Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Spatial Information Research for Temperature and Precipitation Climate Data in Hengduan Mountains

[Objective] The aim was to study the spatial information of temperature and precipitation data in Hengduan mountains. [Method] Considering GIS spatial interpolation and numerical statistics theory, spatial prediction were carried out to the ten years average temperature and precipitation flux observation data in 109 sparse meteorological stations in Hengduan Mountains. Based on the spatial range of geographic position of Hengduan Mountains, and 1∶1 000 000 scale DEM as data sources, and using trend surface simulation and residual ordinary Kriging interpolation correction method, the spatial continuous surface for annual average temperature and precipitation were studied. [Result] It was scientific and reasonable to use certain unevenly distributed sparse climate observation station value, and by dint of trend simulation and residue interpolation method to get climate consecutive spatial result. This method can not only accurate the temperature and precipitation spatial distributions to grid point, but also can reflect macro and micro geography factors and topographic influence factor of variation. Furthermore, it can be predicted error term trend surface reasonable spatial distribution. Simulation results were basically in accordance with the objective law, and can be used for the region climate data spatial informatization simulation. [Conclusion] The study provided scientific spatial basic data for the further study of ecological and vegetation in Hengduan Mountains.

Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

Application and prospects of spatial information technology in CO_(2)sequestration monitoring

This paper systematically reviews the current applications of various spatial information technologies in CO_(2)sequestration monitoring,analyzes the challenges faced by spatial information technologies in CO_(2)sequestration monitoring,and prospects the development of spatial information technologies in CO_(2)sequestration monitoring.Currently,the spatial information technologies applied in CO_(2)sequestration monitoring mainly include five categories:eddy covariance method,remote sensing technology,geographic information system,Internet of Things technology,and global navigation satellite system.These technologies are involved in three aspects:monitoring data acquisition,positioning and data transmission,and data management and decision support.Challenges faced by the spatial information technologies in CO_(2)sequestration monitoring include:selecting spatial information technologies that match different monitoring purposes,different platforms,and different monitoring sites;establishing effective data storage and computing capabilities to cope with the broad sources and large volumes of monitoring data;and promoting collaborative operations by interacting and validating spatial information technologies with mature monitoring technologies.In the future,it is necessary to establish methods and standards for designing spatial information technology monitoring schemes,develop collaborative application methods for cross-scale monitoring technologies,integrate spatial information technologies with artificial intelligence and high-performance computing technologies,and accelerate the application of spatial information technologies in carbon sequestration projects in China.

Optimization of a Network Topology Generation Algorithm Based on Spatial Information Network

Spatial information network(SIN)is a network with high speed and periodicity of node operation.In recent days,China will build a complete asteroid monitoring and warning system and a near-Earth asteroid defense system.This requires launching more low-Earth orbit satellites.In order to adapt to the increase in the number of near-Earth satellites,the dynamic optimization of space informa-tion network topology between satellites will have research significance.Consid-ering the visibility of satellite networking,the connectivity of satellite nodes,and the number of links connected to the whole network,with the goal of minimizing the end-to-end delay between satellite nodes in the network as the optimization goal,a network topology optimization model that meets multiple constraints is constructed,and the model is solved using greedy algorithm and simulated anneal-ing algorithm.In the process of simulated annealing,the networkflow algorithm is innovatively proposed for neighborhood solution.Experiments show that the simulated annealing hybrid neighborhood algorithm is significantly better than the simulated annealing random neighborhood algorithm.

Digital humanities affect: The evolution and revolution of spatial information organization in science fiction

The spatial information of literature refers to the representation of physical or conscious space in literary works,including position,shape,structure,features,and relationships.The spatial information organization and its method are essential to the whole process of literature production,circulation,acceptance,and reproduction,which is conducive to a deeper exploration of the aesthetic essence of literature in the information era.Taking the organization of spatial information in science fiction as an example,this paper investigates the way of interaction between informatics and literature,explores the function of spatial information in the narrative of science fiction,and discovers digital humanistic laws of literary information organization.This study finds that the spatial information of early science fiction is organized in horizontal,vertical,axial,and causal modes;in the era of digital humanities,influenced by information technology such as the internet,virtual reality,and artificial intelligence,literary space has experienced revolutions characteristic of time-space integration,boundary dissolution,dimensional change,and virtualization;the spatial information organization of contemporary science fiction adopts nested,reticulated,parallel,interactive,and reverse patterns;on the whole,it is featured with fluidity,chaos,correlation,and mutation,creating a unique digital humanistic spectacle.

