Ballistic response of armour plates using Generative Adversarial Networks

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V₅₀ ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V₅₀ BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.     

高性能互连网络中端口阻塞故障预测方法

随着系统规模、芯片功耗和链路速率的提升,高性能互连网络的整体故障率也不断上升,传统运维方式将难以为继,给高性能计算系统整体可靠性和可用性带来了巨大挑战。针对网络端口阻塞这类严重网络故障,提出无监督算法的预测模型。该模型从历史信息中挖掘征兆性规律并形成新的特征向量,应用K-means聚类算法对特征向量进行学习归类。在预测时,结合端口当前状态,利用二次指数平滑算法对未来状态进行预测,将得到的新特征向量使用K-means算法预判是否会发生阻塞故障。利用拓扑结构信息,分别对叶交换机和根交换机构建预测子模型,进而提升预测的精确率。结果表明,该预测模型能保持在召回率为88.2%的前提下,达到65.2%的准确率,可为运维人员提供有效的辅助。     

Visual-simulation region proposal and generative adversarial network based ground military target recognition

Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisy-representation. In this paper, a recognition method, involving a novel visual attention mechanism-based Gabor region proposal sub-network (Gabor RPN) and improved refinement generative adversarial sub-network (GAN), is proposed. Novel central–peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset (GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.     

内初班学生学习适应性与心理健康相关研究——以石河子市为例

本研究采用《学习适应性测验》、《中国中学生心理健康量表》对石河子市内初班初一、初二年级少数民族学生进行了测查。结果表明:从总体上看学习适应性与心理健康之间存在显著相关;从内容上看心理健康与学习适应性之间存在显著相关;学习态度、学习环境和心身健康三个方面受到心理健康的影响最大。     

【专题】基于深度学习及FPGA的装备目标检测研究

本文应用深度学习技术实现海天背景下基于可见光、红外方式成像的舰船及角反、烟幕干扰的目标检测,这也是反舰导弹作战使用的关键技术之一。采集的可见光与红外成像目标检测数据集涵盖实施典型干扰下的态势场景,贴近实战;结合四种不同的目标检测机制,选取YOLOV3、Faster R-CNN、SSD及CenterNet四种典型模型分别进行训练与验证,通过对比分析进一步提高弱小目标、复杂干扰态势的的检测,可以实现端到端的高精度装备目标检测模型。在确保精度的前提下基于现场可编程门阵列（FPGA）进行软硬件协同设计,通过对比分析选定基于Vitis AI的实施方案,经过模型的量化、编译与优化,可在保证检测效率的前提下快速实现模型的小型化部署,便于进行装备移植。研究结果表明,该研究内容可有效提高现役反舰导弹目标检测的准确率。     

结合YdUaVa颜色模型和改进MobileNetV3的视频烟雾检测方法

为降低视频烟雾检测中的虚警率和提升检测效率,提出Y^dU^aV^a颜色模型,该模型可以表征烟雾的空间域分布特性和时间域变化特性。利用该颜色模型快速筛选出疑似烟雾图像块,降低虚警率和提升运算效率。提出改进的MobileNetV3网络结构,用于提取图像深度特征并对疑似烟雾图像块进行分类识别,检测视频中是否存在烟雾。视频烟雾检测仿真结果表明:该方法准确率和检测帧率高,虚警率低。     

基于转置卷积神经网络的翼型结冰冰形图像化预测方法

结冰问题严重影响飞机飞行安全,结冰智能预测是飞机智能防除冰系统设计和安全设计的重要依据和支撑。为解决复杂冰形在翼面同一位置的法线方向冰形厚度存在多值的问题,提出基于转置卷积神经网络的翼型结冰冰形图像化预测方法。设计预测模型的神经网络结构、损失函数、数据规范等,直接将影响飞机结冰的飞行和大气条件作为输入,以灰度化的冰形图像作为输出。基于NACA0012翼型,通过数值模拟方法生成冰形数据集,同时利用风洞试验结果对数值模拟方法进行验证,以确保生成数据的可信度。构建以飞行速度、温度、液态水含量、平均水微滴直径和结冰时长5项参数作为输入的预测模型,并进行仿真训练和验证。仿真结果表明:所提翼型结冰预测模型不仅能够快速预测翼型冰形,而且在冰体轮廓、结冰上下极限、冰角位置、结冰厚度等主要特征方面也与数值计算结果符合较好。     

近似推理在水雷参数分析专家系统中的应用

为了尽快分析出未知水雷障碍参数,根据水雷战的特点,提出了建立未知水雷障碍参数分析专家系统的观点,对专家系统的设计方法进行了一定的探讨,并针对专家系统建立中的"瓶颈"问题,提出了基于Vague集插值近似推理的专家系统知识自动获取方法,在介绍推理过程的基础上给出了算例.从推理的结果来看,该方法具有较高的可信度,从而为专家系统的研制提供了一定的方法支持.     

基于扩张观测器的三维有限时间收敛导引律

为了保证视线角速率在弹目碰撞前收敛到零附近的较小邻域内从而达到准平行接近的状态,本文基于自抗扰控制的不确定性估计补偿思想,应用反演控制方法设计了一种考虑导弹自动驾驶仪二阶动态特性和目标机动的三维有限时间收敛导引律。根据有限时间收敛控制理论,严格证明了系统的有限时间收敛特性;为抑制量测噪声,将传统跟踪微分器进行改进并应用于扩张状态观测器与反演控制的设计中。仿真结果表明:在自动驾驶仪响应延迟情况下,所设计的导引律能够导引导弹在有限时间内精确地拦截高速机动目标;改进的跟踪微分器精度高、响应快;基于改进跟踪微分器的扩张观测器估计效果理想。     

等速运动观测站纯方位系统的可观测性

对等速直线运动单观测站纯方位目标运动分析问题进行了讨论。尽管系统是不完全可观测的,却证明了目标部分运动参数是可估计的,即目标与观察者的相对航向Kr、相对速度与初距离之比Vr/Do是可以解算的;并对可估计参数的有解条件进行了分析。