The Arkhangelsk Seismic Network(ASN)of the N.Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences,founded in 2003,includes 10 permanent seismic stations located o...The Arkhangelsk Seismic Network(ASN)of the N.Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences,founded in 2003,includes 10 permanent seismic stations located on the coasts of the White,Barents,and Kara Seas and on the Arctic archipelagos of Novaya Zemlya,Franz Josef Land,and Severnaya Zemlya.The network is registered with the International Federation of Digital Seismograph Networks and the International Seismological Center.We used not only ASN data to process earthquakes but also the waveforms of various international seismic stations.The 13,000 seismic events were registered using ASN data for 2012-2022,and for 5,500 of them,we determined the parameters of the earthquake epicenters from the European Arctic.The spatial distribution of epicenters shows that the ASN monitors not only the main seismically active zones but also weak seismicity on the shelf of the Barents and Kara Seas.The representative magnitude of ASN was ML,rep=3.5.The level of microseismic noise has seasonal variations that affect the registration capabilities of each station included in the ASN and the overall sensitivity of the network as a whole.In summer,the sensitivity of the ASN decreased owing to the increasing microseismic and ambient noises,whereas in winter,the sensitivity of the ASN increased significantly because of the decrease.展开更多
Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed ...Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.展开更多
无人机(Unmanned aerial vehicle,UAV)遥感图像拼接是指将两幅或多幅具有相似场景内容的高分辨率无人机遥感图像拼接为一幅包含更多信息的大视野图像,在军事和地理测绘等领域得到了广泛应用。传统算法通常依赖于手工特征,无法有效地提...无人机(Unmanned aerial vehicle,UAV)遥感图像拼接是指将两幅或多幅具有相似场景内容的高分辨率无人机遥感图像拼接为一幅包含更多信息的大视野图像,在军事和地理测绘等领域得到了广泛应用。传统算法通常依赖于手工特征,无法有效地提取弱纹理图像的特征。若图像之间视差较大时,会导致拼接无法进行。为了解决上述问题,基于计算机视觉组(Visual Geometry Group-16,VGG-16)网络结合孪生网络框架提出了一种用于无人机遥感图像拼接的有监督模型。基于VGG-16网络设计了权值共享的孪生特征提取网络,解决特征提取不充分的问题。设计了能够回归图像之间空间变换关系的回归网络,并使用分组卷积代替普通卷积以提升网络速度。同时,为了解决将图像之间真实变换关系作为标签的图像拼接数据集难以获取的问题,基于一定程度的仿射变换,构建了自己的数据集。实验结果表明,本方法在无人机遥感图像拼接的主观视觉效果以及客观评价指标上均有较好的结果,与ORB算法(Oriented FAST and rotated BRIEF,ORB)和CAU-DHE算法(Content-aware unsupervised deep homography estimation,CAU-DHE)相比,主观视觉上图像拼接精度提升,结构相似性分别提高了约12.4%和2.3%,均方根误差分别降低了约15.0%和4.4%。展开更多
The network structure of the smart substation in common use was introduced,and the technical problems of the shared-network of sampled measured value(SMV)and generic object oriented substation event(GOOSE)were analyze...The network structure of the smart substation in common use was introduced,and the technical problems of the shared-network of sampled measured value(SMV)and generic object oriented substation event(GOOSE)were analyzed,such as the processing ability of network device and the intelligent device,the data real-time property and the network reliability,the effects to the substation in the condition of network fault,etc.On this basis,the feasibility of the shared-network of SMV and GOOSE was discussed,the implement scheme was presented,and eventually the solution of the shared-network of SMV and GOOSE was put forward,which based on the applications of the message priority control,restricting the switch number,virtual local area network(VLAN)and GARP multicast registration protocol(GMRP)classification flow control,flow rate limiting,etc.In the test-bed,the cases of shared-network and separate-network of SMV and GOOSE were compared and analyzed,and the result was valuable for reference.展开更多
Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sen...Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.展开更多
The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-t...The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.展开更多
作为移动IP技术的新的发展方向,移动式网络技术是将节点移动性向网络移动性(NEMO)扩展的关键技术,降低移动式网络在注册和认证过程中的延时,能够提高移动式网络技术的实际应用。提出了新的基于Mob ile IP/AAA模型的移动式网络认证方法,...作为移动IP技术的新的发展方向,移动式网络技术是将节点移动性向网络移动性(NEMO)扩展的关键技术,降低移动式网络在注册和认证过程中的延时,能够提高移动式网络技术的实际应用。提出了新的基于Mob ile IP/AAA模型的移动式网络认证方法,研究了AAAL与AAAH之间的距离以及本地切换率对认证延时的影响,并提交了NS2仿真试验结果和安全性分析。该方法能够实现低切换时延,并保证移动式网络注册和切换中的安全性。展开更多
基金supported by the Russian Federation Ministry of Science and Higher Education Research project N 122011300389-8.
