Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Nico Migenda"'
Autor:
Julian Weller, Nico Migenda, Yash Naik, Tim Heuwinkel, Arno Kühn, Martin Kohlhase, Wolfram Schenck, Roman Dumitrescu
Publikováno v:
Mathematics, Vol 12, Iss 17, p 2663 (2024)
Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases a
Externí odkaz:
https://doaj.org/article/79cfe0bea53044e6991344f8767bd717
Publikováno v:
PLoS ONE, Vol 16, Iss 3, p e0248896 (2021)
"Principal Component Analysis" (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-call
Externí odkaz:
https://doaj.org/article/95e4150aae6943e6ab483738bcbfc3c9
Publikováno v:
2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1921bb7435273cf93617b50765d02cc2
https://doi.org/10.1109/aike52691.2021.00011
https://doi.org/10.1109/aike52691.2021.00011
Publikováno v:
2021 3rd International Conference on Management Science and Industrial Engineering.
There are numerous applications that deal with data captured over time making them potential subject to time series analysis. Detecting unknown events and anomalies in time series data is challenging due to the presence of noise, seasonalities and lo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4ac17e89884c844ec748430ff104ba3e
https://doi.org/10.1145/3460824.3460825
https://doi.org/10.1145/3460824.3460825
Publikováno v:
PLoS ONE
PLoS ONE, Vol 16, Iss 3, p e0248896 (2021)
PLoS ONE, Vol 16, Iss 3, p e0248896 (2021)
“Principal Component Analysis” (PCA) is an established linear technique for dimensionality reduction. It performs an orthonormal transformation to replace possibly correlated variables with a smaller set of linearly independent variables, the so-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33793b8fa52d96753eef435b5e873d25
https://pubmed.ncbi.nlm.nih.gov/33784333
https://pubmed.ncbi.nlm.nih.gov/33784333
Autor:
Wolfram Schenck, Nico Migenda
Publikováno v:
ETFA
The early detection of upcoming failures in technical systems, e.g. in factory automation, is crucial for worker safety and to avoid downtimes. Machine learning (ML) approaches are commonly used in this context to learn the standard behavior of techn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bcb8185fbca8e1da41b497d783554e07
https://doi.org/10.1109/etfa46521.2020.9212129
https://doi.org/10.1109/etfa46521.2020.9212129
Publikováno v:
Intelligent Data Engineering and Automated Learning – IDEAL 2019 ISBN: 9783030336066
IDEAL (1)
IDEAL (1)
Many applications in the Industrial Internet of Things and Industry 4.0 rely on large amounts of data which are continuously generated. The exponential growth in available data and the resulting storage requirements are often underestimated bottlenec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0787a52d7085868fa2b6c90d39d4bb51
https://doi.org/10.1007/978-3-030-33607-3_9
https://doi.org/10.1007/978-3-030-33607-3_9
Autor:
David Pelkmann, Marvin Schone, Tim Voigt, Nico Migenda, Martin Kohlhase, Wolfram Schenck, Matthias Fricke
Publikováno v:
Fachhochschule (FH) Bielefeld
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f3ee59b285a81f2cfd0419966d2faaa
https://www.fh-bielefeld.de/publikationsserver/publication/1373
https://www.fh-bielefeld.de/publikationsserver/publication/1373