TY - CONF A1 - Radtke, Martin A1 - Cakir, Cafer Tufan T1 - Big to Small - Getting Smarter@BAMline N2 - In this talk an overview about artificial intelligence/machine learning applications @BAMline is given. In the first part, the use of neural networks for the quantification of XRF measurements and the decoding of coded-aperture measurements are shown. Then it is shown how Gaussian processes and Bayesian statistics can be used to achieve an optimal alignment of the set-up and in general for optimization of measurements. T2 - Forschungsseminar Institut für Optik und Atomare Physik TU Berlin CY - Berlin, Germany DA - 25.10.2022 KW - Artificial Inelligence KW - Machine Learning KW - Bayesian Statistics KW - Gaussian process KW - Neural network KW - BAMline KW - Synchrotron PY - 2022 AN - OPUS4-56249 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin A1 - Cakir, Cafer Tufan A1 - de Oliveira Guilherme Buzanich, Ana T1 - Getting more efficient – The use of Bayesian optimization and Gaussian processes at the BAMline N2 - For more than 20 years, BAM is operating the BAMline at the synchrotron BESSY II in Berlin Adlershof. During this time, the complexity of the setup and the amount of data generated have multiplied. To increase the effectiveness and in preparation for BESSY III, algorithms from the field of machine learning are increasingly used. After a short introduction to BO and GP, the first example is the automatic alignment of our double multilayer monochromator (DMM). The second example is the optimization of measurement time in XRF scanning. T2 - SNI2022 conference CY - Berlin, Germany DA - 05.09.2022 KW - Artificial Inelligence KW - Machine Learning KW - Bayesian Statistics KW - Gaussian process KW - X-ray fluorescence KW - BAMline PY - 2022 AN - OPUS4-56255 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radtke, Martin T1 - ML@BAMLINE N2 - An overview of the machine learning methods used at BAMline. Examples of automatic alignment and optimisation based on Gaussian processes are discussed. Quantification of XRF measurements and reconstruction for coded aperture imaging based on neural networks are also presented. T2 - Machine Learning Workshop BAM CY - Online meeting DA - 18.03.2021 KW - Artificial Intelligence KW - Machine learning KW - Bayesian Optimization KW - Gaussian process KW - Neural Network PY - 2021 AN - OPUS4-54142 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -