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Steffen Limmer and Nils Einecke , "An Efficient Approach for Peak-load-aware Scheduling of Energy-intensive Tasks in the Context of a Public IEEE Challenge", Energies, vol. 15, no. 10, 2022.
AbstractThe shift towards renewable energy and the decreasing battery prices have led to numerous installations of PV and battery systems in industrial and public buildings. Furthermore, the fluctuation of energy cost is increasing as energy sources based on solar and wind are depending on the weather situation. In order to reduce energy costs it is necessary to plan energy hungry activities taking own PV production, battery capacity and energy market ...
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Sneha Saha, Leandro L. Minku, Xin Yao, Bernhard Sendhoff, Stefan Menzel , "Split-AE: An Autoencoder-based Disentanglement Framework for 3D Shape-to-shape Feature Transfer", International Joint Conference on Neural Networks (IJCNN), 2022.
AbstractRecent advancements in machine learning comprise generative models such as autoencoders (AE) for learning and compressing 3D data to generate low-dimensional latent representations of 3D shapes. Learning latent representations that disentangle the underlying factors of variations in 3D shapes is an intuitive way to achieve generalization in generative models. However, it remains an open problem to learn a generative model of 3D shapes such that t...
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Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck , "Cooperative multi-objective topology optimization using clustering and metamodelling", IEEE 2022 Congress on Evolutionary Computation, 2022.
AbstractTopology optimization optimizes material layout in a design space for a given objective, such as crash energy absorption, and a set of boundary conditions. In industrial applications, multi-objective topology optimization requires expensive simulations to evaluate the objectives and generate multiple Pareto-optimal solutions. So, it is more economical to identify preferred regions on the Pareto front and generate only the desired solutions....
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Matthias Hoffmann, Thomas Schmitt, Kathrin Flaßkamp , "I’ll tell you what I want: Categorization of Pareto Fronts for Automated Rule-based Decision-Making", 2022 18th IFAC Workshop on Control Applications of Optimization, vol. 55, no. 16, pp. 376-381, 2022.
AbstractThe application of Pareto optimization in control engineering requires decision-making as a downstream step since one solution has to be selected from the set of computed Pareto optimal points. Economic Model Predictive Control (MPC) requires repeated optimization and, in multi-objective optimization problems, selection of Pareto optimal points at every time step. Thus, designing an automated selection strategy is favorable. However, it is chal...
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Karsten Kreutz and Julian Eggert , "Fast online IDM parameter estimation for trajectory prediction ", Intelligent Vehicles 2022, 2022.
AbstractIn this paper, we propose and analyze a method for trajectory prediction in longitudinal car-following scenarios. Hereby the prediction is realized by a longitudinal car-following model (Intelligent driver model, IDM) with online estimated parameters. In previous approaches, it has been shown that online parameter adaptation of the IDM is possible but difficult and slow due to the nonlinearity of the parameters, providing only a marginal improvem...
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Kinan Bab, Rawane Issa, Mayank Varia, Kalman György Graffi , "Batched Differentially Private Information Retrieval", USENIX Security '22 Summer, 2022.
AbstractPrivate Information Retrieval (PIR) allows several clients to query a database held by one or more servers, such that the contents of their queries remain private. Prior PIR schemes have achieved sublinear communication and computation by leveraging computational assumptions, federating trust among many servers, relaxing security to permit differentially private leakage, refactoring effort into an offline stage to reduce online costs, or amortizi...
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Qiqi Liu , "Evolutionary optimization of many-objective problems with irregular Pareto fronts", University of Surrey, 2022.
AbstractDecomposition based evolutionary algorithms have proven to be able to strike a good trade-off between convergence and diversity in handling multi-objective or many-objective optimization problems with regular Pareto fronts. However, the performance of decomposition based algorithms becomes less efficient in dealing with many-objective problems with irregular Pareto fronts (we call it irregular problems hereafter for simplicity). In this thesis, w...
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Kyle Poland, Anja Sturm, Aaron Gutknecht, Patricia Wollstadt, Michael Wibral, Abdullah Makkeh , "On a differentiable partial information decomposition for continuous random variables and applications in (artificial) neural networks", Bernstein Conference 2021, 2021.
AbstractUnderstanding information mechanisms inside complex systems often poses intricate questions. In neural systems, information is often represented by an ensemble of agents. Knowledge about how information is distributed amongst those agents can lead to insights about how to distribute relevant information about a problem over available agents. These agents can, for instance, be neurons that are recorded during stimulation, one may imagine spike tra...
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Thomas Schmitt, Tobias Rodemann, Jürgen Adamy , "Automatized Decision Making in Multi-Objective MPC with Preferences", GMA Fachausschuss 1.40 „Systemtheorie und Regelungstechnik", 2021.
AbstractIf multiple objectives have to be considered in Model Predictive Control (MPC), usually this is achieved by using a weighted sum as the cost function of the optimal control problem. where the weights are fixed. However, if the circumstances vary over time, the selected weighting between the objectives might not be desirable anymore. Thus, concepts from Multi-Objective Optimization (MOO) can be used. In MOO, the main goal is to choose the Pareto...
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Samuele Vinanzi, Christian Goerick, Angelo Cangelosi , "The Collaborative Mind: Intention Reading and Trust in Human-Robot Interaction", iScience Special Issue, vol. 24, no. 2, 2021.
AbstractRobots stand at the heart of a techno-scientific revolution which promises to significantly alter the way in which we conceive our society. Recent discoveries point towards a future in which artificial agents will become fully integrated in our social structures, thus becoming important actors in our everyday life. In this scenario, it is of critical importance for these robots to understand us in the most human-like fashion and to be able to ...
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