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Publications

Displaying results 301 to 310 of 661.

  1. Jayasankar Santhosh; David Dzsotjan; Shoya Ishimaru

    Multimodal Assessment of Interest Levels in Reading: Integrating Eye-Tracking and Physiological Sensing

    In: IEEE Access (IEEE), Vol. 11, Pages 93994-94008, IEEE, 9/2023.

  2. Andrew S. Morgan; Daljeet Nandha; Georgia Chalvatzaki; Carlo D'Eramo; Aaron M. Dollar; Jan Peters

    Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning

    In: IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2021), May 30 - June 5, Xi'an, China, Pages 6672-6678, IEEE, 2021.

  3. 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.

  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. Kumail Raza; René Schuster; Didier Stricker

    Multi-scale Iterative Residuals for Fast and Scalable Stereo Matching

    In: Computer Science in Cars Symposium. ACM Computer Science in Cars Symposium (CSCS-2021), November 30, Ingolstadt, Germany, ACM, 2021.

  9. Viktor Eisenstadt; Hardik Arora; Christoph Ziegler; Jessica Bielski; Christoph Langenhan; Klaus-Dieter Althoff; Andreas Dengel

    Comparative Evaluation of Tensor-based Data Representations for Deep Learning Methods in Architecture

    In: V. Stojakovic; B. Tepavcevic (Hrsg.). Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1. Education and Research in Computer Aided Architectural Design in Europe (eCAADe-2021), September 8-10, Novi Sad, Serbia, Pages 45-54, CUMINCAD, 2021.

  10. Hardik Arora; Jessica Bielski; Viktor Eisenstadt; Christoph Langenhan; Christoph Ziegler; Klaus-Dieter Althoff; Andreas Dengel

    Consistency Checker – An automatic constraint-based evaluator for housing spatial configurations

    In: V. Stojakovic; B. Tepavcevic (Hrsg.). Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 2. Education and Research in Computer Aided Architectural Design in Europe (eCAADe-2021), September 8-10, Novi Sad, Serbia, CUMINCAD, 2021.

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