@article{RillSchuderer, author = {Rill, Georg and Schuderer, Matthias}, title = {A Second-Order Dynamic Friction Model Compared to Commercial Stick-Slip Models}, series = {Modelling}, volume = {4}, journal = {Modelling}, number = {3}, publisher = {MDPI}, issn = {2673-3951}, doi = {10.3390/modelling4030021}, pages = {366 -- 381}, abstract = {Friction has long been an important issue in multibody dynamics. Static friction models apply appropriate regularization techniques to convert the stick inequality and the non-smooth stick-slip transition of Coulomb's approach into a continuous and smooth function of the sliding velocity. However, a regularized friction force is not able to maintain long-term stick. That is why dynamic friction models were developed in recent decades. The friction force depends herein not only on the sliding velocity but also on internal states. The probably best-known representative, the LuGre friction model, is based on a fictitious bristle but realizes a too-simple approximation. The recently published second-order dynamic friction model describes the dynamics of a fictitious bristle more accurately. It is based on a regularized friction force characteristic, which is continuous and smooth but can maintain long-term stick due to an appropriate shift in the regularization. Its performance is compared here to stick-slip friction models, developed and launched not long ago by commercial multibody software packages. The results obtained by a virtual friction test-bench and by a more practical festoon cable system are very promising. Thus, the second-order dynamic friction model may serve not only as an alternative to the LuGre model but also to commercial stick-slip models.}, language = {en} } @article{HausladenSchelsAsgharzadeetal., author = {Hausladen, Matthias and Schels, Andreas and Asgharzade, Ali and Buchner, Philipp and Bartl, Mathias and Wohlfartsst{\"a}tter, Dominik and Edler, Simon and Bachmann, Michael and Schreiner, Rupert}, title = {Investigation of Influencing Factors on the Measurement Signal of a CMOS Image Sensor for Measuring Field Emission Currents}, series = {Sensors}, volume = {25}, journal = {Sensors}, number = {5}, publisher = {MDPI}, doi = {10.3390/s25051529}, pages = {17}, language = {en} } @article{MarkovGlazerElbeSchliermann, author = {Markov-Glazer, Alon and Elbe, Anne-Marie and Schliermann, Rainer}, title = {The use of psychological skills in deaflympic sport}, series = {European Journal of Adapted Physical Activity}, volume = {17}, journal = {European Journal of Adapted Physical Activity}, number = {15}, publisher = {European Federation of Adapted Physical Activity}, address = {Olomouc}, doi = {10.5507/euj.2024.012}, pages = {13}, abstract = {This study explores the use of psychological skills among Deaflympic athletes and aims to provide initial insights into sport psychology practices in this overlooked population. To this end, the frequency of psychological skills' usage, usage patterns, and differences between subgroups were examined within this population. The Test of Performance Strategies (TOPS) was administered to 84 German Deaflympic athletes (mean age = 28; 27\% female) along with biographical questions (e.g., hearing status). The findings indicate that Deaflympic athletes regularly utilize psychological skills in training and competition, displaying similar usage to that of Olympic athletes. However, only a minority of Deaflympic athletes train their skills with professional consultants. Additionally, relaxation techniques were employed rarely in comparison to the other investigated skills. This study's findings contribute to the foundation of sport psychology research with Deaflympic athletes and underscore the need for a differentiated approach in tailoring interventions for this population.}, language = {en} } @article{SchwarzPastoetterTranetal., author = {Schwarz, Tobias and Past{\"o}tter, Angelina and Tran, T. H. and Schlosser, Marc and Kaulen, Corinna and Lechner, Alfred and Slabon, A. and Kammler, Martin}, title = {Effect of temperature on the growth of Cupric and Cuprous Nanoparticles in a wet chemical synthesis}, series = {Materials Letters}, volume = {381}, journal = {Materials Letters}, publisher = {Springer Nature}, doi = {10.1016/j.matlet.2024.137672}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-79279}, abstract = {Cuprous oxide (Cu2O) and cupric oxide (CuO) particles were synthesized using a wet chemical surfactant free method at different synthesis temperatures from 25 °C to 60 °C. Morphology, size, and chemical composition of the prepared Cu2O particles were analyzed by FESEM, PXRD, and UV-vis. We discovered that the chemical composition of the Cu2O particles remained unaffected by the synthesis temperature. However, morphology and size of the particles showed a strong temperature dependency. This could be attributed to the temperature induced formation of CuO species from the copper hydroxide (Cu(OH)2) precursor which also functions as copper precursor for the Cu2O particle growth. The cupric oxide species was determined to be the main cause for the formation of micrometer sized particles, whereas with the Cu(OH)2 precursor species for the Cu2O particles nanocubes with smaller edge lengths as well as octahedrons were obtained.