| An Evolutionary Learning Approach to Self-Configuring Image Pipelines in the Context of Carbon Fiber Fault Detection A Margraf, A Stein, E Leonhard, S Geinitz, J Hähner IEEE International Conference on Machine Learning and Applications (ICMLA17 …, 2017 | 22 | 2017 |
| Data Augmentation for Semantic Segmentation in the context of Carbon Fiber Defect Detection using Adversarial Learning S Mertes, A Margraf, C Kommer, S Geinitz, E André 1st International Conference on Deep Learning Theory and Applications 2020, 2020 | 9 | 2020 |
| Toward an organic computing approach to automated design of processing pipelines A Stein, A Margraf, J Moroskow, S Geinitz, J Haehner ARCS Workshop 2018; 31th International Conference on Architecture of …, 2018 | 8 | 2018 |
| Detection of Filament Misalignment in Carbon Fiber Production Using a Stereovision Line Scan Camera System S Geinitz, A Margraf, A Wedel, S Witthus, K Drechsler 19th World Conference on Non-Destructive Testing, 2016 | 6 | 2016 |
| Detection of Surface Defects on Carbon Fiber Rovings using Line Sensors and Image Processing Algorithms A Margraf, S Geinitz, A Wedel, L Engstler SAMPE Conference 17 Stuttgart, 2017 | 4 | 2017 |
| Alternative Data Augmentation for Industrial Monitoring using Adversarial Learning S Mertes, A Margraf, S Geinitz, E André arXiv preprint arXiv:2205.04222, 2022 | 2 | 2022 |
| Evolving processing pipelines for industrial imaging with cartesian genetic programming A Margraf, H Cui, A Stein, J Hähner 2023 IEEE International Conference on Autonomic Computing and Self …, 2023 | 1 | 2023 |
| Weighted mutation of connections to mitigate search space limitations in cartesian genetic programming H Cui, D Pätzel, A Margraf, J Hähner Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic …, 2023 | 1 | 2023 |
| Model-driven optimisation of monitoring system configurations for batch production A Margraf, H Cui, S Heimbach, J Hähner, S Geinitz, S Rudolph | 1 | 2023 |
| Refining mutation variants in Cartesian genetic programming H Cui, A Margraf, J Hähner International Conference on Bioinspired Optimization Methods and Their …, 2022 | 1 | 2022 |
| Online detection and categorisation of defects along carbon fibre production using a high resolution, high width line scan vision system S Geinitz, A Wedel, A Margraf | 1 | 2016 |
| Filter evolution using Cartesian genetic programming for time series anomaly detection A Margraf, H Cui, S Baumann, J Hähner Universität Augsburg, 2023 | | 2023 |
| Equidistant Reorder Operator for Cartesian Genetic Programming H Cui Deutsche Nationalbibliothek, 2023 | | 2023 |
| Towards Understanding Crossover for Cartesian Genetic Programming H Cui Deutsche Nationalbibliothek, 2023 | | 2023 |
| Signal Detection for Tracer-Based-Sorting using Deep Learning and Synthetic Data MAMSG Christian Linder, Frank Gaibler Proceedings of the 14th International Joint Conference on Computational …, 2022 | | 2022 |
| KI in der Fasertechnologie: Texturen mit Zeilenkameras analysieren A Margraf https://www.igcv.fraunhofer.de/content/dam/igcv/de/docs/Pressemitteilungen …, 2020 | | 2020 |
| Towards Self-adaptive Defect Classification in Industrial Monitoring A Margraf, J Hähner, P Braml, S Geinitz 9th International Conference on Data Science, Technology and Applications …, 2020 | | 2020 |
| Evolutionary Learning for Processing Pipelines in Image Segmentation and Monitoring Systems A Margraf Organic Computing: Doctoral Dissertation Colloquium 2018 13, 99, 2019 | | 2019 |
| Automatisch Fusselfrei S Geinitz, A Margraf Carbon Composites Magazin, 59, 2018 | | 2018 |
| Evolutionary Learning for Processing Pipelines in Image Segmentation and Monitoring Systems A Margraf Organic Computing Doctoral Dissertation 2018, 2018 | | 2018 |
