Publikationen

The increasing potential of Information and Communications Technology (ICT) drives higher degrees of digitisation in the manufacturing industry. Such catchphrases as “Industry 4.0” and “smart manufacturing” reflect this tendency. The implementation of these paradigms is not merely an end to itself, but a new way of collaboration across existing department and process boundaries. Converting the process input, internal and output data into digital twins offers the possibility to test and validate the parameter changes via simulations, whose results can be used to update guidelines for shop-floor workers. The result is a Cyber-Physical System (CPS) that brings together the physical shop-floor, the digital data created in the manufacturing process, the simulations, and the human workers. The CPS offers new ways of collaboration on a shared data basis: the workers can annotate manufacturing problems directly in the data, obtain updated process guidelines, and use knowledge from other experts to address issues. Although the CPS cannot replace manufacturing management since it is formalised through various approaches, e. g., Six-Sigma or Advanced Process Control (APC), it is a new tool for validating decisions in simulation before they are implemented, allowing to continuously improve the guidelines.

This paper explores the use of reinforcement learning (RL) models for autonomous racing. In contrast to passenger cars, where safety is the top priority, a racing car aims to minimize the lap-time. We frame the problem as a reinforcement learning task witha multidimensional input consisting of the vehicle telemetry, and a continuous action space. To findout which RL methods better solve the problem and whether the obtained models generalize to drivingon unknown tracks, we put 10 variants of deep deterministic policy gradient (DDPG) to race in two experiments: i) studying how RL methods learn to drive a racing car and ii) studying how the learning scenario influences the capability of the models to generalize. Our studies show that models trained with RL are not only able to drive faster than the baseline open source handcrafted bots but also generalize to unknown tracks.

Distance-based classification is among the most competitive classification methods for time series data. The most critical componentof distance-based classification is the selected distance function.Past research has proposed various different distance metrics ormeasures dedicated to particular aspects of real-world time seriesdata, yet there is an important aspect that has not been considered so far: Robustness against arbitrary data contamination. In thiswork, we propose a novel distance metric that is robust against arbitrarily “bad” contamination and has a worst-case computationalcomplexity of O(n logn). We formally argue why our proposedmetric is robust, and demonstrate in an empirical evaluation thatthe metric yields competitive classification accuracy when appliedin k-Nearest Neighbor time series classification.

This paper presents the Know-Center system submitted for task 5 of the SemEval-2019workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether it contains hateful speech and second,to determine the target and level of aggression used. For this purpose our system utilizes word embeddings and a neural network architecture, consisting of both dilated and traditional convolution layers. We achieved aver-age F1-scores of 0.57 and 0.74 for English and Spanish respectively.

Schrunner and Geiger have contributed equally to this work.