Mixed transition metal oxides (MTMOs) have received intensive attention as promising anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). In this work, we demonstrate a facile one-step water-bath method for the preparation of graphene oxide (GO) decorated Fe2(MoO4)3 (FMO) microflower composite (FMO/GO), in which the FMO is constructed by numerous nanosheets. The resulting FMO/GO exhibits excellent electrochemical performances in both LIBs and SIBs. As the anode material for LIBs, the FMO/GO delivers a high capacity of 1,220 mAh·g–1 at 200 mA·g–1 after 50 cycles and a capacity of 685 mAh·g–1 at a high current density of 10 A·g–1. As the anode material for SIBs, the FMO/GO shows an initial discharge capacity of 571 mAh·g–1 at 100 mA·g–1, maintaining a discharge capacity of 307 mAh·g–1 after 100 cycles. The promising performance is attributed to the good electrical transport from the intimate contact between FMO and graphene oxide. This work indicates that the FMO/GO composite is a promising anode for high-performance lithium and sodium storage.
In this paper, a new multi-sensor calibration approach, called iterative registration and fusion (IRF), is presented. The key idea of this approach is to use surfaces reconstructed from multiple point clouds to enhance the registration accuracy and robustness. It calibrates the relative position and orientation of the spatial coordinate systems among multiple sensors by iteratively registering the discrete 3D sensor data against an evolving reconstructed B-spline surface, which results from the Kalman filter-based multi-sensor data fusion. Upon each registration, the sensor data gets closer to the surface. Upon fusing the newly registered sensor data with the surface, the updated surface represents the sensor data more accurately. We prove that such an iterative registration and fusion process is guaranteed to converge. We further demonstrate in experiments that the IRF can result in more accurate and more stable calibration than many classical point cloud registration methods. 相似文献
The successful launch of the Landsat 8 satellite continues the Earth observation of the Landsat series, which has been taking place for nearly 40 years. With the increase in the band number and the improved spectral range compared with the previous Landsat imagery, it will be possible to expand the application of the new Landsat 8 imagery. The purpose of this study is to explore water extraction based on the new Landsat 8 Operational Land Imager (OLI) imagery. According to the specific inland water conditions (clear water, turbid water, and eutrophic water), a number of highly adaptable water indices are assessed for water extraction using Landsat OLI imagery. The results show that clear water is the easiest to extract among the different types of waterbodies, with the highest average accuracy of 97%. The highest-accuracy methods are the automated water extraction index for shadow pixels (AWEIsh), the normalized difference water index using bands 4 and 7 (NDWI47), and the normalized difference water index using bands 3 and 7 (NDWI37), with accuracies of 98.55%, 95.50%, and 96.61%, corresponding to clear water, turbid water, and eutrophic water, respectively. Through the analysis of the different methods for optimal band selection, the seventh band OLI7 (shortwave infrared 2, SWIR-2) of Landsat OLI shows the best performance in water identification. When applying the water indices to water extraction, Otsu’s algorithm has been used to automatically select the water threshold. Using extensive experiments with Otsu’s algorithm and a manual method, it was found that Otsu’s algorithm can replace manual selection and has the ability to select an accurate threshold for water extraction. 相似文献
Multimedia Tools and Applications - In recent years there has been significant interest in reversible data hiding, and also in particular, reversible data hiding in encrypted images (RDH-EI). This... 相似文献
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