Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Epidemic Characteristics and Spatio-Temporal Patterns of HFRS in Qingdao City,China,2010-2022

Objective This study investigated the epidemic characteristics and spatio-temporal dynamics of hemorrhagic fever with renal syndrome(HFRS)in Qingdao City,China.Methods Information was collected on HFRS cases in Qingdao City from 2010 to 2022.Descriptive epidemiologic,seasonal decomposition,spatial autocorrelation,and spatio-temporal cluster analyses were performed.Results A total of 2,220 patients with HFRS were reported over the study period,with an average annual incidence of 1.89/100,000 and a case fatality rate of 2.52%.The male:female ratio was 2.8:1.75.3%of patients were aged between 16 and 60 years old,75.3%of patients were farmers,and 11.6%had both“three red”and“three pain”symptoms.The HFRS epidemic showed two-peak seasonality:the primary fall-winter peak and the minor spring peak.The HFRS epidemic presented highly spatially heterogeneous,street/township-level hot spots that were mostly distributed in Huangdao,Pingdu,and Jiaozhou.The spatio-temporal cluster analysis revealed three cluster areas in Qingdao City that were located in the south of Huangdao District during the fall-winter peak.Conclusion The distribution of HFRS in Qingdao exhibited periodic,seasonal,and regional characteristics,with high spatial clustering heterogeneity.The typical symptoms of“three red”and“three pain”in patients with HFRS were not obvious.

Dynamic adaptive spatio-temporal graph network for COVID-19 forecasting

Appropriately characterising the mixed space-time relations of the contagion process caused by hybrid space and time factors remains the primary challenge in COVID-19 forecasting.However,in previous deep learning models for epidemic forecasting,spatial and temporal variations are captured separately.A unified model is developed to cover all spatio-temporal relations.However,this measure is insufficient for modelling the complex spatio-temporal relations of infectious disease transmission.A dynamic adaptive spatio-temporal graph network(DASTGN)is proposed based on attention mechanisms to improve prediction accuracy.In DASTGN,complex spatio-temporal relations are depicted by adaptively fusing the mixed space-time effects and dynamic space-time dependency structure.This dual-scale model considers the time-specific,space-specific,and direct effects of the propagation process at the fine-grained level.Furthermore,the model characterises impacts from various space-time neighbour blocks under time-varying interventions at the coarse-grained level.The performance comparisons on the three COVID-19 datasets reveal that DASTGN achieves state-of-the-art results with a maximum improvement of 17.092%in the root mean-square error and 11.563%in the mean absolute error.Experimental results indicate that the mechanisms of designing DASTGN can effectively detect some spreading characteristics of COVID-19.The spatio-temporal weight matrices learned in each proposed module reveal diffusion patterns in various scenarios.In conclusion,DASTGN has successfully captured the dynamic spatio-temporal variations of COVID-19,and considering multiple dynamic space-time relationships is essential in epidemic forecasting.

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography

High speed photography technique is potentially the most effective way to measure the motion parameter of warhead fragment benefiting from its advantages of high accuracy,high resolution and high efficiency.However,it faces challenge in dense objects tracking and 3D trajectories reconstruction due to the characteristics of small size and dense distribution of fragment swarm.To address these challenges,this work presents a warhead fragments motion trajectories tracking and spatio-temporal distribution reconstruction method based on high-speed stereo photography.Firstly,background difference algorithm is utilized to extract the center and area of each fragment in the image sequence.Subsequently,a multi-object tracking(MOT)algorithm using Kalman filtering and Hungarian optimal assignment is developed to realize real-time and robust trajectories tracking of fragment swarm.To reconstruct 3D motion trajectories,a global stereo trajectories matching strategy is presented,which takes advantages of epipolar constraint and continuity constraint to correctly retrieve stereo correspondence followed by 3D trajectories refinement using polynomial fitting.Finally,the simulation and experimental results demonstrate that the proposed method can accurately track the motion trajectories and reconstruct the spatio-temporal distribution of 1.0×10^(3)fragments in a field of view(FOV)of 3.2 m×2.5 m,and the accuracy of the velocity estimation can achieve 98.6%.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Multi-Scale Location Attention Model for Spatio-Temporal Prediction of Disease Incidence

Due to the increasingly severe challenges brought by various epidemic diseases,people urgently need intelligent outbreak trend prediction.Predicting disease onset is very important to assist decision-making.Most of the exist-ing work fails to make full use of the temporal and spatial characteristics of epidemics,and also relies on multi-variate data for prediction.In this paper,we propose a Multi-Scale Location Attention Graph Neural Networks(MSLAGNN)based on a large number of Centers for Disease Control and Prevention(CDC)patient electronic medical records research sequence source data sets.In order to understand the geography and timeliness of infec-tious diseases,specific neural networks are used to extract the geography and timeliness of infectious diseases.In the model framework,the features of different periods are extracted by a multi-scale convolution module.At the same time,the propagation effects between regions are simulated by graph convolution and attention mechan-isms.We compare the proposed method with the most advanced statistical methods and deep learning models.Meanwhile,we conduct comparative experiments on data sets with different time lengths to observe the predic-tion performance of the model in the face of different degrees of data collection.We conduct extensive experi-ments on real-world epidemic-related data sets.The method has strong prediction performance and can be readily used for epidemic prediction.

