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.
Deep Learning-Based Machinery Fault Diagnostics
| Deep Learning-Based Machinery Fault Diagnostics |
| Autore | Chen Hongtian |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 electronic resource (290 p.) |
| Soggetto topico |
Technology: general issues
History of engineering & technology |
| Soggetto non controllato |
process monitoring
dynamics variable time lag dynamic autoregressive latent variables model sintering process hammerstein output-error systems auxiliary model multi-innovation identification theory fractional-order calculus theory canonical variate analysis disturbance detection power transmission system k-nearest neighbor analysis statistical local analysis intelligent fault diagnosis stacked pruning sparse denoising autoencoder convolutional neural network anti-noise flywheel fault diagnosis belief rule base fuzzy fault tree analysis Bayesian network evidential reasoning aluminum reduction process alumina concentration subspace identification distributed predictive control spatiotemporal feature fusion gated recurrent unit attention mechanism fault diagnosis evidential reasoning rule system modelling information transformation parameter optimization event-triggered control interval type-2 Takagi-Sugeno fuzzy model nonlinear networked systems filter gearbox fault diagnosis convolution fusion state identification PSO wavelet mutation LSSVM data-driven operational optimization case-based reasoning local outlier factor abnormal case removal bearing fault detection deep residual network data augmentation canonical correlation analysis just-in-time learning fault detection high-speed trains autonomous underwater vehicle thruster fault diagnostics fault tolerant control robust optimization ocean currents |
| ISBN | 3-0365-5174-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910619469103321 |
Chen Hongtian
|
||
| MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Smart Sensors and Devices in Artificial Intelligence
| Smart Sensors and Devices in Artificial Intelligence |
| Autore | Zhang Dan |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (336 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
microelectromechanical systems
inertial measurement unit long short term memory recurrent neural networks artificial intelligence deep learning CNN LSTM CO2 welding molten pool online monitoring mechanical sensor self-adaptiveness ankle-foot exoskeleton walking assistance visual tracking correlation filter color histogram adaptive hedge algorithm scenario generation autonomous vehicle smart sensor and device wireless sensor networks task assignment distributed reliable energy-efficient audification sensor visualization speech to text text to speech HF-OTH radar AIS radar tracking data fusion fuzzy functional dependencies maritime surveillance surgical robot end-effector clamping force estimation joint torque disturbance observer PSO-BPNN cable tension measurement queue length roadside sensor vehicle detection adverse weather roadside LiDAR data processing air pollution atmospheric data IoT machine learning RNN Sensors smart cities traffic flow traffic forecasting wireless sensor network fruit condition monitoring artificial neural network ethylene gas banana ripening unidimensional ACGAN signal recognition data augmentation link establishment behaviors DenseNet short-wave radio station landing gear adaptive landing vehicle classification FBG smart sensors outlier detection local outlier factor data streams air quality monitoring evacuation path multi-story multi-exit building temperature sensors multi-time-slots planning optimization |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557128403321 |
|
Zhang Dan
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||