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Thomas Jatschka, Tobias Rodemann, Guenther Raidl , "Comparing Machine Learning Models in a Cooperative Optimization Approach for Distributing Service Points", EuroCAST, 2019.
AbstractWe consider a variant of the facility location problem [2]. The task is to find an optimal subset of locations within a certain geographical area for erecting service points in order to satisfy customer demands as best as possible. This general scenario has a wide range of real-world applications. More specifically, we have the setup of stations for mobility purposes in mind, such as erecting bike sharing stations for a public bike sharing system...
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Gan Ruan, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "When and How to Transfer Knowledge in Dynamic Multi-objective Optimization", IEEE Symposium Series on Computational Intelligence , 2019.
AbstractThis paper aims to investigate when and how transfer learning works or fails in dynamic multi-objective optimization. Through computational analyses on a number of dynamic bi- and tri-objective test problems, we show that transfer learning fails on problems with fixed Pareto optimal solutions set and under small environmental changes. We also show that the Gaussian kernel function used in the existing transfer learning-based method is not always ...
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Thomas H Weisswange, Sven Rebhan, Bram Bolder, Nico Andreas Steinhardt, Frank Joublin, Jens Schmüdderich, Christian Goerick , "intelligent Traffic Flow Assist: Optimized Highway Driving Using Conditional Behavior Prediction", IEEE Intelligent Transportation Systems Magazine, vol. in press, 2019.
AbstractMany conventional systems for driver assistance on highways decide for a desired vehicle motion based on static situation configurations only. Adaptive Cruise Control systems, for example, react to vehicles cutting-in from neighboring lanes only when these vehicles enter the ego-lane. Some more recent systems include prediction of future behaviors, but often neglect mutual influences between these and the best behavior for the ego vehicle. This ...
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Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck , "Identifying Topological Prototypes using Deep Point Cloud Autoencoder Networks", LMID 2019: Workshop on Learning and Mining with Industrial Data, 2019.
AbstractData mining of engineering designs generated by topology optimization methods is a challenging task. A topology optimization method maximizes one or more performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints. The performance objective could be stiffness and the constraint could be the allowed mass fraction in the design space. If the constraints are not too restrictive or un...
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Sandra Ittner, Dominik Mühlbacher, Thomas H Weisswange, Mark Vollrath, Alexandra Neukum , "The co-driver: A neglected passenger – Development and evaluation of a co-driver assistance system", 10. VDI-Tagung Mensch-Maschine-Mobilität 2019, 2019.
AbstractEven today, the front seat co-driver is a neglected passenger in the car. There are only a few to no assistance systems, which show information or the driver’s cognitive state to the co-driver. Results of preliminary investigations indicate that the current passive role of the co-driver, probably caused by missing information and missing control, can lead to a feeling of discomfort. Furthermore, it could be showed that this feeling of discomfort ...
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Tobias Rodemann , "A Comparison of Different Many-Objective Optimization Algorithms for Energy System Optimization", Applications of Evolutionary Computation, pp. 1-16, 2019.
AbstractThe usage of renewable energy sources, storage devices, and flexible loads has the potential to greatly improve the overall efficiency of a building complex or factory. However, one needs to consider a multitude of upgrade options and several performance criteria. We therefore formulated this task as a many-objective optimization problem with 10 design parameters and 5 objectives (investment cost, yearly energy costs, \CO emissions, system resil...
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Nils Einecke, Stefan Fuchs, Bram Bolder, Manuel Mühlig, Fabian Eisele , "Living Lab: A 24/7 Human-Machine-Interaction Space in an Office Environment", Proceedings of the Poster and Workshop Sessions of AmI-2019, the 2019 European Conference on Ambient Intelligence, pp. 111-123, 2019.
AbstractIn this work, we present the SmartLobby, an intelligent environment system integrated into the lobby of a research institute. The SmartLobby is running 24/7, i.e.~it can be used any time by anyone without any preparations. The goal of the system is to conduct research in the domain of human machine cooperation. One important first step towards this goal is a detailed human state modeling and estimation. As the system is built into the lobby ...
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Steffen Limmer , "Dynamic Pricing for Electric Vehicle Charging - A Literature Review", Energies Journal, vol. 12, no. 18, pp. 1-24, 2019.
AbstractTime-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in...
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Sneha Saha, Thiago de Jesus de Araujo Rios, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, Xin Yao, Zhao Xu, Patricia Wollstadt , "Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks", 2019 ICDM Workshop: Learning and Mining with Industrial Data (LMID), 2019.
AbstractIn automotive digital development, 3D shape morphing techniques are used to create new designs in order to match design targets, such as aerodynamic or stylistic requirements. Control-point based shape morphing alters existing geometries either through human user interactions or through computational optimization algorithms that optimize for product performance targets. Shape morphing is typically continuous and results ...
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Julian Eggert and Fabian Müller , "A Foresighted Driver Model derived from Integral Expected Risk", IEEE Intelligent Transportation Systems Conference - ITSC 2019, pp. 1223-1230, 2019.
AbstractIn this paper, we derive a compact driver model from a concise theoretical foundation for risk under uncertainty. We show that drivers behave by choosing the current action which optimizes the tradeoff between future integral of expected risk and driving quality. We also show that the model is open to incorporate arbitrary risks and that it generalizes over longitudinal, lateral, and mixed scenarios. This paper is an extension of the FDM (Foresig...
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