@inproceedings{ChenDeisherGeorges2023, author = {Chen, Liu and Deisher, Michael and Georges, Munir}, title = {An End-to-End Neural Network for Image-to-Audio Transformation}, booktitle = {Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6327-7}, doi = {https://doi.org/10.1109/ICASSP49357.2023.10096121}, year = {2023}, language = {en} } @article{DemiralBockJohansson2023, author = {Demiral, Tarik and Bock, J{\"u}rgen and Johansson, Pierre}, title = {Challenges in Flexible Manufacturing Technologies for the Final Assembly in the Commercial Vehicle Industry}, volume = {17}, journal = {Tehnički glasnik}, number = {2}, publisher = {University North}, address = {Koprivnica}, issn = {1848-5588}, doi = {https://doi.org/10.31803/tg-20230417140747}, pages = {260 -- 267}, year = {2023}, abstract = {Increasing customer demands and product diversity as well as emerging technologies and market trends, such as the establishment of new driveline technologies, like e-mobility or hydrogen, present challenges for manufacturing companies in the commercial vehicle industry. Consequently, companies must strengthen their focus to the aspect of flexibility within their manufacturing processes. This paper contributes to the state of the art in flexible manufacturing technologies research, which enables manufacturing companies to deal with these increasing flexibility requirements. Focussing on the area of final assembly the paper takes a holistic perspective and characterizes the readiness of automotive companies to be able to implement flexible manufacturing technologies. The system ecosystem and the process organization of automotive companies are examined with respect to the requirements of flexible manufacturing. Finally, gaps that hinder the implementation of flexible manufacturing technologies are identified and described, and possible solution concepts for the identified gaps are proposed.}, language = {en} } @article{MuellerRichterSchaeferetal.2023, author = {Mueller, Juliane and Richter, Monika and Schaefer, Kathrin and Ganz, Jonathan and Lohscheller, J{\"o}rg and Mueller, Steffen}, title = {How to measure children's feet: 3D foot scanning compared with established 2D manual or digital methods}, volume = {16}, pages = {21}, journal = {Journal of Foot and Ankle Research}, publisher = {BioMed Central}, address = {London}, issn = {1757-1146}, doi = {https://doi.org/10.1186/s13047-023-00618-y}, year = {2023}, abstract = {Background In infants and young children, a wide heterogeneity of foot shape is typical. Therefore, children, who are additionally influenced by rapid growth and maturation, are a very special cohort for foot measurements and the footwear industry. The importance of foot measurements for footwear fit, design, as well as clinical applications has been sufficiently described. New measurement techniques (3D foot scanning) allow the assessment of the individual foot shape. However, the validity in comparison to conventional methods remains unclear. Therefore, the purpose of this study was to compare 3D foot scanning with two established measurement methods (2D digital scanning/manual foot measurements). Methods Two hundred seventy seven children (125 m / 152 f; mean ± SD: 8.0 ± 1.5yrs; 130.2 ± 10.7cm; 28.0 ± 7.3kg) were included into the study. After collection of basic data (sex, age (yrs), body height (cm), body weight (kg)) geometry of the right foot was measured in static condition (stance) with three different measurement systems (fixed order): manual foot measurement, 2D foot scanning (2D desk scanner) and 3D foot scanning (hand-held 3D scanner). Main outcomes were foot length, foot width (projected; anatomical; instep), heel width and anatomical foot ball breadth. Analysis of variances for dependent samples was applied to test for differences between foot measurement methods (Post-hoc analysis: Tukey-Kramer-Test; α=0.05). Results Significant differences were found for all outcome measures comparing the three methods (p<0.0001). The span of foot length