捷联惯导系统姿态算法研究

姿态算法是捷联惯导系统算法中的一个重要组成部分,研究了一种新的递推补偿方法并在BorlandC语言环境下进行了仿真,使其更接近工程实践.     

词典在英语写作和口语中的应用

作为一个重要的学习工具 ,词典不但是学习者的良师 ,而且也是益友。在英语学习和应用中 ,它能帮助解决许多疑难问题 ,而且也能提供很多对英文写作和口语表达有价值的信息。但是 ,很多英语学习者迄今还没有认识到这一点 ,他们不能在英文写作和口语学习中充分利用词典的应用价值。因此 ,了解和掌握词典的简史 ,编写特点 ,及其所包含的丰富信息是非常有必要的。再者 ,尽管我国有许多不同类型和版本的词典 ,但它们的使用率却是非常低的。这的确是一个值得注意和亟待解决的问题     

多相永磁同步电动机的矢量控制及仿真

建立了多相永磁同步电动机定子静止坐标系以及d、q旋转坐标下的数学模型 ,对多相永磁同步电动机的矢量控制系统进行了一般性分析 .针对表面式永磁电动机提出了一种简单高效的转子磁场定向控制方法 .采用Simulink/PSB对系统进行了动态仿真研究     

装甲车辆使用与保障费用的指数回归模型

运用逐步回归和主成分分析的方法,选取了装甲车辆诸多战技性能指标集合中的自变量子集,建立了该子集与使用与保障费用之间指数回归方程组.该模型也可推广应用于装甲车辆综合性能的评估.     

基于马尔科夫链的一种评价方法

应用了数理统计与随机过程理论,从分析动态系统的状态以及状态转移情况给出了一种合理的评价方法,经过实例验证,该方法效果较好.     

支持向量机规则提取

支持向量机是一种黑箱模型,其学习到的知识蕴含在决策函数中,不仅影响了用户对利用支持向量机技术构建智能系统的信心,还阻碍了支持向量机技术在数据挖掘领域的应用。由于对支持向量机规则提取进行研究有助于解决上述问题,因此该领域正成为机器学习和智能计算界的研究热点。分析了具有代表性的支持向量机规则提取算法,并提出该领域未来的研究重点。     

基于深度森林的网络流量分类方法

随着网络应用的迅猛发展,流量分类在网络资源分配、流量调度和网络安全等诸多研究领域受到广泛关注。现有的机器学习流量分类方法对流量数据特征的选取和分布要求苛刻,导致在实际应用中的复杂流量场景下分类精确度和稳定度难以提高。为了解决样本特征属性的复杂性给分类性能带来的不利影响,引入了基于深度森林的流量分类方法。该算法通过级联森林和多粒度扫描机制,能够在样本数量规模和特征属性选取规模有限的情况下,有效地提高流量整体分类性能。通过网络流量公开数据集Moore对支持向量机、随机森林和深度森林机器学习算法进行训练和测试,结果表明基于深度森林的网络流量分类器的分类准确率能够达到96. 36%,性能优于其他机器学习模型。     

【将军论坛】重要经济目标与矢量防护工程

重要经济目标是信息化战争首选的打击对象,这类目标绝大多数位于地面以上,特点突出、规模庞大、难以保密、易损性强,一旦遭受破坏后果将损失惨重,而依靠建造硬壳抵御弹药毁伤的传统防护工程对其难有大的作为。本文从新形势下重要经济目标防护陷于困局的实际出发,系统分析和梳理了传统防护工程和防护技术的特点及其应用的固有薄弱特征,提出矢量防护的创造性解决方案,为重要经济设施和预警设施、新型作战装备等位于地面的易损性目标提供了理论基础。矢量防护工程通过改变弹药的运动方向,避免弹药与目标交汇,将防护工程由“拳击式”硬顶变为“太极式”破解,为未来我国重要经济目标提供了新质防护技术。     

Study on dynamic response of multi-degree-of-freedom explosion vessel system under impact load

In order to study the dynamic response and calculate the axial dynamic coefficient of the monolayer cylindrical explosion vessel, the wall of vessel is simplified as a multi-degree-of-freedom (MDoF) undamped elastic foundation beam. Decoupling the coupled motion equation and using Duhamel's integrals, the solutions in generalized coordinates of the equations under exponentially decaying loads, square wave loads and triangular wave loads are calculated. These solutions are consistent in form with the solutions of single-degree-of-freedom (SDoF) undamped forced vibration simplified model. Based on the model, equivalent MDoF design method (also called MDoF dynamic coefficient method) of cylindrical explosion vessel is proposed. The traditional method can only predict the dynamic coefficient of torus portion around the explosion center, but this method can predict that of the vessel wall at any axial n dividing point position. It is verified that the prediction accuracy of this model is greatly improved compared with the SDoF model by comparing the results of this model with SDoF model and numerical simulation in different working conditions. However, the prediction accuracy decreases as the scaled distance decreases and approaches the end of the vessel, which is related to the accuracy of the empirical formula of the implosion load, the simplification of the explosion load direction, the boundary conditions, and the loading time difference.     

An approach for predicting digital material consumption in electronic warfare

Electronic warfare is a modern combat mode, in which predicting digital material consumption is a key for material requirements planning (MRP). In this paper, we introduce an insensitive loss function (ε) and propose a ε-SVR-based prediction approach. First, we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set, and preprocess it to construct the sample space. Subsequently, we establish the ε-SVR-based prediction model based on “wartime influencing factors - material consumption” and perform model training. In case study, we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach. The results illustrate its higher accuracy and more convenience compared with other current approaches. Taking data acquisition controller prediction as an example, our model has better prediction performance (RMSE = 0.575 7, MAPE (%) = 12.037 6 and R² = 0.996 0) compared with BP neural network model (RMSE = 1.272 9, MAPE (%) = 23.577 5 and R² = 0.980 3) and GM (1, 1) model (RMSE = 2.095 0, MAPE (%) = 24.188 0 and R² = 0.946 6). The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.