Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Maya Kallas"'
Publikováno v:
International Journal of Adaptive Control and Signal Processing. 32:1569-1590
Publikováno v:
MED
This paper addresses the problem of multiple fault isolation based on kernel principal component analysis and proposes a sparse fault estimation method to evaluate the reconstruction-based contribution. The fault magnitude estimation is here formulat
Publikováno v:
20th IFAC World Congress, IFAC 2017
20th IFAC World Congress, IFAC 2017, Jul 2017, Toulouse, France. pp.1025-1030, ⟨10.1016/j.ifacol.2017.08.212⟩
20th IFAC World Congress, IFAC 2017, Jul 2017, Toulouse, France. pp.1025-1030, ⟨10.1016/j.ifacol.2017.08.212⟩
Published in IFAC-PaperOnLine, 50(1):1025-1030; International audience; The principal component analysis (PCA) is a linear technique widely used to retrieve a subspace that maximizes the variance of the data, making the presence of a fault easy to de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0d7cb2e47e824586b64aaa9d6e5eb34
https://hal.archives-ouvertes.fr/hal-01478133
https://hal.archives-ouvertes.fr/hal-01478133
Publikováno v:
12th European Workshop on Advanced Control and Diagnosis, ACD 2015
12th European Workshop on Advanced Control and Diagnosis, ACD 2015, Nov 2015, Pilsen, Czech Republic. ⟨10.1088/1742-6596/659/1/012032⟩
12th European Workshop on Advanced Control and Diagnosis, ACD 2015, Nov 2015, Pilsen, Czech Republic. ⟨10.1088/1742-6596/659/1/012032⟩
International audience; In terms of system diagnosis, several studies are generally performed. The diagnosis is composed of three different parts: detecting, isolating and estimating the value of the faults. If many results have been obtained for lin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c6ec61bf769a512bf0ffe4af10e2774
https://hal.archives-ouvertes.fr/hal-01221044
https://hal.archives-ouvertes.fr/hal-01221044
Publikováno v:
14th European Control Conference, ECC'15
14th European Control Conference, ECC'15, Jul 2015, Linz, Austria. ⟨10.1109/ECC.2015.7331026⟩
ECC
14th European Control Conference, ECC'15, Jul 2015, Linz, Austria. ⟨10.1109/ECC.2015.7331026⟩
ECC
International audience; The principal component analysis (PCA) is a well-know technique to detect, isolate and estimate faults affecting a system. However, PCA identifies only linear structures in a given dataset. In this paper, we propose a new tech
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::690e521d018224b0b6a24ee81924636a
https://hal.archives-ouvertes.fr/hal-01135065
https://hal.archives-ouvertes.fr/hal-01135065
Publikováno v:
11th European Workshop on Advanced Control and Diagnosis, ACD 2014
11th European Workshop on Advanced Control and Diagnosis, ACD 2014, Nov 2014, Berlin, Germany. ⟨10.1088/1742-6596/570/7/072004⟩
11th European Workshop on Advanced Control and Diagnosis, ACD 2014, Nov 2014, Berlin, Germany. ⟨10.1088/1742-6596/570/7/072004⟩
Published in Journal of Physics: Conference Series, 570:072004, 2014.; International audience; Technological advances in the process industries during the past decade haveresulted in increasingly complicated processes, systems and products. Therefore
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b3ce00965f507f279c3f185cdfdfa21
https://hal.archives-ouvertes.fr/hal-01078401
https://hal.archives-ouvertes.fr/hal-01078401
Publikováno v:
24th IEEE Workshop on Machine Learning for Signal Processing, MLSP 2014
24th IEEE Workshop on Machine Learning for Signal Processing, MLSP 2014, Sep 2014, Reims, France. ⟨10.1109/MLSP.2014.6958910⟩
MLSP
24th IEEE Workshop on Machine Learning for Signal Processing, MLSP 2014, Sep 2014, Reims, France. ⟨10.1109/MLSP.2014.6958910⟩
MLSP
International audience; The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::135cbaef98230efd4d80cd86628419bc
https://hal.science/hal-01965998
https://hal.science/hal-01965998
Publikováno v:
Signal Processing
Signal Processing, 2013, 93 (11), pp.3053-3061. ⟨10.1016/j.sigpro.2013.03.032⟩
Signal Processing, Elsevier, 2013, 93 (11), pp.3053-3061. ⟨10.1016/j.sigpro.2013.03.032⟩
Signal Processing, 2013, 93 (11), pp.3053-3061. ⟨10.1016/j.sigpro.2013.03.032⟩
Signal Processing, Elsevier, 2013, 93 (11), pp.3053-3061. ⟨10.1016/j.sigpro.2013.03.032⟩
International audience; This paper proposes nonlinear autoregressive (AR) models for time series, within the framework of kernel machines. Two models are investigated. In the first proposed model, the AR model is defined on the mapped samples in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92d469d017c2c8fdb7f28c02afa92768
https://hal.science/hal-01965577/file/13.sp.kar_yw.pdf
https://hal.science/hal-01965577/file/13.sp.kar_yw.pdf
Publikováno v:
Pattern Recognition
Pattern Recognition, Elsevier, 2013, 46 (11), pp.3066-3080. ⟨10.1016/j.patcog.2013.03.021⟩
Pattern Recognition, 2013, 46 (11), pp.3066-3080. ⟨10.1016/j.patcog.2013.03.021⟩
Pattern Recognition, Elsevier, 2013, 46 (11), pp.3066-3080. ⟨10.1016/j.patcog.2013.03.021⟩
Pattern Recognition, 2013, 46 (11), pp.3066-3080. ⟨10.1016/j.patcog.2013.03.021⟩
International audience; Rules of physics in many real-life problems force some constraints to be satisfied. This paper deals with nonlinear pattern recognition under non-negativity constraints. While kernel principal component analysis can be applied
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d66faa45c987dbf3abb461fa8220cb1e
https://hal.archives-ouvertes.fr/hal-01965576
https://hal.archives-ouvertes.fr/hal-01965576
Publikováno v:
Proc. 37th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Proc. 37th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, Kyoto, Japan. pp.2185-2188, ⟨10.1109/ICASSP.2012.6288346⟩
ICASSP
Proc. 37th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, Kyoto, Japan. pp.2185-2188, ⟨10.1109/ICASSP.2012.6288346⟩
ICASSP
International audience; The autoregressive (AR) model is a well-known technique to analyze time series. The Yule-Walker equations provide a straightforward connection between the AR model parameters and the covariance function of the process. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac936b3bc4483d96767b4bfbb37edaa8
https://hal.science/hal-01966015/document
https://hal.science/hal-01966015/document