105 citations · 441 across the 38 of their papers we have counts for
9 papers · 1 filter
AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models
Stefan Meisenbacher, Benedikt Heidrich, Tim Martin +2
Accurate PhotoVoltaic (PV) power generation forecasting is vital for the efficient operation of Smart Grids. The automated design of such accurate forecasting models for individual…
EasyMLServe: Easy Deployment of REST Machine Learning Services
Oliver Neumann, Marcel Schilling, Markus Reischl +1
Various research domains use machine learning approaches because they can solve complex tasks by learning from data. Deploying machine learning models, however, is not trivial and…
Predicting the power grid frequency of European islands
Thorbjørn Lund Onsaker, Heidi S. Nygård, Damià Gomila +7
Modelling, forecasting and overall understanding of the dynamics of the power grid and its frequency are essential for the safe operation of existing and future power grids. Much p…
Rapid Flow Behavior Modeling of Thermal Interface Materials Using Deep Neural Networks
Simon Baeuerle, Marius Gebhardt, Jonas Barth +2
Thermal Interface Materials (TIMs) are widely used in electronic packaging. Increasing power density and limited assembly space pose high demands on thermal management. Large cooli…
EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths
Katharina Löffler, Ralf Mikut
A systematic analysis of the cell behavior requires automated approaches for cell segmentation and tracking. While deep learning has been successfully applied for the task of cell…
Exploiting Multiple EEG Data Domains with Adversarial Learning
David Bethge, Philipp Hallgarten, Ozan Özdenizci +3
Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as…
ciscNet -- A Single-Branch Cell Instance Segmentation and Classification Network
Moritz Böhland, Oliver Neumann, Marcel P. Schilling +4
Automated cell nucleus segmentation and classification are required to assist pathologists in their decision making. The Colon Nuclei Identification and Counting Challenge 2022 (Co…
Review of automated time series forecasting pipelines
Stefan Meisenbacher, Marian Turowski, Kaleb Phipps +4
Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case require…
Domain-Invariant Representation Learning from EEG with Private Encoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This beco…