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Nils Einecke, Stefan Fuchs, Bram Bolder, Manuel Mühlig, "Living Lab: A 24/7 Human-Machine-Interaction Space in an Office Environment", Proceedings of the Poster and Workshop Sessions of AmI-2019, the 2019 European Conference on Ambient Intelligence, pp. 111-123, 2019.

Abstract

In 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. As the system is built into the lobby ...



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Steffen Limmer, "Dynamic Pricing for Electric Vehicle Charging - A Literature Review", Energies Journal, vol. 12, no. 18, pp. 1-24, 2019.

Abstract

Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in...



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Sneha Saha, Thiago Rios, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck, Xin Yao, Zhao Xu, Patricia Wollstadt, "Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks", 2019 ICDM Workshop: Learning and Mining with Industrial Data (LMID), 2019.

Abstract

In automotive digital development, 3D shape morphing techniques are used to create new designs in order to match design targets, such as aerodynamic or stylistic requirements. Control-point based shape morphing alters existing geometries either through human user interactions or through computational optimization algorithms that optimize for product performance targets. Shape morphing is typically continuous and results ...



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Guo Yu, Yaochu Jin, Markus Olhofer, "Benchmark Problems and Performance Indicators for Search of Knee Points in Multi-objective Optimization", IEEE Transactions on Cybernetics, 2019.

Abstract

During the preference-based optimization, the decision makers (DMs) are hard to understand the problem without priori knowledge and give their preference information. Also, solutions have many features, and the searching space is very large and usually not homogenious. Depending on the features, the solutions are more less important, while important might be problem dependent. This can be eg. knee points, robust areas, etc. Therefore, the prefere...



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Manuel Rudolph and Sebastian Schmitt, "Machine Learning on Near-Term Universal Quantum Computers", 1st DPG Fall Meeting - Quantum Science and Information Technologies, 2019.

Abstract

Implementing 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 l...



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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.

Abstract

The notion of experience has often 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. Notably, realizing experience-based methods suffers from a variety of conceptual key problems. The first one being in regards to what constitutes problem-similarity from an algorithm perspective and the second one being what co...



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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 ro...



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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.

Abstract

From 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.

Abstract

An 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.

Abstract

Over 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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