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Timo Friedrich, Barbara Hammer, Stefan Menzel , "Voxel-based Three-dimensional Neural Style Transfer", International Work-Conference on Artificial Neural Networks, 2021.
AbstractIn recent years, Neural Style Transfer has been successfully applied in the crea-tive process for generating novel artistic 2D images by transferring the style of a painting to an existing content image. These techniques which rely on deep neural networks have been extended to further computational creativity tasks like video, motion and animation stylization. However, only few research has been conduct-ed to utilize Neural Style Transfer in the...
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Andrea Castellani, Sebastian Schmitt, Barbara Hammer , "Estimating the electrical power output of industrial devices with end-to-end time-series classification in the presence of label noise", Machine Learning and Knowledge Discovery in Databases. Research Track, 2021.
AbstractIn complex industrial settings, it is common practice to mon- itor the operation of machines in order to detect undesired states, adjust maintenance schedules, optimize system performance or collect usage statistics of individual machines. In this work, we focus on estimating the power output of a Combined Heat and Power (CHP) machine of a medium-sized company facility by analyzing the total facility power consumption. We formulate the prob...
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Elena Raponi, Mariusz Bujny, Markus Olhofer, Simonetta Boria, Fabian Duddeck , "Hybrid Strategy Coupling EGO and CMA-ES for the Topology Optimization of Crash Structures", Computational Intelligence. IJCCI 2019., Springer, LNCS, LNAI, LNBI, vol. 922, issue 1, 2021.
AbstractTopology Optimization (TO) represents a relevant tool in the design of mechanical structures and, as such, it is currently used in many industrial applications. However, many TO optimization techniques are still questionable when applied to crashworthiness optimization problems due to their complexity and lack of gradient information. The aim of this work is to describe the Hybrid Kriging-assisted Level Set Method (HKG-LSM) and test its perfo...
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Tim Puphal, Benedict Flade, Malte Probst , Volker Willert, Jürgen Adamy, Julian Eggert , "Online and Predictive Warning with Risk Maps for Forced Lane Changes", Transactions on Intelligent Vehicles, 2021.
AbstractIn the past, we have introduced the survival analysis that is able to holistically evaluate car risks (from e.g. collisions and curves) in driving trajectories. Its probabilistic and predictive nature is beneficial over common Time-To-X indicators. However, we did not demonstrate so far functions online on real test cars. In this paper, we therefore present Risk Maps (RM) for warning support in forced lane changes. In this context, low-cost ...
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Xilu Wang, Yaochu Jin, Sebastian Schmitt, Markus Olhofer, Richard Allmendinger , "Transfer Learning Based Surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times", Knowledge-Based Systems, vol. 227, no. 5, pp. 107190, 2021.
AbstractVarious multiobjective optimization algorithms have been proposed with a common assumption that the evaluation of each objective function takes the same period of time. Little attention is paid to more general and realistic optimization scenarios where different objectives are evaluated by different computer simulations or physical experiments with different time complexity. To address this issue, we investigate benchmark scenarios with tw...
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Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck , "A New Acquisition Function for Robust Bayesian Optimization of Unconstrained Problems", Genetic and Evolutionary Computation Conference Companion (GECCO Companion), 2021.
AbstractA new acquisition function is proposed for solving robust optimization problems via Bayesian Optimization. The proposed acquisition function reflects the need for the robust instead of the nominal optimum, and is based on the intuition of utilizing the higher moments of the improvement. The efficacy of Bayesian Optimization based on this acquisition function is demonstrated on four test problems, each affected by three different levels of noise. ...
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Jonas Auda, Martin Weigel, Jessica Cauchard, Stefan Schneegass , "Understanding Drone Landing on the Human Body ", Proceedings of the 23rd International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '21), pp. 13, 2021.
AbstractWe envision the human body as a platform for fast take-off and landing of drones in entertainment and professional uses such as medical emergencies, rescue missions, or supporting police units. This new interaction modality challenges our knowledge of human-drone experiences, in which interaction usually occurs at a distance from the body. This work explores important factors for understanding the interplay between drones and humans. We first inv...
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Hao Tong, Leandro L. Minku, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "A Hybrid Local Search Framework for the Dynamic Capacitated Arc Routing Problem", Genetic and Evolutionary Computation Conference Companion (GECCO Companion), 2021.
AbstractThe static capacitated arc routing problem (CARP) is a challenging combinatorial problem, where vehicles need to be scheduled efficiently for serving a set of tasks with minimal travelling costs. Dynamic CARP (DCARP) considers the occurence of dynamic events during the service process, e.g. traffic congestion, which reduce the quality of the currently applied schedule. Existing research mainly focused on scenarios with large changes but neglecte...
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Malte Probst , Raphael Wenzel, Tim Puphal, Thomas H Weisswange, Nico Andreas Steinhardt, Bram Bolder, Benedict Flade, Julian Eggert , "Automated Driving in Complex Real-World Scenarios using a Scalable Risk-Based Behavior Generation Framework", IEEE Intelligent Transportation Systems Conference 2021, 2021.
AbstractThe task of driving autonomously is difficult due to the vast number of driving situations a system may be facing. Especially higher levels of automation in less restricted scopes remain a topic of active research. In previous work, we introduced a behavior planning system which uses analytic models to evaluate the quality of behavior holistically. It uses these models to generate quality-maximizing behavior instead of selecting among pred...
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Stephen Friess, Peter Tino, Zhao Xu, Stefan Menzel, Bernhard Sendhoff, Xin Yao , "Artificial Neural Networks as Feature Extractors in Continuous Evolutionary Optimization", International Joint Conference on Neural Networks (IJCNN), 2021.
AbstractRecent years have seen the advancement of data-driven paradigms in population-based and evolutionary optimization. This reflects on one hand the mere abundance of available data, but on the other hand also progresses in the refinement of previously available machine learning methods. Surprisingly, deep pattern recognition methods emerging from the studies of neural networks have only been sparingly applied. This comes unexpected, as the complex d...
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