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  • Grabowski, N., Kremser, R., Düssel, R., Mulder, A., Tutsch, D.: Using Random Forest Regression for Predicting and Analysing Reduction Cell Behaviour. Proceeding of 12th Australasian Aluminium Smelting Technology Conference (AASTC 2018), Queenstown, New Zealand, December 2018, to appear.

  • P. Oberdiek and M. Rottmann and Hanno Gottschalk, Detection of a Deep Neural Network’s Classification Uncertainty from Gradient Information, to appear Proc. Artificial Neural Networks in Pattern Recognition, Siena 2018.

  • M. Hinz, D. Brüggemann and S. Bracke: On the Application of Machine Learning Techniques in Condition Monitoring Systems of Complex Machines. PSAM 14, Los Angeles, U.S.A. Proceedings: The 14th Probabilistic Safety Assessment and Management, PSAM 14, September 16th – 21th, 2018, Los Angeles, U.S.A. (2018), to appear.

  • M. Rottmann, K. Kahl and H. Gottschalk, Deep Bayesian active semi-supervised learning , Proc. 17th IEEE International Conference on Machine Learning and Applications (ICML), Orlando Fl 2018.

  • P. Oberdiek and M. Rottmann and Hanno Gottschalk, Detection of a Deep Neural Network’s Classification Uncertainty from Gradient Information, to appear Proc. Artificial Neural Networks in Pattern Recognition, Siena 2018.

  • Tercan, Hasan; Guajardo, Alexandro; Heinisch, Julian; Thiele, Thomas; Hopmann, Christian; Meisen, Tobias (2018): Transfer-Learning: Bridging the Gap between Real and Simulation Data for Machine Learning in Injection Molding. In: Procedia CIRP 72, S. 185–190.https://www.sciencedirect.com/science/article/pii/S2212827118301914
  • Meyes, Richard; Tercan, Hasan; Roggendorf, Simon; Thiele, Thomas; Büscher, Christian; Obdenbusch, Markus et al. (2017): Motion Planning for Industrial Robots using Reinforcement Learning. In: Procedia CIRP 63, S. 107–112.
    https://www.sciencedirect.com/science/article/pii/S221282711730241X

  • Pomp, André; Paulus, Alexander; Kirmse, Andreas; Kraus, Vadim; Meisen, Tobias (2018): Applying Semantics to Reduce the Time to Analytics within Complex Heterogeneous Infrastructures. In: Technologies 6 (3), S. 86. http://www.mdpi.com/2227-7080/6/3/86/pdf
  • Ionita, Andrei; Pomp, André; Cochez, Michael; Meisen, Tobias; Decker, Stefan (2018): Where to Park?: Predicting Free Parking Spots in Unmonitored City Areas. In: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. ACM, S. 22.https://dl.acm.org/citation.cfm?id=3227648
  • Wang, You; Tercan, Hasan; Thiele, Thomas; Meisen, Tobias; Jeschke, Sabina; Schulz, Wolfgang (2017): Advanced data enrichment and data analysis in manufacturing industry by an example of laser drilling process. In: ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K), 2017. IEEE, S. 1–5.
    https://ieeexplore.ieee.org/document/8246990

  • M. Hinz, F. Hienzsch and S. Bracke: Analysis of simulated and recorded data of car fleets based on machine learning algorithms. Proceedings: The 13th Probabilistic Safety Assessment and Management, PSAM 13, Seoul, Korea, October 2nd -7th, 2016 (2016).

  • C. Rosebrock, M. Hinz, F. Reinecke and S. Bracke: Modelling the reliability of lead anodes in the electrowinning process of non-ferrous metals using machine learning. In: M. Cepin, R. Bris; Safety and Reliability – Theory and Applications; ESREL 2017, Portorož, Slovenia, 18th – 22nd June 2017; European Safety and Reliability Association, ESRA (2017).

  • M. Hinz, P. Temminghoff and S. Bracke: Optimization of test procedures based on OBD system specific field data. Proceedings: RAMS 2016 – 62nd Reliability and Maintainability Symposium, Tucson, Arizona, U.S.A. (2016).