@inproceedings{70, author = {{Feldkord, Björn and Markarian, Christine and Meyer auf der Heide, Friedhelm}}, booktitle = {{Proceedings of the 11th Annual International Conference on Combinatorial Optimization and Applications (COCOA)}}, pages = {{17 -- 31}}, title = {{{Price Fluctuations in Online Leasing}}}, doi = {{10.1007/978-3-319-71147-8_2}}, year = {{2017}}, } @article{706, author = {{Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm and Riechers, Sören}}, journal = {{Journal of Combinatorial Optimization}}, number = {{4}}, pages = {{1168--1194}}, publisher = {{Springer}}, title = {{{Cost-efficient Scheduling on Machines from the Cloud}}}, doi = {{10.1007/s10878-017-0198-x}}, volume = {{36}}, year = {{2017}}, } @inproceedings{87, abstract = {{Management of complex network services requires flexible and efficient service provisioning as well as optimized handling of continuous changes in the workload of the service.To adapt to changes in the demand, service components need to be replicated (scaling) and allocated to physical resources (placement) dynamically. In this paper, we propose a fullyautomated approach to the joint optimization problem of scaling and placement, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability andeffectiveness of the proposed approach.}}, author = {{Dräxler, Sevil and Karl, Holger and Mann, Zoltan Adam}}, booktitle = {{Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)}}, title = {{{Joint Optimization of Scaling and Placement of Virtual Network Services}}}, doi = {{10.1109/CCGRID.2017.25}}, year = {{2017}}, } @inproceedings{8752, abstract = {{In this article we develop a gradient-based algorithm for the solution of multiobjective optimization problems with uncertainties. To this end, an additional condition is derived for the descent direction in order to account for inaccuracies in the gradients and then incorporated into a subdivision algorithm for the computation of global solutions to multiobjective optimization problems. Convergence to a superset of the Pareto set is proved and an upper bound for the maximal distance to the set of substationary points is given. Besides the applicability to problems with uncertainties, the algorithm is developed with the intention to use it in combination with model order reduction techniques in order to efficiently solve PDE-constrained multiobjective optimization problems.}}, author = {{Peitz, Sebastian and Dellnitz, Michael}}, booktitle = {{NEO 2016}}, isbn = {{9783319640624}}, issn = {{1860-949X}}, pages = {{159--182}}, title = {{{Gradient-Based Multiobjective Optimization with Uncertainties}}}, doi = {{10.1007/978-3-319-64063-1_7}}, year = {{2017}}, } @inproceedings{983, author = {{Auroux, Sébastien and Scholz, S. and Karl, Holger}}, booktitle = {{Proc. European Wireless}}, title = {{{Assessing Genetic Algorithms for Placing Flow Processing-aware Control Applications}}}, year = {{2017}}, } @article{9862, abstract = {{In order to improve the credibility of modern simulation tools, uncertainties of different kinds have to be considered. This work is focused on epistemic uncertainties in the framework of continuum mechanics, which are taken into account by fuzzy analysis. The underlying min-max optimization problem of the extension principle is approximated by α-discretization, resulting in a separation of minimum and maximum problems. To become more universal, so-called quantities of interest are employed, which allow a general formulation for the target problem of interest. In this way, the relation to parameter identification problems based on least-squares functions is highlighted. The solutions of the related optimization problems with simple constraints are obtained with a gradient-based scheme, which is derived from a sensitvity analysis for the target problem by means of a variational formulation. Two numerical examples for the fuzzy analysis of material parameters are concerned with a necking problem at large strain elastoplasticity and a perforated strip at large strain hyperelasticity to demonstrate the versatility of the proposed variational formulation. }}, author = {{Mahnken, Rolf}}, issn = {{ 2325-3444}}, journal = {{Mathematics and Mechanics of complex systems}}, keywords = {{fuzzy analysis, α-level optimization, quantities of interest, optimization with simple constraints, large strain elasticity, large strain elastoplasticity}}, number = {{3-4}}, title = {{{"A variational formulation for fuzzy analysis in continuum mechanics"}}}, volume = {{5}}, year = {{2017}}, } @article{9976, abstract = {{State-of-the-art mechatronic systems offer inherent intelligence that enables them to autonomously adapt their behavior to current environmental conditions and to their own system state. This autonomous behavior adaptation is made possible by software in combination with complex sensor and actuator systems and by sophisticated information processing, all of which make these systems increasingly complex. This increasing complexity makes the design process a challenging task and brings new complex possibilities for operation and maintenance. