A data and physical model dual-driven based trajectory estimator for long-term navigation

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields,for instance,smart healthcare,emergency rescue,soldier positioning et al.The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors,disturbed local magnetic field,and complex motion modes of the pedestrian.This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE)framework,which can be applied for long-term navigation tasks.A Bi-directional Long Short-Term Memory(Bi-LSTM)based quasi-static magnetic field(QSMF)detection algorithm is developed for extracting useful magnetic observation for heading calibration,and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period.In addition,a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks,and enhanced by the magnetic and trajectory features assisted loop detection algorithm.Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms,and final estimated heading and positioning accuracy indexes reaches 5and less than 2 m under the time period of 30 min,respectively.

A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

THE HYPERMEDIA DATA MODEL BASED ON THE INFINITY IMAGE

This paper presents the hypermedia data model based on the infinity RS image information system we have developed.The hypermedia data model consists of different semantic units called nodes,and the associations between nodes are called links.This paper proposes three kinds of nodes (interior node,physical node and complex node) and two kinds of links (plane network structure link,hyper_cube network structure links).The hypermedia information system,based on the model and the basic data layer (the infiniy RS image),represents a digital globe.An approach to the “Getting Lost in the Hyper_space” problem is presented.The approach using the hypermedia data model is an efficient way of handling a large number of RS images in various geographical information systems.

基于SCORM1.3的协作式学习构造

协作式学习是一种重要的学习模式。实现协作式学习,有利于学习资源的共享和复用。该文在对SCORM1.3所定义的“内容聚集模型”和“运行环境”中的数据模型及通信方法进行扩展的基础上,定义了基于SCORM1.3的协作式学习模型及相应的操作方法,并给出了协作式学习环境中数据、操作、权限、角色和用户等要素的形式化描述,简要介绍了一个基于SCORM1.3的协作式学习原型系统SCCL。

SCORM实时运行环境数据模型的实测研究

SCORM是一套源自美国的E-Learning课件标准,在国际范围内有较大影响。SCORM对数据模型的描述较为抽象,普通用户和开发者较难理解和掌握。应用JavaScript针对SCORM实时运行环境(SCORM RTE)建立的模型检测程序,可以在Moodle平台上观察SCORM数据模型的逻辑表现,并展示出LMS平台与SCORM数据模型的交互规律。

Map Aided Pedestrian Dead Reckoning Using Buildings Information for Indoor Navigation Applications

Navigation systems play an important role in many vital disciplines. Determining the location of a user relative to its physical environment is an important part of many indoor-based navigation services such as user navigation, enhanced 911 (E911), law enforcement, location-based and marketing services. Indoor navigation applications require a reliable, trustful and continuous navigation solution that overcomes the challenge of Global Navigation Satellite System (GNSS) signal unavailability. To compensate for this issue, other navigation systems such as Inertial Navigation System (INS) are introduced, however, over time there is a significant amount of drift especially in common with low-cost commercial sensors. In this paper, a map aided navigation solution is developed. This research develops an aiding system that utilizes geospatial data to assist the navigation solution by providing virtual boundaries for the navigation trajectories and limits its possibilities only when it is logical to locate the user on a map. The algorithm develops a Pedestrian Dead Reckoning (PDR) based on smart-phone accelerometer and magnetometer sensors to provide the navigation solution. Geospatial model for two indoor environments with a developed map matching algorithm was used to match and project navigation position estimates on the geospatial map. The developed algorithms were field tested in indoor environments and yielded accurate matching results as well as a significant enhancement to positional accuracy. The achieved results demonstrate that the contribution of the developed map aided system enhances the reliability, usability, and accuracy of navigation trajectories in indoor environments.

基于 SCORM 的在线试题开发工具的研究

在线练习和测试是网络学习的重要组成部分。但是，目前多数开发工具实现的在线试题课程，学习管理系统很难标 准化地记录测试结果、学习者反馈等重要学习数据。通过探索 SCORM RTE API、SCORM Data Model Elements 以及 SCO 和 LMS 之间的通信机制，最后给出了一个符合 SCORM 的 Dreamweaver 试题开发插件作为范例，实现了标准化的数据跟踪和记录．

