Wissenschaftliche Publikationen

2023

T. Harweg, M. Wagner and F. Weichert, "Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario" , International Journal of Environmental Research and Public Health, vol. 20, no. 1, 2023.
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R. Hergenröder, F. Weichert, K. Wüstefeld and V. Shpacovitch, "Virus Detection" in Machine Learning under Resource Constraints - Applications, De Gruyter, 2023, pp. 21--42.
 doi:10.1515/9783110785982-010
H. Müller and F. Weichert, Vorkurs Informatik - Der Einstieg ins Informatikstudium (2023). .... 6 Springer Vieweg, 2023.
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T. Ambrosat, F. Gierschner, A. Hundrup, T. Harweg, F. Weichert, D. Ungermann and W. Flügge, "Automatisierte Inspektion von stahlbaulichen Strukturen mittels Drohne im Innenbereich" , zfv, vol. 5, 2023.
 10.12902/zfv-0441-2023
N. Sharar, K. Wüstefeld, R. M. Talukder, J. Skolnik, K. Kaufmann, B. Giebel, F. Börger, C. Watzl, F. Weichert, R. Hergenröder and V. Shpacovitch, "The employment of the surface plasmon resonance (SPR) microscopy sensor for the detection of individual extracellular vesicles and non-biological nanoparticles" , Biosensors (accepted for publication), 2023.
D. Matuszczyk and F. Weichert, "Reading Direct-Part Marking Data Matrix Code in the Context of Polymer-Based Additive Manufacturing" , Sensors, vol. 23, no. 3, 2023.
 Datei 10.3390/s23031619
N. Piatkowski, K. Morik, N. Kriege, C. Morris, M. Fey, F. Weichert, N. Bertram, J. Ellert, J. Fischer and L. Pfahler, "Structured Data" in Machine Learning under Resource Constraints - Fundamentals, De Gruyter, 2023, pp. 99--178.
 doi:10.1515/9783110785944-004

2022

N. Tschorn, D. Matuszczyk and F. Weichert, "Deep Learning based Synthetic Image Generation for Defect Detection in Additive Manufacturing Industrial Environments" , International Conference on Mechanical Engineering and Robotics Research, 2022.
C. Rest, D. Fisseler, F. Weichert and G. G. W. Müller, "Illumination-based augmentation for cuneiform deep neural sign classification" , Journal on Computing and Cultural Heritage (accepted for publication), 2022.
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J. Lins, T. Harweg, F. Weichert and K. Wohlgemuth, "Potential of deep learning methods for deep level particle characterization in crystallization" , Applied Sciences (accepted for publication), 2022.

2021

A. Roth, K. Wüstefeld and F. Weichert, "A Data-Centric Augmentation Approach for Disturbed Sensor Image Segmentation" , Journal of Imaging, vol. 7 (10), 2021.
 PDF https://doi.org/10.3390/jimaging7100206
T. Harweg, D. Bachmann and F. Weichert, "Agent-based Simulation of Pedestrian Dynamics for Exposure Time Estimationin Epidemic Risk Assessment" , Journal of Public Health, 2021.
 Link https://doi.org/10.1007/s10389-021-01489-y
M. Fey, J. E. Lenssen, F. Weichert and J. Leskovec, "GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings" , International Conference on Machine Learning (ICML), 2021.
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2020

J. E. Lenssen, C. Osendorfer and J. Masci, "Deep Iterative Surface Normal Estimation" in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
 PDF Link
A. Böckenkamp, "roslaunch2: Versatile, Flexible and Dynamic Launch Configurations for the Robot Operating System" in Robot Operating System (ROS): The Complete Reference,Koubaa, Anis, Eds. Heidelberg: Springer International Publishing, 2020, pp. 165--181.
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Y. Zhao, T. Birdal, J. E. Lenssen, E. Menegatti, L. Guibas and F. Tombari, "Quaternion Equivariant Capsule Networks for 3D Point Clouds" in European Conference on Computer Vision (ECCV), 2020.
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R. Chabra, J. E. Lenssen, E. Ilg, T. Schmidt, J. Straub, S. Lovegrove and R. Newcombe, "Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction" in European Conference on Computer Vision (ECCV), 2020.
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M. Fey, J. Yuen and F. Weichert, "Hierarchical Inter-Message Passing for Learning on Molecular Graphs" in ICML Graph Representation Learning and Beyond (GRL+), 2020.
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M. Fey, J. E. Lenssen, C. Morris, J. Masci and N. Kriege, "Deep Graph Matching Consensus" in International Conference on Learning Representations (ICLR), 2020.
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T. Harweg, A. Peter, D. Bachmann and F. Weichert, CNN-Based Deep Architecture for Health Monitoring of Civil and Industrial Structures using UAVs: 6th International Electronic Conference on Sensors and Applications.
 PDF https://doi.org/10.3390/ecsa-6-06640
M. D. Jedrusiak and F. Weichert, "A Deep Learning Approach for Denoising Air-Coupled Ultrasonic Responds Data" , International Journal of Artificial Intelligence and Applications (IJAIA), vol. 11, no. 4, pp. 15-28, 2020.
 PDF Link
K. Wüstefeld and F. Weichert, "An Automated Rapid Test for Viral Nanoparticles Based on Spatiotemporal Deep Learning" , IEEE Sensors, pp. 1-4, 2020.
 PDF Link 10.1109/SENSORS47125.2020.9278935
M. Kleineberg, M. Fey and F. Weichert, "Adversarial Generation of Continuous Implicit Shape Representations" in Eurographics, 2020.
 PDF Link

2019

C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan and M. Grohe, "Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks" in 33rd AAAI Conference on Artificial Intelligence (AAAI-19), 2019.
 Datei
G. Rinkenauer, T. Plewan, D. Bachmann and F. Weichert, "Vergleich von virtuellen Interaktionssystemen mit einer realen Situation" , Arbeit interdisziplinär analysieren - bewerten - gestalten: 65. Kongress der Gesellschaft für Arbeitswissenschaft, vol. 6, pp. 1-6, 2019.