Transverse shear and normal deformation effects on vibration behaviors of functionally graded micro-beams

A quasi-three dimensional model is proposed for the vibration analysis of functionally graded(FG)micro-beams with general boundary conditions based on the modified strain gradient theory.To consider the effects of transverse shear and nor-mal deformations,a general displacement field is achieved by relaxing the assumption of the constant transverse displacement through the thickness.The conventional beam theories including the classical beam theory,the first-order beam theory,and the higher-order beam theory are regarded as the special cases of this model.The material proper-ties changing gradually along the thickness direction are calculated by the Mori-Tanaka scheme.The energy-based formulation is derived by a variational method integrated with the penalty function method,where the Chebyshev orthogonal polynomials are used as the basis function of the displacement variables.The formulation is validated by some comparative examples,and then the parametric studies are conducted to investigate the effects of transverse shear and normal deformations on vibration behaviors.

Information Purification Network for Remote Sensing Image Super-Resolution

Recently,several well-performing deep convolutional neural networks were proposed for remote sensing image super-resolution(SR).However,these methods rarely consider that remote sensing images are corruptible by additional noise,blurring,and other factors.Therefore,to eliminate the interference of these factors,especially the noise,we propose a novel information purification network(IPN)for remote sensing image SR.The proposed information purification block(IPB)can process channel-wise features differently by channel separation and rescale spatial-wise features adaptively through the proposed multi-scale spatial attention mechanism.We further design an information group to explore a more powerful expressive combination of IPBs.Moreover,long and short skip connections can transmit abundant low-frequency information,making IPBs pay more attention to high-frequency information.We mix the images under various degradation models as training data in the training phase.In this way,the network can directly reconstruct various degraded images.Experiments on AID and UC Merced Land-Use datasets under multiple degradation models demonstrate that the proposed IPN performs better than state-of-the-art methods.

RFCNet:Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

In the field of single remote sensing image Super-Resolution(SR),deep Convolutional Neural Networks(CNNs)have achieved top performance.To further enhance convolutional module performance in processing remote sensing images,we construct an efficient residual feature calibration block to generate expressive features.After harvesting residual features,we first divide them into two parts along the channel dimension.One part flows to the Self-Calibrated Convolution(SCC)to be further refined,and the other part is rescaled by the proposed Two-Path Channel Attention(TPCA)mechanism.SCC corrects local features according to their expressions under the deep receptive field,so that the features can be refined without increasing the number of calculations.The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors.Moreover,a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level.Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality.