Articles | Volume 6, issue 2
https://doi.org/10.5194/jsss-6-269-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.Accelerated optimizations of an electromagnetic acoustic transducer with artificial neural networks as metamodels
Related subject area
Sensor technologies: Modeling and simulation
Acceptance and reverification testing for industrial computed tomography – a simulative study on geometrical misalignments
Numerical analysis of an infrared gas sensor utilizing an indium-tin-oxide-based plasmonic slot waveguide
Determination of optimal crystallographic orientations for LiNbO3 and LiTaO3 bimorph actuators
Design study for a multicomponent transducer for wind turbine test benches
A two-port electrothermal model for suspended MEMS device structures with multiple inputs
J. Sens. Sens. Syst., 11, 171–186,
2022J. Sens. Sens. Syst., 11, 15–20,
2022J. Sens. Sens. Syst., 10, 121–126,
2021J. Sens. Sens. Syst., 9, 239–249,
2020J. Sens. Sens. Syst., 8, 293–304,
2019Cited articles
Auld, B. A.: Acoustic Fields and Waves in Solids, Krieger Publishing Company, 1990.
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, 2001.
Dhayalan, R. and Balasubramaniam, K.: A hybrid finite element model for simulation of electromagnetic acoustic transducer (EMAT) based plate waves, NDT & E International, 43, 519–526, 2010.
Edwards, R., Sophian, A., Dixon, S., Tian, G., and Jian, X.: Dual EMAT and PEC non-contact probe: applications to defect testing, NDT & E International, 39, 45–52, 2006.
Gao, S., Dai, X., Liu, Z., and Tian, G.: High-Performance Wireless Piezoelectric Sensor Network for Distributed Structural Health Monitoring, Int. J. Distrib. Sens. N., 12, 3846804, https://doi.org/10.1155/2016/3846804, 2016.