Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems
| Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems |
| Autore | Li Chaoshun |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (212 p.) |
| Soggetto topico |
Physics
Research and information: general |
| Soggetto non controllato |
'S' characteristics
1D-3D coupling model active power anomaly detection approximate entropy cascaded reservoirs change point detection chaotic particle swarms method comprehensive deterioration index cosine similarity degradation trend prediction doubly-fed variable speed pumped storage power station doubly-fed variable-speed pumped storage ensemble empirical mode decomposition facility agriculture fractional order PID controller (FOPID) gated recurrent unit high proportional renewable power system Hopf bifurcation hybrid system hydraulic oil viscosity hydraulic PTO hydro power hydropower units light gradient boosting machine long and short-term neural network low water head conditions maximal information coefficient maximum information coefficient multi-objective optimization noise reduction nonlinear modeling nonlinear pump turbine characteristics operation strategy parameter sensitivity power yield pressure pulsation pumped storage unit pumped storage units pumped storage units (PSUs) reliability seasonal price sensitivity analysis sparrow search algorithm stability analysis stochastic dynamic programming (SDP) successive start-up thermal-hydraulic characteristics transition stability variational mode decomposition wave energy converter |
| ISBN | 3-0365-5838-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910637780603321 |
Li Chaoshun
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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 |
|
Prates Pedro
|
||
| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||