Titlebook: Statistical Machine Learning for Engineering with Applications; Jürgen Franke,Anita Schöbel Textbook 2024 The Editor(s) (if applicable) an

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Henrike Stephani,Thomas Weibel,Ronald Rösch,Ali Moghiseh

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Alex Sarishvili,Fabian Menzel,Benjamin Adrian,Julia Burr

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Statistical Machine Learning for Engineering with Applications

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Machine Learning for Inline Surface Inspection Systems: Challenges, Approaches, and Application Exan. Therefore, respective parties must be made aware of this issue on the one hand; on the other hand, annotation and reannotation must be easy and useable by non-experts. Related is the issue of parametrization and traceability. Both are not inherent to neural networks but must be provided to some l

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Gaussian Process Regression for the Prediction of Cable Bundle Characteristics,n bundles. We outline our approach to solve this nonlinear identification task with Gaussian Process Regression. Besides a short introduction to the industrial application area, we demonstrate and illustrate the applicability and prediction quality of Gaussian Process Regression for this task.

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Cracks in Concrete,oads with cars but have a hard time deciding whether a thin and dark structure seen in a 2D slice continues in the next one. Training networks by synthetic, simulated images is an elegant way out, yet bears its own challenges. In this contribution, we describe how to generate semisynthetic image dat