@misc{12873,
  abstract     = {{Reliable Banknote Authentication is critical for economic stability. Regarding everyday use, recent studies implemented successful techniques using banknote images taken by mobile phone cameras. One challenge in mobile banknote authentication is that it is impossible to collect images by all series/brands of mobile phones. In this study, classification models are implemented that are able to generalize to the samples from a wide number of mobile phone series even though they are trained with samples from a small group of series. Existing state-of-the-art banknote authentication approaches train a separate model per sub-image of a banknote, using the extracted features of that sub-image. A new approach that trains a single global model on the concatenated features of all the sub-images is presented. Furthermore, ensemble models that combine Linear Discriminant Analysis and Deep Neural Networks are employed in order to maximize the accuracy. Implemented techniques were able to reach up to F1-score of 0.99914 on a Euro banknote data set which contain images from 16 different mobile-phone series. The results also indicate that new global model approach can improve the accuracy of the existing banknote authentication techniques in case of model training with images from restricted/incomplete phone series and brands.}},
  author       = {{Sürmeli, Baris Gün and Gillich, Eugen and Dörksen, Helene}},
  booktitle    = {{Artificial Neural Networks and Machine Learning - ICANN 2023}},
  editor       = {{Iliadis,  Lazaros }},
  isbn         = {{978-3-031-44209-4}},
  issn         = {{1611-3349}},
  location     = {{Heraklion, GREECE}},
  pages        = {{332--343}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Generalisation Approach for Banknote Authentication by Mobile Devices Trained on Incomplete Samples}}},
  doi          = {{10.1007/978-3-031-44210-0_27}},
  volume       = {{14255}},
  year         = {{2023}},
}

@misc{8450,
  author       = {{Schwarzer, Knut and Weishaupt, Imke and Gossen, Arthur and Sürmeli, Baris Gün and Schneider, Jan}},
  location     = {{Frankfurt}},
  title        = {{{Smart Pasteurization - Eine neuartige, autonome Regelung für eine Kurzzeiterhitzung}}},
  year         = {{2022}},
}

@article{6839,
  abstract     = {{Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r<jats:sup>2</jats:sup> 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r<jats:sup>2</jats:sup> 0.98 ± 0.01. Thus, the model accuracy ±10–30 % is well within the standard safety margins. }},
  author       = {{Sürmeli, Baris Gün and Weishaupt, Imke and Schwarzer, Knut and Moriz, Natalia and Schneider, Jan}},
  issn         = {{1751-6552}},
  journal      = {{Journal of Near Infrared Spectroscopy}},
  keywords     = {{Beverage pasteurization, heat impact control, prediction outlier elimination}},
  number       = {{6}},
  pages        = {{339--351}},
  publisher    = {{Sage Publishing}},
  title        = {{{Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy}}},
  doi          = {{10.1177/09670335211057233}},
  volume       = {{29}},
  year         = {{2021}},
}

@inproceedings{5473,
  author       = {{Sürmeli, Baris Gün and Weishaupt, Imke and Schwarzer, Knut and Schneider, Jan}},
  title        = {{{Beverage Classification Using Linear Discriminant Analysis with Covariance Matrix Shrinkage}}},
  year         = {{2019}},
}