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Nils Einecke, Stefan Fuchs, Fabian Eisele , "SmartLobby: Using a 24/7 Remote Head-Eye-Tracking for Content Personalization ", UbiComp/ISWC '19 Adjunct, 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 with head-eye-tracking as key component....
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Duc Nguyen, Marios Kefalas, Steffen Limmer, Asteris Apostolidis, Kaifeng Yang, Markus Olhofer, Thomas Bäck , "A Review: Prognostics and Health Management in Automotive and Aerospace", International journal of prognostics and health management (IJPHM), vol. 10, 2019.
AbstractPrognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year...
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Elena Raponi, Mariusz Bujny, Markus Olhofer, Simonetta Boria, Fabian Duddeck , "Hybrid Kriging-assisted Level Set Method for Topology Optimization", 11th International Conference on Evolutionary Computation Theory and Applications, 2019.
AbstractThis work presents a hybrid optimization approach that couples Efficient Global Optimization (EGO) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Topology Optimization (TO) of mechanical structures. Both of these methods are regarded as good optimization strategies for continuous global optimization of expensive and multimodal problems, e.g. associated with vehicle crashworthiness. CMA-ES is flexible and robust to changing ci...
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Tobias Rodemann, Tom Eckhardt , René Unger, Torsten Schwan , "Using Agent-based Customer Modelling for the Evaluation of EV Charging Systems", Energies, vol. 12, no. 15, issue Multi-Agent Energy System Simulations, 2019.
AbstractThe development of efficient Electric Vehicle (EV) charging infrastructure requires a modeling of customer behavior at an appropriate level of detail. Since only limited information about real customers is available, most simulation approaches employ a stochastic approach by combining known or estimated customer features with random variations. A typical example is to model EV charging customers by an arrival and a targeted departure time, plus ...
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Diana Kleingarn, Nima Nabizadeh, Martin Ernst Heckmann, Dorothea Kolossa , "Speaker-adapted neural-network-based fusion for multimodal reference resolution", The 20th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2019), pp. 210-214, 2019.
AbstractHumans use a variety of approaches to reference objects in the external world, including verbal descriptions, hand and head gestures, eye gaze or any combination of them. The amount of useful information from each modality, however, may vary depending on the specific person and on several other factors. For this reason, it is important to learn the correct combination of inputs for inferring the best-fitting reference. In this paper, we investiga...
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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing , "Robust Outdoor Self-localization In Changing Environments", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), 2019.
AbstractIn outdoor scenarios changing conditions (e.g., seasonal, weather and lighting effects) have a substantial impact on the appearance of a scene, which often prevents successful visual localization. The application of an unsupervised Slow Feature Analysis (SFA) on the images captured by an autonomous robot enables self-localization from a single image. However, changes occurring during the training phase or over a more extended period can affe...
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Chao Wang , "A Framework of the Non-critical Spontaneous Intervention in Highly Automated Driving Scenarios", AutoUi 2019, 2019.
AbstractOne trend in the development of autonomous driving is to take the human completely out-of-the-loop. However, we believe that there are good grounds to keep the human in the loop, at some level of control (in particular tactical control), even in the case of full automation. One reason to do so is that the technology may not be flexible enough to always behave according to human needs and preferences, which may vary across people and situations. T...
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Nazia Attari, Martin Ernst Heckmann, David Schlangen , "From Explainability to Explanation: Using a Dialogue Setting to Elicit Annotations with Justifications.", Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, 2019.
AbstractWith the increasing use of machine learning techniques to support decision making comes an increasing demand for making their predictions more transparent. The field of explainable AI has set itself the goal to open the black box of current prediction methods, as it were, in order to provide not just predictions, but also justifications for them. Typically, however, already the training data for supervised machine learning is collected in a manne...
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Patricia Wollstadt , "Information-theoretic measures for interaction analysis and network reconstruction from time series", ECOLE Summer School, 2019.
AbstractA central step for understanding, describing, and predicting global behavior of multivariate systems evolving over time is to analyze interactions between system components. In this talk, I will introduce recent measures from information theory that provide powerful tools for the analysis of such interactions from time-series data. Methods are model-free, robust to noise, require relatively few data points, are unsupervised, and are thus applicab...
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Martin Ernst Heckmann, Dennis Orth, Nico Andreas Steinhardt, Bram Bolder, Mark Dunn, Dorothea Kolossa , "CORA, a Prototype for a Cooperative Speech-Based On-Demand Intersection Assistant", AutomotiveUI ’19 Adjunct , 2019.
AbstractPreviously, we proposed an on-demand speech-based communication concept for the interaction between the driver and his or her vehicle. Using this concept, drivers can flexibly activate the system via speech whenever they want to receive assistance. The interaction solely relies on speech; no visual information is displayed. We could show via driver simulator studies that an instantiation of this concept as an intersection assistant, supporting...
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