TY - CHAP A1 - Bhavnani, Sidhant A1 - Schiendorfer, Alexander ED - Schaus, Pierre T1 - Towards copeland optimization in combinatorial problems T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022; Proceedings UR - https://doi.org/10.1007/978-3-031-08011-1_4 KW - constraint programming KW - social choice theory KW - OR Y1 - 2022 UR - https://doi.org/10.1007/978-3-031-08011-1_4 SN - 978-3-031-08010-4 SN - 978-3-031-08011-1 N1 - Access provided by Nationallizenz Ebooks Medicine SP - 34 EP - 43 PB - Springer CY - Cham ER - TY - CHAP A1 - Hanke, Julian A1 - Kosak, Oliver A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang T1 - Self-organized Resource Allocation for Reconfigurable Robot Ensembles T2 - 2018 IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) UR - https://doi.org/10.1109/SASO.2018.00022 KW - multi-agent KW - multi-robot KW - resource allocation KW - self-awareness KW - self-configuration Y1 - 2018 UR - https://doi.org/10.1109/SASO.2018.00022 SN - 978-1-5386-5172-8 SP - 110 EP - 119 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Gajek, Carola A1 - Reif, Wolfgang ED - Bischl, Bernd ED - Guhr, Oliver ED - Seibold, Heidi ED - Steinbach, Peter T1 - Turning Software Engineers into Machine Learning Engineers T2 - Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop Y1 - 2021 UR - https://proceedings.mlr.press/v141/schiendorfer21a.html IS - 141 SP - 36 EP - 41 PB - PMLR CY - [s. l.] ER - TY - CHAP A1 - Ella, Miriam A1 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Bischl, Bernd ED - Guhr, Oliver ED - Seibold, Heidi ED - Steinbach, Peter T1 - An Interactive Web Application for Decision Tree Learning T2 - Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop Y1 - 2021 UR - https://proceedings.mlr.press/v141/elia21a.html IS - 141 SP - 11 EP - 16 PB - PMLR CY - [s. l.] 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 SP - 1455 EP - 1458 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Schiex, Thomas ED - de Givry, Simon T1 - Reducing bias in preference aggregation for multiagent soft constraint problems T2 - Principles and Practice of Constraint Programming, 25th International Conference, CP 2019, Proceedings UR - https://doi.org/10.1007/978-3-030-30048-7_30 Y1 - 2019 UR - https://doi.org/10.1007/978-3-030-30048-7_30 SN - 978-3-030-30047-0 SP - 510 EP - 526 PB - Springer CY - Cham 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 - Stieber, Simon A1 - Schröter, Niklas A1 - Schiendorfer, Alexander A1 - Hoffmann, Alwin A1 - Reif, Wolfgang ED - Dong, Yuxiao ED - Mladenić, Dunja ED - Saunders, Craig T1 - FlowFrontNet: Improving Carbon Composite Manufacturing with CNNs T2 - Machine Learning and Knowledge Discovery in Databases, Applied Data Science Track, Proceedings, Part IV UR - https://doi.org/10.1007/978-3-030-67667-4_25 KW - process monitoring KW - convolutional neural networks KW - digital twin KW - manufacturing KW - industrial automation KW - resin transfer molding KW - carbon composites Y1 - 2021 UR - https://doi.org/10.1007/978-3-030-67667-4_25 SN - 978-3-030-67667-4 SN - 978-3-030-67666-7 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 411 EP - 426 PB - Springer CY - Cham 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 - 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 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Do, Phuc ED - Michau, Gabriel ED - Ezhilarasu, Cordelia T1 - Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks T2 - Proceedings of the European Conference of the PHM Society 2022 N2 - Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting. UR - https://doi.org/10.36001/phme.2022.v7i1.3331 KW - machine Learning KW - classification KW - uncertainty KW - estimaton KW - neural network Y1 - 2022 UR - https://doi.org/10.36001/phme.2022.v7i1.3331 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31307 SN - 978-1-936263-36-3 SP - 294 EP - 305 PB - PHM Society CY - State College ER - TY - CHAP A1 - Ruttmann, Julia A1 - Schiendorfer, Alexander ED - Hotho, Andreas ED - Rudolph, Sebastian T1 - SocialCOP: Reusable Building Blocks for Collective Constraint Optimization T2 - KI 2024: Advances in Artificial Intelligence, 47th German Conference on AI Würzburg, Germany, September 25–27, 2024 Proceedings UR - https://doi.org/10.1007/978-3-031-70893-0_15 