应用统计能量分析法预测板材隔声量

应用统计能量分析法SEA(Statistical Energy Analysis)预测板隔声量,先建立板材的SEA模型,将板材的隔声量预测转变成求解声振动功率的线性代数方程组,对方程组的变量(SEA参数)值进行确定,得出板材的隔声量,再根据混响室法测量板材实际隔声量。将预测结果与实验结果进行对比分析,证明统计能量分析法能有效的预测板材隔声量。     

Electronic warfare in the optical band: Main features,examples and selected measurement data

The paper presents the possibilities of, and methods for, acquiring, analysing and processing optical signals in order to recognise, identify and counteract threats on the contemporary battleground. The main ways electronic warfare is waged in the optical band of the electromagnetic wave spectrum have been formulated, including the acquisition of optical emitter signatures, as well as ultraviolet (UV) and thermal (IR) signatures. The physical parameters and values describing the emission of laser radiation are discussed, including their importance in terms of creating optical signatures. Moreover, it has been shown that in the transformation of optical signals into signatures, only their spectral and temporal parameters can be applied. This was confirmed in experimental part of the paper, which includes our own measurements of spectral and temporal emission characteristics for three types of binocular laser rangefinders. It has been further shown that through simple registration and quick analysis involving comparison of emission time parameters in the case of UV signatures in “solar-blind” band, various events can be identified quickly and faultlessly. The same is true for IR signatures, where the amplitudes of the recorded signal for several wavelengths are compared. This was confirmed experimentally for UV signatures by registering and then analyzing signals from several events during military exercises at a training ground, namely Rocket Propelled Grenade (RPG) launches and explosions after hitting targets, trinitrotoluene (TNT) explosions, firing armour-piercing, fin-stabilised, discarding sabots (APFSDS) or high explosive (HE) projectiles. The final section describes a proposed model database of emitters, created as a result of analysing and transforming the recorded signals into optical signatures.     

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.     

自然场景车标数据集的构建及其应用

车标作为车辆身份的关键特征之一,在车辆的监控与辨识中发挥着重要作用。由于自然场景复杂多变,对其中的车标进行准确识别仍具有很大的挑战性。目前公开数据库很少且存在诸多局限,导致研究缺乏可信度和实用性。本文建立了一个面向自然场景的全新数据集,包含多种采集环境下的10 324幅、67类车辆图像。基于此数据集开展应用研究,提出一个目标检测与深度学习相结合的车标识别方法,包括车标区域定位和车标种类预测两大步骤。实验表明,该方法对复杂背景有较强的适应性,在涉及30种车标的分类任务中达到89.0%的总体识别率。     

蒙特卡洛剖分投影法的爆震弹破片平均比动能评估

为了评估爆震弹封装壳体破片致死、致伤半径,并削弱其平均比动能,基于蒙特卡洛剖分投影法建立破片平均比动能计算模型。通过LS-DYNA及自编程联合仿真方法对爆震弹自然破片、半预制破片比动能计算模型进行仿真求解,获得了全破片全时域的质量分布、初始速度、垂直靶分布、平均比动能阈值及安全半径等指标。结果表明:在相同装药参数下,半预制破片相比于自然破片,质量分布、初始速度阈值更低且更集中;在小于等于2.5 m范围内半预制破片平均比动能阈值比自然破片更低,大于2.5 m后半预制破片平均比动能阈值比自然破片更高。相比于自然破片,采用半预制破片可显著减小爆震弹破片的致死半径,但并不能明显增大其安全半径。     

一种基于生物免疫原理的入侵检测新模型

入侵检测系统可以从生物免疫系统的很多特点中得到启发,文中利用生物免疫原理设计了一个新的入侵检测框架模型,该框架在传统信息传输网基础上构建了免疫淋巴网,用来监控和管理传统传输网的行为。模型中还应用了阴性选择、克隆选择等免疫算法,使得该模型对于入侵检测问题有较好的敌我识别功能。     

5级同步感应线圈发射试验装置及控制系统研究

基于同步感应线圈发射原理设计制作了5级电磁线圈发射试验装置,该装置采用光电传感器实时检测弹丸的位置,并将该位置信号反馈给控制系统触发双向可控硅闭合,使脉冲电容器对该级线圈放电。通过试验,给出了单级线圈的弹丸速度和触发位置的关系以及电压和级数对弹丸发射速度的影响。     

基于SMC的红外序列图像目标检测算法

提出一种海天背景下红外序列图像自动目标检测算法。该算法针对输入图像严重的非高斯分布噪声干扰和目标在序列图像中的非线性运动特性,基于SMC(Sequence Monte Carlo)方法通过目标的特征统计值影响蒙特卡罗采样的粒子权值,再利用该权值控制粒子状态变化,最终在目标特征图像中检测和跟踪目标,使用差分图像作为特征图像。试验结果证明,该算法可准确有效地检测目标,适合快速处理的实时性。     

噪声目标转向机动自动检测

根据观测器--目标保持匀速直线运动时目标方位随时间线性变化的一种可量测函数,研究了一种检测目标转向机动的新方法.由于该方法将目标方位随时间变化的非线性关系转换到另一空间的线性关系,这使得检测更加有效、直观,并且能够克服直接采用方位序列检测目标转向机动在特殊情况下失效的限制.最后计算机仿真计算及实测数据验证结果表明,该方法能快速、准确地检测目标的转向机动.     

火灾探测技术研究

火灾探测技术是传感技术和火灾探测算法的结合产物。从火灾探测的基本原理和火灾探测算法这两个方面详细分析了火灾探测技术,为消防工作中火灾探测器选择提供指导,并对火灾探测技术的现状和新兴技术进行了归纳分析,为进一步研究火灾探测技术指明了方向。