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Thomas Schmitt, Tobias Rodemann, Jürgen Adamy, "Application of Pareto Frontiers in an Economic Model Predictive Controlled Microgrid", GMA-Fachausschuss Treffen, GMA, 2019.

Abstract

The increase of renewable energies and a trend to decentralization lead to a need of managable strategies for energy commitment in small microgrids. Intuitively, economic model predictiv control (EMPC) is a profound approach for this task, due to its capabilities of optimizing an control sequence with respect to constraints and future predictions. To apply EMPC, we model a medium-sized company building as a second-order linear time-discrete mod...



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Steffen Limmer, Takahiro Ishihara, Tobias Rodemann, "SimulationX Solver Setting Optimization via Automated Hyperparameter Tuning Approaches", ESI Forum 2019, 2019.

Abstract

An ever-increasing complexity of technical systems requires sophisticated methods to optimize a larger number of design parameters under consideration of many objectives. For a broad class of problems evolutionary algorithms are the method of choice. Their main drawback is a huge computational effort since thousands or more simulation runs are required. It is therefore essential to reduce the simulation times as much as possible. One approach i...



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Thomas Jatschka, Tobias Rodemann, Guenther Raidl, "A Cooperative Optimization Approach for Distributing Service Points in Mobility Applications", EvoCOP, pp. 1-16, 2019.

Abstract

We investigate a variant of the facility location problem concerning the optimal distribution of service points with incomplete information within a certain geographical area. The application scenario is generic in principle, but we have the setup of charging stations for electric vehicles or rental stations for bicycles or cars in mind. When planning such systems, estimating under which conditions which customer demand can be fulfilled is fundam...



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Akinobu Hayashi, Dirk Ruiken, Christian Goerick, Tadaaki Hasegawa, "Online adaptation of uncertain models using neural network priors and partially observable planning", International Conference on Robotics and Automation (ICRA) 2019, 2019.

Abstract

One of the key challenges in realizing a robot that is capable of completing a variety of manipulation tasks in the real world is the need to utilize sufficiently compact and rich world models. If the assumed prediction model does not match real observations, planning systems are unable to perform properly. We propose a system that corrects the models based on information collected from the robot's sensors. We encode prior experiences in a neural...



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Martina Hasenjäger and Taizo Yoshikawa, "Machine learning approaches in human walk modeling", IROS 2019 Cutting Edge Forum "Human Movement Understanding for Intelligent Robots and Systems", 2019.

Abstract

The combination of an increasing life expectancy and a low, decreasing birth rate has lead to aging societies in many countries. The resulting problems of a decreasing workforce and an increasing demand in health care are particularly acute in Japan. To address these problems, we aim to develop advanced physical assist devices to improve the quality of life and to extend activities of daily living and activities in the work place. We regard a hum...



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Viktor Losing, Martina Hasenjäger, Heiko Wersing, Barbara Hammer, "Personalized Online Learning of Whole-Body Motion Classes Using Multiple Inertial Measurement Units", International Conference on Robotics and Automation (ICRA), 2019.

Abstract

Online action classification is an important field of research, enabling the particularly interesting application scenario of controlling wearable devices which actively support the user's motions. The majority of machine learning applications of real-world systems are based on pre-trained average-user models without any personalization. Our long-term goal is to provide a system that adapts to its user's personal behavior patterns on the fly and ...



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Torsten Schwan, Sebastian Schmitt, Andrea Castellani, "Calibration of HVAC system models with monitoring data - Digital Twin meets measurement data", ESI FORUM IN DEUTSCHLAND , 2019.

Abstract

Modern heat, ventilation and air-conditioning (HVAC) systems for buildings requires engineers to use increasingly more complex physical models to evaluate building performance in early design stages as well as during modernization and reconstruction phases. Those models often provide accurate results regarding total annual heat, cold and power consumption. However, achieving very accurate high temporal resolution results and evaluation of smart ...



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Pouya Aghaei-Pour, Tobias Rodemann, Markus Olhofer, Jussi Hakanen, Kaisa Miettinen, "On Surrogate Management in Interactive Multiobjective Building Energy System Design", ECCOMAS Thematic Conference Computational Sciences and AI in Industry (CSAI), U Jyvaeskyla, 2019.

Abstract

When thinking of possible extensions of energy systems, decision making for larger buildings consists of a series of complex investment decisions. The consideration involves multiple objectives like investment and annual operation costs, CO 2 emissions and module lifetime to be considered simultaneous. Thus, in building energy system management, methods of multiobjective optimization are needed to support decision making. We have a system upgrade...



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Guo Yu, Yaochu Jin, Markus Olhofer, "References or Preferences – Rethinking Many-objective Evolutionary Optimization", CEC 2019, 2019.

Abstract

Past decades have witnessed a rapid development in multi- and many-objective evolutionary optimization. The references-assisted and preference-driven strategies are both widely used in dealing with the multi- and many-objective optimization problems. However, few research analyzes the difference between these two strategies. Thus, this paper analyzes and compares both strategies from background, constructions, similarities, differences, to concer...



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Robin Menzenbach, "Benchmarking Sim-2-Real Algorithms on Real-World Platforms", Technical University of Darmstadt, 2019.

Abstract

Learning from simulation is particularly useful, because it is typically cheaper and safer than learning on real-world systems. Nevertheless, the transfer of learned behavior from the simulation to the real word can impose difficulties because of the so-called ’reality gap’. There are multiple approaches trying to close the gap. Although many benchmarks of reinforcement learning algorithms exist, state-of-the-art sim-2-real methods are rarely...



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