}, number = {{7}}, pages = {{7480--7488}}, title = {{{Estimating Electric Motor Temperatures with Deep Residual Machine Learning}}}, doi = {{10.1109/tpel.2020.3045596}}, volume = {{36}}, year = {{2020}}, } @inproceedings{29939, abstract = {{In this paper, a full-bridge modular multilevel converter (MMC) and two half-bridge-based MMCs are evaluated for high-current low-voltage e.g. 100 - 400V DC-applications such as electrolysis, arc welding or datacenters with DC-power distribution. Usually, modular multilevel converters are used in high-voltage DC-applications (HVDC) in the multiple kV-range, but to meet the needs of a high-current demand at low output voltage levels, the modular converter concept requires adaptations. In the proposed concept, the MMC is used to step-down the three-phase medium-voltage of 10kV, and provide up to 1 MW to the load. Therefore, each module is extended by an LLC resonant converter to adapt to the specific electrolyzers DC-voltage range of 142 - 220V and to provide galvanic isolation. The six-arm MMC converter with half-bridge modules can be simplified and optimized by removing three arms, and thus halving the number of modules. In addition, the module voltage ripple and capacitor losses are decreased by 22% and 30% respectively. By rearranging the components of the half-bridge MMC to build a MMC consisting of grid-side full-bridge modules, the voltage ripple is further reduced by 78% and capacitor losses by 64%, while ensuring identical costs and volume for all MMCs. Finally, the LLC resonant converter is designed for the most efficient full-bridge MMC. The LLC can not operate at resonance with a fixed nominal module voltage of 770V because the output voltage is varying between 142 - 220V. By decreasing the module voltage down to 600V, additional points of operation can be operated in resonance, and the remaining are closer to resonance. The option to decrease the module voltage down to 600V, increases the number of required modules per arm from 12 to 15, which requires to balance the losses of the LLCs and the grid-side stages.}}, author = {{Unruh, Roland and Schafmeister, Frank and Böcker, Joachim}}, booktitle = {{2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe)}}, keywords = {{Multilevel converters, Resonant converter, High voltage power converters, ZVS Converters, Combination MMC LLC}}, location = {{Lyon, France}}, publisher = {{IEEE}}, title = {{{Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design}}}, doi = {{10.23919/epe20ecceeurope43536.2020.9215687}}, year = {{2020}}, }