This study investigates the forced vibration of functionally graded hexagonal nano-size plates for the first time.A quasi-three-dimensional(3D)plate theory including stretching effect is used to model the anisotropic ...This study investigates the forced vibration of functionally graded hexagonal nano-size plates for the first time.A quasi-three-dimensional(3D)plate theory including stretching effect is used to model the anisotropic plate as a continuum one where small-scale effects are considered based on nonlocal strain gradient theory.Also,the plate is assumed on a Pasternak foundation in which normal and transverse shear loads are taken into account.The governing equations of motion are obtained via the Hamiltonian principles which are solved using analytical based methods by means of Navier’s approximation.The influences of the exponential factor,nonlocal parameter,strain gradient parameter,Pasternak foundation coefficients,length-to-thickness,and length-to-width ratios on the dynamic response of the nanoplates are examined.In addition,the accuracy of an isotropic approximate instead of the anisotropic model is studied.The dynamic behavior of the system shows that mechanical mathematics-based models may get better results considering the anisotropic model because the dynamic response can cause prominent differences(up to 17%)between isotropic approximation and anisotropic model.展开更多
Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing.However,their effectiveness is often compromised by frequency-selective response...Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing.However,their effectiveness is often compromised by frequency-selective responses caused by fabrication,transmission,and environmental factors.To mitigate these issues,this study introduces a deep reinforcement learning calibration(DRC)method inspired by the deep deterministic policy gradient training strategy.This method continuously and autonomously learns from the system,effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods.In systems based on dispersion compensating fiber,micro-ring resonator array,and Mach-Zehnder interferometer array that use multiwavelength optical carriers as the light source,the DRC method enables the completion of the corresponding signal processing functions within 21 iterations.This method provides efficient and accurate control,making it suitable for applications such as optical convolution computation acceleration,microwave photonic signal processing,and optical network routing.展开更多
文摘This study investigates the forced vibration of functionally graded hexagonal nano-size plates for the first time.A quasi-three-dimensional(3D)plate theory including stretching effect is used to model the anisotropic plate as a continuum one where small-scale effects are considered based on nonlocal strain gradient theory.Also,the plate is assumed on a Pasternak foundation in which normal and transverse shear loads are taken into account.The governing equations of motion are obtained via the Hamiltonian principles which are solved using analytical based methods by means of Navier’s approximation.The influences of the exponential factor,nonlocal parameter,strain gradient parameter,Pasternak foundation coefficients,length-to-thickness,and length-to-width ratios on the dynamic response of the nanoplates are examined.In addition,the accuracy of an isotropic approximate instead of the anisotropic model is studied.The dynamic behavior of the system shows that mechanical mathematics-based models may get better results considering the anisotropic model because the dynamic response can cause prominent differences(up to 17%)between isotropic approximation and anisotropic model.
基金the National Natural Science Foundation of China(62302504,11902358).
文摘Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing.However,their effectiveness is often compromised by frequency-selective responses caused by fabrication,transmission,and environmental factors.To mitigate these issues,this study introduces a deep reinforcement learning calibration(DRC)method inspired by the deep deterministic policy gradient training strategy.This method continuously and autonomously learns from the system,effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods.In systems based on dispersion compensating fiber,micro-ring resonator array,and Mach-Zehnder interferometer array that use multiwavelength optical carriers as the light source,the DRC method enables the completion of the corresponding signal processing functions within 21 iterations.This method provides efficient and accurate control,making it suitable for applications such as optical convolution computation acceleration,microwave photonic signal processing,and optical network routing.
