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.
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 online resource (288 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
artificial generation of variability
auto machine learning classification combustion condition monitoring construction industry continuous casting control chart pattern convolutional neural network curve resolution data augmentation data mining data reconciliation decision support systems digital processing discriminant analysis disruption management disruptions expert systems failure mode and effects analysis (FMEA) failure mode effects analysis fault detection fault diagnosis high-dimensional data imbalanced data Industry 4.0 latent variables models load identification membrane monitoring multi-mode model multi-phase residual recursive model multiscale multivariate data analysis n/a neural networks non-intrusive load monitoring online optical sensors OPTICS pasting process PCA plaster production PLS principal component analysis process control process image process monitoring quality control quality prediction real-time risk priority number rolling bearing semiconductor manufacturing signal detection Six Sigma spatial-temporal data spectroscopy measurements statistical process control statistical process monitoring time series classification yield improvement |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557491503321 |
Reis Marco S
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data-Intensive Computing in Smart Microgrids
| Data-Intensive Computing in Smart Microgrids |
| Autore | Herodotou Herodotos |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (238 p.) |
| Soggetto topico | Technology: general issues |
| Soggetto non controllato |
AMI
automatic generation control battery energy storage systems big data analytics cloud computing data-intensive smart application deep learning demand response demand response programs electricity consumption electricity load forecasting electricity theft detection electricity thefts energy management energy trade contract Extreme Learning Machine feature selection fog computing Genetic Algorithm green community green data center Grid Search imbalanced data intelligent control methods load forecasting microgrid multi-objective energy optimization n/a NB-PLC optimization techniques photovoltaic processing time real time power management real-time systems renewable energy renewable energy sources resource allocation response time scheduling SG single/multi-area power system smart grid smart grids smart meter soft computing control methods Support Vector Machine TL virtual inertial control wind |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557603203321 |
|
Herodotou Herodotos
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Evolutionary Computation & Swarm Intelligence
| Evolutionary Computation & Swarm Intelligence |
| Autore | Caraffini Fabio |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (286 p.) |
| Soggetto topico | Information technology industries |
| Soggetto non controllato |
algorithmic design
analysis approximate matching benchmark suite compact optimization concept drift concept evolution context-triggered piecewise hashing data integration data sampling DE-MPFSC algorithm density based clustering discrete optimization dynamic stream clustering edit distance entanglement degree estimation distribution algorithms evolutionary algorithms evolutionary computation feature selection fitness trend fuzzy hashing genetic algorithm global/local optimization Holm-Bonferroni hybrid algorithms identification imbalanced data instance weighting k-means centroid kinematic parameters large-scale optimization LZJD manipulator Markov process maximum k-coverage memetic algorithms memetic computing meta-heuristic algorithms metaheuristic optimisation metaheuristics multi-objective deterministic optimization, derivative-free multi-thread programming nature-inspired algorithms normalization one billion variables online clustering optimisation parameter tuning particle swarm Particle Swarm Optimization population based algorithms PSO redundant representation robot routing screening criteria sdhash signatures similarity detection simulation-based design optimization Social Network Optimization ssdeep swarm intelligence Swarm Intelligence Wilcoxon rank-sum wireless sensor networks |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557283803321 |
|
Caraffini Fabio
|
||
| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||