@article{HandwerkerKesslerMarzbani2021, author = {Handwerker, Michael and Kessler, J{\"o}rg and Marzbani, Hormoz}, title = {Review of mechanical properties of and optimisation methods for continuous fibre‑reinforced thermoplastic parts manufactured by fused deposition modelling}, volume = {6}, journal = {Progress in Additive Manufacturing}, number = {4}, publisher = {Springer Nature}, address = {Cham}, issn = {2363-9520}, doi = {https://doi.org/10.1007/s40964-021-00187-1}, pages = {663 -- 677}, year = {2021}, abstract = {A new way of manufacturing continuous fibre-reinforced plastics is the embedding of fibres in the filament of a 3D printer. This method could be used in manufacturing composite materials with a thermoplastic matrix containing glass, Kevlar and carbon fibres. This paper provides an overview of research on the mechanical and physical properties of these parts as well as optimisation approaches of additively manufactured thermoplastics. Furthermore, applicable testing and analysis methods and their corresponding standards are included. Several studies, which represent the current state of the art, are reviewed in detail for the analysis of the mechanical performance of different fibre reinforcements. In addition, an overview of different optimisation approaches is given. The ultimate tensile strength of Kevlar and glass fibre-reinforced parts are similar to those of common Aluminium alloys whereas the carbon fibre reinforced parts outperform their aluminium counterparts. Major performance limitations include a poor adhesion between layers as well as a high air void ratio.}, language = {en} } @article{MayerClassenEndisch2021, author = {Mayer, Sebastian and Classen, Tobias and Endisch, Christian}, title = {Modular production control using deep reinforcement learning: proximal policy optimization}, volume = {32}, journal = {Journal of Intelligent Manufacturing}, number = {8}, publisher = {Springer Nature}, address = {Cham}, issn = {1572-8145}, doi = {https://doi.org/10.1007/s10845-021-01778-z}, pages = {2335 -- 2351}, year = {2021}, abstract = {EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent's action, state, and reward design.}, language = {en} } @article{TheilerEndischLewerenz2021, author = {Theiler, Michael and Endisch, Christian and Lewerenz, Meinert}, title = {Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells}, volume = {7}, pages = {22}, journal = {Batteries}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2313-0105}, doi = {https://doi.org/10.3390/batteries7020022}, year = {2021}, abstract = {Float currents are steady-state self-discharge currents after a transient phase—caused by anode overhang, polarization, etc.—is accomplished. The float current is measured in this study with a standard test bench for five 18650 cells (Samsung 25R) at potentiostatic conditions while the temperature is changed in 5 K steps from 5 °C to 60 °C. The entire test is performed in about 100 days resulting in 12 measurement points per cell potential for an Arrhenius representation. The float current follows the Arrhenius law with an activation energy of about 60 kJ/mol. The capacity loss measured at reference condition shows a high correlation to the results of float currents analysis. In contrast to classical calendar aging tests, the performed float current analysis enables determining the aging rate with high precision down to at least 10 °C. Returning from higher temperatures to 30 °C reference temperature shows reducing float currents at 30 °C for increasing temperature steps that may originate from an hysteresis effect that has to be investigated in future publications.}, language = {en} } @article{vonSawitzkyGrauschopfRiener2021, author = {von Sawitzky, Tamara and Grauschopf, Thomas and Riener, Andreas}, title = {"Attention! A Door Could Open."—Introducing Awareness Messages for Cyclists to Safely Evade Potential Hazards}, volume = {6}, pages = {3}, journal = {Multimodal Technologies and Interaction}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {2414-4088}, doi = {https://doi.org/10.3390/mti6010003}, year = {2021}, abstract = {Numerous statistics show that cyclists are often involved in road traffic accidents, often with serious outcomes. One potential hazard of cycling, especially in cities, is "dooring"—passing parked vehicles that still have occupants inside. These occupants could open the vehicle door unexpectedly in the cyclist's path—requiring a quick evasive response by the cyclist to avoid a collision. Dooring can be very poorly anticipated; as a possible solution, we propose in this work a system that notifies the cyclist of opening doors based on a networked intelligent transportation infrastructure. In a user study with a bicycle simulator (N = 24), we examined the effects of three user interface designs compared to a baseline (no notifications) on cycling behavior (speed and lateral position), perceived safety, and ease of use. Awareness messages (either visual message, visual message + auditory icon, or visual + voice message) were displayed on a smart bicycle helmet at different times before passing a parked, still-occupied vehicle. Our participants found the notifications of potential hazards very easy to understand and appealing and felt that the alerts could help them navigate traffic more safely. Those concepts that (additionally) used auditory icons or voice messages were preferred. In addition, the lateral distance increased significantly when a potentially opening door was indicated. In these situations, cyclists were able to safely pass the parked vehicle without braking. In summary, we are convinced that notification systems, such as the one presented here, are an important component for increasing road safety, especially for vulnerable road users.