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 - 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 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Amini, Massih-Reza ED - Canu, Stéphane ED - Fischer, Asja ED - Guns, Tias ED - Kralj Novak, Petra ED - Tsoumakas, Grigorios T1 - A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks T2 - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I UR - https://doi.org/10.1007/978-3-031-26387-3_28 KW - Graph Machine Learning KW - Recommendation KW - Computer-aided Design KW - AI-aided Design Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-26387-3_28 SN - 978-3-031-26387-3 SN - 978-3-031-26386-6 SP - 457 EP - 473 PB - Springer CY - Cham ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Steghöfer, Jan-Philipp A1 - Reif, Wolfgang T1 - Synthesis and Abstraction of Constraint Models for Hierarchical Resource Allocation Problems T2 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence: Volume 2: ICAART N2 - Many resource allocation problems are hard to solve even with state-of-the-art constraint optimisation software upon reaching a certain scale. Our approach to deal with this increasing complexity is to employ a hierarchical “regio-central” mechanism. It requires two techniques: (1) the synthesis of several models of agents providing a certain resource into a centrally and efficiently solvable optimisation problem and (2) the creation of an abstracted version of this centralised model that reduces its complexity when passing it on to higher layers. We present algorithms to create such synthesised and abstracted models in a fully automated way and demonstrate empirically that the obtained solutions are comparable to central solutions but scale better in an example taken from energy management. UR - https://doi.org/10.5220/0004757700150027 Y1 - 2014 UR - https://doi.org/10.5220/0004757700150027 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-42364 SN - 978-989-758-016-1 SP - 15 EP - 27 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Lassner, Christoph A1 - Anders, Gerrit A1 - Reif, Wolfgang A1 - Lienhart, Rainer T1 - Active Learning for Efficient Sampling of Control Models of Collectives T2 - Proceedings: 2015 IEEE 9th International Conference on Self-Adaptive and Self-Organizing Systems: SASO 2015 UR - https://doi.org/10.1109/SASO.2015.13 Y1 - 2015 UR - https://doi.org/10.1109/SASO.2015.13 SP - 51 EP - 60 PB - IEEE CY - Los Alamitos 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 -