This paper presents a new framework for sensor reliability evaluation in classification problems based on evidence theory (or the Dempster-Shafer theory of belief functions). The evaluation is treated as a two-stage training process. First, the authors assess the static reliability from a training set by comparing the sensor classification readings with the actual values of data, which are both represented by belief functions. Information content contained in the actual values of each target is extracted to determine its influence on the evaluation. Next, considering the ability of the sensor to understand a dynamic working environment, the dynamic reliability is evaluated by measuring the degree of consensus among a group of sensors. Finally, the authors discuss why and how to combine these two kinds of reliabilities. A significant improvement using the authors' method is observed in numerical simulations as compared with the recently proposed method.
The interdigital transducer (IDT) can excite Lamb wave in a piezoelectric plate loading with a liquid layer, and the phase velocity of Lamb wave is associated with the properties of the liquid layer. In this paper, the concept of effective permittivity is introduced to study the Lamb wave's potential application in liquid sensing. Considering the measuring of ideal nonviscous liquid, the sensors array is designed to sense the density and the dielectric constant of the liquid layer simultaneously. Using LiNbO 3 as piezoelectric material, in order to improve the sensors array sensitivity and the electro-mechanical coupling coefficient, the optimized results including plate thicknesses and cut orientations are presented by numerical simulation. These studies show that the Lamb wave sensors array can be potential in liquid sensing.
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