On the Development of Context-aware Augmented Reality Applications - Yigitbas, Enes; Jovanovikj, Ivan; Sauer, Stefan; Engels, Gregor (Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes - IFIP WG 13.2/13.5)

Die „Logik“ der Überzeugungskraft - Krimphove, Dieter (Rechtstheorie)

National culture and incentives: Are incentive practices always good? - Prince, Nicholas R.; Bruce Prince, J.; Kabst, Rüdiger (Journal of World Business)

How to Build Trust on Peer-to-Peer Platforms: An Investigation of the Antecedents of Peer and Platform Trust - Purrmann, Maren; Wünderlich, Nancy (Proceedings of the 2020 AMA Winter Academic Conference)

Nonlinear Wavefront Control by Geometric-Phase Dielectric Metasurfaces: Influence of Mode Field and Rotational Symmetry - Liu, Bingyi; Sain, Basudeb; Reineke, Bernhard; Zhao, Ruizhe; Meier, Cedrik; Huang, Lingling; Jiang, Yongyuan; Zentgraf, Thomas (Advanced Optical Materials)
Nonlinear Pancharatnam–Berry phase metasurfaces facilitate the nontrivial phase modulation for frequency conversion processes by leveraging photon‐spin dependent nonlinear geometric‐phases. However, plasmonic metasurfaces show some severe limitation for nonlinear frequency conversion due to the intrinsic high ohmic loss and low damage threshold of plasmonic nanostructures. Here, the nonlinear geometric‐phases associated with the third‐harmonic generation process occurring in all‐dielectric metasurfaces is studied systematically, which are composed of silicon nanofins with different in‐plane rotational symmetries. It is found that the wave coupling among different field components of the resonant fundamental field gives rise to the appearance of different nonlinear geometric‐phases of the generated third‐harmonic signals. The experimental observations of the nonlinear beam steering and nonlinear holography realized in this work by all‐dielectric geometric‐phase metasurfaces are well explained with the developed theory. This work offers a new physical picture to understand the nonlinear optical process occurring at nanoscale dielectric resonators and will help in the design of nonlinear metasurfaces with tailored phase properties.

Beschreibung der Rektifikation viskoser Systeme mit Hydrodynamischen Analogien - Bolenz, Lukas; Gutperl, Sven; Toye, Dominique; Scholl, Stephan; Kenig, Eugeny

Simulation und Bewertung eines Anlagenkonzepts für Trennleistungsmessungen mit nicht-verdampfenden Komponenten - Gutperl, Sven; Bolenz, Lukas; Kriese, Friederike; Jasch, Katharina; Kenig, Eugeny; Scholl, Stephan

Stofftransportmessung am ruhenden und bewegten Einzeltropfen - Heine, Jens; Wecker, Christian; Kenig, Eugeny; Bart, Hans-Joerg

CFD-basierte Untersuchung stofftransportinduzierter Marangoni-konvektion in Flüssig-Flüssig-Systemen - Wecker, Christian; Schulz, Andreas; Heine, Jens; Bart, Hans-Joerg; Kenig, Eugeny

A Hybrid Synthesis Methodology for Approximate Circuits - Awais, Muhammad; Ghasemzadeh Mohammadi, Hassan; Platzner, Marco (Proceedings of the 30th ACM Great Lakes Symposium on VLSI (GLSVLSI) 2020)
Automated synthesis of approximate circuits via functional approximations is of prominent importance to provide efficiency in energy, runtime, and chip area required to execute an application. Approximate circuits are usually obtained either through analytical approximation methods leveraging approximate transformations such as bit-width scaling or via iterative search-based optimization methods when a library of approximate components, e.g., approximate adders and multipliers, is available. For the latter, exploring the extremely large design space is challenging in terms of both computations and quality of results. While the combination of both methods can create more room for further approximations, the Design Space Exploration~(DSE) becomes a crucial issue. In this paper, we present such a hybrid synthesis methodology that applies a low-cost analytical method followed by parallel stochastic search-based optimization. We address the DSE challenge through efficient pruning of the design space and skipping unnecessary expensive testing and/or verification steps. The experimental results reveal up to 10.57x area savings in comparison with both purely analytical or search-based approaches.