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-70893-0_15 SN - 978-3-031-70893-0 SN - 978-3-031-70892-3 SP - 204 EP - 217 PB - Springer CY - Cham ER - TY - CHAP A1 - Schiendorfer, Alexander ED - Tomforde, Sven ED - Sick, Bernhard T1 - A Soft Constraint Approach to Self-organising Systems T2 - Organic Computing, Doctoral Dissertation Colloquium 2015 Y1 - 2015 UR - https://www.uni-kassel.de/ub/publizieren/kassel-university-press/verlagsprogramm?h=9783737600286 SN - 978-3-7376-0029-3 SN - 978-3-7376-0028-6 SP - 3 EP - 11 PB - kassel university press CY - Kassel ER - TY - CHAP A1 - Schiendorfer, Alexander ED - Tomforde, Sven ED - Sick, Bernhard T1 - Constraint Programming for Hierarchical Resource Allocation T2 - Organic Computing, Doctoral Dissertation Colloquium 2014 Y1 - 2014 UR - https://www.uni-kassel.de/ub/publizieren/kassel-university-press/verlagsprogramm/?h=978-3-86219-832-0 SN - 978-3-86219-832-0 SN - 978-3-86219-833-7 SP - 57 EP - 68 PB - kassel university press CY - Kassel ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Lassner, Christoph A1 - Anders, Gerrit A1 - Reif, Wolfgang A1 - Lienhart, Rainer ED - Cardoso, João M. P. T1 - Active Learning for Abstract Models of Collectives T2 - ARCS 2015 - 28th International Conference on Architecture of Computing Systems, Workshop Proceedings Y1 - 2015 UR - https://www.vde-verlag.de/proceedings-de/563657010.html SN - 978-3-8007-3657-7 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/abstract/document/7107102 PB - VDE Verlag CY - Berlin ER - TY - JOUR A1 - Augustine, Nitin A1 - Schwab, Maximilian A1 - Klarmann, Steffen A1 - Pfefferer, Christian A1 - Schiendorfer, Alexander T1 - Impact of Blemish Artefacts on Object Detection Models in Autonomous Driving: A Study on Camera Module Manufacturing Defects JF - Procedia Computer Science N2 - With the rise of autonomous driving technology, concerns over the safety of AI-based systems have increased, particularly regarding their effect on human life and infrastructure. The complexity and unpredictable nature of AI-enabled software functions have disrupted traditional safety evaluation methods. In such a scenario, the quality control methods used at the manufacturing environments of sensors used for deploying AI-based perception functions need to be coupled with the downstream functions. This study focuses on investigating the impact of a specific production-related defect called blemish, which is caused by debris on image sensors of camera modules. The research aims to answer two fundamental questions: 1) How can blemish artifacts be mathematically modeled, and what is the process for developing a suitable dataset that incorporates this defect? 2) How can the influence of blemish artifacts on performance of object detection models be traced back to define quality requirements of the camera modules? This work takes a novel perspective by shifting the focus from improving model resilience to improving hardware quality camera modules by studying the effects of manufacturing defects on downstream functions. This research seeks to establish a framework to derive quality targets for front camera module manufacturing which can be extended to other sensors for similar applications. UR - https://doi.org/10.1016/j.procs.2024.01.061 Y1 - 2024 UR - https://doi.org/10.1016/j.procs.2024.01.061 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46388 SN - 1877-0509 VL - 2024 IS - 232 SP - 616 EP - 625 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Facchinetti, Tullio ED - Cenedese, Angelo ED - Lo Bello, Lucia ED - Vitturi, Stefano ED - Sauter, Thilo ED - Tramarin, Federico T1 - A multi-layer machine learning architecture for near real-time inference in manufacturing based on Apache Kafka and selective classification T2 - 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA61755.2024.10711030 Y1 - 2024 UR - https://doi.org/10.1109/ETFA61755.2024.10711030 SN - 979-8-3503-6123-0 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schiendorfer, Alexander A1 - Lisca, Gheorghe A1 - Outafraout, Karima A1 - Michailov, Lilia A1 - Kätzel, Pascal A1 - Felix, Rudolf ED - Endriss, Ulle ED - Melo, Francisco S. ED - Bach, Kerstin ED - Bugarín-Diz, Alberto ED - Alonso-Moral, José M. ED - Barro, Senén ED - Heintz, Fredrik T1 - Gas Grid Copilot: Can a MORL Agent