The cobalt sulfide/graphene oxide（CoS/GO） nanocomposite was synthesized by a simple hydrothermal reaction.The products as-synthesized were characterized by XRD,SEM,TEM,BET-BJH and TG.The electrochemical property and...The cobalt sulfide/graphene oxide（CoS/GO） nanocomposite was synthesized by a simple hydrothermal reaction.The products as-synthesized were characterized by XRD,SEM,TEM,BET-BJH and TG.The electrochemical property and impedance of the CoS/GO nanocomposite were studied by cyclic voltammetry and EIS analysis,respectively.The results show that the presence of the GO enhances the electrode conductivity,and then improves the capacitance property of the CoS/GO nanocomposite.The galvanostatic charge/discharge measurement results show that the CoS/GO nanocomposite has a high specific capacitance（550Fg^-1） and long cycle life（over 1 000 cycles）.展开更多
The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points ...The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.展开更多
Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithm...Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.展开更多
基金Funded by the National Natural Science Foundation of China(Nos.21275006,21471001,20905001,21071002,21175001,21271004)
文摘The cobalt sulfide/graphene oxide（CoS/GO） nanocomposite was synthesized by a simple hydrothermal reaction.The products as-synthesized were characterized by XRD,SEM,TEM,BET-BJH and TG.The electrochemical property and impedance of the CoS/GO nanocomposite were studied by cyclic voltammetry and EIS analysis,respectively.The results show that the presence of the GO enhances the electrode conductivity,and then improves the capacitance property of the CoS/GO nanocomposite.The galvanostatic charge/discharge measurement results show that the CoS/GO nanocomposite has a high specific capacitance（550Fg^-1） and long cycle life（over 1 000 cycles）.
基金supported by the NUAA Fundamental Research Funds(No.NS2013034)
文摘The research of unmanned aerial vehicles＇（UAVs＇）autonomy navigation and landing guidance with computer vision has important signifcance.However,because of the image blurring,the position of the cooperative points cannot be obtained accurately,and the pose estimation algorithms based on the feature points have low precision.In this research,the pose estimation algorithm of UAV is proposed based on feature lines of the cooperative object for autonomous landing.This method uses the actual shape of the cooperative-target on ground and the principle of vanishing line.Roll angle is calculated from the vanishing line.Yaw angle is calculated from the location of the target in the image.Finally,the remaining extrinsic parameters are calculated by the coordinates transformation.Experimental results show that the pose estimation algorithm based on line feature has a higher precision and is more reliable than the pose estimation algorithm based on points feature.Moreover,the error of the algorithm we proposed is small enough when the UAV is near to the landing strip,and it can meet the basic requirements of UAV＇s autonomous landing.
基金supported by the Aeronautical Science Foundation of China (Grant No.20100152003)the National Natural Foundation of China (Grant Nos.60974105,61074161)the Fundamental Research Funds for the Central Universities (No. NZ2012307)
文摘Multi-sensor image matching based on salient edges has broad prospect in applications, but it is difficult to extract salient edges of real multi-sensor images with noises fast and accurately by using common algorithms. According to the analysis of the features of salient edges, a novel salient edges detection algorithm and its rapid calculation are proposed based on possibility fuzzy C-means （PFCM） kernel clustering using two-dimensional vectors composed of the values of gray and texture. PFCM clustering can overcome the shortcomings that fuzzy C-means （FCM） cluster- ing is sensitive to noises and possibility C-means （PCM） clustering tends to find identical clusters. On this basis, a method is proposed to improve real-time performance by compressing data sets based on the idea of data reduction in the field of mathematical analysis. In addition, the idea that kernel-space is linearly separable is used to enhance robustness further. Experimental results show that this method extracts salient edges for real multi-sensor images with noises more accurately than the algorithm based on force fields and the FCM algorithm; and the proposed method is on average about 56 times faster than the PFCM algorithm in real time and has better robustness.
