民汉大学生自主性学习对比研究——以新疆财经大学民汉学生为例

自主学习是大学生除课堂学习之外最主要的学习形式,也是提高大学生自身素质的重要手段。新疆是多民族聚居地区,民汉大学生学习融合成为新疆教育的一大特色。在学习融合的过程中,民汉学生的自主性学习是否存在差异,成为关注的内容。本文通过调查问卷的形式,对民汉大学生的自主性学习的现状与影响因素进行了对比研究,发现民汉大学生自主学习差异不大,根据问卷调查的情况,提出了加强民汉大学生自主学习的建议。     

履带车辆下长坡液力辅助制动控制策略仿真研究

针对履带车辆下长坡制动过程中不同制动力的分配关系,在液力减速器和整车下坡动力学分析的基础上,研究了基于液力辅助制动的履带车辆下长坡制动控制策略,建立了基于SIMULINK的履带车辆整车动态仿真模型,对液力减速器的持续制动性能和控制策略的有效性进行了仿真．结果表明,车辆下坡持续制动时,适时应用液力减速器,能够有效分流整车制动功率,明显降低机械制动器的工作负荷,满足整车安全恒速下坡的要求．     

增强小波系数的飞行数据奇异值阈值降噪

针对飞行数据降噪问题,提出了增强小波系数的飞行数据奇异值阈值降噪算法.结合小波系数噪声自相关函数进行飞行数据的最佳分解层小波分解,对高频小波系数先进行增强预处理再进行SVD分解,运用奇异熵理论确定奇异值重构阶次,采用奇异值阈值对小波系数进行处理并重构,得到降噪数据.仿真实验表明,本文算法能获得较高的信噪比,改善了数据质量.     

扇形波束和针状波束雷达的地对空干扰暴露区比较

扇形波束和针状波束具有不同的方向性特点,对这两种波束的机载雷达实施干扰,将形成不同的暴露区.机载雷达天线本身的方向性是影响干扰能量进入雷达接收端的直接原因.提出两种波束雷达对接收干扰能量增益的计算方法,依据雷达干扰方程,通过计算仿真,分别绘制并分析两种暴露区的特点和变化规律,认为雷达天线波束方向性对干扰阵地配置、干扰目标选择、干扰时机选择和干扰效果评估都将产生深刻影响.     

一种基于静电引信被动探测的引战配合方法

针对静电引信采用被动探测方式,不能提供引战配合所需信息的难题,根据静电探测原理,设计了一种静电引信双探测器布设阵列,解算出了引战配合所需信息,并结合静电探测器的静电电压响应曲线特性,提出了一种基于零点(脱靶点)的全公式化引战配合延时模型,解决了静电引信被动探测时引战配合信息获取及高效引战配合的难题,同时可以为其他型号引...     

空中反恐中保卫目标重要度指标体系研究

空中恐怖袭击具有突然性和随意性的特点,其目的是通过制造恐怖事件引起巨大影响,带来社会恐慌。通过分析空中恐怖袭击对袭击目标选择的特点,剖析其规律性,从而建立空中反恐保卫目标重要度指标体系和保卫目标重要度评价模型,对保卫目标进行科学有效地划定与排序,便于在空中反恐作战中对保卫目标及早进行确定、实施空中警戒范围划定和战斗部署。     

DP接口多屏显示在火控系统中的应用

将DP多屏显示技术应用到嵌入式载体,介绍了DP接口原理、相关火控系统硬件、软件的设计,DP接口传输设计,实现了界面显示资源与显示处理资源分离的模式;解决了多屏显示传输不可靠的问题,保证火控系统"一拖三"的多屏显示功能正常.     

Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment

It well known that vehicle detection is an important component of the field of object detection. However, the environment of vehicle detection is particularly sophisticated in practical processes. It is compara-tively difficult to detect vehicles of various scales in traffic scene images, because the vehicles partially obscured by green belts, roadblocks or other vehicles, as well as influence of some low illumination weather. In this paper, we present a model based on Faster R-CNN with NAS optimization and feature enrichment to realize the effective detection of multi-scale vehicle targets in traffic scenes. First, we proposed a Retinex-based image adaptive correction algorithm (RIAC) to enhance the traffic images in the dataset to reduce the influence of shadow and illumination, and improve the image quality. Second, in order to improve the feature expression of the backbone network, we conducted Neural Architecture Search (NAS) on the backbone network used for feature extraction of Faster R-CNN to generate the optimal cross-layer connection to extract multi-layer features more effectively. Third, we used the object Feature Enrichment that combines the multi-layer feature information and the context information of the last layer after cross-layer connection to enrich the information of vehicle targets, and improve the robustness of the model for challenging targets such as small scale and severe occlusion. In the imple-mentation of the model, K-means clustering algorithm was used to select the suitable anchor size for our dataset to improve the convergence speed of the model. Our model has been trained and tested on the UN-DETRAC dataset, and the obtained results indicate that our method has art-of-state detection performance.     

Numerical and experimental study on the response characteristics of warhead in the fast cook-off process

The response characteristics of the warhead under thermal stimuli conditions are important to the safety improvement. The goal of this study is to obtain data on the warhead in the fast cook-off process. In this paper, a numerical calculation method is proposed, whose reliability is supported by comparison with experimental results. Through the numerical calculation, the temperature distribution, temperature change, and ignition time are acquired. The numerical results show that the ignition time is 76 s after the warhead started to burn and that the maximum temperature of the explosive's outer surface is 238.3 C at the ignition time. The fast cook-off experiment of the warhead is implemented so as to get the flame temperature and reaction grades that are not available through numerical calculation. The experimental results show that the overpressure fails to reach the preset minimum value which is equivalent to 6 kg of TNT and that the reaction grade is deflagration. The research results have reference value for the design of the warhead and the reduction of detonation risks.