Elucidation of Latent Risk of Navigation Using an Actual Ship Behavior Analysis

In recent years, maritime transportation has played an important role in global economy development. As a result, ship traffic has become more congested. Moreover, ship navigation is susceptible to weather and environmental conditions, and in some cases, it may become dangerous. Therefore, vessels are subjected to high-risk navigation conditions. To understand the latent risk of ship navigation, this study focused on the actual ship behavior. Thus, an analysis of ship behavior was carded out using historical ship navigation based on automatic identification system data. Consequently, a dynamic analysis of the speed and encounter situation was performed. One of the main results of this work was the understanding of the latent risk involved in ships navigating the Seto Inland Sea, which is one of the most congested routes in Japan. Moreover, the risk areas were obtained, and visualized using a geographical information system. The obtained results can be applied to ensure safe navigation and the development of a safe and efficient navigation model.

基于SCORM的在线试题开发工具的研究

在线练习和测试是网络学习的重要组成部分。但目前多数开发工具实现的在线试题课程、学习管理系统很难标准化地记录测试结果、学习者反馈等重要学习数据。通过探索SCORM RTE API,SCORM Data Model Elements以及SCO和LMS之间的通信机制,给出了一个符合SCORM的Dreamweaver试题开发插件的范例,实现了标准化的数据跟踪和记录。

Deep-reinforcement-learning-based UAV autonomous navigation and collision avoidance in unknown environments

In some military application scenarios,Unmanned Aerial Vehicles(UAVs)need to perform missions with the assistance of on-board cameras when radar is not available and communication is interrupted,which brings challenges for UAV autonomous navigation and collision avoidance.In this paper,an improved deep-reinforcement-learning algorithm,Deep Q-Network with a Faster R-CNN model and a Data Deposit Mechanism(FRDDM-DQN),is proposed.A Faster R-CNN model(FR)is introduced and optimized to obtain the ability to extract obstacle information from images,and a new replay memory Data Deposit Mechanism(DDM)is designed to train an agent with a better performance.During training,a two-part training approach is used to reduce the time spent on training as well as retraining when the scenario changes.In order to verify the performance of the proposed method,a series of experiments,including training experiments,test experiments,and typical episodes experiments,is conducted in a 3D simulation environment.Experimental results show that the agent trained by the proposed FRDDM-DQN has the ability to navigate autonomously and avoid collisions,and performs better compared to the FRDQN,FR-DDQN,FR-Dueling DQN,YOLO-based YDDM-DQN,and original FR outputbased FR-ODQN.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

面向云边端协同的多模态数据建模技术及其应用

云边端协同架构中数据类型多样,各级存储资源与计算资源存在差异,给数据管理带来新的挑战.现有数据模型或者数据模型的简单叠加,都难以同时满足云边端中多模态数据管理和协同管理需求.因此,研究面向云边端协同的多模态数据建模技术成为重要问题.其核心在于,如何高效地从云边端三层架构中得到满足应用所需的查询结果.从云边端三层数据的数据类型出发,提出了面向云边端协同的多模态数据建模技术,给出了基于元组的多模态数据模型定义,设计了6种基类,解决多模态数据统一表征困难的问题;提出了云边端协同查询的基本数据操作体系,以满足云边端业务场景的查询需求;给出了多模态数据模型的完整性约束,为查询优化奠定了理论基础.最后,给出了面向云边端协同多模态数据模型的示范应用,并从数据存储时间、存储空间和查询时间这3个方面对所提出的数据模型存储方法进行了验证.实验结果表明,所提方案能够有效地表示云边端协同架构中的多模态数据.

国家金融安全视域下金融科技的风险应对与法治保障

在数字化时代,科技深度赋能金融服务的同时,也易引发国家金融安全风险,包括与传统金融活动结合、构建新型金融网络系统及新兴科技直接应用等场景。金融科技引发国家安全风险的生成机理在于,随着金融体系的中介类型增多、金融风险传导渠道增加,新金融网络体系会产生基于“关联”的风险。金融科技“破坏性创新”的特征,会从金融监管立法与实施两个方面冲击传统金融监管框架,从而加剧监管失灵。国家金融安全的法治保障亟须理论层面的反思与重构,进而形成金融科技的“动态协同”治理模式,从外层金融网络与基础设施安全、中层金融科技创新活动和内层金融服务行为三个层面,构建多层次的金融科技法治保障体系。