differences ranged from 3 to 6mm with 2D scans showing the smallest and 3D scans the largest deviations. Foot width measurements in comparison of 3D and 2D scans showed consistently higher values for 3D measurements with the differences ranging from 1mm to 3mm. Conclusions The findings suggests that when comparing foot data, it is important to consider the differences caused by new measurement methods. Differences of about 0.6cm are relevant when measuring foot length, as this is the difference of a complete shoe size (Parisian point). Hence, correction factors may be required to compare the results of different measurements appropriately. The presented results may have relevance in the field of ergonomics (shoe industry) as well as clinical practice.}, language = {en} } @article{ChughtaiNaseerTamooretal.2023, author = {Chughtai, Iqra Toheed and Naseer, Asma and Tamoor, Maria and Asif, Saara and Jabbar, Mamoona and Shahid, Rabia}, title = {Content-based image retrieval via transfer learning}, volume = {44}, journal = {Journal of Intelligent \& Fuzzy Systems}, number = {5}, publisher = {IOS Press}, address = {Amsterdam}, issn = {1875-8967}, doi = {https://doi.org/10.3233/JIFS-223449}, pages = {8193 -- 8218}, year = {2023}, language = {en} } @inproceedings{AmbrosyKampaJumaretal.2022, author = {Ambrosy, Niklas and Kampa, Thomas and Jumar, Ulrich and Großmann, Daniel}, title = {5G and DetNet: Towards holistic determinism in industrial networks}, booktitle = {2022 IEEE International Conference on Industrial Technology (ICIT)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-72811-948-9}, doi = {https://doi.org/10.1109/ICIT48603.2022.10002776}, year = {2022}, language = {en} } @inproceedings{BucaioniDiSalleIovinoetal.2023, author = {Bucaioni, Alessio and Di Salle, Amleto and Iovino, Ludovico and Kugele, Stefan and Dajsuren, Yanja}, title = {Joint Workshop on Model-Driven Engineering for Software Architecture (MDE4SA) and International Workshop on Automotive System/Software Architectures (WASA)}, booktitle = {Proceedings: IEEE 20th International Conference on Software Architecture Companion}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-6654-6459-8}, issn = {2768-4288}, doi = {https://doi.org/10.1109/ICSA-C57050.2023.00059}, pages = {246 -- 247}, year = {2023}, language = {en} } @inproceedings{PetrovskaHutzelmannKugele2023, author = {Petrovska, Ana and Hutzelmann, Thomas and Kugele, Stefan}, title = {A Theoretical Framework for Self-Adaptive Systems: Specifications, Formalisation, and Architectural Implications}, booktitle = {SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing}, publisher = {ACM}, address = {New York}, isbn = {978-1-4503-9517-5}, doi = {https://doi.org/10.1145/3555776.3577665}, pages = {1440 -- 1449}, year = {2023}, language = {en} } @article{AxmannHarmokoMalhotra2023, author = {Axmann, Bernhard and Harmoko, Harmoko and Malhotra, Rahul}, title = {The Assessment of Robotic Process Automation Projects with a Portfolio Analysis: First Step - Evaluation Criteria Identification and Introduction of the Portfolio Concept}, volume = {17}, journal = {Tehnički glasnik}, number = {2}, publisher = {University North}, address = {Koprivnica}, issn = {1848-5588}, doi = {https://doi.org/10.31803/tg-20230416193006}, pages = {207 -- 214}, year = {2023}, abstract = {RPA's (Robotic Process Automation) usage in organizations has rapidly increased in recent years; as a result, companies have developed high expectations from this technology. However, according to Ernst \& Young (E\&Y), 30-50\% of observed RPA projects initially fail and reveal several risks, which lead to investment losses. Consequently, the RPA project is prematurely retired, and the company is back to the manual process. This premature retirement is mainly because of wrong process selection and the not sufficient company automation (RPA) maturity. Therefore, this paper will introduce the concept of an RPA Portfolio, which will assess the complexity of business processes with a company's automation (RPA) maturity. The RPA Portfolio is a new innovative concept to simplify and visualize the business process selection for RPA projects, and will help to introduce successfully the right RPA projects.