基于流程仿真的物资接收系统可靠性预计方法

针对船舶物资接收时间计算的问题,新增加了对设备发生故障的考虑,通过引入接收时间可靠性的概念,将其转换为船舶物资接收系统的可靠性预计问题。为了解决上述可靠性预计问题,提出了基于流程仿真的系统可靠性预计方法。分析了物资接收系统的工作流程,依据工作流程实现了仿真程序,用蒙特卡洛法对物资接收系统的可靠性进行了求解,并分析了设备不同的可靠性水平对物资接收系统可靠性的影响。仿真结果表明,增加了对设备故障的考虑后,对物资接收时间的估计更加接近实际;基于流程仿真的系统可靠性预计方法的模型更容易理解,所得结论更加具体。     

电传动装甲车辆负载功率预测实时能量管理策略

针对电传动装甲车辆负载功率预测功能缺失导致控制作用滞后的问题,提出一种具有较高负载功率预测精度的实时能量管理策略。在分析整车结构的基础上,采用理论分析和数据拟合方法,建立各动力源数学模型。将差分自回归移动平均模型和自适应马尔可夫链两种预测方法相结合,设计非平稳趋势性负载功率组合预测方法。在非线性模型预测控制框架下,构建多目标优化函数,采用序列二次规划法在有限时域内实时求解最优控制指令,优化多动力源协调控制过程。依托硬件在环仿真平台进行多路面行驶实验,对比有无功率预测的能量管理控制效果。结果表明,改进的实时能量管理策略对未来负载功率具有较好的预见性,能够显著优化多动力源协调控制过程,提升车辆燃油经济性,稳定母线电压和电池荷电状态,对传统模型预测控制下的工程应用场景具有一定借鉴意义。     

The SSA-BP-based potential threat prediction for aerial target considering commander emotion

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system. However, the traditional threat prediction methods mostly ignore the effect of commander's emotion. They only predict a target's present threat from the target's features itself, which leads to their poor ability in a complex situation. To aerial targets, this paper proposes a method for its potential threat prediction considering commander emotion (PTP-CE) that uses the Bi-directional LSTM (BiLSTM) network and the backpropagation neural network (BP) optimized by the sparrow search algorithm (SSA). Furthermore, we use the BiLSTM to predict the target's future state from real-time series data, and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model. Therefore, the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion. The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction, regardless of commander's emotional effect.     

对岸电子进攻中航母阵位确定方法?

针对航母编队对岸电子进攻作战提出了一种航母编队阵位确定的方法。基于航母编队在对岸电子进攻中的任务,提出了航母编队与岸边距离的基本要求,建立了航母编队与岸边距离的数学模型,仿真计算了模型中各个参数对航母编队与岸边距离的影响。这些模型可用于航母编队在对岸电子进攻中确定航母的阵位,满足航母编队作战使用的需要。     

A novel gradient approach for optimal design and sensitivity analysis of EWMA control charts

Conventional control charts are often designed to optimize out‐of‐control average run length (ARL), while constraining in‐control ARL to a desired value. The widely employed grid search approach in statistical process control (SPC) is time‐consuming with unsatisfactory accuracy. Although the simulation‐based ARL gradient estimators proposed by Fu and Hu [Manag Sci 45 (1999), 395–413] can alleviate this issue, it still requires a large number of simulation runs to significantly reduce the variance of gradient estimators. This article proposes a novel ARL gradient estimation approach based on integral equation for efficient analysis and design of control charts. Although this article compares with the results of Fu and Hu [Manag Sci 45 (1999), 395–413] based on the exponentially weighted moving average (EWMA) control chart, the proposed approach has wide applicability as it can generally fit into any control chart with Markovian property under any distributions. It is shown that the proposed method is able to provide a fast, accurate, and easy‐to‐implement algorithm for the design and analysis of EWMA charts, as compared to the simulation‐based gradient estimation method. Moreover, the proposed gradient estimation method facilitates the computation of high‐order derivatives that are valuable in sensitivity analysis. The code is written in Matlab, which is available on request. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 223–237, 2014     

基于灰色神经网络的装甲部队油料消耗预测

油料消耗量的精确预测直接影响装甲部队后勤保障能力的提升,而传统预测模型精度不高,应用范围也有一定的局限,难以满足信息化战争精确保障的需要。提出一种装甲部队油料消耗预测的组合模型,对历史油料消耗数据和油耗影响因素进行统计分析,求出各影响因素与油耗量的关联度作为权重系数;通过改进GM(1,1)模型预测某部队下一次军事行动的油耗量;用GM(1,1)模型的预测值、加权后的各影响因素值和油耗实际值训练网络,对下一次想定的军事行动油耗量进行预测。通过平均相对误差计算表明,组合预测模型比单一的GM(1,1)模型预测精度高,能够较好地指导部队进行下一步的油料供管工作。     

单站单圈卫星初轨确定方法研究

为实现低轨卫星轨道的快速确定和预报,基于单站单圈雷达站测量数据采用遗传算法和最小二乘法对卫星初轨确定及轨道预测问题进行了研究。通过比较分析不同观测误差下两种方法的仿真结果,验证了初轨确定过程中两种方法的可行性,同时指出了各自的优缺点和适用条件,为不同观测条件下卫星最优初轨确定的方法提供了参考。     

利用星角距不变性标定星敏感器内部参数

提出了一种恒星角距不变的星敏感器内部参数的地面标定方法。较常见的星角距的标定方法不同的是,该方法利用对在0°附近的角度变化较敏感的恒星角距的正弦值建立观测方程;采用两步法分别估计焦距、主点和高阶畸变系数,并采用迭代优化的方法,获得最优估计值。为了确保标定精度,对观测时刻的导航星进行了视位置的修正。实验结果表明,采用星角距的正弦值建立观测方程,估计精度较高,采用考虑高阶畸变的改进正弦法标定后的星角距误差的均方根值为2.2×10-5rad。     

基于相似性的装备剩余寿命预测研究

为了更有效估计装备的剩余寿命,对基于相似性的寿命预测方法进行研究。首先介绍基于相似性的寿命预测方法的基本概念和流程,然后研究基于高斯核回归的退化轨迹提取方法,在传统欧氏距离的基础上,考虑时间范围的影响,改进相似度计算方式,最后用高斯核密度估计得到剩余寿命的区间估计值.一个数值仿真试验表明,基于相似性的方法能够利用失效历史样本对装备的剩余寿命进行有效预测.     

LEACH—DRT算法中基于神经网络的数据预测算法

为了保证无线传感器网络中数据的完整性,针对基于LEACH路由协议的动态轮时间算法存在的问题,提出一种基于人工神经网络的数据预测算法。该动态轮时间算法中,部分簇因调整后的轮时间不足以完成数据的采集而丢失数据。数据预测算法结合传感器节点数据具有时空相关性的特点,将时空延迟算子引入神经网络模型,并通过建立的神经网络模型对数据进行预测。仿真时采用伯克利英特尔实验室的传感器数据,通过Mafl软件对模型进行测试并分析仿真结果。实验结果表明：该算法对连续多个数据的预测效果理想,预测误差始终保持在较低水平。