Yazar "Pereira, C. M." seçeneğine göre listele

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    Representation of the characteristics of piezoelectric fiber composites with neural networks
    (AMER INST PHYSICS, 2007) Yapici, A.; Bickraj, K.; Yenilmez, A.; Li, M.; Tansel, I. N.; Martin, S. A.; Pereira, C. M.
    Ideal sensors for the future should be economical, efficient, highly intelligent and capable of obtaining their operation power from the environment. The use of piezoelectric fiber composites coupled with a low power microprocessor and backpropagation type neural networks is proposed for the development of a simple sensor to estimate the characteristics of harmonic forces. Three neural networks were used for the estimation of amplitude, gain and variation of the load in the time domain. The average estimation errors of the neural networks were less than 8% in all of the studied cases.
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    Resign of energy scavengers with the help of finite element packages
    (AMER INST PHYSICS, 2007) Yenilmez, A.; Yapici, A.; Tansel, I. N.; Martin, S. A.; Pereira, C. M.; Roth, L. E.
    Self-powering sensors have been desired for future platform sensor networks to minimize wiring and related problems. The selection of the proper area of piezoelectric patches at various operating conditions is an important challenge since the selection of a large patch area increases the complexity and weight. The small patch area could not provide enough energy to operate the electronics continuously. Many Finite Element Method (FEM) packages are capable of estimating the electricity after the stress or strain distribution is calculated. In this paper, the required energy for smart sensors is briefly discussed and the use of FEM is suggested for selection of the size and best location of the piezoelectric patch. The study indicated that the oscillation frequency affects the mode shape and the generated energy drastically. FEM is very useful to determine the mode shapes and the selection of patch locations with maximum dynamic strain.