}, language = {en} } @article{VoglSchulzSchaefferetal., author = {Vogl, Yannick and Schulz, Carsten and Schaeffer, Thomas and Geiger, Benjamin}, title = {Prediction of the wear behavior of a conveyor belt with flexible rollers}, series = {Multibody System Dynamics}, journal = {Multibody System Dynamics}, publisher = {Springer}, address = {Cham}, issn = {1384-5640}, doi = {10.1007/s11044-025-10097-7}, pages = {16}, abstract = {This paper introduces a method to predict wear behavior in conveyor belt systems using a lumped mass modeling approach. While previous research has focused primarily on lateral belt walking, this study shifts attention to belt deformation and its associated wear. Both significantly affect system efficiency and component lifespan. The authors propose using local frictional power as a wear indicator, leveraging its direct relation to frictional work in established wear models. To solely demonstrate the method, the study simulates a conveyor belt with three flexible rollers and a deformable belt modeled through rigid spheres connected by spring-damper elements. The authors visualize frictional power density across the belt width, distinguishing between running and transverse directions. The results demonstrate that the frictional power distribution depends heavily on discretization quality, particularly due to the polygon effect inherent in the lumped mass approach. A convergence analysis reveals the minimum necessary discretization of the belt, ensuring reliable qualitative results. To support the credibility of the work, this study compares theoretical expectations and initial wear observations from a real belt with the results from the shown approach. The plausibility check already shows promising results. The proposed methodology provides an adaptable framework to evaluate wear in belt-like structures. It can be readily adapted to a variety of multibody dynamics applications and integrated into larger MBS models that include the overall drivetrain and engine control. Future work will focus on refining discretization strategies and contact models, as well as validation of the wear model to enable quantitative predictions.}, language = {en} } @article{MendelRauberSouzaJretal., author = {Mendel, Robert and Rauber, David and Souza Jr., Luis Antonio de and Papa, Jo{\~a}o Paulo and Palm, Christoph}, title = {Error-Correcting Mean-Teacher: Corrections instead of consistency-targets applied to semi-supervised medical image segmentation}, series = {Computers in Biology and Medicine}, volume = {154}, journal = {Computers in Biology and Medicine}, number = {March}, publisher = {Elsevier}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2023.106585}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-57790}, pages = {13}, abstract = {Semantic segmentation is an essential task in medical imaging research. Many powerful deep-learning-based approaches can be employed for this problem, but they are dependent on the availability of an expansive labeled dataset. In this work, we augment such supervised segmentation models to be suitable for learning from unlabeled data. Our semi-supervised approach, termed Error-Correcting Mean-Teacher, uses an exponential moving average model like the original Mean Teacher but introduces our new paradigm of error correction. The original segmentation network is augmented to handle this secondary correction task. Both tasks build upon the core feature extraction layers of the model. For the correction task, features detected in the input image are fused with features detected in the predicted segmentation and further processed with task-specific decoder layers. The combination of image and segmentation features allows the model to correct present mistakes in the given input pair. The correction task is trained jointly on the labeled data. On unlabeled data, the exponential moving average of the original network corrects the student's prediction. The combined outputs of the students' prediction with the teachers' correction form the basis for the semi-supervised update. We evaluate our method with the 2017 and 2018 Robotic Scene Segmentation data, the ISIC 2017 and the BraTS 2020 Challenges, a proprietary Endoscopic Submucosal Dissection dataset, Cityscapes, and Pascal VOC 2012. Additionally, we analyze the impact of the individual components and examine the behavior when the amount of labeled data varies, with experiments performed on two distinct segmentation architectures. Our method shows improvements in terms of the mean Intersection over Union over the supervised baseline and competing methods. Code is available at https://github.com/CloneRob/ECMT.