Remote sensing change detection (RSCD) focuses on identifying regions that have undergone changes between two remote sensing images captured at different times. Recently, convolutional neural networks (CNNs) have shown promising results in the challenging task of RSCD. However, these methods do not efficiently fuse bitemporal features and extract useful information that is beneficial to subsequent RSCD tasks. In addition, they did not consider multilevel feature interactions in feature aggregation and ignore relationships between difference features and bitemporal features, which, thus, affects the RSCD results. To address the above problems, a difference-guided multiscale aggregation attention network, DGMA2-Net, is developed. Bitemporal features at different levels are extracted through a Siamese convolutional network and a multiscale difference fusion module (MDFM) is then created to fuse bitemporal features and extract, in a multiscale manner, difference features containing rich contextual information. After the MDFM treatment, two difference aggregation modules (DAMs) are used to aggregate difference features at different levels for multilevel feature interactions. The features through DAMs are sent to the difference-enhanced attention modules (DEAMs) to strengthen the connections between bitemporal features and difference features and further refine change features. Finally, refined change features are superimposed from deep to shallow and a change map is produced. In validating the effectiveness of DGMA2-Net, a series of experiments are conducted on three public RSCD benchmark datasets [LEVIR building change detection dataset (LEVIR-CD), Wuhan University building change detection dataset (BCDD), and Sun Yat-Sen University dataset (SYSU-CD)]. The experimental results demonstrate that DGMA2-Net surpasses the current eight state-of-the-art methods in RSCD. Our code is released at https://github.com/yikuizhai/DGMA2-Net .

Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting, and utilize auxiliary information of related tasks to optimize the main task. In this paper, we present a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty. In the experiment, beauty prediction is the main task, and gender recognition is the auxiliary. For multi-task training, we employ multi-task loss weights automatic learning strategy to improve the performance of FBP. Finally, we replace the softmax classifier with a random forest. We conduct experiments on the Large Scale Facial Beauty Database (LSFBD) and SCUT-FBP5500 database. Results show that our method has achieved good results on LSFBD, the accuracy of FBP is up to 68.23%. Our 2M BeautyNet structure is suitable for multiple inputs of different databases.

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model’s recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

This paper aims to explain the design of a novel time-varying sliding mode control of variable parameter (VP-TVSMC), which can effectively solve the anti-swing and positioning problem of distributed-mass double pendulum bridge crane system with its quick responsiveness and strong robustness to external interference. More specifically, this model initiates with the establishment of the dynamic equation of double pendulum crane model based on distributed-mass, then followed by the design of a time-varying parameter to realize the dynamic adjustment of the sliding mode surface and enhance the adjustment ability of the sliding mode surface, which is conducive to the global robustness of the double pendulum crane system under VP-TVSMC. With Lyapunov method and LaSalle’s invariance principle, the asymptotic stability of the system can be sufficiently proved. Finally, the adoption of three kinds of external interference signals and uncertain system parameters successfully verified the preeminent control performance and global robustness against external interference of the proposed controller. The simulation results indicate that compared with the conventional CSMC and PDSMC, the proposed control method can reduce the driving force of the trolley, ensure the rapid and precise positioning of the trolley, as well as restrain the load swing angle within 5° in an effective manner. In addition, compared with the symbolic function sgn( $S$ ), the designed continuous function th( $S$ ) possesses a better anti-chattering effect, thus strengthening of the control performance of VP-TVSMC.

Owing to the complexity of finger vein patterns in shape and spatial dependence, the existing methods suffer from an inability to obtain accurate and stable finger vein features. This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). Firstly, to reduce missing pixels during ROI extraction, the method of sliding window summation is employed to filter and adjusted with self-built tools. In addition, the traditional baselines are endowed with different weights to automatically assign labels. Secondly, the deformable convolution network, through replacing the plain counterparts in the standard U-Net mode, can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins’ scales and shapes. Moreover, the above features can be further mined and accumulated by combining the recurrent neural network (RNN) and the residual network (ResNet). With the steps mentioned above, the fully convolutional neural network is constructed. Finally, the CRF with Gaussian pairwise potential conducts mean-field approximate inference as the RNN, and then is embedded as a part of the FCN, so that the model can fully integrate CRF with FCNs, which provides the possibility to involve the usual back-propagation algorithm in training the whole deep network end-to-end. The proposed models in this paper were tested on three public finger vein datasets SDUMLA, MMCBNU and HKPU with experimental results to certify their superior performance on finger-vein verification tasks compared with other equivalent models including U-Net.