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Our sensor placement approach using MTC extends to the multi-level sensing model and an improved identification performance is obtained via reduced number of sensors (in comparison to single-level sensing model).
First, we summarize a single-level sensing model and discuss an efficient fast greedy approach for solving the MTC problem.
Then, based on the above analysis, we address the k-coverage problem using a more realistic sensing model, known as probabilistic sensing model.
This study presents a soft sensing model of coal moisture for utility boilers.
Firstly, the sensing model was established based on the conductivity analysis of the CFRP composites.
In particular, we investigate the bi-level sensing model to illustrate the efficacy of employing multi-level sensors for the identification of failure events.
First, we consider a deterministic (or disk) sensing model, where the sensing range is modeled as a disk.
Based on the energy band levels of the AuNCs and electron tunneling processes, a detailed photoelectrochemical sensing model was given.
In this direction, we first examine the physical model of the disturbance propagating in the network as a result of a failure event, and outline the multi-level sensing model that captures several event features.
Second, we develop a multi-level sensing model that captures additional physical features of the disturbance event, such as the time lapsed between the occurrence of disturbance and its detection by the sensor.
Sensing model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com