}, language = {en} } @article{GrassmannMengelkampBrandletal., author = {Graßmann, Felix and Mengelkamp, Judith and Brandl, Caroline and Harsch, Sebastian and Zimmermann, Martina E. and Linkohr, Birgit and Peters, Annette and Heid, Iris M. and Palm, Christoph and Weber, Bernhard H. F.}, title = {A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography}, series = {Ophtalmology}, volume = {125}, journal = {Ophtalmology}, number = {9}, publisher = {Elsevier}, doi = {10.1016/j.ophtha.2018.02.037}, pages = {1410 -- 1420}, abstract = {Purpose Age-related macular degeneration (AMD) is a common threat to vision. While classification of disease stages is critical to understanding disease risk and progression, several systems based on color fundus photographs are known. Most of these require in-depth and time-consuming analysis of fundus images. Herein, we present an automated computer-based classification algorithm. Design Algorithm development for AMD classification based on a large collection of color fundus images. Validation is performed on a cross-sectional, population-based study. Participants. We included 120 656 manually graded color fundus images from 3654 Age-Related Eye Disease Study (AREDS) participants. AREDS participants were >55 years of age, and non-AMD sight-threatening diseases were excluded at recruitment. In addition, performance of our algorithm was evaluated in 5555 fundus images from the population-based Kooperative Gesundheitsforschung in der Region Augsburg (KORA; Cooperative Health Research in the Region of Augsburg) study. Methods. We defined 13 classes (9 AREDS steps, 3 late AMD stages, and 1 for ungradable images) and trained several convolution deep learning architectures. An ensemble of network architectures improved prediction accuracy. An independent dataset was used to evaluate the performance of our algorithm in a population-based study. Main Outcome Measures. κ Statistics and accuracy to evaluate the concordance between predicted and expert human grader classification. Results. A network ensemble of 6 different neural net architectures predicted the 13 classes in the AREDS test set with a quadratic weighted κ of 92\% (95\% confidence interval, 89\%-92\%) and an overall accuracy of 63.3\%. In the independent KORA dataset, images wrongly classified as AMD were mainly the result of a macular reflex observed in young individuals. By restricting the KORA analysis to individuals >55 years of age and prior exclusion of other retinopathies, the weighted and unweighted κ increased to 50\% and 63\%, respectively. Importantly, the algorithm detected 84.2\% of all fundus images with definite signs of early or late AMD. Overall, 94.3\% of healthy fundus images were classified correctly. Conclusions Our deep learning algoritm revealed a weighted κ outperforming human graders in the AREDS study and is suitable to classify AMD fundus images in other datasets using individuals >55 years of age.}, subject = {Senile Makuladegeneration}, language = {en} } @article{MaierPerretSimonetal., author = {Maier, Johannes and Perret, Jerome and Simon, Martina and Schmitt-R{\"u}th, Stephanie and Wittenberg, Thomas and Palm, Christoph}, title = {Force-feedback assisted and virtual fixtures based K-wire drilling simulation}, series = {Computers in Biology and Medicine}, volume = {114}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, doi = {10.1016/j.compbiomed.2019.103473}, pages = {1 -- 10}, abstract = {One common method to fix fractures of the human hand after an accident is an osteosynthesis with Kirschner wires (K-wires) to stabilize the bone fragments. The insertion of K-wires is a delicate minimally invasive surgery, because surgeons operate almost without a sight. Since realistic training methods are time consuming, costly and insufficient, a virtual-reality (VR) based training system for the placement of K-wires was developed. As part of this, the current work deals with the real-time bone drilling simulation using a haptic force-feedback device. To simulate the drilling, we introduce a virtual fixture based force-feedback drilling approach. By decomposition of the drilling task into individual phases, each phase can be handled individually to perfectly control the drilling procedure. We