大规模 MIMO系统中用户分层变长时隙分配方法研究

大规模MIMO系统时分双工模式中分配的物理时隙长度是固定的,不能根据不同时长的信道相干时间灵活地调整长度,二者长度的不匹配导致部分时频资源的浪费,因此系统的总体容量不能达到最优。针对此问题本文提出一种用户分层变长时隙分配方法,即根据不同终端的移动性对终端进行分层,并为不同层中的移动终端灵活地分配不同时长的物理时隙,使物理时隙长度与移动终端所对应信道的相干时长相匹配,从而减少时频资源的浪费,提高了资源利用率。同时,避免了低速移动终端过频繁的信道估计,降低了信道训练序列开销,使系统的总体数据容量得到很大的提升。实验仿真结果验证了本文所提方法的有效性。     

新时代与公共政策的创新

公共政策,既是时代的产物,又为时代服务。新时代要求公共政策的创新必须更加聚焦,一切以调动广大人民群众的积极性,增强民族的凝聚力、创造力为出发点;要求公共政策的创新以人民为中心,一切以人民是否满意,是否具有获得感、幸福感、安全感、自豪感为落脚点;要求公共政策的创新,应当以社会问题为重点,以推动解决主要矛盾、化解社会各领域的主要问题为根本要求;要求公共政策的研究应当提高定位、开阔视野,跟上时代步伐,多出系统性、前瞻性研究成果和高质量研究人才,为党和国家的科学决策贡献更多的智慧力量。     

采用LSRSERM方法进行多雷达数据配准

基于雷达网系统误差分析,提出了一种最小二乘相对系统误差配准法,并给出了一种多雷达数据配准方案。仿真和实际数据证明,该方法可以使同一目标观测航迹的分裂程度减小一个数量级。     

灰色神经网络的鱼雷经济寿命预测

为减少军费开支、降低鱼雷全寿命费用,从经济性角度提出了鱼雷最佳服役年限模型。利用灰色等维新息GM(1,1)模型对鱼雷年度使用维修费用进行预测;采用分组的思想将原始数据分为多组,采用神经网络对灰色模型的预测残差进行修正,以提高预测精度。通过实际算例预测了鱼雷经济寿命,从而证明了模型的实际应用价值。     

模拟退火优化BP神经网络在航向控制系统中的应用

目前,广泛运用于神经网络中的误差反向传播算法(BP算法)训练时间较长,且易陷入局部最优.为了克服BP算法的固有缺陷,文中提出了在BP算法中加入模拟退火算法权因子.在航向控制系统中进行了仿真,数据显示该算法比单纯BP算法更能优化控制器性能参数和全局搜索能力,收敛速度更快,精度提高比较明显.     

Network routing for insurgency: An adversarial risk analysis framework

Problems in counterterrorism and corporate competition have prompted research that attempts to combine statistical risk analysis with game theory in ways that support practical decision making. This article applies these methods of adversarial risk analysis to the problem of selecting a route through a network in which an opponent chooses vertices for ambush. The motivating application is convoy routing across a road network when there may be improvised explosive devices and imperfect intelligence about their locations. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

洛杉矶警察局反恐策略及对我国公安反恐工作的启示

洛杉矶警察局在9.11事件之后,不断积累预防和打击恐怖主义的经验,提高自身反恐能力,并在实际的预防打击恐怖主义斗争中做出了重要贡献。恐怖主义是全世界的敌人。针对中国的恐怖主义威胁现状,我国公安机关作为预防和打击恐怖主义的重要力量,要研究和借鉴他们好的经验,坚决打击国内恐怖主义的发展势头,以维护中国的安全稳定。     

一类带配送中心运输问题的容量扩张模型研究

运输问题一般采用表上作业法来解决,考虑一类带配送中心的运输问题,若仍采用表上作业法,会使问题复杂化.文中采用一种构造辅助网络的方法:在运输网络中将每个配送中心均拆分成两个点,连接两点形成新弧,构造出新的网络,并给每条弧赋予参数,将此类运输问题转换为最小费用流模型来解决,可以使问题模型和运算简单化.在此基础上,考虑运输网络中配送中心和边的容量扩张问题.     

Optimizing natural gas pipeline transmission with nonuniform elevation: A new initialization approach

In this paper, we develop an iterative piecewise linear approximation approach with a novel initialization method to solve natural gas pipeline transmission problems with the nonuniform network elevation. Previous approaches, such as energy minimization methods, cannot be applied to solve problems with the nonuniform network elevation because they exclude pressure range constraints, and thus provide solutions far from optimum. We propose a new initialization model that considers pressure range constraints and improves the optimality of the solutions and the computational efficiency. Furthermore, we extend the energy minimization methods and provide the necessary conditions under which the extended methods operate in networks with the nonuniform elevation. We test the performances of the methods with previously reported pipeline networks from the literature, with the open data set GasLib, and with our industrial collaborator. The initialization approach is shown to be more efficient than the method with fixed initial breakpoints. The newly proposed initialization approach generates solutions with a higher accuracy than the extended energy minimization methods, especially in large‐size networks. The proposed method has been applied to natural gas transmission planning by the China National Petroleum Corporation and has brought a direct profit increase of 330 million U.S. dollars in 2015‐2017.     

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.