@article{RiekeStollenwerkDahmenetal.2018,
  author    = {Rieke, Christian and Stollenwerk, Dominik and Dahmen, Markus and Pieper, Martin},
  title     = {Modeling and optimization of a biogas plant for a demand-driven energy supply},
  series = {Energy},
  volume    = {145},
  journal   = {Energy},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0360-5442},
  doi       = {10.1016/j.energy.2017.12.073},
  pages     = {657 -- 664},
  year      = {2018},
  abstract  = {Due to the Renewable Energy Act, in Germany it is planned to increase the amount of renewable energy carriers up to 60\%. One of the main problems is the fluctuating supply of wind and solar energy. Here biogas plants provide a solution, because a demand-driven supply is possible. Before running such a plant, it is necessary to simulate and optimize the process. This paper provides a new model of a biogas plant, which is as accurate as the standard ADM1 model. The advantage compared to ADM1 is that it is based on only four parameters compared to 28. Applying this model, an optimization was installed, which allows a demand-driven supply by biogas plants. Finally the results are confirmed by several experiments and measurements with a real test plant.},
  language  = {en}
}
@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}
}