@inproceedings{PanzerBenderGronau2021,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Deep reinforcement learning in production planning and control},
  series = {Proceedings of the Conference on Production Systems and Logistics},
  booktitle = {Proceedings of the Conference on Production Systems and Logistics},
  publisher = {Institutionelles Repositorium der Leibniz Universit{\"a}t Hannover},
  address   = {Hannover},
  issn      = {2701-6277},
  doi       = {10.15488/11238},
  pages     = {535 -- 545},
  year      = {2021},
  abstract  = {Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.},
  language  = {en}
}
@misc{GoeritzBergerGegeetal.2018,
  author    = {G{\"o}ritz, Anna and Berger, Stella A. and Gege, Peter and Grossart, Hans-Peter and Nejstgaard, Jens C. and Riedel, Sebastian and R{\"o}ttgers, R{\"u}diger and Utschig, Christian},
  title     = {Retrieval of water constituents from hyperspectral in-situ measurements under variable cloud cover},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {941},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-45983},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-459837},
  pages     = {21},
  year      = {2018},
  abstract  = {Remote sensing and field spectroscopy of natural waters is typically performed under clear skies, low wind speeds and low solar zenith angles. Such measurements can also be made, in principle, under clouds and mixed skies using airborne or in-situ measurements; however, variable illumination conditions pose a challenge to data analysis. In the present case study, we evaluated the inversion of hyperspectral in-situ measurements for water constituent retrieval acquired under variable cloud cover. First, we studied the retrieval of Chlorophyll-a (Chl-a) concentration and colored dissolved organic matter (CDOM) absorption from in-water irradiance measurements. Then, we evaluated the errors in the retrievals of the concentration of total suspended matter (TSM), Chl-a and the absorption coefficient of CDOM from above-water reflectance measurements due to highly variable reflections at the water surface. In order to approximate cloud reflections, we extended a recent three-component surface reflectance model for cloudless atmospheres by a constant offset and compared different surface reflectance correction procedures. Our findings suggest that in-water irradiance measurements may be used for the analysis of absorbing compounds even under highly variable weather conditions. The extended surface reflectance model proved to contribute to the analysis of above-water reflectance measurements with respect to Chl-a and TSM. Results indicate the potential of this approach for all-weather monitoring.},
  language  = {en}
}
@article{KonakvandeWaterDoeringetal.2023,
  author    = {Konak, Orhan and van de Water, Robin and D{\"o}ring, Valentin and Fiedler, Tobias and Liebe, Lucas and Masopust, Leander and Postnov, Kirill and Sauerwald, Franz and Treykorn, Felix and Wischmann, Alexander and Gjoreski, Hristijan and Luštrek, Mitja and Arnrich, Bert},
  title     = {HARE},
  series = {Sensors},
  volume    = {23},
  journal   = {Sensors},
  number    = {23},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s23239571},
  pages     = {23},
  year      = {2023},
  abstract  = {Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE's multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.},
  language  = {en}
}
@article{SchladebachBarsan2023,
  author    = {Schladebach, Marcus and B{\^a}rsan, Catinca},
  title     = {Der Mondbergbau als v{\"o}lkerrechtliche Herausforderung},
  series = {Zeitschrift f{\"u}r Bergrecht},
  volume    = {164},
  journal   = {Zeitschrift f{\"u}r Bergrecht},
  number    = {2},
  publisher = {Carl Heymanns Verlag},
  address   = {K{\"o}ln},
  issn      = {0340-3939},
  pages     = {97 -- 107},
  year      = {2023},
  language  = {de}
}