Spatio-Temporal Change of Dispersal Areas of Greater Kudu (Tragelaphus strepsiceros) in Lake Bogoria Landscape, Kenya

Decline in wildlife populations is manifest globally, regionally and locally. A wildlife decline of 68% has been reported in Kenya’s rangelands with Baringo County experiencing more than 85% wildlife loss in the last four decades. Greater Kudu (Tragelaphus strepsiceros) is endemic to Lake Bogoria landscape in Baringo County and constitutes a major tourist attraction for the region necessitating use of its photo on the County’s logo and thus a flagship species. Tourism plays a central role in Baringo County’s economy and is a major source of potential growth and employment creation. The study was carried out to assess spatio-temporal change of dispersal areas of Greater Kudu (GK) in Lake Bogoria landscape in the last four years for enhanced adaptive management and improved livelihoods. GK population distribution primary data collected in December 2022 and secondary data acquired from Lake Bogoria National Game Reserve (LBNGR) for 2019 and 2020 were digitized using in a Geographic Information System (GIS). Measures of dispersion and point pattern analysis (PPA) were used to analyze dispersal of GK population using GIS. Spatio-temporal change of GK dispersal in LBNR was evident thus the null hypothesis was rejected. It is recommended that anthropogenic activities contributing to GK’s habitat degradation be curbed by providing alternative livelihood sources and promoting community adoption of sustainable technologies for improved livelihoods.

Research on the Spatio-Temporal Evolution and Driving Forces of Green Spaces in the Central Urban Area of Zunyi City

Green space,as a medium for carrying out urban functions and guiding urban development,is becoming a scarce resource along with the urbanization process and the intensification of environmental problems.In the face of the spatial mismatch between high demand and low supply,it is of great significance to clarify the evolution mechanism of green space to undertake national spatial planning,protect the natural strategic resources in the urban fringe area,and promote the sustainable development of the“three living spaces.”The study focuses on the Zunyi City Center,selecting the 20 years of rapid development following its establishment as a city as the study period.It explores the dynamic evolution of green space and the main driving forces during different periods using remote-sensing image data.The study shows that from 2003 to 2023,the total scale of green space has an obvious decreasing trend along with the expansion of the urban built-up area.A large amount of arable land is being converted to construction land,resulting in a sudden decrease in arable land area.In the past 10 years,the comprehensive land use dynamics have accelerated.Still,the spatial difference has gradually narrowed,indicating that the overall development intensity of Zunyi City’s central urban area has increased.There is a gradual spread of the trend to the hilly areas.The limiting effect of the mountainous natural environment on the city’s development has gradually diminished under the superposition of external factors,such as economic development,industrial technological upgrading,and policy orientation so the importance of the effective protection and rational utilization of urban green space has become more prominent.

Traveling Wave Solutions of a SIR Epidemic Model with Spatio-Temporal Delay

In this paper, we studied the traveling wave solutions of a SIR epidemic model with spatial-temporal delay. We proved that this result is determined by the basic reproduction number R0and the minimum wave speed c*of the corresponding ordinary differential equations. The methods used in this paper are primarily the Schauder fixed point theorem and comparison principle. We have proved that when R0>1and c>c*, the model has a non-negative and non-trivial traveling wave solution. However, for R01and c≥0or R0>1and 0cc*, the model does not have a traveling wave solution.

基于Geodatabase的CAD到ARCGIS数据入库研究

城市基础地理信息系统建设的核心在于数据和基于数据的服务,而目前拥有的前端数据以CAD格式为主,所以研究CAD到G IS的数据直接转换势在必行。本文研究了从CAD到ARCG IS过程中的地理编码方案和规则库,采用COM组件技术,利用ARCG IS的Geodatabase数据模型,用VB和AO编程解决了格式转换、构面处理、属性提取和入库等问题。

基于GeoDatabase和ArcSDE的湿地GIS数据库技术研究与应用实例

以松嫩湿地为背景,介绍了GeoDatabase结合ArcSDE技术建立G IS数据库的方法,详细阐述了如何实现属性数据和空间数据一体化存储的理论细节,在此基础上提出了两者的改进连接方案;同时将该方法与传统的G IS数据存储方式相比较来说明其先进性。进而通过实例证明了此数据库技术在应用系统开发中的实用性。

基于Geodatabase的城市综合地下管线信息系统的设计与实现

利用GIS技术管理地下管线是提高城市建设效率的重要手段.本文以厦门市综合地下管线信息系统的建设为背景,研究管线数据与地形图数据的统一数据建库、以及信息资源的共享应用.为此,利用ArcGIS引入的Geodatabase数据模型,探讨数据的组织形式,根据不同的应用需求提出系统的框架结构,采用B/S和C/S架构相结合方式进行系统总体设计和功能实现,解决了地下管线信息管理、应用和法规保证的动态更新等问题,为城市地下管线的系统建设提供了借鉴方案和基本思路.

