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Tim Janke, Bastian Brindley, Tobias Rodemann, Florian Steinke , "Incentivizing the adoption of local flexibility options: A quantitative case study", IEEE International Conference on European Energy Market, 2018.
AbstractEnergy systems with a large share of generation from renewable sources require a flexible behavior of the demand side. Office buildings can technically contribute through the installation of local co-generation and storage in combination with energy management systems. However, businesses will only consider the adoption of flexibility options if there is a clear monetary benefit. We evaluate different combinations of flexibility options for a t...
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Andrea Schnall and Martin Ernst Heckmann , "Feature Space SVM Adaptation for Speaker Adapted Word Prominence Detection ", Computer Speech and Language, 2018.
AbstractProsodic cues like the word prominence play a fundamental role in human communication, e.g. to express important information. Since different speakers use a wide variety of features to express prominence, there is a large difference in performance between speaker dependently and speaker independently trained models. To cope with these variations without training a new speaker dependent model, in speech recognition speaker adaptation techniqu...
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Timo Friedrich, Nikola Aulig, Stefan Menzel , "On the Potential and Challenges of Neural Style Transfer for Three-dimensional Shape Data ", EngOpt 2018, 2018.
AbstractIn the field of two-dimensional image and video processing, convolutional neural networks have been successfully applied to generate novel images by composing content and style of two different sources, a process called artistic or neural style transfer. However a usage of these methods for three-dimensional objects is not straight forward due to the unstructured mesh representations of typical shape data. Hence, efficient geometry representation...
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Mariusz Bujny, Nikola Aulig, Markus Olhofer, Fabian Duddeck , "Learning-Based Topology Variation in Evolutionary Level Set Topology Optimization", Genetic and Evolutionary Computation Conference (GECCO), 2018.
AbstractThe main goal in structural Topology Optimization is to find an optimal distribution of material within a defined design domain, under specified boundary conditions. This task is frequently solved with gradient-based methods, but for some problems, e.g. in the domain of crash Topology Optimization, analytical sensitivity information is not available. The recent Evolutionary Level Set Method (EA-LSM) uses Evolutionary Strategies and a representati...
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Moritz Bühler and Thomas H Weisswange , "Online inference of human belief for cooperative robots", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), 2018.
AbstractFor human-robot cooperation, inferring a human's cognitive state is very important for an efficient and natural interaction. Similar to human-human cooperation, understanding what the partner plans and knows, if he is situation aware, is necessary to prevent collisions, offer support at the right time, correct mistakes before they happen or choose the best actions for oneself as early as possible. We propose a model-based belief filter to ...
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Andreas Johannes Richter, Stefan Dresselhaus, Stefan Menzel, Mario Botsch , "Orthogonalization of Linear Representations for Efficient Evolutionary Design Optimization", The Genetic and Evolutionary Computation Conference (GECCO) 2018, 2018.
AbstractReal-world evolutionary design optimizations of complex shapes can efficiently be solved using linear deformation representations, but the optimization performance crucially depends on the initial deformation setup. For instance, when modeling the deformation by radial basis functions (RBF) the convergence speed depends on the condition number of the involved kernel matrix, which previous work therefore tried to optimize through careful placeme...
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Michael Gienger, Dirk Ruiken, Tamas Bates, Mohamed Regaieg, Michael Meißner, Jens Kober, Philipp Seiwald, Arne-Christoph Hildebrandt , "Human-Robot cooperative object manipulation with contact changes", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), 2018.
AbstractThis paper presents a system for cooperatively manipulating large objects with a human and a robot. This physical interaction system is designed to handle, transport or rotate objects in cooperation with a human. Unique points are the bi-manual physical cooperation, the sequential characteristic of the cooperation including re-grasping, and a novel architecture combining force interaction queues, interactive search-based planning, and online traj...
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Stephan Hasler, Jennifer Kreger, Ute Bauer-Wersing , "Interactive Incremental Online Learning of Objects Onboard of a Cooperative Autonomous Mobile Robot", International Conference on Neural Information Processing, 2018.
AbstractDetecting objects and referring to them in a dialog is a crucial requirement for robotic systems that cooperate with humans. For this, in an unrestricted natural environment the innate concepts of the robot must be extended and adapted over time. In this paper we describe an autonomous mobile robot system that performs online interactive incremental learning of objects. We argue that this combination strongly contributes to the variation of appea...
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Matti Krüger, Christiane Wiebel, Heiko Wersing , "Approach for Enhancing the Perception and Prediction of Traffic Dynamics with a Tactile Interface", 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2018.
AbstractParticipation in road traffic frequently requires fast and accurate understanding of environmental object characteristics. Here we introduce an assistance function and corresponding interface targeted at enhancing a driver's perception and understanding of environment dynamics in order to improve driving safety and performance. The core functionality of this assistance function lies in the tactile communication of spatio-temporal proximity inform...
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Barbara Hammer, Heiko Wersing, Jan Philip Goepfert , "Mitigating Concept Drift via Rejection", International Conference on Artitificial Neural Networks ICANN 2018, 2018.
AbstractLearning in non-stationary environments is challenging, because in them the common assumption of independent and identically distributed data does not hold; when concept drift is present it necessitates continuous system updates. In recent years, several powerful approaches -- such as ensemble techniques or intelligent memory models -- have been proposed and are able to deal with different types of drift. However, these models typically classify ...
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