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- 저자 : A. Mai-Duc (검색결과 2 건)
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Multiferroic CoFe2O4-Pb(Zr,Ti)O3 Nanostructures
Pham Duc Thang,Mai T. N. Pham,G. Rijnders,D. H. A. Blank,Nguyen Huu Duc,J. C. P. Klaasse,E. Bruck 한국물리학회 2008 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.52 No.5
Multiferroic CoFe₂O₄-Pb(Zr,Ti)O₃ films were prepared on TiO₂-terminated (001) Nb-doped SrTiO3 substrates by using pulsed laser deposition (PLD). The lms were epitaxial and exhibited a large in-plane magnetic anisotropy and good ferroelectric properties. A decrease in the magneti- zation around the ferroelectric Curie temperature indicated magnetoelectric coupling between the magnetostrictive and the piezoelectric phases, which allows interconversion of energy stored in the electric and the magnetic elds and provides great potential for applications as next-generation multi-functional devices. Multiferroic CoFe₂O₄-Pb(Zr,Ti)O₃ films were prepared on TiO₂-terminated (001) Nb-doped SrTiO3 substrates by using pulsed laser deposition (PLD). The lms were epitaxial and exhibited a large in-plane magnetic anisotropy and good ferroelectric properties. A decrease in the magneti- zation around the ferroelectric Curie temperature indicated magnetoelectric coupling between the magnetostrictive and the piezoelectric phases, which allows interconversion of energy stored in the electric and the magnetic elds and provides great potential for applications as next-generation multi-functional devices.
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L. Nguyen-Ngoc,H. Tran-Ngoc,T. Bui-Tien,A. Mai-Duc,M. Abdel Wahab,Huan X. Nguyen,G. De Roeck 국제구조공학회 2021 Smart Structures and Systems, An International Jou Vol.28 No.1
In this paper, a novel approach to damage identification in structures using Particle Swarm Optimization (PSO) combined with Artificial neural network (ANN) is proposed. With recent substantial advances, ANN has been extensively utilized in a wide variety of fields. However, because of the application of backpropagation algorithms based on gradient descent techniques, ANN may be trapped in local minima when seeking the best solution. This may reduce the accuracy of ANN. Therefore, we propose employing an evolutionary algorithm, namely PSO to deal with the local minimum problems of ANN. PSO is employed to improve the training parameters of ANN consisting of weight and bias ratios by reducing the deviation between calculated and desired results. These training parameters are then used to train the network. Since PSO applies global search techniques to look for the best solution, it can assist the network in avoiding local minima by looking for a beneficial starting point. In order to assess the effectiveness of the proposed approach, both numerical and experimental models with different damage scenarios are employed. The results show that ANN -PSO not only significantly reduces computational time compared to PSO but also possibly identifies damages in the considered structures more accurately than ANN and PSO separately.
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