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability, which in turn increases reliability. The development of such an adaption strategy requires appropriate methods to evaluate reliability based on currently selected system behavior. A common approach to implement such adaptivity is to base system behavior on different working points that are obtained using multiobjective optimization. During operation, selection among these allows a changed operating strategy. To allow for multiobjective optimization, an accurate system model including system reliability is required. This model is repeatedly evaluated by the optimization algorithm. At present, modeling of system reliability and synchronization of the models of behavior and reliability is a laborious manual task and thus very error-prone. Since system behavior is crucial for system reliability, an integrated model is introduced that integrates system behavior and system reliability. The proposed approach is used to formulate reliability-related objective functions for a clutch test rig that are used to compute feasible working points using multiobjective optimization.}}, author = {{Kaul, Thorben and Meyer, Tobias and Sextro, Walter}}, journal = {{SAGE Journals}}, keywords = {{Integrated model, reliability, system behavior, Bayesian network, multiobjective optimization}}, pages = {{390 -- 399}}, title = {{{Formulation of reliability-related objective functions for design of intelligent mechatronic systems}}}, doi = {{10.1177/1748006X17709376}}, volume = {{Vol. 231(4)}}, year = {{2017}}, } @inproceedings{9983, author = {{Schulze, Sebastian and Sextro, Walter and Kister, K.}}, booktitle = {{Proceedings of the 12th International Symposium on Automotive Lighting 2017}}, title = {{{Model based optimization of dynamics in adaptive headlamps}}}, year = {{2017}}, } @inproceedings{65, abstract = {{Heterogeneous compute nodes in form of CPUs with attached GPU and FPGA accelerators have strongly gained interested in the last years. Applications differ in their execution characteristics and can therefore benefit from such heterogeneous resources in terms of performance or energy consumption. While performance optimization has been the only goal for a long time, nowadays research is more and more focusing on techniques to minimize energy consumption due to rising electricity costs.This paper presents reMinMin, a novel static list scheduling approach for optimizing the total energy consumption for a set of tasks executed on a heterogeneous compute node. reMinMin bases on a new energy model that differentiates between static and dynamic energy components and covers effects of accelerator tasks on the host CPU. The required energy values are retrieved by measurements on the real computing system. In order to evaluate reMinMin, we compare it with two reference implementations on three task sets with different degrees of heterogeneity. In our experiments, MinMin is consistently better than a scheduler optimizing for dynamic energy only, which requires up to 19.43% more energy, and very close to optimal schedules.}}, author = {{Lösch, Achim and Platzner, Marco}}, booktitle = {{Proceedings of the 28th Annual IEEE International Conference on Application-specific Systems, Architectures and Processors (ASAP)}}, title = {{{reMinMin: A Novel Static Energy-Centric List Scheduling Approach Based on Real Measurements}}}, doi = {{10.1109/ASAP.2017.7995272}}, year = {{2017}}, } @inproceedings{6565, author = {{Jäger, Axel and Johannesmann, Sarah and Claes, Leander and Webersen, Manuel and Henning, Bernd and Kupnik, Mario}}, booktitle = {{2017 IEEE IUS~Proceedings}}, title = {{{Evaluating the Influence of 3D-Printing Parameters on Acoustic Material Properties}}}, year = {{2017}}, } @inproceedings{6638, author = {{Krauter, Stefan}}, booktitle = {{VDE-Proceedings of NEIS 2017 – Conference on Sustainable Energy Supply and Energy Storage Systems by IEEE-PES. Hamburg (Deutschland), 21.–22. September, 2017.}}, location = {{Hamburg}}, title = {{{Comparison of Conversion Efficiencies and Energy Yields of Micro-Inverters for Photovoltaic Modules}}}, year = {{2017}}, } @inproceedings{6639, author = {{Krauter, Stefan and Ameli, Ali}}, booktitle = {{VDE-Proceedings of NEIS 2017 – Conference on Sustainable Energy Supply and Energy Storage Systems by IEEE-PES. Hamburg (Deutschland), 21.–22. September, 2017.}}, location = {{Hamburg}}, title = {{{Smart Charging Management System of Plugged-in EVs for Optimal Operation of Future Power Systems.