A Sensing and Monitoring System for Hydrodynamic Flow Based on Imaging and Ultrasound

Lay-Ekuakille, A.; Telesca, Vito; Giorgio, Giuseppina Anna

doi:10.3390/s19061347

Cited by 7 publications

(3 citation statements)

References 29 publications

(38 reference statements)

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“…The results included in this section intend to demonstrate the use of accurate and low computationally cost algorithms to monitor the flow of an artificial channel, serving mainly to collect water coming from springs and to [ 23 , 24 ] spill it into the sea. They fall within the hydrodynamics characterization of the channel with video-sensors and image-sensors [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach

Lay-Ekuakille

Okitadiowo

Ugwiri

et al. 2021

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach

Lay-Ekuakille

Okitadiowo

Ugwiri

et al. 2021

Sensors

Self Cite

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“…There, sensor measurements are combined at various levels (raw data, state vector, decision level). In [30], some findings are presented about monitoring a channel hydrodynamic behaviour by means of sensors based on imaging and ultrasound.…”

Section: Related Workmentioning

confidence: 99%

Advanced sensor-based maintenance in real-world exemplary cases

et al. 2020

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Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects.

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“…For rivers and channels affluent to the modeled domain, one should prescribe flow velocity or discharge rate [8]. For stream gauging, most common instruments for water velocity measurement employ mechanical, acoustic, and stage-based volumetric techniques [9] but also include systems based on imaging techniques. The transmission of water level sensors data based on IoT technology help to establish operational monitoring networks [10], which can also serve to modeling purposes.…”

Section: Variables Of Equations For Shallow Water Modeling and Instrumentioning

confidence: 99%

Sampling Methods for Metocean Data Aiming at Hydrodynamic Modeling of Estuarine and Coastal Areas

Pecly

Rosman

Ribeiro

2020

Sensors

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Field observations require adequate metocean data gathering to promote the link between environmental diagnostic and prognostic obtained from modeling techniques. In general, model confidence can be improved by using data which present better quality and by improved parametrizations. This paper discusses and suggests timing routines for data gathering which are enough to describe the hydrodynamic behavior of estuarine and coastal areas. From the environmental diagnostics viewpoint, a sampling procedure is defined to the temporal scales providing data with adequate resolution to describe the natural process without signal aliasing. The proposed sampling procedure was based on the analysis of a data set of tides, currents, waves, water temperature, and meteorological variables observed at several stations along the Brazilian coast. The instrument setup was based mainly on the results of the harmonic analysis of tides. It is shown that the setup of instruments for simultaneous measurements of currents and waves requires special attention particularly in sites that present low currents and the action of waves. A subset of data gathered in shallow bays was used to estimate the surface turbulent stress by using a classical and a slightly modified parametrization for the wind drag coefficient. Under near neutral atmospheric stability conditions and high tide excursion, the surface turbulent stress obtained with the classical and the modified parametrization differed but the current profiles are expected to be only partially affected by wind-induced drift currents.

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A Sensing and Monitoring System for Hydrodynamic Flow Based on Imaging and Ultrasound

Cited by 7 publications

References 29 publications

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach

Advanced sensor-based maintenance in real-world exemplary cases

Sampling Methods for Metocean Data Aiming at Hydrodynamic Modeling of Estuarine and Coastal Areas

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