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Manuel Rudolph, Sebastian Schmitt, Fred Jendrzejewski , "Machine Learning on Near-Term Universal Quantum Computers", 1st DPG Fall Meeting - Quantum Science and Information Technologies, 2019.
AbstractImplementing near-term quantum computers with a small number of qubits and imperfect gate fidelities for real world challenges has been a flourishing field of research in recent years. Quantum-classical hybrid algorithms with shallow quantum circuits for state preparation are being used with success in fields like quantum chemistry and machine learning. This work focuses on the use of near-term quantum computers for unsupervised machine learn...
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Stephen Friess, Peter Tino, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "Learning Transferable Variation Operators in a Continuous Genetic Algorithm", IEEE Symposium Series on Computational Intelligence, 2019.
AbstractThe notion of experience has long been neglected within the domain of evolutionary computation. While in machine learning a large variety of methods has emerged in the recent years under the umbrella of transfer learning, a similar notion for experience reuse has been missing in optimization. Notably, realizing experience-based methods suffers from a variety of conceptual key problems. The first one being in regards to what constitutes problem-si...
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Fabian Müller and Julian Eggert , "Behavior investigation of a risk-aware driving model for trajectory prediction", FAST-zero 2019, 2019.
AbstractThe prevention of risky situations is one of the main tasks in autonomous driving and intelligent driving as- sistant systems. Uncertainty in the traffic participants behavior and sensor noise leads to critical situations, which have to be anticipated by appropriate risk prediction approaches. The risk prediction itself requires dedicated driver models which are interaction sensitive and computationally cheap, to efficiently simulate how a sce...
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Mathias Franzius , "Towards Beauty: Robot Following Aesthetics Gradients", International Conference on Advanced Robotics (ICAR) , 2019.
Abstract[Publication on SPLESH project] Increasing numbers of devices are equipped with cameras generating large amounts of images. State of the art technologies allow to automatically identify relevant and aesthetically pleasing images after they were stored. Here, we demonstrate a robot that estimates the gradient of image aesthetics in its environment and actively navigates towards the maximum. Aesthetics navigation is integrated into a modified robo...
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Can Wang, Steffen Limmer, Mitra Baratchi, Thomas Bäck, Holger Hoos, Markus Olhofer , "Automated Machine Learning for Short-term Electric Load Forecasting", IEEE Symposium Series on Computational Intelligence (SSCI) 2019, 2019.
AbstractFrom detecting skin cancer, to translating languages, to forecasting electricity consumption, machine learning is enabling advanced capabilities of computer systems across a broad range of important real-world applications. In this work, we present machine learning models for forecasting the electricity consumption. Short-term electric load forecasting has been a fundamental concern in power operation systems for over a century. Energy load for...
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Sebastian Schrom and Stephan Hasler , "Domain Mixture: An Overlooked Scenario in Domain Adaptation", IEEE International Conference on Machine Learning and Applications (ICMLA), 2019.
AbstractAn image based object classification system that is trained on one domain usually shows a decreased performance when transferred to other domains during test if their belonging data distributions differ significantly. There exist various domain adaptation approaches that improve generalization from a source to a target domain. However, those approaches consider during transfer only the case where at least from one domain all supervised samples of...
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Elena Raponi, Mariusz Bujny, Markus Olhofer, Nikola Aulig, Simonetta Boria, Fabian Duddeck , "Kriging-Assisted Topology Optimization of Crash Structures", Computer Methods in Applied Mechanics and Engineering, 2019.
AbstractOver the recent decades, Topology Optimization (TO) has become an important tool in the design and analysis of mechanical structures. Although structural TO is already used in many industrial applications, it needs much more investigation in the context of vehicle crashworthiness. Indeed, crashworthiness optimization problems present strong nonlinearities and discontinuities, and gradient-based methods cannot be applied. The aim of this work is t...
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Thomas Schmitt, Tobias Rodemann, Jürgen Adamy , "Application of Pareto Frontiers in an Economic Model Predictive Controlled Microgrid", GMA-Fachausschuss Treffen, GMA, 2019.
AbstractThe increase of renewable energies and a trend to decentralization lead to a need of managable strategies for energy commitment in small microgrids. Intuitively, economic model predictiv control (EMPC) is a profound approach for this task, due to its capabilities of optimizing an control sequence with respect to constraints and future predictions. To apply EMPC, we model a medium-sized company building as a second-order linear time-discrete mod...
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Steffen Limmer, Takahiro Ishihara, Tobias Rodemann , "SimulationX Solver Setting Optimization via Automated Hyperparameter Tuning Approaches", ESI Forum 2019, 2019.
AbstractAn ever-increasing complexity of technical systems requires sophisticated methods to optimize a larger number of design parameters under consideration of many objectives. For a broad class of problems evolutionary algorithms are the method of choice. Their main drawback is a huge computational effort since thousands or more simulation runs are required. It is therefore essential to reduce the simulation times as much as possible. One approach i...
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Thomas Jatschka, Tobias Rodemann, Guenther Raidl , "A Cooperative Optimization Approach for Distributing Service Points in Mobility Applications", EvoCOP, pp. 1-16, 2019.
AbstractWe investigate a variant of the facility location problem concerning the optimal distribution of service points with incomplete information within a certain geographical area. The application scenario is generic in principle, but we have the setup of charging stations for electric vehicles or rental stations for bicycles or cars in mind. When planning such systems, estimating under which conditions which customer demand can be fulfilled is fundam...
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