作战任务总量的迭代收敛性

为降低作战任务的复杂度,提高任务执行效率,研究了作战任务总量及其迭代收敛问题.首先,采用图形和设计结构矩阵,阐明反导作战子任务的复杂联系.再引入任务转移矩阵建立任务总量模型,总结迭代收敛条件.最后,综合考虑资源和时间约束,规划子任务优先级并优化作战资源配置.结合实例分析了任务总量迭代收敛规律的可行性和有效性,为反导作战任务规划提供了理论指导.     

One-bit splitting deceptive jamming against SAR

One-bit quantization is a promising technique due to its performance retention and complexity reduction in a deceptive jammer against synthetic aperture radar (SAR). In this paper, the 1-bit quantization technology is utilized to agilely generate split false targets in the SAR imagery once the radar signal is intercepted, which reduces the complexity of the jammer significantly with guaranteed focusing quality. A single-frequency threshold is used to decompose harmonics incurred by the 1-bit quantization, and its parameters are adjusted through different pulse repetition intervals to provide steerable modulations. In this way, the SAR signal is split into coupled false scatterers during the 1-bit interception. By further deploying amplitude, time-delay, and Doppler frequency modulations on the 1-bit intercepted signal, the split false targets are created. The proposed approach is compared with different deceptive jamming methods to show its validity in effectiveness and cost, and numerical experiments are also presented for verification.     

几何非线性机械臂有限段传递矩阵法

提出了几何非线性柔性机械臂动力学有限段传递矩阵建模方法,该方法既保留了有限段法自动计及几何非线性和动力刚化的优势,又保留了传递矩阵法建模的方便灵活.算例表明,计算时间与自由度成正比,在已知运动规律的情况下,可直接求得所需的驱动力矩,反之亦然.该方法无需建立系统总体动力学方程,易于工程技术人员掌握,具有一定实用价值.     

Performance evaluation of a production/inventory system with periodic review and endogenous lead times

This paper considers a discrete time, single item production/inventory system with random period demands. Inventory levels are reviewed periodically and managed using a base‐stock policy. Replenishment orders are placed with the production system which is capacitated in the sense that there is a single server that sequentially processes the items one at a time with stochastic unit processing times. In this setting the variability in demand determines the arrival pattern of production orders at the queue, influencing supply lead times. In addition, the inventory behavior is impacted by the correlation between demand and lead times: a large demand size corresponds to a long lead time, depleting the inventory longer. The contribution of this paper is threefold. First, we present an exact procedure based on matrix‐analytic techniques for computing the replenishment lead time distribution given an arbitrary discrete demand distribution. Second, we numerically characterize the distribution of inventory levels, and various other performance measures such as fill rate, base‐stock levels and optimal safety stocks, taking the correlation between demand and lead times into account. Third, we develop an algorithm to fit the first two moments of the demand and service time distribution to a discrete phase‐type distribution with a minimal number of phases. This provides a practical tool to analyze the effect of demand variability, as measured by its coefficient of variation, on system performance. We also show that our model is more appropriate than some existing models of capacitated systems in discrete time. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

侵彻子母弹封锁机场跑道最小升降窗口搜索算法研究

针对侵彻子母弹封锁机场跑道中判断是否存在最小升降窗口的"两点比较"算法,提出更为符合实际毁伤情况的"网格-矩阵"算法。采用随机分布的数学模型和计算机仿真模型,利用Monte Carlo法模拟侵彻子母弹子弹的落点。最后在Matlab7.0环境下对两种算法进行了仿真试验和对比分析,提出两种算法在火力辅助决策中的使用建议。     

在分离式Hopkinson拉杆实验中确定临界实验条件的经验方法

在分离式Hopkinson拉杆实验中,常常需要获得试样在经历一个拉力脉冲拉断情况下的最低应变率,而在分离式Hopkinson拉杆装置中确定对应最低应变率的发射压力需要进行多次尝试性实验。为了更好地解决这一问题,提出一种简单的经验方法,可以通过一次拉断的实验结果计算出一个拉力脉冲拉断试样需要的最小发射压力及相应的应变率,进一步可以预测不同发射压力下的应变率。对4种铝合金试样进行拉伸实验,结果表明,利用提出的经验方法得到的应变率结果与实验结果吻合较好,可以有效地对发射压力和实验应变率进行预估。     

矩阵变换特征与码序列联合学习的卷积码识别方法

现有基于深度学习的卷积码识别方法仍存在参数规模较大、识别性能较弱等不足。针对该问题,提出了一种基于矩阵变换特征与码序列联合学习的卷积码识别方法。将接收到的码字序列排列成矩阵形式,利用软信息剔除可靠性较低的码字,通过一种新的矩阵变换算法得到特征矩阵。在识别时,将原始码字矩阵和特征矩阵输入到具有多模态数据联合学习能力的网络模型,在神经网络中完成特征的提取融合与卷积码的识别。仿真结果表明,所提方法性能明显优于现有基于深度学习的识别方法,特别是对于高码率卷积码;当码率较低时,同样优于传统识别方法。当信噪比达到5 dB时,25种不同参数卷积码的识别率均可达到100%。     

三维点云深度模型压缩算法

随着计算机三维视觉的广泛应用,近几年基于深度学习的点云处理算法得到了大量研究,而耗时耗存储的缺陷较大程度限制了其在移动端的部署应用。基于改进损失函数的总体思路,提出了一种新的点云深度模型压缩框架,将知识蒸馏方法引入二值量化模型中,同时考虑点云聚合操作的特殊性引入了辅助损失项,改进的损失函数共包括预测损失项、蒸馏损失项和辅助损失项三部分。实验结果表明,和已有算法相比,所提算法可以获取更高的精度,同时对当前点云主流深度网络模型也具有良好的扩展性。     

块Toeplitz矩阵低复杂度求逆的卫星导航空时抗干扰算法

采样协方差矩阵求逆是空时抗干扰算法的基本运算单元,但由于其运算量随时域抽头个数急剧增长,直接限制了空时抗干扰技术在卫星导航接收机中的应用。针对该问题,提出了基于块Toeplitz矩阵快速求逆的空时抗干扰方法。通过采用新的协方差矩阵近似计算方法,使得该矩阵同时为块Toeplitz矩阵与Hermite矩阵,并运用块Toeplitz矩阵的快速求逆算法,将时域抽头个数为K的计算复杂度从O[K3]降至O[K2]。理论分析和仿真结果表明,在阵元数为4、时域抽头为15的典型情况下,相比现有矩阵求逆方法,该算法的抗干扰性能损耗小于1d B,但计算量可降低约2/3。     

边境安全风险评估矩阵模型构建研究

提出边境安全风险和边境安全风险评估的概念。围绕边境安全风险状态和边境安全风险类型两个维度构建边境安全风险评估的矩阵模型。其中,边境安全风险状态维度包括边境安全风险源活跃度、边境安全风险源聚集度、边境安全风险供应链成熟度、边境安全风险源异常度;边境安全风险类型包括外生型边境安全风险、内生型边境安全风险、网络型边境安全风险、跨境型边境安全风险。并论述了边境安全风险评估矩阵模型的应用。