基于数据流模型的航线网络结构可视分析方法

针对航线网络结构多维属性关联分析问题,提出一种基于数据流模型的多视图协同网络结构可视分析方法。引入数据流模型,增强多维数据分析时的交互和多视图协同效果;构建基于数据流模型的航线网络结构可视分析系统用于方法验证。该方法对比和实验结果表明,利用航线网络数据流实时交互和多视图协同分析网络结构数据,完成聚类、相关性、异常值分析任务,能更好地分析数据之间的关联关系,有助于理解航线网络的结构和特征。最后,通过以上方法分析网络结构特点与不足,提出航线网络优化建议,能更好地帮助用户完成可视分析任务,探索数据分布特征、发现多维数据之间的关联关系。

滨里海盆地东缘速度建模方法研究与应用

滨里海盆地东缘阿克若尔地区石炭系碳酸盐岩顶面低幅度构造具有较大的油气勘探潜力,由于上覆下二叠统地层分布复杂,横向速度变化大,使得石炭系低幅度构造成图存在很大难度;为了解决研究区构造快速成图的问题,通过3种速度建模方法对比,优选出分频层速度反演方法,建立研究区的速度模型,解决了分频层速度反演受常规层速度反演方法横向连续性的限制问题,对下二叠统碳酸盐岩异常体的速度刻画更为准确;同时,分频层速度反演方法与井—震速度谱协同速度建模方法和常规井约束层速度反演相对比,提高了研究区下伏石炭系低幅度构造的深度域成图精度,取得了较好的效果。

考虑数据稀疏性的图书推荐协同过滤算法仿真

图书推荐算法易忽略数据稀疏性问题,导致推荐结果与用户感兴趣内容之间存在较大的偏差。在考虑数据稀疏性的基础上提出一种图书推荐协同过滤算法,对数据预处理,通过对用户和用户之间综合信任度分析,利用分布估计算法对用户兴趣建模;构建用户兴趣簇类集,划分用户兴趣,从中选择出与检索对象最接近的邻居;计算邻近项目得分,按照从大到小的顺序排列,排名靠前的资源项即为图书推荐结果。实验结果表明,所提方法在推荐500本图书时,用时在12s内,且降低了平均绝对误差和均方根误差,实现了最精准的图书推荐。

我国数据跨境流动协同治理现实困境及纾解路径研究

[目的/意义]在数据跨境流动助推全球化发展这一背景下,探索我国数据跨境流动协同治理路径,为提升我国数据跨境流动协同治理效能提供参考。[方法/过程]本文引入SFIC协同治理理论模型并对其进行适用化修正,通过剖析我国数据跨境流动协同治理的现实困境,提出基于SFIC修正模型的数据跨境流动协同治理纾解路径。[结果/结论]本文构建出“初始条件-制度设计-催化领导-协同过程-评估反馈”五维一体的纾解路径框架,并有针对性地提出培育协同治理文化、激发协同主体治理积极性、搭建统一的协同治理平台等多项具体举措,旨在促进我国数据跨境流动协同治理的长效化、可持续化健康发展。

数据驱动下图书馆智慧化协同治理模式探究

数据驱动下图书馆智慧化协同治理,就是发挥数据驱动和协同治理两大优势,协同共建高质量新数据,关联赋能存量数据,提高图书馆数据资源的有效供给,促进数据资源价值化,实现图书馆数字化到智慧化升级。文章从数据驱动下图书馆智慧化协同治理模式构建的策略路径、服务流程和实现功能方面对数据驱动下图书馆数据协同治理平台构建进行了探析,以期为图书馆系统融入“中华文化数据库”提供思路和启发。

基于HCPN的复杂BPMN协作模型数据流建模与验证

为了保证复杂BPMN协作模型的正确性,不仅要涵盖多实例和子进程等复杂元素,还要在检测控制流错误的同时检测数据流错误。但业务流程建模标注(BPMN 2.0)缺乏形式化语义的描述,这对模型正确性的验证造成了阻碍。因此,给出了一种具有弧权重的层次化着色Petri网(HCPN)的定义,它既可以对数据流进行形式化表示,又可以对多实例和子进程结构进行建模。进一步提出了从BPMN协作模型到HCPN模型的形式化映射方法。然后基于HCPN模型的弧权重给出了缺失、丢失和冗余3种数据流错误的定义,并提出了对应的检测算法。最后,设计了一个自动化建模与验证的框架,通过一个案例研究说明了该方法的有效性。