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Data-Driven Fault Detection and Reasoning for Industrial Monitoring
| Data-Driven Fault Detection and Reasoning for Industrial Monitoring |
| Autore | Wang Jing <1974 April 21-> |
| Pubbl/distr/stampa | Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (277 pages) |
| Altri autori (Persone) |
ZhouJinglin
ChenXiaolu |
| Collana | Intelligent Control and Learning Systems |
| Soggetto topico |
Robotics
Artificial intelligence |
| Soggetto non controllato |
Multivariate causality analysis
Process monitoring Manifold learning Fault diagnosis Data modeling Fault classification Fault reasoning Causal network Probabilistic graphical model Data-driven methods Industrial monitoring Open Access |
| ISBN | 981-16-8044-2 |
| Classificazione | TEC009000TEC037000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910520099003321 |
Wang Jing <1974 April 21->
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| Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Generalized principal component analysis / René Vidal, Yi Ma, S. Shankar Sastry
| Generalized principal component analysis / René Vidal, Yi Ma, S. Shankar Sastry |
| Autore | Vidal, René |
| Pubbl/distr/stampa | New York, : Springer, 2016 |
| Descrizione fisica | XXXII, 566 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Ma, Yi
Sastry, S. Shankar |
| Soggetto topico |
14-XX - Algebraic geometry [MSC 2020]
15-XX - Linear and multilinear algebra; matrix theory [MSC 2020] 62B10 - Statistical aspects of information-theoretic topics [MSC 2020] 62H12 - Estimation in multivariate analysis [MSC 2020] 62-XX - Statistics [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 30C10 - Polynomials and rational functions of one complex variable [MSC 2020] 62H35 - Image analysis in multivariate analysis [MSC 2020] 62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62Jxx - Linear inference, regression [MSC 2020] 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020] 62Fxx - Parametric inference [MSC 2020] 30C40 - Kernel functions in one complex variable and applications [MSC 2020] 14N20 - Configurations and arrangements of linear subspaces [MSC 2020] |
| Soggetto non controllato |
Hybrid system identification
Image and video segmentation Linear subspace models Low-rank matrix theory Manifold learning Principal component analysis Robust principal component analysis Sparse representation theory Spectral clustering Subspace arrangements Subspace clustering |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0114796 |
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Vidal, René
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| New York, : Springer, 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Generalized principal component analysis / René Vidal, Yi Ma, S. Shankar Sastry
| Generalized principal component analysis / René Vidal, Yi Ma, S. Shankar Sastry |
| Autore | Vidal, René |
| Pubbl/distr/stampa | New York, : Springer, 2016 |
| Descrizione fisica | XXXII, 566 p. : ill. ; 24 cm |
| Altri autori (Persone) |
Ma, Yi
Sastry, S. Shankar |
| Soggetto topico |
14-XX - Algebraic geometry [MSC 2020]
14N20 - Configurations and arrangements of linear subspaces [MSC 2020] 15-XX - Linear and multilinear algebra; matrix theory [MSC 2020] 30C10 - Polynomials and rational functions of one complex variable [MSC 2020] 30C40 - Kernel functions in one complex variable and applications [MSC 2020] 62-XX - Statistics [MSC 2020] 62B10 - Statistical aspects of information-theoretic topics [MSC 2020] 62Fxx - Parametric inference [MSC 2020] 62H12 - Estimation in multivariate analysis [MSC 2020] 62H25 - Factor analysis and principal components; correspondence analysis [MSC 2020] 62H35 - Image analysis in multivariate analysis [MSC 2020] 62J05 - Linear regression; mixed models [MSC 2020] 62J07 - Ridge regression; shrinkage estimators (Lasso) [MSC 2020] 62Jxx - Linear inference, regression [MSC 2020] |
| Soggetto non controllato |
Hybrid system identification
Image and video segmentation Linear subspace models Low-rank matrix theory Manifold learning Principal component analysis Robust principal component analysis Sparse representation theory Spectral clustering Subspace arrangements Subspace clustering |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN00114796 |
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Vidal, René
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| New York, : Springer, 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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Mathematical Principles of Topological and Geometric Data Analysis / Parvaneh Joharinad, Jürgen Jost
| Mathematical Principles of Topological and Geometric Data Analysis / Parvaneh Joharinad, Jürgen Jost |
| Autore | Joharinad, Parvaneh |
| Pubbl/distr/stampa | Cham, : Springer, 2023 |
| Descrizione fisica | ix, 281 p. : ill. ; 24 cm |
| Soggetto topico |
55N31 - Persistent homology and applications, topological data analysis [MSC 2020]
57-XX - Manifolds and cell complexes [MSC 2020] 62-XX - Statistics [MSC 2020] 62R40 - Topological data analysis [MSC 2020] |
| Soggetto non controllato |
Category Theory
Curvature of metric spaces Dimension reduction Graph theory Homology Kernel technique Laplace operators Manifold learning Riemannian geometry |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNICAMPANIA-VAN00278879 |
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Joharinad, Parvaneh
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| Cham, : Springer, 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
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