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Thiago Rios, Bas van Stein, Stefan Menzel, Thomas Bäck, Bernhard Sendhoff, Patricia Wollstadt, "Feature Visualization for 3D Point Cloud Autoencoders", International Joint Conference on Neural Networks (IJCNN), 2020.

Abstract

In order to reduce the dimensionality of 3D point cloud representations, autoencoder architectures generate increasingly abstract, compressed features of the input data. Visualizing these features is central to understanding the learning process, however, while successful visualization techniques exist for neural networks applied to computer vision tasks, similar methods for geometric, especially non-Euclidean, input data are currently lacking. H...



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Martina Hasenjäger, Martin Heckmann, Heiko Wersing, "A survey of personalized driver assistance systems", IEEE Transactions on Intelligent Vehicles, vol. 5, no. 2, pp. 335 - 344, 2020.

Abstract

The field of advanced driver assistance systems (ADAS) has matured towards more and more complex assistance functions, applied with wider scope and a strongly increasing number of possible users due to wider market penetration. To deal with such a large variety of use conditions and usage patterns, personalization methods have been developed to ensure optimal user experience. In this paper we develop a general conceptual framework to personalizat...



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Thiago Rios, Stefan Menzel, Bernhard Sendhoff, "Engineering Data and Descriptors (ECOLE Deliverable 1.1)", ECOLE Project Deliverables, 2020.

Abstract

Our research in the ECOLE project aims at generating computational models for capturing the notion of experience, which is embedded on data collected over the course of sets of optimizations, and exploiting said experience in similar, yet more challenging, optimization tasks. This vision of experience follows the analogy of an engineer who also collects, abstracts and utilizes her/his professional experience built while working on different types...



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Thomas Schmitt, Tobias Rodemann, Jürgen Adamy, "Multi-Objective Model Predictive Control for Microgrids", at-Automatisierungstechnik, vol. 68, no. 8, pp. 687-702, 2020.

Abstract

Economic model predictive control is applied to a simplified linear microgrid model. Monetary costs and thermal comfort are simultaneously optimized by using Pareto optimal solutions in every time step. The effects of different metrics and normalization schemes for selecting knee points from the Pareto front are investigated. For German industry pricing with nonlinear peak costs, a linear programming trick is applied to reformulate the optimiz...



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Ye Tian, Shichen Peng, Tobias Rodemann, Xingyi Zhang, Yaochu Jin, "Automatic Algorithm Selection for Evolutionary Multi-Objective Optimization", 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 3225-3232, 2020.

Abstract

In the last two decades, many evolutionary algorithms have shown promising performance in solving a variety of multi-objective optimization problems (MOPs). Since there does not exist an evolutionary algorithm having the best performance on all the MOPs, it is unreasonable to use a single evolutionary algorithm to tackle all the MOPs. While many real-world MOPs are computationally expensive, selecting the best evolutionary algorithm from multip...



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Chao Wang, Stephan Hasler, Manuel Mühlig, Frank Joublin, Antonello Ceravola, Jörg Deigmöller, Lydia Fischer, "Designing Interaction for Multi-agent System in an Office Environment", CHINESE CHI 2020, 2020.

Abstract

Future intelligent system will involve very various types of artificial agents, such as mobile robots, smart home infrastructure, or personal devices, which share data and collaborate with each other to execute certain tasks. Designing an efficient human-machine interface, which can support users to express needs to the system, supervise the collaboration progress of different entities, and evaluate the result, will be challenging. This paper pre...



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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, pp. 3531-3544, 2020.

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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Timo Friedrich, Stefan Menzel, Sebastian Schmitt, "Rapid Creation Of Vehicle Line-ups By Eigenspace Projections For Style Transfer ", Design 2020 - 16th international design conference, no. 1, pp. 867-876, 2020.

Abstract

In product development, an automated generation of shape variations enables a rapid assessment of potentially appealing future design directions. In the present paper, we propose a framework for computing a product line-up of 3D automotive body shapes based on spectral methods for mesh processing. Our proposed method utilizes the visual features extracted as style from a designed car model and projects them onto differently shaped car models, e.g...



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Benedict Flade, Axel Koppert, Gorka Isasmendi, Anweshan Das, David Bétaille, Gijs Dubbelman, Oihana Otaegui, Julian Eggert, "Vision-Enhanced Low-Cost Localization in Crowdsourced Maps", IEEE Intelligent Transportation Systems Magazine, vol. 12, no. 3, pp. 70 - 80, 2020.

Abstract

Lane-level localization of vehicles with low-cost sensors is a challenging task. In situations in which Global Navigation Satellite Systems (GNSS) suffer from weak observation geometry or the influence of reflected signals, the fusion of heterogeneous information presents a suitable approach for improving the localization accuracy. We propose a solution based on a monocular front-facing camera, a low-cost inertial measurement unit (IMU) and a sin...



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Simon Manschitz, "Learning, generating and adapting wave gestures for expressive human-robot interaction", ACM/IEEE International Conference on Human Robot Interaction, 2020.

Abstract

While many humanoid robots can perform basic wave gestures, these gestures are usually hard-coded behaviors. Consequently, the gesture looks rather stiff since there is no variance in the execution of the movement. This study proposes a novel imitation learning approach for the stochastic generation of human-like rhythmic wave gestures and their modulation for effective nonverbal communication through a probabilistic formulation using joint angle...



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