}, language = {en} } @inproceedings{KasparMunozOsorioBock2021, author = {Kaspar, Manuel and Mu{\~n}oz Osorio, Juan D. and Bock, J{\"u}rgen}, title = {Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, publisher = {IEEE}, address = {Piscataway (NJ)}, isbn = {978-1-7281-6212-6}, issn = {2153-0866}, doi = {https://doi.org/10.1109/IROS45743.2020.9341260}, pages = {4383 -- 4388}, year = {2021}, language = {en} } @inproceedings{KendrickFrohnmaierGeorges2021, author = {Kendrick, Caroline and Frohnmaier, Mariano and Georges, Munir}, title = {Audio-Visual Recipe Guidance for Smart Kitchen Devices}, booktitle = {ICNLSP 2021: Proceedings of the 4th International Conference on Natural Language and Speech Processing}, publisher = {ACL}, address = {Stroudsburg}, isbn = {978-1-955917-18-6}, doi = {https://aclanthology.org/2021.icnlsp-1.30}, pages = {257 -- 261}, year = {2021}, language = {en} } @unpublished{BalajiBliesGoerietal.2021, author = {Balaji, Thangapavithraa and Blies, Patrick and G{\"o}ri, Georg and Mitsch, Raphael and Wasserer, Marcel and Sch{\"o}n, Torsten}, title = {Temporally coherent video anonymization through GAN inpainting}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2106.02328}, year = {2021}, language = {en} } @unpublished{LeinenCozzolinoSchoen2021, author = {Leinen, Fabian and Cozzolino, Vittorio and Sch{\"o}n, Torsten}, title = {VolNet: Estimating Human Body Part Volumes from a Single RGB Image}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2107.02259}, year = {2021}, language = {en} } @article{SenelKefferpuetzDoychevaetal.2023, author = {Senel, Numan and Kefferp{\"u}tz, Klaus and Doycheva, Kristina and Elger, Gordon}, title = {Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking}, volume = {11}, pages = {501}, journal = {Processes}, number = {2}, publisher = {MDPI}, address = {Basel}, issn = {2227-9717}, doi = {https://doi.org/10.3390/pr11020501}, year = {2023}, abstract = {Sensor data fusion is essential for environmental perception within smart traffic applications. By using multiple sensors cooperatively, the accuracy and probability of the perception are increased, which is crucial for critical traffic scenarios or under bad weather conditions. In this paper, a modular real-time capable multi-sensor fusion framework is presented and tested to fuse data on the object list level from distributed automotive sensors (cameras, radar, and LiDAR). The modular multi-sensor fusion architecture receives an object list (untracked objects) from each sensor. The fusion framework combines classical data fusion algorithms, as it contains a coordinate transformation module, an object association module (Hungarian algorithm), an object tracking module (unscented Kalman filter), and a movement compensation module. Due to the modular design, the fusion framework is adaptable and does not rely on the number of sensors or their types. Moreover, the method continues to operate because of this adaptable design in case of an individual sensor failure. This is an essential feature for safety-critical applications. The architecture targets environmental perception in challenging time-critical applications. The developed fusion framework is tested using simulation and public domain experimental data. Using the developed framework, sensor fusion is obtained well below 10 milliseconds of computing time using an AMD Ryzen 7 5800H mobile processor and the Python programming language. Furthermore, the object-level multi-sensor approach enables the detection of changes in the extrinsic calibration of the sensors and potential sensor failures. A concept was developed to use the multi-sensor framework to identify sensor malfunctions. This feature will become extremely important in ensuring the functional safety of the sensors for autonomous driving.