@article{KahmannRauschPluemeretal.2022,
  author    = {Kahmann, Stephanie L. and Rausch, Valentin and Pl{\"u}mer, Jonathan and M{\"u}ller, Lars P. and Pieper, Martin and Wegmann, Kilian},
  title     = {The automized fracture edge detection and generation of three-dimensional fracture probability heat maps},
  series = {Medical Engineering \& Physics},
  volume    = {2022},
  journal   = {Medical Engineering \& Physics},
  number    = {110},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1350-4533},
  pages     = {7 Seiten},
  year      = {2022},
  abstract  = {With proven impact of statistical fracture analysis on fracture classifications, it is desirable to minimize the manual work and to maximize repeatability of this approach. We address this with an algorithm that reduces the manual effort to segmentation, fragment identification and reduction. The fracture edge detection and heat map generation are performed automatically. With the same input, the algorithm always delivers the same output. The tool transforms one intact template consecutively onto each fractured specimen by linear least square optimization, detects the fragment edges in the template and then superimposes them to generate a fracture probability heat map. We hypothesized that the algorithm runs faster than the manual evaluation and with low (< 5 mm) deviation. We tested the hypothesis in 10 fractured proximal humeri and found that it performs with good accuracy (2.5 mm ± 2.4 mm averaged Euclidean distance) and speed (23 times faster). When applied to a distal humerus, a tibia plateau, and a scaphoid fracture, the run times were low (1-2 min), and the detected edges correct by visual judgement. In the geometrically complex acetabulum, at a run time of 78 min some outliers were considered acceptable. An automatically generated fracture probability heat map based on 50 proximal humerus fractures matches the areas of high risk of fracture reported in medical literature. Such automation of the fracture analysis method is advantageous and could be extended to reduce the manual effort even further.},
  language  = {en}
}
@inproceedings{BlankeSchmidtGoettscheetal.2022,
  author    = {Blanke, Tobias and Schmidt, Katharina S. and G{\"o}ttsche, Joachim and D{\"o}ring, Bernd and Frisch, J{\´e}r{\^o}me and van Treeck, Christoph},
  title     = {Time series aggregation for energy system design: review and extension of modelling seasonal storages},
  series = {Energy Informatics},
  volume    = {5},
  booktitle = {Energy Informatics},
  number    = {1, Article number: 17},
  editor    = {Weidlich, Anke and Neumann, Dirk and Gust, Gunther and Staudt, Philipp and Sch{\"a}fer, Mirko},
  publisher = {Springer Nature},
  issn      = {2520-8942},
  doi       = {10.1186/s42162-022-00208-5},
  pages     = {1 -- 14},
  year      = {2022},
  abstract  = {Using optimization to design a renewable energy system has become a computationally demanding task as the high temporal fluctuations of demand and supply arise within the considered time series. The aggregation of typical operation periods has become a popular method to reduce effort. These operation periods are modelled independently and cannot interact in most cases. Consequently, seasonal storage is not reproducible. This inability can lead to a significant error, especially for energy systems with a high share of fluctuating renewable energy. The previous paper, "Time series aggregation for energy system design: Modeling seasonal storage", has developed a seasonal storage model to address this issue. Simultaneously, the paper "Optimal design of multi-energy systems with seasonal storage" has developed a different approach. This paper aims to review these models and extend the first model. The extension is a mathematical reformulation to decrease the number of variables and constraints. Furthermore, it aims to reduce the calculation time while achieving the same results.},
  language  = {en}
}