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pro vyhledávání: '"Bader, Sebastian"'
In this paper we argue that Neuro-Symbolic AI (NeSy-AI) should be applied for patient monitoring. In this context, we introduce patient monitoring as a special case of Human Activity Recognition and derive concrete requirements for this application a
Externí odkaz:
http://arxiv.org/abs/2407.11995
Artificial intelligence (AI) based assistive systems, so called intelligent assistive technology (IAT) are becoming increasingly ubiquitous by each day. IAT helps people in improving their quality of life by providing intelligent assistance based on
Externí odkaz:
http://arxiv.org/abs/2303.03929
In an effort to advocate the research for a deep learning-based machine failure detection system, we present a case study of our proposed system based on a tiny sound dataset. Our case study investigates a variational autoencoder (VAE) for augmenting
Externí odkaz:
http://arxiv.org/abs/2209.11527
Denoising is the process of removing noise from sound signals while improving the quality and adequacy of the sound signals. Denoising sound has many applications in speech processing, sound events classification, and machine failure detection system
Externí odkaz:
http://arxiv.org/abs/2208.04462
Publikováno v:
2022. Proceedings of the 7th International Workshop on Sensor-based Activity Recognition and Artificial Intelligence. Association for Computing Machinery, New York, NY, USA
The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and
Externí odkaz:
http://arxiv.org/abs/2207.08816
We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object properties and relations. Thus, estimating the hidden system state from
Externí odkaz:
http://arxiv.org/abs/2202.00332
Developing artificial intelligence based assistive systems to aid Persons with Dementia (PwD) requires large amounts of training data. However, data collection poses ethical, legal, economic, and logistic issues. Synthetic data generation tools, in t
Externí odkaz:
http://arxiv.org/abs/2107.05346
Autor:
Dyrba, Martin, Hanzig, Moritz, Altenstein, Slawek, Bader, Sebastian, Ballarini, Tommaso, Brosseron, Frederic, Buerger, Katharina, Cantré, Daniel, Dechent, Peter, Dobisch, Laura, Düzel, Emrah, Ewers, Michael, Fliessbach, Klaus, Glanz, Wenzel, Haynes, John-Dylan, Heneka, Michael T., Janowitz, Daniel, Keles, Deniz B., Kilimann, Ingo, Laske, Christoph, Maier, Franziska, Metzger, Coraline D., Munk, Matthias H., Perneczky, Robert, Peters, Oliver, Preis, Lukas, Priller, Josef, Rauchmann, Boris, Roy, Nina, Scheffler, Klaus, Schneider, Anja, Schott, Björn H., Spottke, Annika, Spruth, Eike J., Weber, Marc-André, Ertl-Wagner, Birgit, Wagner, Michael, Wiltfang, Jens, Jessen, Frank, Teipel, Stefan J.
Background: Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important rea
Externí odkaz:
http://arxiv.org/abs/2012.10294
Autor:
Zhang, Ying, Wang, Wei, Wu, Xin, Lei, Yaguo, Cao, Junyi, Bowen, Chris, Bader, Sebastian, Yang, Bin
Publikováno v:
In Renewable and Sustainable Energy Reviews September 2023 183
Autor:
Bader, Sebastian R., Maleshkova, Maria
The disruptive potential of the upcoming digital transformations for the industrial manufacturing domain have led to several reference frameworks and numerous standardization approaches. On the other hand, the Semantic Web community has made signific
Externí odkaz:
http://arxiv.org/abs/1909.00690