Scheda di dettaglio – i prodotti della ricerca
| Dato | Valore |
|---|---|
| Title | Video-sensing characterization for hydrodynamic features: Particle tracking-based algorithm supported by a machine learning approach |
| Abstract | The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the deposition of materials within the bed of the channel, which might lead to critical floods. A reliable monitoring system can thus help to protect properties and, in the most critical cases, save lives. A sensing system capable of monitoring the flow conditions and the possible geo-environmental constraints within a channel can operate using still images or video imaging. The latter approach better supports the above two features, but the acquisition of still images can display a better accuracy. To increase the accuracy of the video imaging approach, we propose an improved particle tracking algorithm for flow hydrodynamics supported by a machine learning approach based on a convolutional neural network-evolutionary fuzzy integral (CNN-EFI), with a sub-comparison performed by multi-layer perceptron (MLP). Both algorithms have been applied to process the video signals captured from a CMOS camera, which monitors the water flow of a channel that collects rain water from an upstream area to discharge it into the sea. The channel plays a key role in avoiding upstream floods that might pose a serious threat to the neighboring infrastructures and population. This combined approach displays reliable results in the field of environmental and hydrodynamic safety. |
| Source | Sensors (Basel) 21 (12) |
| Keywords | Flow measurement and classificationHydrodynamic monitoringMachine learningParticle trackingSensing systemsSensors |
| Journal | Sensors (Basel) |
| Editor | Molecular Diversity Preservation International (MDPI),, Basel, |
| Year | 2021 |
| Type | Articolo in rivista |
| DOI | 10.3390/s21124197 |
| Authors | Lay-Ekuakille, Aimé; Djungha Okitadiowo, John; Avoci Ugwiri, Moïse; Maggi, Sabino; Masciale, Rita; Passarella, Giuseppe |
| Text | 456688 2021 10.3390/s21124197 Scopus 2 s2.0 85108102090 Flow measurement and classification Hydrodynamic monitoring Machine learning Particle tracking Sensing systems Sensors Video sensing characterization for hydrodynamic features Particle tracking based algorithm supported by a machine learning approach Lay Ekuakille, Aime; Djungha Okitadiowo, John; Avoci Ugwiri, Moise; Maggi, Sabino; Masciale, Rita; Passarella, Giuseppe Istituto di Ricerca Sulle Acque, Bari; Universita Telematica Internazionale UNINETTUNO; Universita del Salento; Consiglio Nazionale delle Ricerche; Universita degli Studi di Reggio Calabria; Universita degli Studi di Salerno The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow downs due to the deposition of materials within the bed of the channel, which might lead to critical floods. A reliable monitoring system can thus help to protect properties and, in the most critical cases, save lives. A sensing system capable of monitoring the flow conditions and the possible geo environmental constraints within a channel can operate using still images or video imaging. The latter approach better supports the above two features, but the acquisition of still images can display a better accuracy. To increase the accuracy of the video imaging approach, we propose an improved particle tracking algorithm for flow hydrodynamics supported by a machine learning approach based on a convolutional neural network evolutionary fuzzy integral CNN EFI , with a sub comparison performed by multi layer perceptron MLP . Both algorithms have been applied to process the video signals captured from a CMOS camera, which monitors the water flow of a channel that collects rain water from an upstream area to discharge it into the sea. The channel plays a key role in avoiding upstream floods that might pose a serious threat to the neighboring infrastructures and population. This combined approach displays reliable results in the field of environmental and hydrodynamic safety. 21 Published version http //www.scopus.com/record/display.url eid=2 s2.0 85108102090 origin=inward Articolo in rivista Molecular Diversity Preservation International MDPI , 1424 8220 Sensors Basel Sensors Basel Sensors Basel Sensors. Basel giuseppe.passarella PASSARELLA GIUSEPPE sabino.maggi MAGGI SABINO rita.masciale MASCIALE RITA |