MapGIS到Geodatabase数据自动批量转换实践研究

国土资源数据库建设成果主要以MapGIS和Geodatabase(GDB)格式进行存储,两者之间的数据转换十分频繁。经测试表明,MapGIS通过中间格式向GDB转换存在问题,因此本文提出直接读取MapGIS加密二进制文件,通过MapGIS SDK和ArcGIS Engine相结合开发转换程序的技术路线,实现MapGIS数据批量自动转换到GDB。在分析MapGIS数据组织、GDB数据组织、实体几何类型对照、几何对象转换对照、属性字段对照等关键技术的基础上,采用多源空间数据无缝集成技术(SIMS)进行程序设计,对转换流程进行优化,开发出了MapGIS到GDB的数据转换软件MapGIS2GDB,该成果能够满足省市级国土资源数据库建设对MapGIS数据转换的需求。

基于Geodatabase和ArcSDE的城市地质空间数据库设计

城市基础地质数据是"数字城市"的基础性数据。随着地质资料数据的急剧增多,迫切需要建立有效的地质空间数据库。为建立科学合理的地质空间数据库,描述了Geodatabase空间数据模型和ArcSDE技术,以沈阳市的工程勘察数据为例,在分析城市地质管理内容的基础上,探讨了城市地质Geodatabase空间数据库的建立过程,实现了空间数据和属性数据的集成化一体存储和管理,为实现城市基础地质数据的科学管理和应用提供了保障。

采用Geodatabase技术构建流域水文系统地理数据库——以太湖地区西苕溪流域为例

如何表达流域复杂的系统结构是实现流域信息与模型集成,构建流域决策支持系统需要研究的首要问题.在分析现有流域数据库存在问题的基础上,以太湖流域西南部的西苕溪流域为研究区,采用面向对象的Geodatabase地理数据技术,通过分析流域系统的组成要素及过程,提出面向流域水文、水质应用需求的数据库信息组织体系;应用Arcgis的Archydro水文分析模块,基于国家基础地理数据库中的数字地形提取流域要素信息,构建了包括河流流线、集水区出水口、监测台站位置、湖库出口等要素的完整水文网络,并分析水文网络要素上下游关系,对流域集水区与河流的水力联系进行表达;通过分析流域监测台站空间信息、监测项目、时间序列的信息特征,设计Geodatabase的表结构和连接类,实现流域空间特征与状态序列的一体化表达,研究可以为流域数据库建设及流域决策支持系统信息平台构建提供一些技术参考.

基于Geodatabase的面向对象时空数据模型

针对时空数据模型与时态地理信息系统研究中存在的几类问题,提出了一种通用的面向对象时空数据模型GOO-STDM。该模型从时空对象的基本属性和行为出发,运用面向对象方法,将地学对象封装为空间、专题、时间的整体,满足地学对象的what/where/when语义,具有良好的扩展性。在GOO-STDM基础上,利用Geodatabase模型,采用定制ArcGIS的方法,实现了支持双时态语义的原型TGIS系统,既能满足时空表达的需要,又继承了ArcGIS系统的功能。

基于Geodatabase模型的流域水文系统数据组织与实现

简要介绍了构建流域水文系统的必要性、可行性和技术支撑,提出应用Geodatabase模型来进行流域数据组织的观点。以Geodatabase为基础,集成流域中各种要素,定义它们之间相互作用关系,建立流域数据库。特别是对河流网络,水文数据组织,水文数据建模等进行了重点研究,并提出了其技术流程,为流域的研究和建设提供了经验借鉴和新的思路。

基于ArcSDE Geodatabase的城市规划管理GIS数据库的应用研究

采用ArcInfo平台的Geodatabase数据模型和ArcSDE技术,探讨将AutoCAD图形数据导入数据库,并以VB+MapObjects平台为例介绍如何从数据库中实现空间数据的存取。

基于Geodatabase模型的空间数据库设计

在对空间数据库技术应用研究的基础上,设计林业Geodatabase空间数据库,实现了空间数据和属性数据的集成化一体存储;对林业GIS应用存在的难点,也进行了较深入的讨论,并提出了相应的解决方案。