Assist a Dispatcher in Managing a Gas Grid? T2 - ECAI 2024: 27th European Conference on Artificial Intelligence, 19–24 October 2024, Santiago de Compostela, Spain, Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), Proceedings N2 - The distribution of fuel gases is undergoing major changes due to decarbonization efforts: Non-fossil gases such as biomethane or renewable hydrogen can lead to the reuse of existing gas infrastructure for gas storage, transport, and distribution to reduce greenhouse gas emissions while maintaining a high energy security. For safe and efficient operation, we propose Gas Grid Copilot (GGC) as a demonstrator of a multi-objective reinforcement learning agent that trains in a simulated gas grid environment to control a grid by modifying its inflow into a mass storage. Multiple, possibly conflicting reward signals are included. Their conflicts and synergies of rewards are analyzed using techniques from multi-criteria decision making, more specifically a conflict interaction matrix based on extended fuzzy logic. That way, dispatchers of a gas grid can explore the effects of reward prioritizations and their consequences safely. UR - https://doi.org/10.3233/FAIA241025 Y1 - 2024 UR - https://doi.org/10.3233/FAIA241025 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-53069 SN - 978-1-64368-548-9 SP - 4443 EP - 4446 PB - IOS Press CY - Amsterdam ER - TY - CHAP A1 - Steffel, Pauline A1 - Bock, Jürgen A1 - Schiendorfer, Alexander ED - Almeida, Luis ED - Indria, Marina ED - de Sousa, Mario ED - Visioli, Antonio ED - Ashjaei, Mohammad ED - Santos, Pedro T1 - Towards a Configurable and Reusable RL Training Infrastructure for AMRs in ROS2 T2 - 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA65518.2025.11205702 Y1 - 2025 UR - https://doi.org/10.1109/ETFA65518.2025.11205702 SN - 979-8-3315-5383-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Dachtler, Kristina A1 - Schiendorfer, Alexander ED - Almeida, Luis ED - Indria, Marina ED - de Sousa, Mario ED - Visioli, Antonio ED - Ashjaei, Mohammad ED - Santos, Pedro T1 - A Framework to integrate Machine Learning Decision Preferences in Manufacturing Use Cases T2 - 2025 IEEE 30th International Conference on Emerging Technologies and Factory Automation (ETFA) UR - https://doi.org/10.1109/ETFA65518.2025.11205573 Y1 - 2025 UR - https://doi.org/10.1109/ETFA65518.2025.11205573 SN - 979-8-3315-5383-8 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Lodes, Lukas A1 - Hass, Erik A1 - Dachtler, Kristina A1 - Schiendorfer, Alexander T1 - SmartManPy – Open Source Synthetic Manufacturing Data JF - Procedia Computer Science N2 - Data Science projects in manufacturing often suffer from difficult access to the necessary high-quality data, such as sensor readings or product quality records. In addition, strict rules due to data protection and intellectual property concerns hinder broad use of the provided data for innovative research, especially in the academic context. To enable the research and evaluation of approaches for a variety of Industry 4.0 use cases, we developed SmartManPy, an extension to the ManPy discrete event simulation environment. We extended the scope of the project to meet our needs and are now able to simulate production lines and the resulting data in just a few lines of code. SmartManPy (Smart Manufacturing Python) includes complex synthetic feature generation, quality control and failures based on recorded features, and data drift simulation. We additionally added a basic environment to train AI agents using reinforcement learning. Our code is released to the public on GitHub. UR - https://doi.org/10.1016/j.procs.2025.01.245 Y1 - 2025 UR - https://doi.org/10.1016/j.procs.2025.01.245 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57650 SN - 1877-0509 VL - 2025 IS - 253 SP - 1830 EP - 1839 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Dachtler, Kristina A1 - Ortner, Michael A1 - Ferri, Massimo A1 - Eberst, Christof A1 - Schiendorfer, Alexander T1 - Data-centric and Goal-oriented AI for Robotic Repair Tasks T2 - ISR Europe 2023, 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 – 27, 2023 in Stuttgart Y1 - 2023 SN - 978-3-8007-6141-8 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/document/10363053 PB - VDE Verlag CY - Berlin ER -