}, language = {en} } @inproceedings{UnoldWintergerstBelzneretal.2021, author = {Unold, Florian von and Wintergerst, Monika and Belzner, Lenz and Groh, Georg}, title = {DYME: a dynamic metric for dialog modeling learned from human conversations}, booktitle = {Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8-12, 2021, Proceedings, Part V}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-92307-5}, doi = {https://doi.org/10.1007/978-3-030-92307-5_30}, pages = {257 -- 264}, year = {2021}, language = {en} } @article{KleinerStuckenbergerKomsiyskaetal.2021, author = {Kleiner, Jan and Stuckenberger, Magdalena and Komsiyska, Lidiya and Endisch, Christian}, title = {Advanced monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling}, volume = {7}, pages = {31}, journal = {Batteries}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2313-0105}, doi = {https://doi.org/10.3390/batteries7020031}, year = {2021}, abstract = {Novel intelligent battery systems are gaining importance with functional hardware on the cell level. Cell-level hardware allows for advanced battery state monitoring and thermal management, but also leads to additional thermal interactions. In this work, an electro-thermal framework for the modeling of these novel intelligent battery cells is provided. Thereby, a lumped thermal model, as well as a novel neural network, are implemented in the framework as thermal submodels. For the first time, a direct comparison of a physics-based and a data-driven thermal battery model is performed in the same framework. The models are compared in terms of temperature estimation with regard to accuracy. Both models are very well suited to represent the thermal behavior in novel intelligent battery cells. In terms of accuracy and computation time, however, the data-driven neural network approach with a Nonlinear AutoregRessive network with eXogeneous input (NARX) shows slight advantages. Finally, novel applications of temperature prediction in battery electric vehicles are presented and the applicability of the models is illustrated. Thereby, the conventional prediction of the state of power is extended by simultaneous temperature prediction. Additionally, temperature forecasting is used for pre-conditioning by advanced cooling system regulation to enable energy efficiency and fast charging.}, language = {en} } @article{AxenieBauerRodriguezMartinez2021, author = {Axenie, Cristian and Bauer, Roman and Rodr{\´i}guez Mart{\´i}nez, Mar{\´i}a}, title = {The Multiple Dimensions of Networks in Cancer: A Perspective}, volume = {13}, pages = {1559}, journal = {Symmetry}, number = {9}, publisher = {MDPI}, address = {Basel}, issn = {2073-8994}, doi = {https://doi.org/10.3390/sym13091559}, year = {2021}, abstract = {This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue "Networks in Cancer: From Symmetry Breaking to Targeted Therapy". The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.}, language = {en} } @inproceedings{BrunnerDenkHuberetal.2019, author = {Brunner, Pascal and Denk, Florian and Huber, Werner and Kates, Ronald}, title = {Virtual safety performance assessment for automated driving in complex urban traffic scenarios}, booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-7024-8}, doi = {https://doi.org/10.1109/ITSC.2019.8917517}, pages = {679 -- 685}, year = {2019}, language = {en} } @inproceedings{DenkHuberBrunneretal.2020, author = {Denk, Florian and Huber, Werner and Brunner, Pascal and Kates, Ronald}, title = {The role of perceptual failure and degrading processes in urban traffic accidents: a stochastic computational model for virtual experiments}, booktitle = {2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4149-7}, doi = {https://doi.org/10.1109/ITSC45102.2020.9294498}, year = {2020}, language = {en} } @inproceedings{BrunnerLoeckenDenketal.2021, author = {Brunner, Pascal and L{\"o}cken, Andreas and Denk, Florian and Kates, Ronald and Huber, Werner}, title = {Analysis of experimental data on dynamics and behavior of e-scooter riders and applications to the impact of automated driving functions on urban road safety}, booktitle = {2020 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6673-5}, doi = {https://doi.org/10.1109/IV47402.2020.9304835}, pages = {219 -- 225}, year = {2021}, language = {en} } @article{HuberHaaseGleich2017, author = {Huber, Stephan and Haase, Axel and Gleich, Bernhard}, title = {Analysis of 2D NMR relaxation data using Chisholm approximations}, volume = {2017}, journal = {Journal of Magnetic Resonance}, number = {281}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1096-0856}, doi = {https://doi.org/10.1016/j.jmr.2017.05.006}, pages = {66 -- 74}, year = {2017}, language = {en} } @article{HuberMinStaatetal.2019, author = {Huber, Stephan and Min, Changwook and Staat, Christoph and Oh, Juhyun and Castro, Cesar and Haase, Axel and Weissleder, Ralph and Gleich, Bernhard and Lee, Hakho}, title = {Multichannel digital heteronuclear magnetic resonance biosensor}, volume = {2019}, journal = {Biosensors and Bioelectronics}, number = {126}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-4235}, doi = {https://doi.org/10.1016/j.bios.2018.10.052}, pages = {240 -- 248}, year = {2019}, language = {en} } @inproceedings{WenzelSchoenLealTaixeetal.2021, author = {Wenzel, Patrick and Sch{\"o}n, Torsten and Leal-Taix{\´e}, Laura and Cremers, Daniel}, title = {Vision-based mobile robotics obstacle avoidance with deep reinforcement learning}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-9077-8}, issn = {2577-087X}, doi = {https://doi.org/10.1109/ICRA48506.2021.9560787}, pages = {14360 -- 14366}, year = {2021}, language = {en} } @book{OPUS4-1275, title = {"Die große Transformation"}, subtitle = {Beitrag von Unternehmen und Wissenschaft zur Nachaltigen Entwicklung: Tagungsband zur Tagung am 08.11.2019}, editor = {Schober, Walter}, publisher = {Cuvillier}, address = {G{\"o}ttingen}, isbn = {978-3-7369-7318-3}, doi = {https://cuvillier.de/de/shop/publications/8433-die-grosse-transformation}, pages = {155}, year = {2021}, language = {de} } @article{MokhtariContiBhogarajuetal.2019, author = {Mokhtari, Omid and Conti, Fosca and Bhogaraju, Sri Krishna and Meier, Markus and Schweigart, Helmut and Tetzlaff, Ulrich and Elger, Gordon}, title = {Characterization of tin-oxides and tin-formate crystals obtained from SnAgCu solder alloy under formic acid vapor}, volume = {43}, journal = {New Journal of Chemistry}, number = {26}, publisher = {RSC}, address = {London}, issn = {1369-9261}, doi = {https://doi.org/10.1039/C9NJ02135C}, pages = {10227 -- 10231}, year = {2019}, language = {en} } @article{BhogarajuContiKotadiaetal.2020, author = {Bhogaraju, Sri Krishna and Conti, Fosca and Kotadia, Hiren R. and Keim, Simon and Tetzlaff, Ulrich and Elger, Gordon}, title = {Novel approach to copper sintering using surface enhanced brass micro flakes for microelectronics packaging}, volume = {2020}, pages = {156043}, journal = {Journal of Alloys and Compounds}, number = {844}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-4669}, doi = {https://doi.org/10.1016/j.jallcom.2020.156043}, year = {2020}, language = {en} } @inproceedings{WurstBalasubramanianBotschetal.2021, author = {Wurst, Jonas and Balasubramanian, Lakshman and Botsch, Michael and Utschick, Wolfgang}, title = {Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder}, booktitle = {2021 IEEE Intelligent Vehicles Symposium (IV)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-5394-0}, doi = {https://doi.org/10.1109/IV48863.2021.9575730}, pages = {1304 -- 1311}, year = {2021}, language = {en} } @inproceedings{AubrevilleBertramKlopfleischetal.2018, author = {Aubreville, Marc and Bertram, Christof and Klopfleisch, Robert and Maier, Andreas}, title = {SlideRunner}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2018: Algorithmen - Systeme - Anwendungen}, subtitle = {a tool for massive cell annotations in whole slide images}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-56537-7}, doi = {https://doi.org/10.1007/978-3-662-56537-7_81}, pages = {309 -- 314}, year = {2018}, abstract = {Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitosis detection, it is assumed that detection accuracy could significantly benefit from larger annotation databases that are currently however very troublesome to produce. Further, multiple independent (blind) expert labels are a big asset for such databases, yet there is currently no tool for this kind of annotation available. To ease this tedious process of expert annotation and grading, we introduce SlideRunner, an open source annotation and visualization tool for digital histopathology, developed in close cooperation with two pathologists. SlideRunner is capable of setting annotations like object centers (for e.g. cells) as well as object boundaries (e.g. for tumor outlines). It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.}, language = {en} } @article{AubrevilleKnipferOetteretal.2017, author = {Aubreville, Marc and Knipfer, Christian and Oetter, Nicolai and Jaremenko, Christian and Rodner, Erik and Denzler, Joachim and Bohr, Christopher and Neumann, Helmut and Stelzle, Florian and Maier, Andreas}, title = {Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning}, volume = {7}, pages = {11979}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-017-12320-8}, year = {2017}, abstract = {Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3\% (sensitivity 86.6\%, specificity 90\%).}, language = {en} } @inproceedings{DietlBotschDietrichetal.2005, author = {Dietl, Guido and Botsch, Michael and Dietrich, F. A. and Utschick, Wolfgang}, title = {Robust and reduced-rank matrix Wiener filter based on the conjugate gradient algorithm}, booktitle = {2005 IEEE 6th Workshop on Signal Processing Advances in Wireless Communications}, publisher = {IEEE}, address = {Piscataway}, isbn = {0-7803-8867-4}, doi = {https://doi.org/10.1109/SPAWC.2005.1506201}, pages = {555 -- 559}, year = {2005}, language = {en} }