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Viktor Losing, Barbara Hammer, Heiko Wersing, "Tackling Heterogeneous Concept Drift with the Self Adjusting Memory (SAM)", Knowledge and Information Systems, vol. 54, no. 1, pp. 171-201, 2018.

Abstract

Data 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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Jörg Deigmöller, Herbert Janßen, Nils Einecke, Oliver Fuchs, "Road Surface Scanning using Stereo Cameras for Motorcycles", Proceedings of the 14th International Conference on Computer Vision Theory and Applications, 2018.

Abstract

Active and semi-active suspension systems for vehicles became quite popular in the recent years as they allow for a smoother and safer ride compared to conventional suspension systems. The performance of an active/semi-active suspension system can be even more improved if the road condition in front of the vehicle is known. Currently only a few luxury cars combine fully active suspension with stereo cameras for such a predictive adaptatio...



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Martin Heckmann, "Audio-visual Word Prominence Detection from Clean and Noisy Speech", Computer Speech and Language, 2018.

Abstract

In this paper we introduce the audio-visual detection of word prominence and investigate how the additional visual information can be used to increase the robustness when acoustic background noise is present. We evaluate the detection performance for each modality individually and also perform experiments using feature and decision fusion. Our experiments are based on a corpus with 11 English speakers which contains in addition to the speech si...



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Antonello Ceravola, Frank Joublin, Heiko Wersing, Stephan Hasler, Behzad Dariush, Yi-Ting Chen, "A Data Management Infrastructure for Intelligent Systems", Japanese Society of Artificial Intelligence (JSAI), pp. 45-54, 2018.

Abstract

In this paper we describe the design principles, implementation choices and general challenges we encountered in the creation of a data management infrastructure for recording data streams from test vehicles, robots and other platforms. The trigger for this data management infrastructure project was twofold: First from the proper setup of new test cars equipped with many sensors, delivering high bandwidth data recordings and second from achieving...



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Steffen Limmer and Tobias Rodemann, "Robust Evolutionary Optimization Based on Coevolution", Proceedings of EvoApplications 2018, pp. 813-831, 2018.

Abstract

A way to deal with uncertainties in the fitness function of an optimization problem is robust optimization, which optimizes the expected value of the fitness. In the context of evolutionary optimization, it is a common practice to compute the expected value of the fitness approximately with the help of Monte-Carlo simulation. This approach requires a lot of evaluations of the fitness function in order to evaluate an individual and thus it can be ...



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Simon Manschitz, Michael Gienger, Jens Kober, Jan Peters, "Mixture of Attractors: A novel Movement Primitive Representation for Learning Motor Skills from Demonstrations", IEEE Robotics and Automation Letters (RA-L), 2018.

Abstract

Most learning from demonstration approaches still require too many demonstrations to learn a skill or fail to generalize it to new situations. In this paper, we introduce Mixture of Attractors, a novel movement primitive representation, which allows for learning complex skills from very few demonstrations. The movement primitive representation inherently supports multiple coordinate frames, enabling the system to generalize a skill to unseen obje...



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Michael Gienger and Thomas Buschmann, "The TUM Bipeds Johnnie and Lola", Humanoid Robotics: a Reference (Book Chapter), Springer, 2018.

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Christian Limberg, Heiko Wersing, Helge Ritter, "Efficient Accuracy Estimation for Instance-Based Incremental Active Learning", European Symposium on Artificial Neural Networks, 2018.

Abstract

Estimating systems accuracy is crucial for applications of in- cremental learning. In this paper, we introduce the Distogram Estimation (DGE) approach to estimate the accuracy of instance-based classifiers. By calculating relative distances to samples it is possible to train an offline regression model, capable of predicting the classifiers accuracy on unseen data. Our approach requires only a few supervised samples for training and can ins...



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Tobias Rodemann and René Unger, "Smart Company Digital Twin - Supporting Controller Development and Testing Using FMI ", Japanese Society of Automotive Engineers Spring Meeting, 2018.

Abstract

The challenges of reducing greenhouse gases (GHG) are approached on many fronts, from electrification of mobility to energy management in buildings, and smart grids for more efficient operation of energy production and distribution networks. Considered in isolation these efforts may be inefficient and might even result in severe instabilities of the grid. Fortunately, when integrating electric mobility, smart buildings and smart grids the integra...



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Mariusz Bujny, Nikola Aulig, Markus Olhofer, Fabian Duddeck, "Identification of optimal topologies for crashworthiness with the evolutionary level set method ", International Journal of Crashworthiness, pp. 1-22, 2017.

Abstract

Structural topology optimisation methods are well established in many engineering disciplines. However, for highly non-linear problems, including crashworthiness, methods are less developed and still subject to active research. In particular, due to the simplifications made in the state-of-the-art methods as well as heuristic character of most of them, the optimality of the obtained structures is arguable. In this paper, a topology optimisation ...



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