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Volume 167 (2025), Edition 04

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ISSN-Print
0042-1766
ISSN-Online
0042-1766
Publisher
VDI fachmedien, Düsseldorf
Language
German
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Bibliography (14 entries)

  1. [1] VDI/VDE 2617 Blatt 11 -03.2011: Genauigkeit von Koordinatenmessgeräten-Kenngrößen und deren Prüfung. Ermittlung der Unsicherheit von Messungen auf Koordinatenmessgeräten durch Messunsicherheitsbilanzen. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-23
  2. [1] Tiedje, O.; Paustian, S.; Rosenkranz, S.; Hecker, M.; Tropea, C.: Detection of production relevant deviations in paint sprays, J Coat Technol Res, 2024. DOI: https://doi.org/10.1007/s11998-024-01015-1. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  3. [2] Niggemann, O.; Maier, A.; Vodencarevic, A.; Jantscher, B.: Fighting the Modeling Bottleneck – Learning Models for Production Plants, 2011. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  4. [3] Hinton, G.; Sejnowski, T. J.: Unsupervised Learning. The MIT Press, 1999. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  5. [4] Braverman, V.: Sliding Window Algorithms. In: Kao, M.-Y. (Ed.), Encyclopedia of Algorithms. Springer New York, New York, NY, pp. 2006–2011, 2016. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  6. [5] Rumelhart, D. E.; McClelland, J. L.: Learning Internal Representations by Error Propagation.In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, pp. 318–362. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  7. [6] Sakurada, M.; Yairi, T.: Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction. In: Proceedings of the MLSDA 2014 2nd Workshop on Machine Learning for Sensory Data Analysis. MLSDA‘14: Machine Learning for Sensory Data Analysis, Gold Coast Australia QLD Australia. 02 12 2014 02 12 2014. ACM, New York, NY, USA, pp. 4–11, 2014. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
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  10. [9] Zhuang, F.; Qi, Z.; Duan, K.; Xi, D.; Zhu, Y.; Zhu, H.; Xiong, H.; He, Q.: A Comprehensive Survey on Transfer Learning. Proc. IEEE 109 (1), pp. 43–76, 2021. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  11. [10] Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; Lerer, A.: Automatic differentiation in PyTorch. The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) (31), 2017. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  12. [11] Akiba, T.; Sano, S.; Yanase, T.; Ohta, T.; Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework, pp. 10, 2019. Internet: http://arxiv.org/pdf/1907.10902v1. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  13. [12] Montgomery, D.C.: Introduction to statistical quality control, Eighth edition ed. Wiley, Hoboken, NJ, 2019. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67
  14. [13] Zimmer, L.; Kotz, S.; Lovelace, C.R.: Process Capability Indices in Theory and Practice. Technometrics 42 (2) 206, 2000. Open Google Scholar doi.org/10.37544/0042-1766-2025-04-67

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