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Advanced Process Monitoring for Industry 4.0
| Advanced Process Monitoring for Industry 4.0 |
| Autore | Reis Marco S |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (288 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
spatial-temporal data
pasting process process image convolutional neural network Industry 4.0 auto machine learning failure mode effects analysis risk priority number rolling bearing condition monitoring classification OPTICS statistical process control control chart pattern disruptions disruption management fault diagnosis construction industry plaster production neural networks decision support systems expert systems failure mode and effects analysis (FMEA) discriminant analysis non-intrusive load monitoring load identification membrane data reconciliation real-time online monitoring Six Sigma multivariate data analysis latent variables models PCA PLS high-dimensional data statistical process monitoring artificial generation of variability data augmentation quality prediction continuous casting multiscale time series classification imbalanced data combustion optical sensors spectroscopy measurements signal detection digital processing principal component analysis curve resolution data mining semiconductor manufacturing quality control yield improvement fault detection process control multi-phase residual recursive model multi-mode model process monitoring |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557491503321 |
Reis Marco S
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big Data Analytics and Information Science for Business and Biomedical Applications
| Big Data Analytics and Information Science for Business and Biomedical Applications |
| Autore | Ahmed S. Ejaz |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (246 p.) |
| Soggetto topico |
Humanities
Social interaction |
| Soggetto non controllato |
high-dimensional
nonlocal prior strong selection consistency estimation consistency generalized linear models high dimensional predictors model selection stepwise regression deep learning financial time series causal and dilated convolutional neural networks nuisance post-selection inference missingness mechanism regularization asymptotic theory unconventional likelihood high dimensional time-series segmentation mixture regression sparse PCA entropy-based robust EM information complexity criteria high dimension multicategory classification DWD sparse group lasso L2-consistency proximal algorithm abdominal aortic aneurysm emulation Medicare data ensembling high-dimensional data Lasso elastic net penalty methods prediction random subspaces ant colony system bayesian spatial mixture model inverse problem nonparamteric boostrap EEG/MEG data feature representation feature fusion trend analysis text mining |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557614803321 |
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Ahmed S. Ejaz
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big Data Analytics and Information Science for Business and Biomedical Applications II
| Big Data Analytics and Information Science for Business and Biomedical Applications II |
| Autore | Ahmed S. Ejaz |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (196 p.) |
| Soggetto topico |
Information technology industries
Computer science |
| Soggetto non controllato |
bandwidth selection
correlation edge-preserving image denoising image sequence jump regression analysis local smoothing nonparametric regression spatio-temporal data linear mixed model ridge estimation pretest and shrinkage estimation multicollinearity asymptotic bias and risk LASSO estimation high-dimensional data big data adaptation dividend estimation options markets weighted least squares online health community social support network analysis cancer functional principal component analysis functional predictor linear mixed-effects model mobile device sparse group regularization wearable device data Bayesian modeling functional regression gestational weight infant birth weight joint modeling longitudinal data maternal weight gain transfer learning deep learning pretrained neural networks chest X-ray images lung diseases causal structure learning consistency FCI algorithm high dimensionality nonparametric testing PC algorithm fMRI functional connectivity brain network Human Connectome Project statistics |
| ISBN | 3-0365-5550-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637784003321 |
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Ahmed S. Ejaz
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Recent Advances and Applications of Machine Learning in Metal Forming Processes
| Recent Advances and Applications of Machine Learning in Metal Forming Processes |
| Autore | Prates Pedro |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (210 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology Mining technology & engineering |
| Soggetto non controllato |
sheet metal forming
uncertainty analysis metamodeling machine learning hot rolling strip edge defects intelligent recognition convolutional neural networks deep-drawing kriging metamodeling multi-objective optimization FE (Finite Element) AutoForm robust analysis defect prediction mechanical properties prediction high-dimensional data feature selection maximum information coefficient complex network clustering ring rolling process energy estimation metal forming thermo-mechanical FEM analysis artificial neural network aluminum alloy mechanical property UTS topological optimization artificial neural networks (ANN) machine learning (ML) press-brake bending air-bending three-point bending test sheet metal buckling instability oil canning artificial intelligence convolution neural network hot rolled strip steel defect classification generative adversarial network attention mechanism deep learning mechanical constitutive model finite element analysis plasticity parameter identification full-field measurements |
| ISBN | 3-0365-5772-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637782503321 |
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Prates Pedro
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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