Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Tveten, Martin"'
Autor:
Tveten, Martin, Stakkeland, Morten
Machine learning and statistical methods can be used to enhance monitoring and fault prediction in marine systems. These methods rely on a dataset with records of historical system behaviour, potentially containing periods of both fault-free and faul
Externí odkaz:
http://arxiv.org/abs/2406.08030
We propose a new, computationally efficient, sparsity adaptive changepoint estimator for detecting changes in unknown subsets of a high-dimensional data sequence. Assuming the data sequence is Gaussian, we prove that the new method successfully estim
Externí odkaz:
http://arxiv.org/abs/2306.04702
Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series effic
Externí odkaz:
http://arxiv.org/abs/2010.06937
Autor:
Tveten, Martin, Glad, Ingrid K.
When applying principal component analysis (PCA) for dimension reduction, the most varying projections are usually used in order to retain most of the information. For the purpose of anomaly and change detection, however, the least varying projection
Externí odkaz:
http://arxiv.org/abs/1908.02029
Autor:
Tveten, Martin
PCA is often used in anomaly detection and statistical process control tasks. For bivariate data, we prove that the minor projection (the least varying projection) of the PCA-rotated data is the most sensitive to distributional changes, where sensiti
Externí odkaz:
http://arxiv.org/abs/1905.06318
Akademický článek
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Autor:
Tveten, Martin
Publikováno v:
Tveten, Martin. Multi-Stream Sequential Change Detection -- Using Sparsity and Dimension Reduction. Master thesis, University of Oslo, 2017
Externí odkaz:
http://hdl.handle.net/10852/57802
https://www.duo.uio.no/bitstream/handle/10852/57802/5/Martin-Tveten-ENDELIG-masteroppgave.pdf
https://www.duo.uio.no/bitstream/handle/10852/57802/5/Martin-Tveten-ENDELIG-masteroppgave.pdf
Autor:
Tveten, Martin1 (AUTHOR) martintv@math.uio.no
Publikováno v:
Stat. 2019, Vol. 8 Issue 1, p1-10. 10p.
Autor:
Hellton, Kristoffer Herland, Tveten, Martin, Stakkeland, Morten, Engebretsen, Solveig, Haug, Ola, Aldrin, Magne Tommy
Publikováno v:
Hellton, Kristoffer Herland Tveten, Martin Stakkeland, Morten Engebretsen, Solveig Haug, Ola Aldrin, Magne Tommy . Real-time prediction of propulsion motor overheating using machine learning. Jo
Jo
Jo
Externí odkaz:
http://hdl.handle.net/10852/88688
https://www.duo.uio.no/bitstream/handle/10852/88688/1/Sensor__Motor_Temperatur_Prediction.pdf
https://www.duo.uio.no/bitstream/handle/10852/88688/1/Sensor__Motor_Temperatur_Prediction.pdf