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

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Machine Learning for Inline Surface Inspection Systems: Challenges, Approaches, and Application Exaduction are usually fixed in terms of clock rate and response time. Furthermore, these methods need a lot of data, while typically, the data situation is thin in the beginning as well as continuously unbalanced: defects occur rarely, thereby providing few example data for learning, while the desired

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Gaussian Process Regression for the Prediction of Cable Bundle Characteristics,l product development and simulation-based design, it is necessary to know the characteristic physical parameters, like effective bending or torsion stiffness, of these cable systems. In early stages of the development process as well as for highly customized individual cable configurations, measuri

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Machine Learning Methods for Prediction of Breakthrough Curves in Reactive Porous Media,y often the concentration of the species at the inlet is known, and the so-called breakthrough curves, measured at the outlet, are the quantities which could be measured or computed numerically. The measurements and the simulations could be time-consuming and expensive, and machine learning approach

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Segmentation and Aggregation in Text Classification,ple classification algorithms are known, in application those suffer from the different length of the documents. One way to handle those is the segmentation setup. As a result, the classification algorithm gives a probability for each segmentation and every class. To classify the whole document, an

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Optimal Experimental Design Supported by Machine Learning Regression Models,odel parameters to the measured data. Optimal experimental design (OED) provides methods to obtain precise parameter estimates for mathematical models with as few experiments as possible, which in turn saves costs and time. Classical algorithms for OED require pre-computations on a finite design gri

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