文摘The Arkhangelsk Seismic Network(ASN)of the N.Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences,founded in 2003,includes 10 permanent seismic stations located on the coasts of the White,Barents,and Kara Seas and on the Arctic archipelagos of Novaya Zemlya,Franz Josef Land,and Severnaya Zemlya.The network is registered with the International Federation of Digital Seismograph Networks and the International Seismological Center.We used not only ASN data to process earthquakes but also the waveforms of various international seismic stations.The 13,000 seismic events were registered using ASN data for 2012-2022,and for 5,500 of them,we determined the parameters of the earthquake epicenters from the European Arctic.The spatial distribution of epicenters shows that the ASN monitors not only the main seismically active zones but also weak seismicity on the shelf of the Barents and Kara Seas.The representative magnitude of ASN was ML,rep=3.5.The level of microseismic noise has seasonal variations that affect the registration capabilities of each station included in the ASN and the overall sensitivity of the network as a whole.In summer,the sensitivity of the ASN decreased owing to the increasing microseismic and ambient noises,whereas in winter,the sensitivity of the ASN increased significantly because of the decrease.
文摘Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.
文摘无人机(Unmanned aerial vehicle,UAV)遥感图像拼接是指将两幅或多幅具有相似场景内容的高分辨率无人机遥感图像拼接为一幅包含更多信息的大视野图像,在军事和地理测绘等领域得到了广泛应用。传统算法通常依赖于手工特征,无法有效地提取弱纹理图像的特征。若图像之间视差较大时,会导致拼接无法进行。为了解决上述问题,基于计算机视觉组(Visual Geometry Group-16,VGG-16)网络结合孪生网络框架提出了一种用于无人机遥感图像拼接的有监督模型。基于VGG-16网络设计了权值共享的孪生特征提取网络,解决特征提取不充分的问题。设计了能够回归图像之间空间变换关系的回归网络,并使用分组卷积代替普通卷积以提升网络速度。同时,为了解决将图像之间真实变换关系作为标签的图像拼接数据集难以获取的问题,基于一定程度的仿射变换,构建了自己的数据集。实验结果表明,本方法在无人机遥感图像拼接的主观视觉效果以及客观评价指标上均有较好的结果,与ORB算法(Oriented FAST and rotated BRIEF,ORB)和CAU-DHE算法(Content-aware unsupervised deep homography estimation,CAU-DHE)相比,主观视觉上图像拼接精度提升,结构相似性分别提高了约12.4%和2.3%,均方根误差分别降低了约15.0%和4.4%。
文摘The network structure of the smart substation in common use was introduced,and the technical problems of the shared-network of sampled measured value(SMV)and generic object oriented substation event(GOOSE)were analyzed,such as the processing ability of network device and the intelligent device,the data real-time property and the network reliability,the effects to the substation in the condition of network fault,etc.On this basis,the feasibility of the shared-network of SMV and GOOSE was discussed,the implement scheme was presented,and eventually the solution of the shared-network of SMV and GOOSE was put forward,which based on the applications of the message priority control,restricting the switch number,virtual local area network(VLAN)and GARP multicast registration protocol(GMRP)classification flow control,flow rate limiting,etc.In the test-bed,the cases of shared-network and separate-network of SMV and GOOSE were compared and analyzed,and the result was valuable for reference.
基金National"863"Program of China(No.2006AA12Z130)the Natural Science Foundation of Jiangxi Province.China(No.G J J08039)the Digital Land Key Lab of Jiangxi Province,China(No.DLLJ200605).
文摘Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.
文摘The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.
文摘作为移动IP技术的新的发展方向,移动式网络技术是将节点移动性向网络移动性(NEMO)扩展的关键技术,降低移动式网络在注册和认证过程中的延时,能够提高移动式网络技术的实际应用。提出了新的基于Mob ile IP/AAA模型的移动式网络认证方法,研究了AAAL与AAAH之间的距离以及本地切换率对认证延时的影响,并提交了NS2仿真试验结果和安全性分析。该方法能够实现低切换时延,并保证移动式网络注册和切换中的安全性。