}}}, year = {{2017}}, } @phdthesis{10594, abstract = {{Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. Since – in contrast to the solution of a single objective optimization problem – the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging. This is even more the case when many problems have to be solved, when the number of objectives is high, or when the objectives are costly to evaluate. Consequently, this thesis is devoted to the identification and exploitation of structure both in the Pareto set and the dynamics of the underlying model as well as to the development of efficient algorithms for solving problems with additional parameters, with a high number of objectives or with PDE-constraints. These three challenges are addressed in three respective parts. In the first part, predictor-corrector methods are extended to entire Pareto sets. When certain smoothness assumptions are satisfied, then the set of parameter dependent Pareto sets possesses additional structure, i.e. it is a manifold. The tangent space can be approximated numerically which yields a direction for the predictor step. In the corrector step, the predicted set converges to the Pareto set at a new parameter value. The resulting algorithm is applied to an example from autonomous driving. In the second part, the hierarchical structure of Pareto sets is investigated. When considering a subset of the objectives, the resulting solution is a subset of the Pareto set of the original problem. Under additional smoothness assumptions, the respective subsets are located on the boundary of the Pareto set of the full problem. This way, the “skeleton” of a Pareto set can be computed and due to the exponential increase in computing time with the number of objectives, the computations of these subsets are significantly faster which is demonstrated using an example from industrial laundries. In the third part, PDE-constrained multiobjective optimal control problems are addressed by reduced order modeling methods. Reduced order models exploit the structure in the system dynamics, for example by describing the dynamics of only the most energetic modes. The model reduction introduces an error in both the function values and their gradients, which has to be taken into account in the development of algorithms. Both scalarization and set-oriented approaches are coupled with reduced order modeling. Convergence results are presented and the numerical benefit is investigated. The algorithms are applied to semi-linear heat flow problems as well as to the Navier-Stokes equations. }}, author = {{Peitz, Sebastian}}, title = {{{ Exploiting structure in multiobjective optimization and optimal control}}}, doi = {{10.17619/UNIPB/1-176}}, year = {{2017}}, } @inproceedings{10676, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{2017 International Conference on Field Programmable Technology (ICFPT)}}, keywords = {{Linux, cache storage, microprocessor chips, multiprocessing systems, LEON3-Linux based multicore processor, MiBench suite, block sizes, cache adaptation, evolvable caches, memory-to-cache-index mapping function, processor caches, reconfigurable cache mapping optimization, reconfigurable hardware technology, replacement strategies, standard Linux OS, time a complete hardware implementation, Hardware, Indexes, Linux, Measurement, Multicore processing, Optimization, Training}}, pages = {{215--218}}, title = {{{Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor}}}, doi = {{10.1109/FPT.2017.8280144}}, year = {{2017}}, } @inproceedings{10761, author = {{Kaufmann, Paul and Ho, Nam and Platzner, Marco}}, booktitle = {{Adaptive Hardware and Systems (AHS)}}, publisher = {{IEEE}}, title = {{{Evaluation Methodology for Complex Non-deterministic Functions: A Case Study in Metaheuristic Optimization of Caches}}}, doi = {{10.1109/AHS.2017.8046380}}, year = {{2017}}, } @techreport{11735, abstract = {{This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic operations the derivative of the eigenvalue problem with complex-valued eigenvectors is one of the key results of this report. The potential of this approach is shown with experimental results on the CHiME-3 challenge database. There, the beamforming task is used as a front-end for an ASR system. With the developed derivatives a joint optimization of a speech enhancement and speech recognition system w.r.t. the recognition optimization criterion is possible.}}, author = {{Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, title = {{{On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming}}}, year = {{2017}}, } @inproceedings{11736, abstract = {{In this paper we show how a neural network for spectral mask estimation for an acoustic beamformer can be optimized by algorithmic differentiation. Using the beamformer output SNR as the objective function to maximize, the gradient is propagated through the beamformer all the way to the neural network which provides the clean speech and noise masks from which the beamformer coefficients are estimated by eigenvalue decomposition. A key theoretical result is the derivative of an eigenvalue problem involving complex-valued eigenvectors. Experimental results on the CHiME-3 challenge database demonstrate the effectiveness of the approach. The tools developed in this paper are a key component for an end-to-end optimization of speech enhancement and speech recognition.}}, author = {{Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation}}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @phdthesis{16070, author = {{Weiß Borkowski, Nathalie }}, isbn = {{978-3-8440-5013-4}}, publisher = {{Shaker Verlag Band 2017/22}}, title = {{{Analyse des Verformungsverhaltens von Übergangszonen partiell pressgehärteter Strukturen}}}, year = {{2017}}, } @inproceedings{16075, author = {{Ahlers, Dominik and Tröster, Thomas}}, location = {{Nördlingen}}, title = {{{Aspekte der Produktentwicklung in der additiven Fertigung}}}, year = {{2017}}, }