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Emily Nutwell, Nikola Aulig, Duane Detwiler , "Topology Optimization of a Bumper Beam System Considering a Nonlinear Design Requirement ", NAFEMS World Congress 2017, 2017.
AbstractMethods of topology optimization are used to generate an optimal layout of material within a defined design space subject to single or multiple load cases. These methods are increasingly utilized in the field of concept design for automotive development. A vehicle’s bumper system is often subjected to loading conditions which require the structure to be efficient at absorbing energy. While an optimal bumper system maximizes energy absorbed duri...
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Hans-Georg Beyer and Bernhard Sendhoff , "Towards a steady-state analysis of an evolution strategy on a robust optimization problem with noise-induced multi-modality", IEEE Transactions on Evolutionary Computation, 2017.
AbstractA steady state analysis of the optimization quality of a classical self-adaptive Evolution Strategy (ES) on a class of robust optimization problems is presented. A novel technique for calculating progress rates for non-quadratic noisy fitness landscapes is presented. This technique yields asymptotically exact results in the infinite population size limit. This technique is applied to a class of functions with noise-induced multi-modality. The ...
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Ran Cheng, Tobias Rodemann, Michael Fischer, Markus Olhofer, Yaochu Jin , "Evolutionary Many-objective Optimization of Hybrid Electric Vehicle Control: from General Optimization to Preference Articulation", IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 2, pp. 97-111, 2017.
AbstractA key element in hybrid car design is the energy management controller that has to guarantee peak performance for an increasing number of conflicting objectives, e.g., fuel consumption, battery stress, emissions, noise, et al. Recently, a seven-objective controller model has been suggested to promote optimal controls of hybrid cars. Nowadays, such an optimization problem with more than three conflicting objectives is often known as a many-object...
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Hans-Georg Beyer and Bernhard Sendhoff , "Simplify Your Covariance Matrix Adaptation Evolution Strategy", IEEE Transactions on Evolutionary Computation, 2017.
AbstractThe standard Covariance Matrix Adaptation Evolution Strategy (CMA-ES) comprises two evolution paths, one for the learning of the mutation strength and one for the rank- 1 update of the covariance matrix. In this paper is is shown that one can approximately transform this algorithm in such a manner that one of the evolution paths and the covariance matrix itself disappear. That is, the covariance update and the covariance matrix square root...
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Andreas Johannes Richter, Jascha Achenbach, Stefan Menzel, Mario Botsch , "Multi-objective Representation Setups for Deformation-based Design Optimization", 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 514-528, 2017.
AbstractThe increase of complexity in virtual product design requires high-quality optimization algorithms capable to find the global parameter solution for a given problem. Population-based evolutionary design optimization targets to solve these kinds of application problems, offering efficient algorithms striving for high-quality solutions. The representation, which defines the encoding of the design and the mapping from parameter space to design spac...
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Mikael Kaandorp, Stefan Menzel, Sebastian Schmitt , "An Aerodynamic Perspective on Shape Deformation Methods ", 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2017.
AbstractShape deformation methods are commonly used for aerodynamic design optimization of physical bodies. These methods promise a good trade-off between high design variations and a minimal set of optimization parameters. Free-form deformation and deformations based on radial basis functions are among the current state-of-the-art linear deformation methods. Recently, both methods have been analyzed on their capabilities in a simulation-based design opt...
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Florian Damerow, Tim Puphal, Julian Eggert , "Risk-based Driver Assistance for Approaching Intersections of Limited Visibility", International Conference on Vehicular Electronics and Safety 2017, pp. 178-184, 2017.
AbstractThis work addresses the general problem of risk evaluation in traffic scenarios for the case of limited observability of the scene due to a restricted sensory coverage. Here we especially concentrate on intersection scenarios, which are visually difficult to access. To distinguish the area of sight, we employ publicly available digital map data which include, besides the general road geometry, information about buildings potentially blocking t...
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Martin Ernst Heckmann, Dennis Orth, Heiko Wersing, Dorothea Kolossa , "Situated speech-based Driver Assistant Systems: The development of a personalized left-turning assistant", ATZ - Automobiltechnische Zeitschrift, 2017.
AbstractWe recently developed the concept of ``Assistance on Demand''. This describes an advanced driver assistance system (ADAS) which supports the driver in an inner city scenario only if they ask for assistance. A key element is the control of the ADAS via speech which allows the driver to flexibly formulate his requests for assistance while the situation develops. Our application scenario is turning left at unsignalized urban intersections. After the...
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Mathias Franzius, Mark Dunn, Roman Dirnberger, Nils Einecke , "Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers", 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 361-369, 2017.
AbstractAutonomous lawn mowers have become a solid product over the past years. Yet, they lack intelligent functions like obstacle recognition and avoidance or robust mapping and localization. One reason for these missing capabilities are the challenging situations encountered outdoors. Furthermore, the intelligent functions need to be robust enough that they do not need any expert intervention whatsoever. This makes functions based on image processing p...
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Viktor Losing, Barbara Hammer, Heiko Wersing , "SAM: How to Deal with Diverse Drift Types", International Joint Conference on Artificial Intelligence, 2017.
AbstractData Mining in non-stationary data streams is particularly relevant in the context of Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm. SAM-kN...
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