TY - CHAP A1 - Lang, Christian A1 - Steinborn, Florian A1 - Steffens, Oliver A1 - Lang, Elmar Wolfgang ED - Valenzuela, O. ED - Rojas, F. ED - Herrera, L.J. ED - Pomares, H. ED - Rojas, I. T1 - Applying a 1D-CNN Network to Electricity Load Forecasting T2 - Theory and Applications of Time Series Analysis N2 - This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN architectures, the input data is one-dimensional. A parameter scanning of network parameters is conducted in order to gain information about the influence of the kernel size, number of filters and number of nodes. Furthermore, different dropout methods are applied to the CNN and are evaluated. The results show that a good forecast quality can already be achieved with basic CNN architectures, the dropout improves the forecast. The method works not only for smooth sum loads of many hundred consumers, but also for the load of single apartment buildings. KW - Energy load forecasting KW - STLF KW - Neural networks KW - CNN KW - Convolutional networks Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-56219-9_14 SP - 205 EP - 218 PB - Springer 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 -