@misc{AlJobayerBlossPorschetal., author = {Al Jobayer, Md Adif and Bloß, Katharina and Porsch, Christopher and Maaten, Ernst van der and Maaten-Theunissen, Marieke van der and Bonhage, Alexander and Raab, Alexandra and Schneider, Anna and Raab, Thomas}, title = {Drought sensitivity of Pinus sylvestris L. on century old charcoal rich Technosols in the North-German Lowland}, series = {EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022}, journal = {EGU General Assembly 2022, Vienna, Austria, 23-27 May 2022}, doi = {10.5194/egusphere-egu22-7978}, abstract = {Soils on relict charcoal hearths (RCHs), i.e. aged charcoal rich Technosols, feature documented legacy effects regarding their physical and chemical soil properties. These forest soils result from the pre-/early-industrial production of charcoal in upright standing hearths. Today, RCHs are found in forested areas as circular microrelief structures with average diameters of about 10 meters that contain technogenic soil horizons of about 20 cm thickness. RCH soils are most distinctly characterized by their large content of pyrogenic soil organic matter and consequently increased total carbon contents. The resulting changes in the soils' cation exchange capacity, soil acidity, total nitrogen contents and total element stocks are often significant. Furthermore, changes in soil physical properties like a lower bulk density and a higher porosity result in changed soil-water- and temperature regimes. Combined, these effects potentially result in unique soil microhabitats that are restricted to relatively small, clearly defined areas in many forest ecosystems. Recent studies on RCH soils increasingly focus on potential changes in microbiological and vegetational composition and abundances and report diverse effects. Here, we study the effects of century old charcoal rich Technosols on the drought sensitivity of Scots pine (Pinus sylvestris L.) on three RCH sites located in Lower Lusatia, Saxony (Germany). Through dendrochronologial analysis of increment cores and historical climate data, we study climate-growth relationships for trees on RCH soils and reference forest soils for two periods (AD 1963-1992 \& AD 1990-2019). Furthermore, we determine pointer years and components of resilience to quantify the trees' reaction to extreme weather conditions. This analysis was accompanied by soil sampling and analysis on the RCH sites and adjacent reference forest soils. Here, we present preliminary results and discuss them in the context of findings from other studies on RCH soils.}, language = {en} } @misc{LehmannPorschMai, author = {Lehmann, Marlon Antonin and Porsch, Ronny and Mai, Christopher}, title = {Assembly Process Digitization Through Self-learning Assistance Systems in Production}, series = {Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization \& Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021}, journal = {Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems : Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV2021) and the 10th World Mass Customization \& Personalization Conference (MCPC2021), Aalborg, Denmark, October/November 2021}, editor = {Andersen, Ann-Louise and Andersen, Rasmus and Brunoe, Thomas Ditlev and Stoettrup Schioenning Larsen, Maria and Nielsen, Kjeld and Napoleone, Alessia and Kjeldgaard, Stefan}, publisher = {Springer International Publishing}, isbn = {978-3-030-90699-3}, doi = {10.1007/978-3-030-90700-6_24}, pages = {216 -- 223}, abstract = {Abstract. As product specifications change, manufacturing processes have to adapt. In manual production tasks, the human worker is forced to adapt at the same pace. Fast-changing work tasks lead to high stress and therefore increase failures. Digital assistance systems aim to support the human workforce by providing assembly instructions at the right time and in the right place to reduce the cognitive load. The latest digital assistance systems provide multimodal humanmachine interfaces, such as augmented reality, haptic feedback, and voice control to provide information or react to the user's input.However, those digital assistance systems require the manufacturing information themselves, which are mostly provided through text-based or graphical programming. Both manufacturing experts and programmers are needed to create a digital assistance system workflow or adapt it to changes. This process is costly, time-consuming, and inflexible. This work presents a gesture recognition based approach for a self-learning digital assistance system. Therefore, assembly gestures are classified based on anatomical grip descriptions. Assembly sequences are recognized and learned by the digital assistance system using machine learning techniques. The learned procedures are used to automatically generate work instructions and guide the worker through the assembly task.}, language = {en} }