}, language = {en} } @inbook{Schoen2022, author = {Sch{\"o}n, Torsten}, title = {Artificial Intelligence Inspired by Human Learning}, booktitle = {AI. Mobility. Science. Brazil - Germany 2021/22}, editor = {Schober, Walter}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, isbn = {978-3-00-071542-6}, pages = {34 -- 37}, year = {2022}, language = {en} } @article{PloetnerAlHaddadAntoniouetal.2020, author = {Ploetner, Kay O. and Al Haddad, C. and Antoniou, C. and Frank, F. and Fu, M. and Kabel, Stefanie and Llorca, C. and Moeckel, R. and Moreno, A. T. and Pukhova, A. and Rothfeld, R. and Shamiyeh, M. and Straubinger, A. and Wagner, Harry and Zhang, Q.}, title = {Long-term application potential of urban air mobility complementing public transport: an upper Bavaria example}, volume = {11}, journal = {CEAS Aeronautical Journal}, number = {4}, publisher = {Springer}, address = {Wien}, issn = {1869-5590}, doi = {https://doi.org/10.1007/s13272-020-00468-5}, pages = {991 -- 1007}, year = {2020}, language = {en} } @article{XuWangPerardGayotetal.2022, author = {Xu, Xiang and Wang, Lu and P{\´e}rard-Gayot, Ars{\`e}ne and Membarth, Richard and Li, Cuiyu and Yang, Chenglei and Slusallek, Philipp}, title = {Temporal Coherence-Based Distributed Ray Tracing of Massive Scenes}, volume = {30}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {2}, publisher = {IEEE}, address = {Piscataway}, issn = {1941-0506}, doi = {https://doi.org/10.1109/TVCG.2022.3219982}, pages = {1489 -- 1501}, year = {2022}, language = {en} } @inproceedings{KugeleGrunske2023, author = {Kugele, Stefan and Grunske, Lars}, title = {20th Workshop on Automotive Software Engineering (ASE'23)}, booktitle = {Software Engineering 2023: Fachtagung des GI-Fachbereichs Softwaretechnik}, editor = {Engels, Gregor and Hebig, Regina and Tichy, Matthias}, publisher = {Gesellschaft f{\"u}r Informatik}, address = {Bonn}, isbn = {978-3-88579-726-5}, url = {https://dl.gi.de/items/a826d18c-1633-4dc8-8efe-e745f26e66ce}, pages = {137 -- 138}, year = {2023}, abstract = {Software-based systems play an increasingly important role and enable most innovations in modern cars. This workshop will address various topics related to automotive software development. The participants will discuss appropriate methods, techniques, and tools needed to address the most current challenges for researchers and practitioners.}, language = {en} } @unpublished{KoecherBelyaevHermannetal.2022, author = {K{\"o}cher, Aljosha and Belyaev, Alexander and Hermann, Jesko and Bock, J{\"u}rgen and Meixner, Kristof and Volkmann, Magnus and Winter, Michael and Zimmermann, Patrick and Grimm, Stephan and Diedrich, Christian}, title = {A Reference Model for Common Understanding of Capabilities and Skills in Manufacturing}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2209.09632}, year = {2022}, language = {en} } @inproceedings{SunPierothSchmidetal.2022, author = {Sun, Xiyue and Pieroth, Fabian Raoul and Schmid, Kyrill and Wirsing, Martin and Belzner, Lenz}, title = {On Learning Stable Cooperation in the Iterated Prisoner's Dilemma with Paid Incentives}, booktitle = {Proceedings: 2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops: ICDCSW 2022}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {978-1-6654-8879-2}, issn = {2332-5666}, doi = {https://doi.org/10.1109/ICDCSW56584.2022.00031}, pages = {113 -- 118}, year = {2022}, language = {en} } @article{StieberSchroeterFausteretal.2022, author = {Stieber, Simon and Schr{\"o}ter, Niklas and Fauster, Ewald and Bender, Marcel and Schiendorfer, Alexander and Reif, Wolfgang}, title = {Inferring material properties from FRP processes via sim-to-real learning}, volume = {128}, journal = {The International Journal of Advanced Manufacturing Technology}, number = {3-4}, publisher = {Springer}, address = {London}, issn = {1433-3015}, doi = {https://doi.org/10.1007/s00170-023-11509-8}, pages = {1517 -- 1533}, year = {2022}, abstract = {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.}, language = {en} }