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Publications

Displaying results 311 to 320 of 661.

  1. Hendrik Wöhrle; Mariela De Lucas Alvarez; Fabian Schlenke; Alexander Walsemann; Michael Karagounis; Frank Kirchner (Hrsg.)

    Surrogate Model based Co-Optimization of Deep Neural Network Hardware Accelerators

    IEEE International Midwest Symposium on Circuits and Systems (MWSCAS-2021), August 9-11, USA, IEEE, 8/2021.

  2. Nijat Mehdiyev; Johannes Lahann; Andreas Emrich; David Enke; Peter Fettke; Peter Loos

    Time Series Classification using Deep Learning for Process Planning: A Case from the Process Industry

    In: Procedia Computer Science. Complex Adaptive Systems (CAS-2017), USA, Pages 242-249, Vol. 114, 2017.

  3. Babu Ajish; Frank Kirchner

    Terrain Adaption Controller for a Walking Excavator Robot using Deep Reinforcement Learning

    In: 2021 20th International Conference on Advanced Robotics (ICAR). International Conference On Advanced Robotics (ICAR-2021), December 7-10, Ljubljana, Slovenia, Pages 64-70, IEEE Xplore, ieeexplore.ieee.org/document/9659399, 12/2021.

  4. Jens Popper; Carsten Harms; Martin Ruskowski

    Enabling reliable Visual Quality Control in Smart Factories through TSN

    In: Roberto Teti; Doriana M. D'Addona (Hrsg.). Procedia CIRP, Vol. 88 - 13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 17-19 July 2019, Gulf of Naples, Italy, Pages 549-553, Elsevier B.V. 2020.

  5. Jens Popper; Martin Ruskowski

    Using Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Problems

    In: Roberto Teti; Doriana M. D'Addona (Hrsg.). CIRP Proceedings. CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME), Pages 63-67, Vol. 112, Special Issue 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 14-16 July 2019, Gulf of Naples, Italy, Elsevier B.V. 2021.

  6. Jens Popper; Vassilios Yfantis; Martin Ruskowski (Hrsg.)

    Simultaneous Production and AGV Scheduling using Multi-Agent Deep Reinforcement Learning

    CIRP Conference on Manufactoring Systems (CIRP CMS-2021), 54th CIRP Conference on Manufacturing Systems, 2021, located at CIRP, September 22-24, Athens, Greece, ELSEVIER, 2021.

  7. Jens Popper; William Motsch; Alexander David; Teresa Petzsche; Martin Ruskowski (Hrsg.)

    Utilizing Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Under Sustainable Viewpoints

    International Conference on Electrical, Computer, Communications and Mechatronics Engineering, located at 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, October 7-8, Belle Mare, Mauritius, IEEE, 2021.

  8. Kareem Amin; Stelios Kapetanakis; Klaus-Dieter Althoff; Andreas Dengel; Miltos Petridis

    Dynamic process workflow routing using Deep Learning

    In: Artificial Intelligence XXXV. SGAI International Conference on Artificial Intelligence (AI-2018), December 11-13, Cambridge, United Kingdom, Springer, 2018.

  9. Gabriel Mittag; Sebastian Möller

    Non-intrusive Estimation of Packet Loss Rates in Speech Communication Systems Using Convolutional Neural Networks

    In: 2018 IEEE International Symposium on Multimedia (ISM). IEEE International Symposium on Multimedia (ISM-2018), December 10-12, Taichung, Taiwan, Province of China, Pages 105-109, ISBN 978-1-5386-6857-3, IEEE, 2018.

  10. Dayananda Herurkar; Shoya Ishimaru; Andreas Dengel

    Combining Software-Based Eye Tracking and a Wide-Angle Lens for Sneaking Detection

    In: Proc. UbiComp2018 Adjunct (Hrsg.). The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing. International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp-2018), October 8-12, Singapore, Singapore, Pages 54-57, ISBN 978-1-4503-5966-5, ACM, 2018.

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