TY - CHAP A1 - Schiendorfer, Alexander A1 - Steghöfer, Jan-Philipp A1 - Knapp, Alexander A1 - Nafz, Florian A1 - Reif, Wolfgang ED - Bramer, Max ED - Petridis, Miltos T1 - Constraint Relationships for Soft Constraints T2 - Research and Development in Intelligent Systems XXX: Incorporating Applications and Innovations in Intelligent Systems XXI: Proceedings of AI-2013 UR - https://doi.org/10.1007/978-3-319-02621-3_17 Y1 - 2013 UR - https://doi.org/10.1007/978-3-319-02621-3_17 SN - 978-3-319-02621-3 SP - 241 EP - 255 PB - Springer CY - Cham ER - TY - CHAP A1 - Knapp, Alexander A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang T1 - Quality over Quantity in Soft Constraints T2 - Proceedings: 2014 IEEE 26th International Conference on Tools with Artificial Intelligence: ICTAI 2014 UR - https://doi.org/10.1109/ICTAI.2014.75 Y1 - 2014 UR - https://doi.org/10.1109/ICTAI.2014.75 SN - 978-1-4799-6572-4 SP - 453 EP - 460 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Knapp, Alexander A1 - Steghöfer, Jan-Philipp A1 - Anders, Gerrit A1 - Siefert, Florian A1 - Reif, Wolfgang ED - De Nicola, Rocco ED - Hennicker, Rolf T1 - Partial Valuation Structures for Qualitative Soft Constraints T2 - Software, Services, and Systems: Essays Dedicated to MartinWirsing on the Occasion of His Retirement from the Chair of Programming and Software Engineering UR - https://doi.org/10.1007/978-3-319-15545-6_10 Y1 - 2015 UR - https://doi.org/10.1007/978-3-319-15545-6_10 SN - 978-3-319-15545-6 SN - 978-3-319-15544-9 SP - 115 EP - 133 PB - Springer CY - Cham ER - TY - JOUR A1 - Schiendorfer, Alexander A1 - Knapp, Alexander A1 - Anders, Gerrit A1 - Reif, Wolfgang T1 - MiniBrass: Soft constraints for MiniZinc JF - Constraints UR - https://doi.org/10.1007/s10601-018-9289-2 KW - Soft constraints KW - Modeling languages KW - MiniZinc Y1 - 2018 UR - https://doi.org/10.1007/s10601-018-9289-2 SN - 1572-9354 VL - 23 IS - 4 SP - 403 EP - 450 PB - Springer CY - Dodrecht ER - TY - CHAP A1 - Baschin, Anja A1 - Baschin, Michelle A1 - Böselt, Reinhard A1 - Felix, Rudolf A1 - Fernandez, Cesareo A1 - Gehring, Sven A1 - Görtz, Alexander A1 - Harpeng, Lars A1 - Hei, Yuguang A1 - Hildebrandt, Niclas A1 - Hügging, Thomas A1 - Kätzel, Pascal A1 - Kolberg, Kristoffer-Robin A1 - Kuoza, Leonid A1 - Luzius, Lukas A1 - Lisca, Gheorghe A1 - Michailov, Lilia A1 - Multhaup, Werner A1 - Outafraout, Karima A1 - Proch, Fabian A1 - Schiendorfer, Alexander A1 - Simmanek, Marcel A1 - Streubel, Tom T1 - Industrielle Künstliche Intelligenz für sichere Gasnetze T2 - Tagungsband 18. Symposium Energieinnovation 2024 Y1 - 2024 UR - https://www.tugraz.at/events/eninnov2024/nachlese/download-beitraege/stream-a/#c590685 PB - Technische Universität Graz CY - Graz ER - TY - CHAP A1 - Schönmann, Alexander A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Dumitrescu, Roman ED - Hölzle, Katharina T1 - KI-Augmentation der Reifegradbewertung von Technologien: Können KI-Sprachmodelle Expertenwissen ersetzen? T2 - Vorausschau und Technologieplanung: 17. Symposium für Vorausschau und Technologieplanung, 14. und 15. September 2023, Berlin UR - https://doi.org/10.17619/UNIPB/1-1821 Y1 - 2023 UR - https://doi.org/10.17619/UNIPB/1-1821 SN - 978-3-947647-32-3 SP - 371 EP - 387 PB - Heinz-Nixdorf-Institut CY - Paderborn ER - TY - CHAP A1 - Stieber, Simon A1 - Hoffmann, Alwin A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang A1 - Beyrle, Matthias A1 - Faber, Jan A1 - Richter, Michaela A1 - Sause, Markus T1 - Towards real-time process monitoring and machine learning for manufacturing composite structures T2 - Proceedings 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA46521.2020.9212097 Y1 - 2020 UR - https://doi.org/10.1109/ETFA46521.2020.9212097 SN - 978-1-7281-8956-7 SN - 1946-0759 SP - 1455 EP - 1458 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Nicosia, Giuseppe ED - Pardalos, Panos ED - Umeton, Renato ED - Giuffrida, Giovanni ED - Sciacca, Vincenzo T1 - A chained neural network model for photovoltaic power forecast T2 - Machine Learning, Optimization, and Data Science: 5th International Conference, LOD 2019, Siena, Italy, September 10–13, 2019, Proceedings UR - https://doi.org/10.1007/978-3-030-37599-7_47 Y1 - 2019 UR - https://doi.org/10.1007/978-3-030-37599-7_47 SN - 978-3-030-37598-0 SN - 0302-9743 SP - 566 EP - 578 PB - Springer CY - Cham ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Zimmermann, Gerhard A1 - Lu, Yan A1 - Lo, George T1 - Fault Diagnosis in HVAC Systems Based on the Heat Flow Model T2 - Proceedings of SimBuild Conference 2012, 5th Conference of IBPSA USA, Madison, Wisconsin, USA August 1-3, 2012 Y1 - 2012 UR - https://publications.ibpsa.org/conference/?id=simbuild2012 SP - 440 EP - 447 PB - IBPSA CY - [s. l.] ER - TY - JOUR A1 - Stieber, Simon A1 - Schröter, Niklas A1 - Fauster, Ewald A1 - Bender, Marcel A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang T1 - Inferring material properties from FRP processes via sim-to-real learning JF - The International Journal of Advanced Manufacturing Technology N2 - Fiber reinforced polymers (FRP) provide favorable properties such as weight-specific strength and stiffness that are central for certain industries, such as aerospace or automotive manufacturing. Liquid composite molding (LCM) is a family of often employed, inexpensive, out-of-autoclave manufacturing techniques. Among them, resin transfer molding (RTM), offers a high degree of automation. Herein, textile preforms are saturated by a fluid polymer matrix in a closed mold.Both impregnation quality and level of fiber volume content are of crucial importance for the final part quality. We propose to simultaneously learn three major textile properties (fiber volume content and permeability in X and Y direction) presented as a three-dimensional map based on a sequence of camera images acquired in flow experiments and compare CNNs, ConvLSTMs, and Transformers. Moreover, we show how simulation-to-real transfer learning can improve a digital twin in FRP manufacturing, compared to simulation-only models and models based on sparse real data. The overall best metrics are: IOU 0.5031 and Accuracy 95.929 %, obtained by pretrained transformer models. UR - https://doi.org/10.1007/s00170-023-11509-8 KW - Sequence-to-Image Learning KW - Architecture comparison KW - FRP KW - LCM KW - Transfer Learning KW - Industry 4.0 KW - Digital Twin Y1 - 2022 UR - https://doi.org/10.1007/s00170-023-11509-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32402 SN - 1433-3015 VL - 128 IS - 3-4 SP - 1517 EP - 1533 PB - Springer CY - London ER -