5GOS: Demonstrating multi-domain orchestration of end-to-end virtual RAN services - Camps-Mur, Daniel; Canellas, Ferran; Machwe, Azahar; Paracuellos, Jorge; Choumas, Kostas; Giatsios, Dimitris; Korakis, Thanasis; Razzaghi Kouchaksaraei, Hadi (the 6th IEEE International Conference on Network Softwarization (IEEE NetSoft 2020))
Despite recent progress in orchestration of Virtual Network Functions (VNFs) and in multi-technology SDN connectivity, the automated provisioning of end-to-end network services composed of virtual functions deployed across distributed compute locations remains an open challenge. This problem is especially relevant to support the deployment of future 5G networks, comprising virtual access and core network functions connected through a potentially multi-domain transport network. In this paper, we present and demonstrate the 5GOS, a lightweight end-to-end orchestration framework that enables the automated provisioning of virtual radio access network services. Using an experimental multi-domain testbed we demonstrate that the 5GOS can provision multi-domain virtual Wi-Fi and LTE services in less than three minutes.
Keywords: Orchestration; multi-domain; cellular network virtualization; SDN; 5G

Machine Learning for Dynamic Resource Allocation in Network Function Virtualization - Schneider, Stefan Balthasar; Satheeschandran, Narayanan Puthenpurayil; Peuster, Manuel; Karl, Holger (IEEE Conference on Network Softwarization (NetSoft))
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.

Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case - Zafeiropoulos, A.; Fotopoulou, E.; Peuster, Manuel; Schneider, Stefan Balthasar; Gouvas, P.; Behnke, D.; Müller, M.; Bök, P.; Trakadas, P.; Karkazis, P.; Karl, Holger (IEEE Conference on Network Softwarization (NetSoft))

Numerische Simulation von Gravidestillationsapparaten mit Metallschäumen - Wende, Marc; Kenig, Eugeny (Jahrestreffen der ProcessNet-Fachgruppen Fluidverfahrenstechnik, Adsorption und Extraktion)

Timing in Information Security: An Event Study on the Impact of Information Security Investment Announcements - Szubartowicz, Eva; Schryen, Guido (Journal of Information System Security)
Timing plays a crucial role in the context of information security investments. We regard timing in two dimensions, namely the time of announcement in relation to the time of investment and the time of announcement in relation to the time of a fundamental security incident. The financial value of information security investments is assessed by examining the relationship between the investment announcements and their stock market reaction focusing on the two time dimensions. Using an event study methodology, we found that both dimensions influence the stock market return of the investing organization. Our results indicate that (1) after fundamental security incidents in a given industry, the stock price will react more positively to a firm’s announcement of actual information security investments than to announcements of the intention to invest; (2) the stock price will react more positively to a firm’s announcements of the intention to invest after the fundamental security incident compared to before; and (3) the stock price will react more positively to a firm’s announcements of actual information security investments after the fundamental security incident compared to before. Overall, the lowest abnormal return can be expected when the intention to invest is announced before a fundamental information security incident and the highest return when actual investing after a fundamental information security incident in the respective industry.
Keywords: Event Study; Information Security; Investment Announcements; Stock Price Reaction; Value of Information Security Investments

Gender, competitiveness, and task difficulty: Evidence from the field - Hoyer, Britta; van Huizen, Thomas; Keijzer, Linda; Rezaei, Sarah; Rosenkranz, Stephanie; Westbrock, Bastian (Labour Economics)
This study examines the gender gap in competitiveness in an educational setting and tests whether this gap depends on the difficulty of the task at hand. For this purpose, we administered a series of experiments during the final exam of a university course. We confronted three cohorts of undergraduate students with a set of bonus questions and the choice between an absolute and a tournament grading scheme for these questions. To test the moderating impact of task difficulty, we (randomly) varied the difficulty of the questions between treatment groups. We find that, on average, women are significantly less likely to select the tournament scheme. However, the results show that the gender gap in tournament entry is sizable when the questions are relative easy, but much smaller and statistical insignificant when the questions are difficult.

