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Jens Engel , "Integration of Electrical Vehicles into a Model Predictive Control Building Energy System", TU Darmstadt, 2020.
AbstractIn the future mobility infrastructure, electrical vehicles (EVs) will play an increasingly important role. This trend can also be seen in the number of newly registered EVs per year in Germany, as Figure 1.1 illustrates. This will not only impact the mobility infrastructure, but very importantly, also the energy infrastructure, namely the power grid. The reason for this is the large energy demand these EVs will generate through their large batter...
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Sibghat Ullah, Duc Anh Nguyen, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck , "Exploring Dimensionality Reduction Techniques for Efficient Surrogate-Assisted Optimization", IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
AbstractConstructing surrogate models of high dimensional optimization problems is challenging due to the computational complexity involved. This paper empirically investigates the practicality of major dimensionality reduction techniques for encapsulating the high dimensional design space into compact representations. Such low dimensional representations of the design space can be utilized for constructing the surrogate models efficiently. Based on hist...
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Xiaofen Lu, Ke Tang, Stefan Menzel, Xin Yao , "A New Co-evolutionary Optimization Method for the Dynamic Vehicle Routing Problem", IEEE Symposium Series on Computational Intelligence (SSCI), 2020.
AbstractIn the basic vehicle routing problem, the best routes need to be found for a fleet of vehicles to serve a set of customers. The dynamic vehicle routing problem is a variant of the vehicle routing problem, in which part or all the information defining the routing problem might change over time. This requires an optimizer to perform a fast search of new routes once the change happens. For its robustness with respect to noise, evolutionary algorithm...
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Fabio Muratore, Michael Gienger, Jan Peters , "Assessing Transferability from Simulation to Reality for Reinforcement Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020.
AbstractLearning robot control policies from physics simulations is of great interest for the robotics community as it mayrender the learning process faster, cheaper, and safer by alleviating the need for expensive real-world experiments. However, the direct transfer of the learned behavior from simulation to realityis a major challenge. Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the ‘Simulat...
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Nima Nabizadeh, Martin Ernst Heckmann, Dorothea Kolossa , "Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies", Joint Workshop on Multiword Expressions and Electronic Lexicons (MWE-LEX 2020) at COLING 2020 (Barcelona, Spain), vol. 1, issue 1, pp. 22–26, 2020.
AbstractWhile repairing a device, humans employ a series of tools that corresponds to the arrangement of the device components. Such sequences of tool usage can be learned from repair manuals, so that at each step, observing the previously applied tools, a sequential model can predict the next required tool. In this paper, we improve the tool prediction performance of such methods by additionally taking the hierarchical relationships among the tools into...
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Tapabrata Ray, Hemant Kumar Singh, Ahsanul Habib, Tobias Rodemann, Markus Olhofer , "Online intensification of search around solutions of interest for multi/many-objective optimization", 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1-8, 2020.
AbstractIn practical multi/many-objective optimization problems, a decision maker is often only interested in a handful of solutions of interest (SOI) instead of the entire Pareto Front (PF). It is therefore of significant research interest to design algorithms that can automatically detect SOIs and search around them instead of attempting to find the entire PF. However, this is challenging for a number of reasons. First and foremost, the interpretation ...
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Kalman György Graffi and Newton Masinde , "LibreSocial: A Peer-to-Peer Framework for Online Social Networks", Concurrency and Computation: Practice and Experience, vol. 32, no. 24, pp. 1-26, 2020.
AbstractDistributed online social networks (DOSNs) were first proposed to solve the problem of privacy, security and scalability. A significant amount of research was undertaken to offer viable DOSN solutions that were capable of competing with the existing centralized OSN applications such as Facebook, LinkedIn and Instagram. This research led to the emergence of the use of peer-to- peer (P2P) networks as a possible solution, upon which several OSNs s...
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Thomas Schnürer, Malte Probst , Horst-Michael Groß , "Relational Concepts in Deep Reenforcement Learning: Emergence and Representation", CARLA Workshop, 2020.
AbstractRelational Architectures have shown to be quite successful in deep reinforcement learning recently. We are interested in comparing and evaluating the different types of relational representations such approaches can learn as well as the training process that allows for their emergence. This Extended Abstract outlines the work and experiments we will present ate the workshop....
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Mathias Franzius, Benjamin Metka, Muhammad Haris, Ute Bauer-Wersing , "Unsupervised Learning of Metric Representations with Slow Features from Omnidirectional Views", 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020.
AbstractUnsupervised learning of Self-Localization with SlowFeature Analysis (SFA) using omnidirectional camera input has been shown to be a viable alternative to established SLAM approaches. Previous models for SFA self-localization purely relied on omnidirectional visual input. The model led to globally consistent localization in SFA space but the lack of odometry integration reduced the local accuracy. However, odometry integration and other down...
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Hesham Elsayed, Martin Weigel, Florian Müller, Martin Schmitz, Karola Marky, Sebastian Günther, Jan Riemann, Max Mühlhäuser , "VibroMap: Understanding Spacing of Vibrotactile Actuators Across the Body", Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol. 4, no. 4, 2020.
AbstractDespite the great potential of on-body vibrotactile displays for a variety of applications, research lacks an understanding of the spacing between vibrotactile actuators. Through two controlled experiments, we systematically investigate vibrotactile perception on the wrist, forearm, upper arm, back, torso, thigh, and leg, each in transverse and longitudinal body orientation. In the first experiment, we address the maximum distance between vibrati...
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