Optimization of multi-state generation problem based on spatial information network topology

Spatial information network is a kind of satellite network with high speed node movement and fast dynamic topology change.With the increasing number of low-orbit satellites,the research on the subnets topology and dynamic optimization of space information networks has become an important direction to study the destructibility of spatial information network.In this paper,two common objective functions in inter-satellite link assignment,network observation position and network communication factor are studied,and a multi-objective optimization model is constructed.Depth first search,simulated annealing,NSGA-II and adaptive optimization simulated annealing were used to analyze and solve the model.By comparing the solving efficiency of the model through simulation experiments,the difference of the results caused by the four algorithms is verified.

Fuzzy c-means clustering with non local spatial information for noisy image segmentation

As an effective image segmentation method, the standard fuzzy c-means （FCM） clustering algorithm is very sensitive to noise in images. Several modified FCM algorithms, using local spatial information, can overcome this problem to some degree. However, when the noise level in the image is high, these algorithms still cannot obtain satisfactory segmentation performance. In this paper, we introduce a non local spatial constraint term into the objective function of FCM and propose a fuzzy c- means clustering algorithm with non local spatial information （FCM_NLS）. FCM_NLS can deal more effectively with the image noise and preserve geometrical edges in the image. Performance evaluation experiments on synthetic and real images, especially magnetic resonance （MR） images, show that FCM NLS is more robust than both the standard FCM and the modified FCM algorithms using local spatial information for noisy image segmentation.

Optically spatial information selection with hybridly polarized beam in atomic vapor

Vector beams with spatially variant polarization have attracted much attention in recent years, with potential applications in both classical optics and quantum optics. In this work, we study a polarization selection of spatial intensity distribution by utilizing a hybridly polarized beam as a coupling beam and a circularly polarized beam as a probe beam in87 Rb atom vapor. We experimentally observe that the spatial intensity distribution of the probe beam after passing through atoms can be modulated by the hybridly polarized beam due to the optical pumping effect. Then, the information loaded in the probe beam can be designedly filtrated by an atomic system with a high extinction ratio. A detailed process of the optical pumping effect in our configurations and the corresponding absorption spectra are presented to interpret our experimental results, which can be used for the spatial optical information locally extracted based on an atomic system, which has potential applications in quantum communication and computation.

On generalised and specialised spatial information grids: are geo-services ready?

After the set-up of a spatial data infrastructure(SDI)and a national information infrastructure(NII)in many countries,the provision of geo-services became one of the most important and attractive tasks.With the integration of global positioning system(GPS),geographic information system(GIS)and remote sensing(RS),we can,in principle,answer any geo-spatial related question:when and where what object has which changes?An intelligent geo-service agent could provide end-users with the most necessary information in the shortest time and at the lowest cost.Unfortunately there is still a long way to go to achieve such goals.The central component in such geo-services is the integration of the spatial information system with a computing grid via wire-and wireless communication networks.This paper will mainly discuss the grid technology and its integration with spatial information technology,expounding potential problems and possible resolutions.A novel categorising of information grids in the context of geospatial information is proposed:generalised and specialised spatial information grids.

Selective sampling with Gromov–Hausdorff metric:Efficient dense-shape correspondence via Confidence-based sample consensus

Background Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” scenario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used. Methods A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence. Results The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods. Conclusions The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

Oceanographic ontology-based spatial knowledge query

The construction of oceanographic ontologies is fundamental to the ＂digital ocean＂. Therefore, on the basis of introduction of new concept of oceanographic ontology, an oceanographic ontology-based spatial knowledge query （OOBSKQ） method was proposed and developed. Because the method uses a natural language to describe query conditions and the query result is highly integrated knowledge, it can provide users with direct answers while hiding the complicated computation and reasoning processes, and achieves intelligent, automatic oceanographic spatial information query on the level of knowledge and semantics. A case study of resource and environmental application in bay has shown the implementation process of the method and its feasibility and usefulness.

Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery

Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier （MLC）.

