@inproceedings{22480,
  abstract     = {{In this publication important aspects for the implementation of inductive locating are explained. The miniaturized sensor platform called Sens-o-Spheres is used as an application of this locating method. The sensor platform is applied in bioreactors in order to obtain the environmental parameters, which makes a localization by magnetic fields necessary. Since the properties of magnetic fields in the localization area are very different from the wave characteristics, the principle of inductive localization is investigated in this publication and explained by using electrical equivalent circuit diagrams. Thereby, inductive localization uses the coupling or the mutual inductivities between coils, which is noticeable by an induced voltage. Therefore some properties and procedures are explained to extract the location of Sens-o-Spheres or other industrial sensor platforms from the couplings of the coils. One method calculates the location from an adapted ratio calculation and the other method uses neural networks and stochastic filters to obtain the results. In the end, these results are evaluated and compared.}},
  author       = {{Lange, Sven and Schröder, Dominik and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}},
  booktitle    = {{22nd IEEE International Conference on Industrial Technology (ICIT)}},
  isbn         = {{9781728157306}},
  keywords     = {{Location awareness, Coils, Couplings, Nonuniform electric fields, Magnetic separation, Neural networks, Training data}},
  location     = {{Valencia, Spain }},
  publisher    = {{IEEE}},
  title        = {{{Development of Methods for Coil-Based Localization by Magnetic Fields of Miniaturized Sensor Platforms in Bioprocesses}}},
  doi          = {{10.1109/icit46573.2021.9453609}},
  year         = {{2021}},
}

@inproceedings{22481,
  abstract     = {{During the industrial processing of materials for the manufacture of new products, surface defects can quickly occur. In order to achieve high quality without a long time delay, it makes sense to inspect the work pieces so that defective work pieces can be sorted out right at the beginning of the process. At the same time, the evaluation unit should come close the perception of the human eye regarding detection of defects in surfaces. Such defects often manifest themselves by a deviation of the existing structure. The only restriction should be that only matt surfaces should be considered here. Therefore in this work, different classification and image processing algorithms are applied to surface data to identify possible surface damages. For this purpose, the Gabor filter and the FST (Fused Structure and Texture) features generated with it, as well as the salience metric are used on the image processing side. On the classification side, however, deep neural networks, Convolutional Neural Networks (CNN), and autoencoders are used to make a decision. A distinction is also made between training using class labels and without. It turns out later that the salience metric are best performed by CNN. On the other hand, if there is no labeled training data available, a novelty classification can easily be achieved by using autoencoders as well as the salience metric and some filters.}},
  author       = {{Sander, Tom and Lange, Sven and Hilleringmann, Ulrich and Geneis, Volker and Hedayat, Christian and Kuhn, Harald and Gockel, Franz-Barthold}},
  booktitle    = {{22nd IEEE International Conference on Industrial Technology (ICIT)}},
  isbn         = {{9781728157306}},
  keywords     = {{Image Processing, Defect Detection, wooden surfaces, Machine Learning, Neural Networks}},
  location     = {{Valencia, Spain }},
  publisher    = {{IEEE}},
  title        = {{{Detection of Defects on Irregular Structured Surfaces by Image Processing Methods for Feature Extraction}}},
  doi          = {{10.1109/icit46573.2021.9453646}},
  year         = {{2021}},
}