report about the related finite state machine (FSM), describe the haptic feedback of each state and explain, how to avoid jerking of the haptic force-feedback during state transition. The usage of the virtual fixture approach results in a good haptic performance and a stable drilling behavior. This was confirmed by 26 expert surgeons, who evaluated the virtual drilling on the simulator and rated it as very realistic. To make the system even more convincing, we determined real drilling feed rates through experimental pig bone drilling and transferred them to our system. Due to a constant simulation thread we can guarantee a precise drilling motion. Virtual fixtures based force-feedback calculation is able to simulate force-feedback assisted bone drilling with high quality and, thus, will have a great potential in developing medical applications.}, subject = {Handchirurgie}, language = {en} } @article{RueckertRueckertPalm, author = {R{\"u}ckert, Tobias and R{\"u}ckert, Daniel and Palm, Christoph}, title = {Methods and datasets for segmentation of minimally invasive surgical instruments in endoscopic images and videos: A review of the state of the art}, series = {Computers in Biology and Medicine}, volume = {169}, journal = {Computers in Biology and Medicine}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.compbiomed.2024.107929}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-69830}, pages = {24}, abstract = {In the field of computer- and robot-assisted minimally invasive surgery, enormous progress has been made in recent years based on the recognition of surgical instruments in endoscopic images and videos. In particular, the determination of the position and type of instruments is of great interest. Current work involves both spatial and temporal information, with the idea that predicting the movement of surgical tools over time may improve the quality of the final segmentations. The provision of publicly available datasets has recently encouraged the development of new methods, mainly based on deep learning. In this review, we identify and characterize datasets used for method development and evaluation and quantify their frequency of use in the literature. We further present an overview of the current state of research regarding the segmentation and tracking of minimally invasive surgical instruments in endoscopic images and videos. The paper focuses on methods that work purely visually, without markers of any kind attached to the instruments, considering both single-frame semantic and instance segmentation approaches, as well as those that incorporate temporal information. The publications analyzed were identified through the platforms Google Scholar, Web of Science, and PubMed. The search terms used were "instrument segmentation", "instrument tracking", "surgical tool segmentation", and "surgical tool tracking", resulting in a total of 741 articles published between 01/2015 and 07/2023, of which 123 were included using systematic selection criteria. A discussion of the reviewed literature is provided, highlighting existing shortcomings and emphasizing the available potential for future developments.}, subject = {Deep Learning}, language = {en} } @inproceedings{HoengWiedererEderetal., author = {H{\"o}ng, Simon K. and Wiederer, Jonas and Eder, Friedrich and Obergriesser, Mathias and Linner, Thomas}, title = {Towards AI-enhanced facade planning : integrating human expertise with machine learning-driven parametric modeling}, series = {EC³ \& CIB W78 : 2025 European Conference on Computing in Construction \& 42nd CIB W78 IT in Construction Conference}, volume = {6}, booktitle = {EC³ \& CIB W78 : 2025 European Conference on Computing in Construction \& 42nd CIB W78 IT in Construction Conference}, publisher = {European Council for Computing in Construction}, isbn = {978-9-083451-31-2}, issn = {2684-1150}, doi = {10.35490/EC3.2025.320}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-84862}, pages = {192 -- 199}, abstract = {Planning modern facade systems is complex, requiring optimization across multiple domains.This paper proposes an AI-enhanced workflow for facade planning, harnessing computer vision and human input via a Large Language Model.A generative AI system then guides a parametric model to produce 3D facade designs. Automated checks provide feedback to a Reinforcement Learning system, to iteratively determine optimal solutions.These solutions are verified and finalized by human expertise, ensuring improved outcomes with reduce planning time and effort.The approach illustrates how combining advanced AI methods with human expertise can address the multifactorial challenges of facade design within current industry practices.}, language = {en} }