TY - CHAP A1 - Wostmann, Rene A1 - Schlunder, Philipp A1 - Temme, Fabian A1 - Klinkenberg, Ralf A1 - Kimberger, Josef A1 - Spichtinger, Andrea A1 - Goldhacker, Markus A1 - Deuse, Jochen T1 - Conception of a Reference Architecture for Machine Learning in the Process Industry T2 - 2020 IEEE International Conference on Big Data (Big Data): 10.12.2020 - 13.12.2020 Atlanta, GA, USA N2 - The increasing global competition demands continuous optimization of products and processes from companies in the process industry. Where conventional methods of Lean Management and Six Sigma reach their limits, new opportunities and challenges arise through increasing connectivity in the Industrial Internet of Things and machine learning. The majority of industrial projects do not reach the deployment or are isolated solutions, as the structures for data integration, training, deployment and maintenance of models are not established. This paper presents the conception of a reference architecture for machine learning in the process industry to support companies in implementing their own specific structures. The focus is on the development process and an exemplary implementation in the brewing industry. KW - Big data KW - Companies KW - Industrial Internet of Things KW - Industries KW - Machine learning KW - Optimization KW - process industry KW - Production KW - Reference architecture KW - Six sigma KW - Training Y1 - 2020 SN - 978-1-7281-6251-5 U6 - https://doi.org/10.1109/bigdata50022.2020.9378290 SP - 1726 EP - 1735 PB - IEEE ER - TY - JOUR A1 - Wein, Simon A1 - Tomé, Ana Maria A1 - Goldhacker, Markus A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - A Constrained ICA-EMD Model for Group Level fMRI Analysis JF - Frontiers in Neuroscience N2 - Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components. Y1 - 2020 U6 - https://doi.org/10.3389/fnins.2020.00221 SN - 1662-453X SN - 1662-4548 VL - 14 SP - 1 EP - 10 PB - frontiers ER -