@article{22656, author = {{Julin, S and Korpi, A and Shen, B and Liljeström, V and Ikkala, O and Keller, Adrian and Linko, V and Kostiainen, MA}}, issn = {{2040-3364}}, journal = {{Nanoscale}}, number = {{10}}, pages = {{4546--4551}}, title = {{{DNA origami directed 3D nanoparticle superlattice via electrostatic assembly.}}}, doi = {{10.1039/c8nr09844a}}, volume = {{11}}, year = {{2019}}, } @inproceedings{22749, author = {{Wortmann, Fabio and Ellermann, Kai Fabian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Vorausschau und Technologieplanung, 15. Symposium für Vorausschau und Technologieplanung, 21.-22. November 2019, Berlin}}, editor = {{Gausemeier, Jürgen and Bauer, Wilhelm and Dumitrescu, Roman}}, location = {{Berlin}}, publisher = {{Heinz Nixdorf Institut}}, title = {{{Typisierung und Strukturierung digitaler Plattformen im Kontext Business-to-Business}}}, year = {{2019}}, } @inproceedings{22970, author = {{Biemelt, Patrick and Mertin, Sven and Rüddenklau, Nico and Gausemeier, Sandra and Trächtler, Ansgar}}, booktitle = {{Proceedings of the International Conference on Advances in System Simulation (SIMUL)}}, location = {{Valencia, Spain}}, publisher = {{IARIA}}, title = {{{Objective Evaluation of a Novel Filter-Based Motion Cueing Algorithm in Comparison to Optimization-Based Control in Interactive Driving Simulation}}}, year = {{2019}}, } @article{10593, abstract = {{We present a new framework for optimal and feedback control of PDEs using Koopman operator-based reduced order models (K-ROMs). The Koopman operator is a linear but infinite-dimensional operator which describes the dynamics of observables. A numerical approximation of the Koopman operator therefore yields a linear system for the observation of an autonomous dynamical system. In our approach, by introducing a finite number of constant controls, the dynamic control system is transformed into a set of autonomous systems and the corresponding optimal control problem into a switching time optimization problem. This allows us to replace each of these systems by a K-ROM which can be solved orders of magnitude faster. By this approach, a nonlinear infinite-dimensional control problem is transformed into a low-dimensional linear problem. Using a recent convergence result for the numerical approximation via Extended Dynamic Mode Decomposition (EDMD), we show that the value of the K-ROM based objective function converges in measure to the value of the full objective function. To illustrate the results, we consider the 1D Burgers equation and the 2D Navier–Stokes equations. The numerical experiments show remarkable performance concerning both solution times and accuracy.}}, author = {{Peitz, Sebastian and Klus, Stefan}}, issn = {{0005-1098}}, journal = {{Automatica}}, pages = {{184--191}}, title = {{{Koopman operator-based model reduction for switched-system control of PDEs}}}, doi = {{10.1016/j.automatica.2019.05.016}}, volume = {{106}}, year = {{2019}}, } @article{10595, abstract = {{In this article we show that the boundary of the Pareto critical set of an unconstrained multiobjective optimization problem (MOP) consists of Pareto critical points of subproblems where only a subset of the set of objective functions is taken into account. If the Pareto critical set is completely described by its boundary (e.g., if we have more objective functions than dimensions in decision space), then this can be used to efficiently solve the MOP by solving a number of MOPs with fewer objective functions. If this is not the case, the results can still give insight into the structure of the Pareto critical set.}}, author = {{Gebken, Bennet and Peitz, Sebastian and Dellnitz, Michael}}, issn = {{0925-5001}}, journal = {{Journal of Global Optimization}}, number = {{4}}, pages = {{891--913}}, title = {{{On the hierarchical structure of Pareto critical sets}}}, doi = {{10.1007/s10898-019-00737-6}}, volume = {{73}}, year = {{2019}}, } @inproceedings{12875, abstract = {{Signal dereverberation using the Weighted Prediction Error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani, Tomohiro}}, booktitle = {{ICASSP 2019, Brighton, UK}}, title = {{{Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR}}}, year = {{2019}}, } @inproceedings{15422, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{World Congress on Nature and Biologically Inspired Computing (NaBIC)}}, publisher = {{Springer}}, title = {{{Optimization of Application-specific L1 Cache Translation Functions of the LEON3 Processor}}}, year = {{2019}}, } @proceedings{14829, editor = {{Scheideler, Christian and Berenbrink, Petra}}, isbn = {{978-1-4503-6184-2}}, publisher = {{ACM}}, title = {{{The 31st ACM Symposium on Parallelism in Algorithms and Architectures, SPAA 2019, Phoenix, AZ, USA, June 22-24, 2019}}}, doi = {{10.1145/3323165}}, year = {{2019}}, } @inbook{14890, author = {{Kuhlemann, Stefan and Sellmann, Meinolf and Tierney, Kevin}}, booktitle = {{Lecture Notes in Computer Science}}, isbn = {{9783030300470}}, issn = {{0302-9743}}, title = {{{Exploiting Counterfactuals for Scalable Stochastic Optimization}}}, doi = {{10.1007/978-3-030-30048-7_40}}, year = {{2019}}, } @techreport{14902, author = {{Mair, Christina and Scheffler, Wolfram and Senger, Isabell and Sureth-Sloane, Caren}}, title = {{{Analyse der Veränderung der zwischenstaatlichen Gewinnaufteilung bei Einführung einer standardisierten Gewinnverteilungsmethode am Beispiel des Einsatzes von 3D-Druckern}}}, volume = {{42}}, year = {{2019}}, } @unpublished{16341, abstract = {{We present a technique for rendering highly complex 3D scenes in real-time by generating uniformly distributed points on the scene's visible surfaces. The technique is applicable to a wide range of scene types, like scenes directly based on complex and detailed CAD data consisting of billions of polygons (in contrast to scenes handcrafted solely for visualization). This allows to visualize such scenes smoothly even in VR on a HMD with good image quality, while maintaining the necessary frame-rates. In contrast to other point based rendering methods, we place points in an approximated blue noise distribution only on visible surfaces and store them in a highly GPU efficient data structure, allowing to progressively refine the number of rendered points to maximize the image quality for a given target frame rate. Our evaluation shows that scenes consisting of a high amount of polygons can be rendered with interactive frame rates with good visual quality on standard hardware.}}, author = {{Brandt, Sascha and Jähn, Claudius and Fischer, Matthias and Meyer auf der Heide, Friedhelm}}, booktitle = {{arXiv:1904.08225}}, title = {{{Rendering of Complex Heterogenous Scenes using Progressive Blue Surfels}}}, year = {{2019}}, } @inproceedings{13123, abstract = {{Given the recent development in embedded devices, wireless senor nodes are no longer limited to data collection but they can also do processing (e.g., smartphones). Accordingly, new types of applications take an advantage of the processing and flexibility provided by the wireless network. A common property between these applications is that the processing is not running on only one single node, but it is broken-down into smaller tasks that can run over multiple nodes, i.e., exploiting the in-network processing. We study a special variant of in-network processing, where the application is given by a graph; the processing tasks have predefined connections to be executed in a predefined sequence. The problem of embedding an application graph into a network is commonly known as Virtual Network Embedding (VNE). In this paper, we present a Genetic Algorithm (GA) solution to solve this wireless VNE problem, where we take into account the interference and multi-cast properties. We show that the GA has a good performance and fast execution compared to the optimization problem.}}, author = {{Afifi, Haitham and Horbach, Konrad and Karl, Holger}}, booktitle = {{2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (WiMob 2019)}}, title = {{{A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding}}}, year = {{2019}}, } @inproceedings{13250, author = {{Ansótegui, Carlos and Heymann, Britta and Pon, Josep and Sellmann, Meinolf and Tierney, Kevin}}, booktitle = {{Learning and Intelligent Optimization}}, isbn = {{978-3-030-05347-5}}, pages = {{309--325}}, publisher = {{Springer International Publishing}}, title = {{{Hyper-Reactive Tabu Search for MaxSAT}}}, doi = {{10.1007/978-3-030-05348-2_27}}, year = {{2019}}, } @article{13434, author = {{Mäck, Markus and Caylak, Ismail and Edler, Philipp and Freitag, Steffen and Hanss, Michael and Mahnken, Rolf and Meschke, Günther and Penner, Eduard}}, issn = {{0936-7195}}, journal = {{GAMM-Mitteilungen}}, title = {{{"Optimization with constraints considering polymorphic uncertainties"}}}, doi = {{10.1002/gamm.201900005}}, year = {{2019}}, } @inproceedings{13442, author = {{Manavi, Seyed Alborz and Kenig, Eugeny}}, booktitle = {{Computer Aided Chemical Engineering}}, isbn = {{9780128186343}}, issn = {{1570-7946}}, publisher = {{29th European Symposium on Computer Aided Process Engineering}}, title = {{{Numerical Simulation of Forced Convection in a Microchannel with Realistic Roughness of 3D Printed Surface}}}, doi = {{10.1016/b978-0-12-818634-3.50138-7}}, year = {{2019}}, } @inproceedings{13443, abstract = {{This work considers the problem of control and resource allocation in networked systems. To this end, we present DIRA a Deep reinforcement learning based Iterative Resource Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards large-scale problems where control and scheduling need to act jointly to optimize performance. DIRA can be used to schedule general time-domain optimization based controllers. In the present work, we focus on control designs based on suitably adapted linear quadratic regulators. We apply our algorithm to networked systems with correlated fading communication channels. Our simulations show that DIRA scales well to large scheduling problems.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Quevedo, Daniel}}, booktitle = {{Proceedings of the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems}}, keywords = {{Networked control systems, deep reinforcement learning, large-scale systems, resource scheduling, stochastic control}}, location = {{Chicago, USA}}, title = {{{Deep reinforcement learning for scheduling in large-scale networked control systems}}}, year = {{2019}}, } @inproceedings{10093, author = {{Beyer, Dirk and Jakobs, Marie-Christine and Lemberger, Thomas and Wehrheim, Heike}}, booktitle = {{Software Engineering and Software Management (SE/SWM 2019), Stuttgart, Germany, February 18-22, 2019}}, editor = {{Becker, Steffen and Bogicevic, Ivan and Herzwurm, Georg and Wagner, Stefan}}, pages = {{151----152}}, publisher = {{GI}}, title = {{{Combining Verifiers in Conditional Model Checking via Reducers}}}, doi = {{10.18420/se2019-46}}, volume = {{P-292}}, year = {{2019}}, } @inproceedings{10094, author = {{Sharma, Arnab and Wehrheim, Heike}}, booktitle = {{Software Engineering and Software Management, {SE/SWM} 2019, Stuttgart, Germany, February 18-22, 2019}}, editor = {{Becker, Steffen and Bogicevic, Ivan and Herzwurm, Georg and Wagner, Stefan}}, pages = {{157--158}}, publisher = {{{GI}}}, title = {{{Testing Balancedness of ML Algorithms}}}, doi = {{10.18420/se2019-48}}, volume = {{{P-292}}}, year = {{2019}}, } @inproceedings{30002, abstract = {{Utilisation of SiC semiconductors' fast switching speeds and high switching frequencies as a consequent are often in discussion. But which switching frequency is really optimal in terms of converter volume, losses and costs? Based on the example of a buck converter, this question is investigated, and a tool for loss calculation and design is described in this paper. A Pareto optimization of the converter is performed where the switching frequency is one of several design parameters. The buck converter can be realized by multiple rails interleaved, and several switches can be placed in parallel. Considered converter modes are continuous conduction mode, that allows hard-switching, ZVS, and incomplete ZVS depending on the switching frequency. Based on Pareto optimizations, a design is selected, and a laboratory sample of 5.5 kW for application in an EV battery charger is built up. Efficiencies of 99.5 % are achieved with switching frequencies of around 100 kHz.}}, author = {{Strothmann, Benjamin and Schafmeister, Frank and Böcker, Joachim}}, booktitle = {{2019 IEEE Applied Power Electronics Conference and Exposition (APEC)}}, publisher = {{IEEE}}, title = {{{Pareto Design and Switching Frequencies for SiC MOSFETs Applied in an 11 kW Buck Converter for EV-Charging}}}, doi = {{10.1109/apec.2019.8721850}}, year = {{2019}}, } @inproceedings{30334, author = {{Keuck, Lukas and Schafmeister, Frank and Böcker, Joachim}}, booktitle = {{Proc. 34th IEEE Applied Power Electronics Conference (APEC)}}, location = {{Anaheim, CA, USA}}, pages = {{1415 -- 1422}}, publisher = {{IEEE}}, title = {{{Computer-Aided Design and Optimization of an Integrated Transformer with Distributed Air Gap and Leakage Path for an LLC Resonant Converter}}}, year = {{2019}}, }