Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Florian Sobieczky"'
Publikováno v:
SoftwareX, Vol 28, Iss , Pp 101915- (2024)
We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. AISLEX detects anomalous data samples when the l
Externí odkaz:
https://doaj.org/article/7f02b795d5c846aaa09f69c9c1392085
Autor:
Maqbool Khan, Arshad Ahmad, Florian Sobieczky, Mario Pichler, Bernhard A. Moser, Ivo Bukovsky
Publikováno v:
IEEE Access, Vol 10, Pp 88738-88749 (2022)
The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory create
Externí odkaz:
https://doaj.org/article/ca23140a35594f2ebedb85991dc70824
Autor:
Michael Affenzeller, Michael Bögl, Lukas Fischer, Florian Sobieczky, Kaifeng Yang, Jan Zenisek
Publikováno v:
2022 24th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC).
Publikováno v:
Procedia Computer Science. 180:466-475
In various industrial production environments a burn-in phase of a specific settling-length precedes a stable (i.e. steady-state, stationary) process mode. The identification of the corresponding physical parameters may be difficult to perform in the
Publikováno v:
Procedia Computer Science. 180:1003-1012
Using a local surrogate approach from explainable AI, a new prediction method for the performance of start-up companies based on psychological profiles is proposed. The method assumes the existence of an interpreted ‘base model’, the predictions
A Deep Neural Network as Surrogate Model for Forward Simulation of Borehole Resistivity Measurements
Publikováno v:
Procedia Manufacturing
BIRD: BCAM's Institutional Repository Data
instname
BIRD: BCAM's Institutional Repository Data
instname
Inverse problems appear in multiple industrial applications. Solving such inverse problems require the repeated solution of the forward problem. This is the most time-consuming stage when employing inversion techniques, and it constitutes a severe li
Publikováno v:
Procedia Manufacturing. 42:524-527
A transformation of unimodal multivariate data is introduced for increased precision in the estimation of the exponential decay type of the underlying density. The transformation renders the contour lines of the probability density function more unif
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031143427
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30b8b77c791880a19f2f16ea05b16eaa
https://doi.org/10.1007/978-3-031-14343-4_2
https://doi.org/10.1007/978-3-031-14343-4_2
Autor:
Muhammad Qasim, Maqbool Khan, Waqar Mehmood, Florian Sobieczky, Mario Pichler, Bernhard Moser
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031143427
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aa4249d0d02035e9de718fd19e85dc35
https://doi.org/10.1007/978-3-031-14343-4_3
https://doi.org/10.1007/978-3-031-14343-4_3
Publikováno v:
SSDBM
Missing data (MD) is a prevalent problem and can negatively affect the trustworthiness of data analysis. In industrial use cases, faulty sensors or errors during data integration are common causes for systematically missing values. The majority of MD