TY - CHAP A1 - Lang, Christian A1 - Steinborn, Florian A1 - Steffens, Oliver A1 - Lang, Elmar Wolfgang T1 - Electricity Load Forecasting - An Evaluation of Simple 1D-CNN Network Structures T2 - International Conference on Time Series and Forecasting (ITISE 2019), Proceedings of Papers Vol. 2, 25-27 September 2019, Granada (Spain) N2 - This paper presents a convolutional neural network (CNN)which can be used for forecasting electricity load profiles 36 hours intothe future. In contrast to well established CNN architectures, the inputdata is one-dimensional. A parameter scanning of network parameters isconducted in order to gain information about the influence of the kernelsize, number of filters, and dense size. The results show that a goodforecast quality can already be achieved with basic CNN architectures.The method works not only for smooth sum loads of many hundredconsumers, but also for the load of apartment buildings KW - energy load forecasting KW - STLF KW - neural networks KW - CNN KW - con-volutional networks Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-16649 UR - https://arxiv.org/abs/1911.11536 UR - http://itise.ugr.es/ITISE2019_vol2.pdf SN - 978-84-17970-78-9 SP - 797 EP - 806 ER - TY - CHAP A1 - Wein, S. A1 - Tomé, Ana Maria A1 - Goldhacker, Markus A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - Hybridizing EMD with cICA for fMRI Analysis of Patient Groups T2 - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 23-27 July 2019, Berlin, Germany N2 - Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow. KW - biomedical MRI KW - data analysis KW - independent component analysis KW - medical image processing Y1 - 2019 U6 - https://doi.org/10.1109/EMBC.2019.8856355 SP - 194 EP - 197 PB - IEEE ER - TY - CHAP A1 - Goldhacker, Markus A1 - Tomé, Ana Maria A1 - Greenlee, Mark W. A1 - Lang, Elmar Wolfgang T1 - Early meta-level: deeper understanding of connectivity-states and consequences for state definition T2 - 21st Annual Meeting of the Organization for Human Brain Mapping, June 14-18, 2015, Honolulu, Hawaii Y1 - 2015 U6 - https://doi.org/10.13140/RG.2.1.2561.3929 PB - Academic Press CY - San Diego, CA ER -