Validating Test Case Migration via Mutation Analysis - Jovanovikj, Ivan; Nagaraj, Achyuth; Yigitbas, Enes; Anjorin, Anthony; Sauer, Stefan; Engels, Gregor (Proceedings of the 1st IEEE/ACM International Conference on Automation of Software Test AST)

A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices - Nomikos, Nikolaos; Michailidis, Emmanouel T.; Trakadas, Panagiotis; Vouyioukas, Demosthenes; Karl, Holger; Martrat, Josep; Zahariadis, Theodore; Papadopoulos, Konstantinos; Voliotis, Stamatis (Vehicular Communications)
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.

Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey - Li, Feng; Yu, Dongxiao; Yang, Huan; Yu, Jiguo; Karl, Holger; Cheng, Xiuzhen (IEEE Wireless Communications)
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.

Designing City Center Area Recommendation Systems - zur Heiden, Philipp; Berendes, Carsten Ingo; Beverungen, Daniel (Proceedings of the 15th International Conference on Wirtschaftsinformatik)
To decide in which part of town to open stores, high street retailers consult statistical data on customers and cities, but they cannot analyze their customers’ shopping behavior and geospatial features of a city due to missing data. While previous research has proposed recommendation systems and decision aids that address this type of decision problem – including factory location and assortment planning – there currently is no design knowledge available to prescribe the design of city center area recommendation systems (CCARS). We set out to design a software prototype considering local customers’ shopping interests and geospatial data on their shopping trips for retail site selection. With real data on 500 customers and 1,100 shopping trips, we demonstrate and evaluate our IT artifact. Our results illustrate how retailers and public town center managers can use CCARS for spatial location selection, growing retailers’ profits and a city center’s attractiveness for its citizens.
Keywords: Town Center Management; High Street Retail; Recommender Systems; Geospatial Recommendations; Design Science Research

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control - Klus, Stefan; Nüske, Feliks; Peitz, Sebastian; Niemann, Jan-Hendrik; Clementi, Cecilia; Schütte, Christof (Physica D: Nonlinear Phenomena)
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.

Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator - Peitz, Sebastian; Klus, Stefan (Lecture Notes in Control and Information Sciences)
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.

Deep model predictive flow control with limited sensor data and online learning - Bieker, Katharina; Peitz, Sebastian; Brunton, Steven L.; Kutz, J. Nathan; Dellnitz, Michael (Theoretical and Computational Fluid Dynamics)
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.

Gathering Anonymous, Oblivious Robots on a Grid - Castenow, Jannik; Fischer, Matthias; Harbig, Jonas; Jung, Daniel; Meyer auf der Heide, Friedhelm (Theoretical Computer Science)

Transformation of Actors’ Roles in Service Systems: A Multi-Level Analysis - Wolf, Verena; Bartelheimer, Christian (16th International Research Conference in Service Management)

Numerische Untersuchungen des Stofftransports in Flüssig-Flüssig-Systemen unter Berücksichtigung der Marangonikonvektion - Wecker, Christian; Hoppe, Anna; Schulz, Andreas; Heine, Jens; Bart, Hans-Jörg; Kenig, Eugeny

Ein PLIC-basierter Ansatz zur Erfassung des Stoffübergangs an bewegten Phasengrenzflächen - Schulz, Andreas; Wecker, Christian; Kenig, Eugeny

Data-Driven Model Predictive Control using Interpolated Koopman Generators - Peitz, Sebastian; Otto, Samuel E.; Rowley, Clarence W. (SIAM Journal on Applied Dynamical Systems)
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.

Matching Strategies of Heterogeneous Agents under Incomplete Information in a University Clearinghouse - Hoyer, Britta; Stroh-Maraun, Nadja (Games and Economic Behavior)
We analyze the actual behavior of agents in a matching mechanism, using data from a clearinghouse at the Faculty of Business Administration and Economics at a German university, where a variant of the Boston mechanism is used. We supplement this data with data generated in a survey among the students who participated in the clearinghouse. We find that under the current mechanism over 74% of students act strategically by misrepresenting at least one of their preferences. Nevertheless, not all students are able to improve their outcome by doing so. We show that this is mainly due to the incomplete information of students and naiveté. Sophisticated students actually reach significantly better outcomes than naive students. Thus, we find evidence that naive students are exploited by sophisticated students in an incomplete information setting.

