Auflistung nach Titel

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  • Deep learning approach to predict optical attenuation in additively manufactured planar waveguides 
    Pflieger, Keno; Evertz, Andreas; Overmeyer, Ludger (Washington, DC : Optical Soc. of America, 2024)
    The booming demand for efficient, scalable optical networks has intensified the exploration of innovative strategies that seamlessly connect large-scale fiber networks with miniaturized photonic components. Within this ...
  • Deep Learning Based Classification of Pedestrian Vulnerability Trained on Synthetic Datasets 
    Schleusner, Jens; Neu, Lothar; Behmann, Nicolai; Blume, Holger (Piscataway, NJ : IEEE, 2019)
    The reliable detection of vulnerable road users and the assessment of the actual vulnerability is an important task for the collision warning algorithms of driver assistance systems. Current systems make assumptions about ...
  • Deep learning based feature matching and its application in image orientation 
    Chen, Lie; Rottensteiner, Franz; Heipke, Christian (Katlenburg-Lindau : Copernicus Publications, 2020)
    Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description ...
  • Deep learning for geometric and semantic tasks in photogrammetry and remote sensing 
    Heipke, Christian; Rottensteiner, Franz (Wuhan : Wuhan Univ. Journals Press, 2020)
    During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote ...
  • A Deep Learning Framework for automated collection and analysis of traffic data based on identifying and classifying Delivery Vehicles in Logistics 
    Jin, Meng; Mauch, Lars Andreas; Bienzeisler, Bernd (Hannover : publish-Ing., 2021)
    In urban logistics, analyzing urban traffic data plays an important role in achieving higher schedule reliability and delivery time efficiency. To increase the diversity of urban traffic data, we developed a solution for ...
  • Deep Learning Gauss–Manin Connections 
    Heal, Kathryn; Kulkarni, Avinash; Sertöz, Emre Can (Cham (ZG) : Springer International Publishing AG, 2022)
    The Gauss–Manin connection of a family of hypersurfaces governs the change of the period matrix along the family. This connection can be complicated even when the equations defining the family look simple. When this is the ...
  • Deep Learning-Based Tracking of Multiple Objects in the Context of Farm Animal Ethology 
    Ali, R.; Dorozynski, M.; Stracke, J.; Mehltretter, M. (Katlenburg-Lindau : Copernicus Publications, 2022)
    Automatic detection and tracking of individual animals is important to enhance their welfare and to improve our understanding of their behaviour. Due to methodological difficulties, especially in the context of poultry ...
  • Deep Learning-Based Weld Contour and Defect Detection from Micrographs of Laser Beam Welded Semi-Finished Products 
    Nowroth, Christian; Gu, Tiansheng; Grajczak, Jan; Nothdurft, Sarah; Twiefel, Jens; Hermsdorf, Jörg; Kaierle, Stefan; Wallaschek, Jörg (Basel : MDPI, 2022)
    Laser beam welding is used in many areas of industry and research. There are many strategies and approaches to further improve the weld seam properties in laser beam welding. Metallography is often needed to evaluate welded ...
  • Deep molecular phylogeny of the Pterygota 
    Simon, Sabrina (Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2010)
    [no abstract]
  • A deep q-learning-based optimization of the inventory control in a linear process chain 
    Dittrich, M.-A.; Fohlmeister, S. (Heidelberg : Springer, 2021)
    Due to growing globalized markets and the resulting globalization of production networks across different companies, inventory and order optimization is becoming increasingly important in the context of process chains. ...
  • Deep Reinforcement Learning In Production Planning And Control: A Systematic Literature Review 
    Panzer, Marcel; Bender, Benedict; Gronau, Norbert (Hannover : publish-Ing., 2021)
    Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions ...
