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.
Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
| Artificial Intelligence for Smart and Sustainable Energy Systems and Applications |
| Autore | Lytras Miltiadis |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (258 p.) |
| Soggetto non controllato |
artificial neural network
home energy management systems conditional random fields LR ELR energy disaggregation artificial intelligence genetic algorithm decision tree static young’s modulus price scheduling self-adaptive differential evolution algorithm Marsh funnel energy yield point non-intrusive load monitoring mud rheology distributed genetic algorithm MCP39F511 Jetson TX2 sustainable development artificial neural networks transient signature load disaggregation smart villages ambient assisted living smart cities demand side management smart city CNN wireless sensor networks object detection drill-in fluid ERELM sandstone reservoirs RPN deep learning RELM smart grids multiple kernel learning load feature extraction NILM energy management energy efficient coverage insulator Faster R-CNN home energy management smart grid LSTM smart metering optimization algorithms forecasting plastic viscosity machine learning computational intelligence policy making support vector machine internet of things sensor network nonintrusive load monitoring demand response |
| ISBN | 3-03928-890-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404078103321 |
Lytras Miltiadis
|
||
| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Internet of Things and Artificial Intelligence in Transportation Revolution
| Internet of Things and Artificial Intelligence in Transportation Revolution |
| Autore | Lytras Miltiadis |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (232 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
decision-making
autonomous navigation collision avoidance scene division deep reinforcement learning maritime autonomous surface ships internet of things crowdsourcing indoor localization data fusion security authentication Inertial Measurement Units road transportation traffic signal control speed guidance vehicle arrival time connected vehicle unmanned ships DDPG autonomous path planning end-to-end at-risk driving deep support vector machine driver drowsiness driver stress multi-objective genetic algorithm multiple kernel learning urban freeway hybrid dynamic system state transition unknown inputs observer vehicle density maritime vessel flows intelligent transportation systems deep learning automatic license plate recognition intelligent vehicle access histogram of oriented gradients artificial neural networks convolutional neural networks time-frequency Inertial Measurement Unit (IMU) road anomalies |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557134703321 |
|
Lytras Miltiadis
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||