Convex Hull Formation for Programmable Matter - Daymude, Joshua J.; Gmyr, Robert; Hinnenthal, Kristian; Kostitsyna, Irina; Scheideler, Christian; Richa, Andréa W. (Proceedings of the 21st International Conference on Distributed Computing and Networking)

Enabling XCSF to Cope with Dynamic Environments via an Adaptive Error Threshold - Hansmeier, Tim; Kaufmann, Paul; Platzner, Marco (GECCO '20: Proceedings of the Genetic and Evolutionary Computation Conference Companion)

Integrating Haptic Signals with V2X-based Safety Systems for Vulnerable Road Users - Oczko, Marie-Christin H.; Stratmann, Lukas; Franke, Mario; Heinovski, Julian; Buse, Dominik S.; Klingler, Florian; Dressler, Falko (2020 International Conference on Computing, Networking and Communications (ICNC))

Cloud-Native Threat Detection and Containment for Smart Manufacturing - Müller, Marcel; Behnke, Daniel; Bök, Patrick-Benjamin; Schneider, Stefan Balthasar; Peuster, Manuel; Karl, Holger (IEEE Conference on Network Softwarization (NetSoft) Demo Track)
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.

Approximating Weighted Completion Time for Order Scheduling with Setup Times - Pukrop, Simon; Mäcker, Alexander; Meyer auf der Heide, Friedhelm (Proceedings of the 46th International Conference on Current Trends in Theory and Practice of Computer Science (SOFSEM))

Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge - Schumacher, Jan; Rezat, Sebastian (Zeichen und Sprache im Mathematikunterricht)
Diagrammatisches Schließen wird im Zusammenhang mit dem Lernen von Mathmematik und ihrer Symbolsprache als wesentliche Theorie der Wissenskonstruktion diskutiert. Dabei wird häufig davon ausgegangen, dass die Wissenskonstruktion im Sinne diagrammatischen Schließens erfolgt. Deskriptive Rekonstruktionen diagrammatischen Schließens bei Lernenden stellen jedoch ein Desiderat der mathematikdidaktischen Forschung dar. Der vorliegende Beitrag befasst sich mit der Fragestellung, wie sich diagrammatisches Schließen bei Lernenden rekonstruieren lässt. Als mögliche Werkzeuge für eine solche Rekonstruktion werden Toulmins Argumentationsschema und Vergnauds Schema-Begriff exemplarisch angewandt, um das diagrammatische Schließen eines Schülerpaars beim Einstieg in die Subtraktion negativer Zahlen zu rekonstruieren. Abschließend wird die tatsächliche Eignung der beiden Ansätze zur Rekonstruktion diagrammatischen Schließens diskutiert.

Visual Analysis of Argumentation in Essays - Kiesel, Dora; Riehmann, Patrick; Wachsmuth, Henning; Stein, Benno; Fröhlich, Bernd (IEEE Transactions of Visualization & Computer Graphics)

Criteria as a prelude for guiding taxonomy evaluation - Szopinski, Daniel; Schoormann, Thorsten; Kundisch, Dennis (Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS))

Network formation with NIMBY constraints - Block, Lukas
We study the structure of power networks in consideration of local protests against certain power lines (’not-in-my-backyard’). An application of a network formation game is used to determine whether or not such protests arise. We examine the existence of stable networks and their characteristics, when no player wants to make an alteration. Stability within this game is only reached if each player is sufficiently connected to a power source but is not linked to more players than necessary. In addition we introduce an algorithm that creates a stable network.
Keywords: Network formation; NIMBY; Power networks; Nash stability

Time efficient laser modification of steel surfaces for advanced bonding in hybrid materials - Voswinkel, D.; Kloidt, D.; Grydin, O.; Schaper, M. (Production Engineering)
Abstract Laser surface treatment of metals is one option to improve their properties for adhesive bonding. In this paper, a pulsed YVO4 Laser source with a wavelength of 1064 nm and a maximum power of 25 W was utilized to increase the surface area of the steel HCT490X in order to improve its bonding properties with a carbon fibre reinforced polymer (CFRP). Investigated was the influence of the scanning speed of the laser source on the bonding properties. For this purpose, the steel surfaces were ablated at a scanning speed between 1500 and 4500 mm/s. Afterwards the components were bonded with the adhesive HexBond™ 677. After lap shear tests were carried out on the specimen, the surfaces were inspected using scanning electron microscopy (SEM). The experiments revealed that the bonding quality can be improved with a high scanning speed, even when the surface is not completely ablated.

