TY - JOUR AU - Madeira Firmino, Nadine AU - Bauknecht, Jürgen ID - 4448 IS - 1 JF - Pädagogischer Blick TI - Entwicklung, Ausmaß und Determinanten der Arbeitszufriedenheit von Erzieherinnen und Erziehern. ER - TY - CONF AU - Erichsmeier, Fabian AU - Kukushkin, Maksim AU - Fiedler, Johannes AU - Enders, Matthias AU - Goertz, Simon AU - Bogdan, Martin AU - Schmid, Thomas AU - Kaschuba, Reinhard ED - Heinemann, Dag ED - Polder, Gerrit ID - 4444 SN - 9781510670181 T2 - Photonic Technologies in Plant and Agricultural Science TI - Automating the purity analysis of oilseed rape through usage of hyperspectral imaging ER - TY - CONF AU - Behrens, Grit AU - Theis, Niklas AU - Boschert, Andreas AU - Zehner, Mike ID - 4400 SN - 978-3-948176-25-9 T2 - Tagungsunterlagen 39. PV-Symposium TI - Wolkensegementierung und -matching mit Deep Learning in All Sky Images ER - TY - CONF AU - Behrens, Grit AU - Hepp, Dennis AU - Hempelmann, Sebastian AU - Friedrich , Werner ID - 4399 SN - 978-3-948176-25-9 T2 - Tagungsunterlagen 39. PV-Symposium, 2024 Kloster Banz, Bad Staffelstein TI - Anwendung von Convolutional Neural Networks zur Schneeerkennung auf Photovoltaikflächen ER - TY - CHAP AU - Orlowski, Cezary AU - Behrens, Grit AU - Karatzas, Kostas ED - Wohlgemuth, Volker ED - Kranzlmüller, Dieter ED - Höb, Maximilian ID - 4397 SN - 2196-8705 T2 - Advances and New Trends in Environmental Informatics 2023. Sustainable Digital Society TI - Commonalities and Differences in ML-Pipelines for Air Quality Systems ER - TY - CONF AU - Behrens, Grit AU - Karaztas, Kostans AU - Orlowski, Cezary ED - Bui, Tung X. ID - 4395 SN - 978-0-9981331-7-1 T2 - 57th Hawaii International Conference on System Sciences, {HICSS} 2024, Hilton Hawaiian Village Waikiki Beach Resort, Hawaii, USA, January 3-6, 2024 TI - Business Intelligence Fog IoT node development model for Big Data processing of air quality in scientific partnerships ER - TY - THES AB - Current solutions for holistic and real-time planning of dynamic manufacturing processes are reaching their limits. This is particularly applicable to complex sociotechnical production environments with flexible material flows as well as undetermined events and fluctuations. Methods of optimization under uncertainty are very computationally intensive and crucial interactions with the real world are insufficiently considered. This lack of field synchronicity reduces the quality of production schedules, leads to manual efforts firefighting), and has a negative impact on the logistical performance. The present work is based on four journal articles that demonstrate novel methods and models for improving field-synchronous scheduling. Through the combination of instruments from operations research and machine learning, generic and predictive algorithms are developed to improve the efficiency and effectiveness of planning procedures. The findings suggest that regression models can replace computation-heavy stochastic simulations in obtaining robustness metrics. Additionally, using reinforcement learning, uncertainty-robust and realistic production schedules for human-centered manufacturing can be generated in a short time. For this purpose, discrete simulation models are used, which are data-driven initialized based on a general control logic. The algorithms can be integrated into a virtual factory, which serves as a digital representation of the real world and is the basis for smart and field-synchronous scheduling systems. In this context, a prototype distributed system for the planning of dynamic manufacturing processes can be presented, which is being tested by industry research partners and further developed in collaboration. Beyond the publications, further research needs can be derived. In order to ensure the transferability of the methods, they need to be evaluated in the context of additional and more comprehensive environments. From a scientific and practical perspective, it is a crucial challenge to develop holistic and proactive scheduling systems that orchestrate a comprehensive set of data-driven analysis and decision-making processes. In this regard, the presented methods and models need to be further developed and integrated into a generic overall concept. The work identifies four focus areas that future research should address in an interdisciplinary manner: (1) Generic simulation models, (2) Human-centered optimization, (3) Field-synchronous scheduling, and (4) System development and rollout. AU - Grumbach, Felix ID - 4392 KW - Produktionsplanung und -steuerung KW - Operations Research KW - Machine Learning KW - Reinforcement Learning TI - Feldsynchrone Ablaufplanung dynamischer Fertigungsprozesse mit Techniken des maschinellen Lernens [kumulative Dissertation] ER - TY - BOOK AU - Eisfeld, Michael ID - 4434 TI - BIM - Einstieg kompakt für Tragwerksplaner ER - TY - JOUR AU - Hamilton, K. AU - Phipps, D. J. AU - Schmidt, P. AU - Bamberg, Sebastian AU - Ajzen, I. ID - 4403 IS - 1 JF - Psychology & Health SN - 0887-0446 TI - First test of the theory of reasoned goal pursuit: predicting physical activity VL - 39 ER - TY - CONF AU - Karau, Fabian AU - Leuer, Michael ID - 4391 SN - 9783910103023 T2 - Fit für die Zukunft: Praktische Lösungen für die industrielle Automation TI - Recheneffiziente Implementierung einer approximierten modellprädiktiven Regelung auf einem Industrie-PC ER -