大规模串行分布式检测的性能

串行分布式（Ｎ＝２,３,４）检测的最优协调门限组难以求取,因而只能就由少量传感器组成的小规模串行网,通过数值计算来考察检测性能。本文在一定条件下,给出了求取串行分布式检测性能的解析方法,进而可得到大规模串行分布式检测的性能,使我们能考察和了解检测性能在积累数增加时的变化趋势。     

提高武警院校办学效益路径研究

通过对不同类型不同地域武警院校的实地调研,包括问卷调查以及与不同层级的相关人员进行座谈和个案访谈,分析研究可知,当前影响军校办学效益的主要问题是办学规模偏小、师生比过低、院校评价指标不够科学合理等。武警院校提升办学效益的空间很大。     

升压变换装置研制过程中难点问题的解决

结合大功率数字式全电力炮控系统所用的升压变换装置(270 V/32 A)的研制,详细阐述了尖峰电压、偏磁现象、波形振荡产生的机理,并从理论上提出了解决上述问题的方法.研制过程中,通过推挽式变压器的合理设计、控制方案的完善、总体布局的优化等一系列技术措施,较好地抑制了尖峰电压、偏磁、波形振荡的产生;试验结果表明所采取措施的有效性和正确性.     

结合离差最大化的多属性群体决策TOPSIS威胁评估

针对传统理想解逼近排序法(TOPSIS)在确定多个属性权重系数上存在主观性和盲目性,并且所作决策多为单一决策,精确度不高等问题,提出结合离差最大化原理的多属性群体决策TOPSIS改进模型,利用离差最大化原理确定属性权值,同时引进群体决策方法。最后将该模型运用于机群协同空战的威胁评估中,建立了多目标威胁评估数学模型,并进行了全空战过程的动态仿真分析,结果表明所建立的扩展模型是合理可行的。     

点邻域尺度差异描述的点云配准算法

针对传统特征描述符计算复杂度高、配准精度低的问题,提出一种基于不同尺度点邻域特征信息差异的点云配准算法。在特征描述符方面,对关键点选取不同尺度的邻域空间,计算各尺度空间之间的特征值归一化向量差异和法向量夹角,建立点邻域尺度差异描述符,特征描述符计算简单且节省时间。在关键点选取方面,根据曲面形状指数设计了一种寻找关键点的方法,提取的点具有很好的代表性。在对应关系寻找方面,提出一种基于欧式距离的对应点二重筛选方法,找出对应点对集,设计了基于全局距离的全局最优点云变换矩阵求取方法。实验结果表明,点邻域尺度差异描述的点云配准算法具有良好的配准精度和稳健的噪声鲁棒性。     

非高斯码元检测的马尔可夫链蒙特卡洛算法

针对实际超低频接收机不仅受非高斯噪声的影响,还受接收机内部和外部环境中高斯噪声影响的问题,对噪声采用非高斯分布和高斯分布的混合模型建模,根据混合模型的性质,设计了一种利用马尔可夫链蒙特卡洛方法的超低频信号码元盲检测算法。盲检测算法在贝叶斯层次模型下,采用Gibbs抽样和M-H抽样更新参数,同步估计信道衰落系数和噪声模型参数,并实现对信号码元的检测。算法迭代效率快、精度高。通过与最优检测算法性能比较,盲检测算法性能优异,对超低频信号接收具有重要的现实意义。     

基于感知信息共享的C2网络特征分析

指挥控制（C2，Commandand Control）关系网络连接数的增加会增强指控节点之间的信息共享，但也会增加节点信息处理和交换负荷，如果达到一定程度，则会造成节点本身的“信息过载”从而影响C2网络性能。对此，通过研究C2网络在两种不同处理方式下的共享感知信息平均提交时间，用网络节点响应时间的均方差表征一个C2网络的共享态势感知时间的一致性，最后对两种不同结构C2网络特征参数的计算，说明了网络连接增加会导致信息提交时间的延长，但一致性会增强。     

助推滑翔飞行器复合控制系统线性化小偏差运动方程推导与控制系统设计

助推滑翔技术是制导武器实现增加射程、提高机动突防能力的关键技术之一,对飞行器姿态控制有较高的要求,需要设计复合控制方式的姿态控制系统.针对采用空气舵与燃气舵联动控制的飞行器,建立了动力学模型,详细推导了完整的三通道线性化小偏差运动方程,结合典型弹道数据给出了动力系数图像,分析了助推滑翔弹道各飞行段中飞行器的稳定性,在此基础上选取再入段低空飞行特征点进行了姿控系统设计,仿真结果验证了线性化小偏差运动方程的正确性和控制系统的有效性,为进行此类飞行器的稳定性分析与姿态控制系统设计提供了有益的参考.     

Maximizing deviation method for multiple attribute decision making under q-rung orthopair fuzzy environment

Because of the uncertainty and subjectivity of decision makers in the complex decision-making environment, the evaluation information of alternatives given by decision makers is often fuzzy and uncertain. As a generalization of intuitionistic fuzzy set (IFSs) and Pythagoras fuzzy set (PFSs), q-rung orthopair fuzzy set (q-ROFS) is more suitable for expressing fuzzy and uncertain information. But, in actual multiple attribute decision making (MADM) problems, the weights of DMs and attributes are always completely unknown or partly known, to date, the maximizing deviation method is a good tool to deal with such issues. Thus, combine the q-ROFS and conventional maximizing deviation method, we will study the maximizing deviation method under q-ROFSs and q-RIVOFSs in this paper. Firstly, we briefly introduce the basic concept of q-rung orthopair fuzzy sets (q-ROFSs) and q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs). Then, combine the maximizing deviation method with q-rung orthopair fuzzy information, we establish two new decision making models. On this basis, the proposed models are applied to MADM problems with q-rung orthopair fuzzy information. Compared with existing methods, the effectiveness and superiority of the new model are analyzed. This method can effectively solve the MADM problem whose decision information is represented by q-rung orthopair fuzzy numbers (q-ROFNs) and whose attributes are incomplete.     

On the exponentially weighted moving variance

MacGregor and Harris (J Quality Technol 25 (1993) 106–118) proposed the exponentially weighted mean squared deviation (EWMS) and the exponentially weighted moving variance (EWMV) charts as ways of monitoring process variability. These two charts are particularly useful for individual observations where no estimate of variability is available from replicates. However, the control charts derived by using the approximate distributions of the EWMS and EWMV statistics are difficult to interpret in terms of the average run length (ARL). Furthermore, both control charting schemes are biased procedures. In this article, we propose two new control charts by applying a normal approximation to the distributions of the logarithms of the weighted sum of chi squared random variables, which are respectively functions of the EWMS and EWMV statistics. These new control charts are easy to interpret in terms of the ARL. On the basis of the simulation studies, we demonstrate that the proposed charts are superior to the EWMS and EWMV charts and they both are nearly unbiased for the commonly used smoothing constants. We also compare the performance of the proposed charts with that of the change point (CP) CUSUM chart of Acosta‐Mejia (1995). The design of the proposed control charts is discussed. An example is also given to illustrate the applicability of the proposed control charts. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009