Malink, Marko/ Vasudevan, Anubav, "The Peripatetic Program in Categorial Logic: Leibniz on Propositional Terms" (The Review of Symbolic Logic 13) - Peckhaus, Volker (Zentralblatt für Mathematik und ihre Grenzgebiete [Zbl. 145.03014])

Influence of Part Microstructure on Mechanical Properties of PA6X Laser Sintered Specimens - Kummert, Chrstina; Diekmann, Wolfgang; Tews, Karina; Schmid, Hans-Joachim (29th Annual International Solid Freeform Fabrication Symposium)

Toward a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control - Traue, Arne; Book, Gerrit; Kirchgässner, Wilhelm; Wallscheid, Oliver (IEEE Transactions on Neural Networks and Learning Systems)

Analysis of the Magnetic Skin Effekt in Motors and Inductors - Böcker, Joachim (2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM))

Variation-Aware Test for Logic Interconnects using Neural Networks - A Case Study - Sprenger, Alexander; Sadeghi-Kohan, Somayeh; Reimer, Jan Dennis; Hellebrand, Sybille (IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT’20), October 2020)

Estimating Electric Motor Temperatures with Deep Residual Machine Learning - Kirchgässner, Wilhelm; Wallscheid, Oliver; Böcker, Joachim (IEEE Transactions on Power Electronics)

Evaluation of MMCs for High-Power Low-Voltage DC-Applications in Combination with the Module LLC-Design - Unruh, Roland; Schafmeister, Frank; Böcker, Joachim (2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe))
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.
Keywords: Multilevel converters; Resonant converter; High voltage power converters; ZVS Converters; Combination MMC LLC

Dual Interleaved 3.6 kW LLC Converter Operating in Half-Bridge, Full-Bridge and Phase-Shift Mode as a Single-Stage Architecture of an Automotive On-Board DC-DC Converter - Rehlaender, Philipp; Tikhonov, Sergey; Schafmeister, Frank; Böcker, Joachim (2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe))

Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors - Brosch, Anian; Hanke, Sören; Wallscheid, Oliver; Böcker, Joachim (IEEE Transactions on Power Electronics)

Emulation of Microgrids for Research and Validation of Control and Operation Strategies - Stille, Karl Stephan Christian; Weber, Daniel; Lange, Jarren; Vogt, Thorsten; Wallscheid, Oliver; Böcker, Joachim (2020 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM))

A 3,6 kW Single-Stage LLC Converter Operating in Half-Bridge, Full-Bridge and Phase-Shift Mode for Automotive Onboard DC-DC Conversion - Rehlaender, Philipp; Grote, Tobias; Tikhonov, Sergey; Mario, Schröder; Schafmeister, Frank; Böcker, Joachim (PCIM Europe digital days 2020)
Automotive DC-DC converters linking the traction battery to the auxiliary battery are characterized by the wide input and output voltage ranges resulting from the varying state-of-charge of the traction and auxiliary battery. The wide voltage transfer ratio needs to be covered for the entire load range conventionally requiring two-stage converter architectures. Considering a less complex single-stage solution potentially enabling cost and weight advantages, traditional LLC converters are unsuitable topologies since it results in a too wide operating frequency range. Most alternative topology candidates show comparable difficulties. To overcome this issue, the gain range of the LLC with full-bridge inverter can be extended by operation in half-bridge mode for low voltage transfer ratios. Phase-shift operation is utilized for intermediate gains and low loads. This paper describes a detailed design methodology for the resonant tank. The experimental results with a peak efficiency of 96.5 % and a power density of 2.1 kW/l prove the proposed concept.