@article{KhayyamJamaliBabHadiasharetal.2020,
  author    = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo},
  title     = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0},
  series = {IEEE Access},
  volume    = {8},
  journal   = {IEEE Access},
  number    = {Art. 9108222},
  publisher = {IEEE},
  address   = {New York, NY},
  issn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.2999898},
  pages     = {111381 -- 111393},
  year      = {2020},
  abstract  = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.},
  language  = {en}
}
@article{PfaffGidaszewskiSchmidt2020,
  author    = {Pfaff, Raphael and Gidaszewski, Lars and Schmidt, Bernd},
  title     = {Ber{\"u}cksichtigung von No Fault Found im Diagnose- und Instandhaltungssystem von Schienenfahrzeugen},
  series = {ETR - Eisenbahntechnische Rundschau},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {5},
  publisher = {DVV Media Group},
  address   = {Hamburg},
  issn      = {0013-2845},
  pages     = {56 -- 59},
  year      = {2020},
  abstract  = {Intermittierende und nicht reproduzierbare Fehler, auch als No Fault Found bezeichnet, treten in praktisch allen Bereichen auf und sorgen f{\"u}r hohe Kosten. Diese sind h{\"a}ufig auf unpr{\"a}zise Fehlerbeschreibungen zur{\"u}ckzuf{\"u}hren. Im vorliegenden Beitrag werden Anpassungen der Vorgehensweise bei der Entwicklung und Anpassungen des Diagnosesystems vorgeschlagen.},
  language  = {de}
}
@article{KhayyamJamaliBabHadiasharetal.2020,
  author    = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalil, Mahdi and Naebe, Minoo},
  title     = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0},
  series = {IEEE Access},
  journal   = {IEEE Access},
  publisher = {IEEE},
  address   = {New York, NY},
  isbn      = {2169-3536},
  doi       = {10.1109/ACCESS.2020.2999898},
  pages     = {1 -- 12},
  year      = {2020},
  abstract  = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.},
  language  = {en}
}
@inproceedings{DinghoferHartung2020,
  author    = {Dinghofer, Kai and Hartung, Frank},
  title     = {Analysis of Criteria for the Selection of Machine Learning Frameworks},
  series = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  booktitle = {2020 International Conference on Computing, Networking and Communications (ICNC)},
  doi       = {10.1109/ICNC47757.2020.9049650},
  pages     = {373 -- 377},
  year      = {2020},
  language  = {en}
}
@article{WilbringEnningPfaffetal.2020,
  author    = {Wilbring, Daniela and Enning, Manfred and Pfaff, Raphael and Schmidt, Bernd},
  title     = {Neue Perspektiven f{\"u}r die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation},
  series = {ETR - Eisenbahntechnische Rundschau},
  volume    = {69},
  journal   = {ETR - Eisenbahntechnische Rundschau},
  number    = {3},
  issn      = {0013-2845},
  pages     = {15 -- 19},
  year      = {2020},
  language  = {de}
}
@inproceedings{FingerdeVriesVosetal.2020,
  author    = {Finger, Felix and de Vries, Reynard and Vos, Roelof and Braun, Carsten and Bil, Cees},
  title     = {A comparison of hybrid-electric aircraft sizing methods},
  series = {AIAA Scitech 2020 Forum},
  booktitle = {AIAA Scitech 2020 Forum},
  doi       = {10.2514/6.2020-1006},
  pages     = {31 Seiten},
  year      = {2020},
  language  = {en}
}
@inproceedings{KreyerMuellerEsch2020,
  author    = {Kreyer, J{\"o}rg and M{\"u}ller, Marvin and Esch, Thomas},
  title     = {A Map-Based Model for the Determination of Fuel Consumption for Internal Combustion Engines as a Function of Flight Altitude},
  series = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019},
  booktitle = {Deutscher Luft- und Raumfahrtkongress 2019, „Luft- und Raumfahrt - technologische Br{\"u}cke in die Zukunft", Darmstadt, 30. September bis 2. Oktober 2019},
  publisher = {Deutsche Gesellschaft f{\"u}r Luft- und Raumfahrt - Lilienthal-Oberth e.V},
  address   = {Bonn},
  doi       = {10.25967/490162},
  pages     = {13 Seiten},
  year      = {2020},
  language  = {en}
}