Information Theory of Cartography:An Information-theoretic Framework for Cartographic Communication

Map is one of the communication means created by human being.Cartographers have been making efforts on the comparison of maps to natural languages so as to establish a"cartographic language"or"map language".One of such efforts is to adopt the Shannon’s Information Theory originated in digital communication into cartography so as to establish an entropy-based cartographic communication theory.However,success has been very limited although research work had started as early as the mid-1960 s.It is then found that the bottleneck problem was the lack of appropriate measures for the spatial(configurational)information of(graphic and image)maps,as the classic Shannon entropy is only capable of characterizing statistical information but fails to capture the configurational information of(graphic and image)maps.Fortunately,after over 40-year development,some bottleneck problems have been solved.More precisely,generalized Shannon entropies for metric and thematic information of(graphic)maps have been developed and the first feasible solution for computing the Boltzmann entropy of image maps has been invented,which is capable of measuring the spatial information of not only numerical images but also categorical maps.With such progress,it is now feasible to build the"Information Theory of Cartography".In this paper,a framework for such a theory is proposed and some key issues are identified.For these issues,some have already been tackled while others still need efforts.As a result,a research agenda is set for future action.After all these issues are tackled,the theory will become matured so as to become a theoretic basis of cartography.It is expected that the Information Theory of Cartography will play an increasingly important role in the discipline of cartography because more and more researchers have advocated that information is more fundamental than matter and energy.

SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis ( CCA ) based methods for low-resolution ( LR ) face recognition involve face images with different resolutions ( or multi-resolutions ), i.e.LR and high-resolution ( HR ) .For single-resolution face recognition , researchers have shown that utilizing spatial information is beneficial to improving the recognition accuracy , mainly because the pixels of each face are not independent but spatially correlated.However , for a multi-resolution scenario , there are no related works.Therefore , a method named spatial regularization of canonical correlation analysis ( SRCCA ) is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces.Furthermore , the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.

Spatial Analysis of Federal Protected Areas and Priority Areas for Biodiversity Conservation in Brazil

This study presents a spatial analysis of priority areas for biodiversity conservation (PABCs) in Brazil and their coverage by federal protected areas as an indicator of the level of protection afforded to biodiversity in the country and the convergence of environmental protection policies in the sphere of federal government. Georeferenced data were processed using a geographic information system, enabling the calculation of areas, analyses of superimpositions, localizations, and the obtainment of other information using spatial features manipulated in this system. A comparative analysis is done of the PABCs mapped in two periods (2003 and 2007) to ascertain the evolution of this public policy instrument in detecting environmental priorities in protected areas. The improved coverage of PABCs by protected areas in the more recent mapping indicates a good convergence of environmental policies, which are enhanced by technical improvements to mapping procedures and methods for identifying such areas. As a result, the priority areas for biodiversity conservation could become a protected area regulated and recognized by the federal government.

Spatial Data Sharing on Grid

The Internet technology has already changed the Information Society in profound ways, and will continue to do so. Nowadays many people foresee that there is a similar trajectory for the next generation of Internet - Grid Technology. As an emerging computational and networking infrastructure, Grid Computing is designed to provide pervasive, uniform and reliable access to data, computational and human resources distributed in a dynamic, heterogeneous environment. On the other hand, the development of Geographic Information System （GIS） has been highly influenced by the evolution of information technology such as the Internet, telecommunications, software and various types of computing technology. In particular, in the distributed GIS domain, the development However, due to the closed and centralized has made significant impact in the past decade. legacy of the architecture and the lack of interoperability, modularity, and flexibility, current distributed GIS still cannot fully accommodate the distributed, dynamic, heterogeneous and speedy development in network and computing environments. Hence, the development of a high performance distributed GIS system is still a challenging task. So, the development of Grid computing technology undoubtedly provides a unique opportunity for distributed GIS, and a Grid Computing based GIS paradigm becomes inevitable. This paper proposes a new computing platform based distributed GIS framework - the Grid Geographic Information System （G^2IS）.

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.

Monitoring of Pinus massoniana spatial pattern changes based on RS and GIS techniques

Our research focused on Pinus massoniana information extracted from remote sensing images based on the knowledge detection and decision tree algorithm and established a spatial pattern model, combining quantitative theoretical ecology with remote sensing （RS） and geometric information system （GIS） techniques. Applying information extraction methods and a spatial pattern model, we studied P. massoniana spatial patterns changes before and after the invasion by pine wood nematode （Bursaphelenchus xylophilus） in Fuyang and Zhoushan counties, Zhejiang Province, east China. The P. massoniana spatial patterns are clustering, whether the invasion happened or not. But the degree of clustering is different. Our results show good agreement with field data. Applying the results, we analyzed the relationship between spatial patterns and the invasion level. Then we drew the elementary conclusion that there are two kinds of patterns for pine wood nematode to spread： continuous and discontinuous diffusion. This approach can help monitor and evaluate the changes in ecological systems.