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In this paper, we consider building extraction from high spatial resolution remote sensing images. At present, most building extraction methods are based on artificial features. However, the diversity and complexity of buildings mean that building extraction methods still face great challenges, so methods based on deep learning have recently been proposed. In this paper, a building extraction framework based on a convolution neural network and edge detection algorithm is proposed. The method is called Mask R-CNN Fusion Sobel. Because of the outstanding achievement of Mask R-CNN in the field of image segmentation, this paper improves it and then applies it in remote sensing image building extraction. Our method consists of three parts. First, the convolutional neural network is used for rough location and pixel level classification, and the problem of false and missed extraction is solved by automatically discovering semantic features. Second, Sobel edge detection algorithm is used to segment building edges accurately so as to solve the problem of edge extraction and the integrity of the object of deep convolutional neural networks in semantic segmentation. Third, buildings are extracted by the fusion algorithm. We utilize the proposed framework to extract the building in high-resolution remote sensing images from Chinese satellite GF-2, and the experiments show that the average value of IOU (intersection over union) of the proposed method was 88.7% and the average value of Kappa was 87.8%, respectively. Therefore, our method can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy. up objects of different sizes, then extract them by using the unique spectral information, shape and texture features of the buildings [3]. Due to different shooting angles, light and other factors, remote sensing images in different periods have considerable internal variability, and buildings have a variety of structure, texture and spectral information, therefore, the methods above cannot perform well in the extraction of complex buildings.In recent years, deep learning technology has ushered in a new wave of revival. At present, deep learning technology represented by deep convolutional neural networks has achieved excellent results in the field of computer vision [4][5][6]. Compared with the traditional method of feature extraction in artificial design, deep convolutional neural networks can obtain the structure, texture, semantics and other information of the object through multiple convolutional layers, and their performance is closer to visual interpretation in object recognition. The experiments in [7,8] had the advantages of deep convolutional neural networks in the field of object detection, but also revealed the problems of local absence and edge blur in image segmentation. This phenomenon is especially serious when the hardware equipment is insufficient or the dataset is small. Figure 1 shows this phenomenon using a mask image [9]. The main reasons for poor...

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Charging methods that were employed earlier provided constant energy to a battery, but the negative impact was the activation of chemical reactions that lead to battery passivation. Thus, in order to mitigate the battery aging, moderate energy for charging a lithium‐ion battery has been theoretically calculated using density functional theory. The charging process was associated with a Li‐ion transfer model between two electrodes. When charging, Li‐ions from the positive electrode, pass through a separator/electrolyte, transfer via a solid electrolyte interface (SEI), and intercalate into the negative electrode. Calculations provided activation energies of migration through the SEI and intercalation of graphite. The inverse reaction of electrolyte reduction was introduced in the process to prevent impedance formation. The unit of power of the external charger can be converted to voltage. Therefore, the proposed novel charging strategy used positive voltage for migration and negative voltage for the inverse reaction of reduction. The charging strategy can be approximated to a sinusoidal waveform, which is effective in reviving Li‐ion battery and prolonging the cycle life.

Since DCNNs (deep convolutional neural networks) have been successfully applied to various academic and industrial fields, semantic segmentation methods, based on DCNNs, are increasingly explored for remote-sensing image interpreting and information extracting. It is still highly challenging due to the presence of irregular target shapes, and similarities of inter-and intra-class objects in largescale high-resolution satellite images. A majority of existing methods fuse the multi-scale features that always fail to provide satisfactory results. In this paper, a dual attention deep fusion semantic segmentation network of large-scale satellite remote-sensing images is proposed (DASSN_RSI). The framework consists of novel encoderdecoder architecture, and a weight-adaptive loss function based on focal loss. To refine high-level semantic and low-level spatial feature maps, the deep layer channel attention module (DLCAM) and shallow layer spatial attention module (SLSAM) are designed and appended with specific blocks. Then the DUpsampling is incorporated to fuse feature maps in a lossless way. Peculiarly, the weight-adaptive focal loss (W-AFL) is inferred and embedded successfully, alleviating the class-imbalanced issue as much as possible. The extensive experiments are conducted on Gaofen image dataset (GID) datasets (Gaofen-2 satellite images, coarse set with five categories and refined set with fifteen categories). And the results show that our approach achieves state-of-the-art performance compared to other typical variants of encoder-decoder networks in the numerical evaluation and visual inspection. Besides, the necessary ablation studies are carried out for a comprehensive evaluation.