  • Deep sea explosive eruptions may be not so different from subaerial eruptions 
    Iezzi, Gianluca; Lanzafame, Gabriele; Mancini, Lucia; Behrens, Harald; Tamburrino, Stella; Vallefuoca, Mattia; Passara, Salvatore; Signanini, Patrizio; Ventura, Guido ([London] : Macmillan Publishers Limited, part of Springer Nature, 2020)
    The dynamics of deep sea explosive eruptions, the dispersion of the pyroclasts, and how submarine eruptions differ from the subaerial ones are still poorly known due to the limited access to sea environments. Here, we ...
  • The deep space quantum link: prospective fundamental physics experiments using long-baseline quantum optics 
    Mohageg, Makan; Mazzarella, Luca; Anastopoulos, Charis; Gallicchio, Jason; Hu, Bei-Lok; Jennewein, Thomas; Johnson, Spencer; Lin, Shih-Yuin; Ling, Alexander; Marquardt, Christoph; Meister, Matthias; Newell, Raymond; Roura, Albert; Schleich, Wolfgang P.; Schubert, Christian; Strekalov, Dmitry V.; Vallone, Giuseppe; Villoresi, Paolo; Wörner, Lisa; Yu, Nan; Zhai, Aileen; Kwiat, Paul (Heidelberg {[u.a.] : SpringerOpen, 2022)
    The National Aeronautics and Space Administration’s Deep Space Quantum Link mission concept enables a unique set of science experiments by establishing robust quantum optical links across extremely long baselines. Potential ...
  • Deep weathering in the semi-arid Coastal Cordillera, Chile 
    Krone, Laura V.; Hampl, Ferdinand J.; Schwerdhelm, Christopher; Bryce, Casey; Ganzert, Lars; Kitte, Axel; Übernickel, Kirstin; Dielforder, Armin; Aldaz, Santiago; Oses-Pedraza, Rómulo; Perez, Jeffrey Paulo H.; Sanchez-Alfaro, Pablo; Wagner, Dirk; Weckmann, Ute; von Blanckenburg, Friedhelm ([London] : Macmillan Publishers Limited, part of Springer Nature, 2021)
    The weathering front is the boundary beneath Earth’s surface where pristine rock is converted into weathered rock. It is the base of the “critical zone”, in which the lithosphere, biosphere, and atmosphere interact. ...
  • Deep-learning continuous gravitational waves 
    Dreissigacker, Christoph; Sharma, Rahul; Messenger, Chris; Zhao, Ruining; Prix, Reinhard (College Park, MD : American Physical Society, 2019)
    We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current ...
  • Deep-learning continuous gravitational waves : Multiple detectors and realistic noise 
    Dreissigacker, Christoph; Prix, Reinhard (College Park, MD : American Physical Society, 2020)
    The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that deep neural networks (DNNs) can perform all-sky searches directly on ...
  • Deep-learning-based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography 
    Laves, Max-Heinrich; Ihler, Sontje; Kahrs, Lüder A.; Ortmaier, Tobias (Bellingham, Wash. : SPIE, 2019)
    In microsurgery, lasers have emerged as precise tools for bone ablation. A challenge is automatic control of laser bone ablation with 4D optical coherence tomography (OCT). OCT as high resolution imaging modality provides ...
  • Deep-learning-based instrument detection for intra-operative robotic assistance 
    Badilla-Solórzano, Jorge; Spindeldreier, Svenja; Ihler, Sontje; Gellrich, Nils-Claudius; Spalthoff, Simon (Heidelberg [u.a.] : Springer, 2022)
    Purpose: Robotic scrub nurses have the potential to become an attractive solution for the operating room. Surgical instrument detection is a fundamental task for these systems, which is the focus of this work. We address ...
  • Defeasible Reasoning with Large Language Models - Initial Experiments and Future Directions 
    Antoniou, Grigoris; Batsakis, Sotiris (Aachen, Germany : RWTH Aachen, 2023)
    As Large Language Models gain prominence in the AI landscape, it is essential to understand their capabilities and limitations, among others in terms of reasoning. This paper is a first step towards understanding the ...
  • Defektstrukturen auf dünnen isolierenden Filmen 
    Kramer, Judith (Hannover : Universität, 2003)
    [no abstract]