融合空间偏好和语义的个体活动识别方法

个体活动识别对用户画像、个性化推荐、异常行为检测、群体行为分析和基于活动的资源配置优化具有重要价值。提出了一种基于稀疏的社交媒体签到数据的个体活动语义识别方法,从签到数据中提取活动行为的时间周期性和趋势性特征,并采用空间偏好量化算法,从个体与群体活动的空间关联中提取群体和个体的空间访问偏好,使用自然语言嵌入工具BERT模型提取访问兴趣点的语义。时间特征、空间偏好特征和访问兴趣点名称语义特征共同构成表征群体、个体偏好的时空联合特征,通过极限梯度提升分类器对其进行分类,得到活动语义识别结果。在Foursquare数据集上的对比实验和消融实验中验证了所提活动语义识别模型可以有效提升活动语义识别的准确性。     

越南卫星现状及未来发展

卫星具有覆盖范围广、可长时间稳定运行、无国界限制、安全性高等独特优势,是提升部队作战效能的＂倍增器＂,也是解决大范围战场信息支撑能力问题的有效手段和最佳途径。21世纪初,高速增长的经济推动越南航天事业驶入了发展的快车道,不仅实现了卫星发射的零突破,还相继发射了多颗通信卫星、遥感卫星和试验型纳卫星。文章主要对越南当前的卫星现状进行总结归纳,并分析其未来的发展计划。     

战争制胜机理何以发生变化

信息化战争已经登上了当今世界的舞台,这是军事领域发生的革命性变化。在信息化战争条件下,传统的战争制胜机理发生的新的变化。     

未来数字化战场中的指挥控制语言研究

针对未来数字化战场中机器人部队指挥控制与协同作战这一新兴研究领域需解决的机-机协同和人-机智能融合等问题,综述国内外相关领域的研究现状,分析了构建我军指挥控制语言的必要性,研究了机器人部队指挥控制语言的技术架构与关键技术,并提出了指挥控制语言的一种形式化语法和语义标注方法和通信协议,并结合海军舰艇作战示例分析了指挥控制语言的可行性及有效性。     

基于兰切斯特方程的网络中心战模型

为了更全面、客观地评价信息优势和决策优势对网络中心战进程的影响,在分析经典兰切斯特方程的基础上,引入信息?决策概率,提出了基于兰切斯特方程的网络中心战模型,仿真实验表明该模型能够有效地描述信息?决策概率在网络中心战进程中发挥的作用,为研究分析网络中心作战过程提供了理论参考。     

浅析消防业务信息系统的管理运维

消防业务信息系统日常运维任务是确保实时数据的可用性和完整性,信息能够得到及时处理应用,保障信息系统的正常运行及维护系统资源的安全.加强消防业务系统日常运维的管理标准,规范运维工作的方法,可以减少系统运维中不确定性因素,是提高消防业务工作质量和水平的重要保障.     

部队网络信息管理规律及策略研究

网络信息管理是新形势下部队管理工作的一项重要内容,对于促进官兵全面发展、加强部队行政管理、提升信息化建设具有重要意义,加强部队网络信息管理必须遵循共享与防范相结合、标本兼治和网络信息技术与官兵素质同步提高规律,从组织领导、官兵思想、安全防护、制度落实和网管人员建设五个方面构筑部队网络信息管理有效策略.     

Impact of reseller's and sales agent's forecasting accuracy in a multilayer supply chain

We consider a three‐layer supply chain with a manufacturer, a reseller, and a sales agent. The demand is stochastically determined by the random market condition and the sales agent's private effort level. Although the manufacturer is uninformed about the market condition, the reseller and the sales agent conduct demand forecasting and generate private demand signals. Under this framework with two levels of adverse selection intertwined with moral hazard, we study the impact of the reseller's and the sales agent's forecasting accuracy on the profitability of each member. We show that the manufacturer's profitability is convex on the reseller's forecasting accuracy. From the manufacturer's perspective, typically improving the reseller's accuracy is detrimental when the accuracy is low but is beneficial when it is high. We identify the concrete interrelation among the manufacturer‐optimal reseller's accuracy, the volatility of the market condition, and the sales agent's accuracy. Finally, the manufacturer's interest may be aligned with the reseller's when only the reseller can choose her accuracy; this alignment is never possible when both downstream players have the discretion to choose their accuracy. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 207–222, 2014     

Assessing the value of demand sharing in supply chains

We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order‐up‐to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi‐ARMA in—quasi‐ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014     

基于星历辅助的导航星座星间链路捕获初始信息求解算法

针对星间链路信号动态范围大、捕获时间要求短之间的矛盾,提出了一种捕获初始信息的求解算法。该算法基于导航星座的星历资源和动态特性,采用迭代的方法对信号传输时延和多普勒频率进行估计。分析指出,该算法实现过程简单,收敛速度快,估计精度高,两次迭代即可实现时延估计精度达到纳秒级,大大降低了捕获实现的难度和复杂度,适合星上资源要求比较严格的场合。分析基于北斗导航星座模型展开,对我国北斗导航星间链路的设计提供了参考。