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Ahmed Sadik and Christian Goerick , "Multi-Robot System Architecture Design in SysML and BPMN", Advances in Science, Technology and Engineering Systems Journal (ASTESJ) - Special Issue on Multidisciplinary Sciences and Engineering, 2021.
AbstractMulti-Robot System (MRS) is a complex system that contains many different software and hardware components. This main problem addressed in this article is the MRS design complexity. The proposed solution provides a modular modeling and simulation technique that is based on formal system engineering method, therefore the MRS design complexity is decomposed and reduced. Modeling the MRS has been achieved via two formal Archi...
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Fabio Muratore , "An Empirical Analysis ofMeasure-Valued Derivatives for Policy Gradients", International Joint Conference on Neural Networks (IJCNN), 2021.
AbstractAUTHORS: Jo ̃ao Carvalho, Davide Tateo, Fabio Muratore, and Jan Peters The success of policy gradient methods in the context of Reinforcement Learning, particularly in robotic tasks, lies in computing a precise (low variance) and accurate (low bias) gradient estimate. Traditional policy gradient algorithms obtain a gradient estimate by employing the likelihood-ratio trick, which is known to produce unbiased but high variance estimates. In th...
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Duc Nguyen, Ewout Zwanenburg, Steffen Limmer, Wessel Luijben, Markus Olhofer, Thomas Bäck , "A Combination of Fourier Transform and Machine Learning for Fault Detection and Diagnosis of Induction Motors", 2021 8th International Conference on Dependable Systems and Their Applications (DSA), pp. 344-351, 2021.
AbstractInduction motors are widely used in different industry areas and can experience various kinds of faults in stators and rotors. In general, fault detection and diagnosis techniques for induction motors can be supervised by measuring quantities such as noise, vibration, and temperature. The installation of mechanical sensors in order to assess the health conditions of a machine is typically only done for expensive or load-critical machines, where t...
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Chao Wang and Pengcheng An , "A Mobile Tool that Helps Nonexperts Make Sense of Pretrained CNN by Interacting with Their Daily Surroundings", The ACM International Conference on Mobile Human-Computer Interaction, 2021.
AbstractConvolutional Neural Network(CNN) is widely used for object recognition. However, it is very difficult for a human to understand the mechanism of CNN which is a deep learning algorithm. Class Activation Map is a promising technique to visualize the discriminative image regions used by CNN to identify an object. In this work, we present a mobile- and a web-based interactive game, which can compare human's perception of regional information for rec...
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Jan Philip Goepfert, Heiko Wersing, Barbara Hammer , "Locally adaptive nearest neighbours", Neurocomputing, 2021.
AbstractWhen training automated systems, it has been shown to be beneficialto adapt the representation of data by learning a problem-specific metric.This metric is global. We extend this idea and, for the widely used familyof k nearest neighbors algorithms, develop a method that allows learninglocallyadaptive metrics. To demonstrate important aspects of how ourapproach works, we conduct a number of experiments on synthetic datasets, and we show its usefu...
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Saif Sidhik, Mohan Sridharan, Dirk Ruiken , "Towards a Framework for Changing-Contact Robot Manipulation", 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), 2021.
AbstractWe describe a framework for changing-contact robot manipulation tasks, which require the robot to make and break contacts with objects and surfaces. The discontinuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy for such tasks. For any target motion trajectory, the framework incrementally improves its prediction of when contacts will occur. This prediction and a model relati...
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Thomas Jatschka, Guenther Raidl, Tobias Rodemann , "A General Cooperative Optimization Approach for Distributing Service Points in Mobility Applications", Algorithms, 2021.
AbstractWe present a cooperative optimization approach (COA) for distributing service points for mobility applications, which generalizes and refines a previously proposed method. COA is an iterative framework for optimizing service point locations, combining an optimization component with user interaction on a large scale and a machine learning component that learns user needs and provides the objective function for the optimization. The previously pro...
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Benedict Flade, Simon Kohaut, Julian Eggert , "Error Decomposition for Hybrid Localization Systems", IEEE Intelligent Transportation Systems Conference (ITSC), 2021.
AbstractFuture advanced driver assistance systems and autonomous vehicles rely on accurate localization which can be divided into three classes: a) viewpoint localization with regard to local references (e.g., via vision-based localization), b) absolute localization with regard to a global reference system (e.g., via satellite navigation), and c) hybrid localization, which presents a combination of the former two. Hybrid localization shares character...
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Nisal Menuka Gamage, Deepana Ishtaweera, Martin Weigel, Anusha Withana , "So Predictable! Continuous 3D Hand Trajectory Prediction in Virtual Reality ", Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology (UIST '21), pp. 12, 2021.
AbstractWe contribute a novel user- and activity-independent kinematics-based regressive model for continuously predicting ballistic hand movements in virtual reality (VR). Compared to prior work on end-point prediction, continuous hand trajectory prediction in VR enables an early estimation of future events such as collisions between the user’s hand and virtual objects such as UI widgets. We developed and validated our prediction model through a user st...
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Mariusz Bujny, Muhammad Salman Yousaf, Fabian Duddeck , "Similarity-based Topology Optimization for Crash and Statics", ECCOMAS Congress 2020, 2021.
AbstractTopology Optimization (TO) [1] is an important technique that redistributes the material within a design domain to optimize certain objective functions under specified constraints. In industry, efficient gradient-based techniques using SIMP (Solid Isotropic Material with Penalization) interpolation with Optimality Criteria (OC) [1] or heuristic approaches such as Hybrid Cellular Automata (HCA) [2] are used to optimize structures without taking i...
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