基于邻接矩阵的软件测试环境规划设计方法

在分析软件测试环境的基础上，研究了软件试验中测试用例设计面临的突出问题，从软件测试环境的规划出发，基于有向图的邻接矩阵，设计满足一定覆盖度标准的软件测试环境规划方法，可为软件试验提供技术参考和研究方向。     

对象边框标注数据的弱监督图像语义分割

针对图像语义分割应用中像素级标注数据费时昂贵的问题,主要研究以对象边框标注数据为代表的弱监督模型下的图像语义分割方法。使用基于金字塔的密集采样全卷积网络提取图像的像素级特征,并用GrabCut算法转化对弱监督数据进行数据标记,通过将图像特征和标记数据进行联合训练,构建了基于金字塔密集采样全卷积网络的对象边框标注弱监督图像语义分割模型,并在公开数据集上进行了验证。实验结果表明,所构建的弱监督模型与DET3-Proposed模型、全矩形转化模型以及Bbox-Seg模型相比,达到了更好的分割效果。     

Effect of nano powder (Al2O3-CaO) addition on the mechanical properties of the polymer blend matrix composite

This work describes the preparation and study of the properties of composite nanoparticles prepared by the sol-gel method which consists of two materials (Al₂O₃-CaO), and study the effect of these nanoparticles on the mechanical behavior of a polymer blend (EP 4% + 96% UPE). The powder was evaluated by X-ray diffraction analysis, scanning electron microscopy analysis (SEM), particle size analysis, and energy dispersive X-ray analysis (EDX). The mechanical behavior of the composite material was assessed by tensile test, bending test and hardness test. The evaluation results of the composite nanoparticles showed good distribution of the chemical composition between aluminum oxide and calcium oxide, smoothness in particles' size at calcination in high and low temperatures, formation of different shapes of nanoparticles and different (kappa and gamma) phases of the Al₂O₃ particles. The results of mechanical behavior tests showed marked improvement in the mechanical properties of the resulted composite material, especially at 1.5%, compared with polymer blend material without nano powder addition. The tensile properties improved about (24 and 14.9) % and bending resistance about (23.5 and 16.8) % and hardness by (25 and 22) % when adding particles of size (63.8 and 68.6) respectively. Therefore, this reflects the efficiency of the proposed method to manufacture the nanocomposite powder and the possibility of using this powder as a strengthening material for the composite materials and using these composite materials in bio applications, especially in the fabrication of artificial limbs.     

基于统计加权的单传感器数据融合算法

单传感器数据融合是十分重要的实际问题。为了提高数据处理的敏感性和精度,在研究基于关系矩阵的数据融合方法的基础上,提出了基于统计加权的数据融合方法,并对两种方法进行了对比分析。实例仿真表明,基于统计加权的数据融合方法优于基于关系矩阵的数据融合方法,该方法能减小无效测量值的范围,并且对测量值的变化更敏感。     

非线性时滞系统模糊控制的一种新方法

针对一类状态变量具有时滞的非线性系统，研究了其模糊控制器的设计问题。采用线性矩阵不等式（LMI）的方法，设计了与状态变量导数有关的模糊控制器，得到了系统存在模糊控制器的充分条件，并利用线性矩阵不等式的解给出具体的形式，最后通过仿真说明了控制器的有效性。     

基于多目标权重分析的舰载反潜武器优先级选择

针对舰载反潜武器优先级选择问题，利用多目标权重分析法，通过构建影响因素的层次结构，建立多目标权重分析模型，确定指标矩阵和相对优越度矩阵，对反潜武器优先级选择方案进行评定，并进行了实例分析，得到的反潜武器选择方案综合体现了反潜武器作战能力和指挥员作战决策意图。     

基于DSP的SMI方法快速实现研究

空时自适应处理(STAP)权值计算有数据域和均方域两种方法,分别以QR分解和样本协方差矩阵求逆(SMI)方法为代表.QR分解方法可以映射到脉动阵上并行实现,但实现复杂且设计成本较高;SMI方法实现则相对简单,但需要对样本协方差矩阵直接求逆.首先考察了不同矩阵求逆方法的内在并行性,基于DSP支持的片内并行技术,提出并实现了SMI方法的单DSP分块并行处理,进一步给出了数值稳定性分析和改善方法,实验结果证明了方法的有效性.     

基于卷积神经网络的卫星网络协调态势评估方法

为充分发掘利用海量卫星网络数据,提高决策效率,加强空间频轨资源获取与储备的分析手段,尤其是对地球静止轨道资源的协调获取问题,提出基于机器学习算法的卫星网络态势评估策略。通过对卫星网络协调因素进行特征分析,选择卷积神经网络(Convolution Neural Network, CNN)为目标算法模型,并建立算法模型的训练数据集及Label规则,采用分裂信息增益度量方法对数据进行降维处理,建立CNN评估模型,并进行了验证分析。结果表明,CNN模型对卫星网络协调态势评估问题测试的正确率高达80%以上,具有较高的评估效能。随着数据量的增多,CNN评估效果逐步提升,是一种在卫星网络协调态势分析、资源储备的有效评估方法。     

具时变扰动动态的多变量控制系统性能评价

针对具有两种时变扰动动态--突变扰动和线性时变扰动的多变量控制系统性能评价方法进行研究.两种扰动的不同动态特性决定了要采取不同的评价方法.突变扰动的情况,利用Olaleye和Xu整体优化SISO过程的思想建立MIMO的实用MV基准;线性时变扰动的情况,将关联矩阵引入视系统为LTV过程,提出一套针对LTV扰动建立时变MIMO MV基准的系统化算法和过程.理论和仿真研究表明所提出的方法能正确和有效的评价具有时变扰动的多变量系统.     

Gabor-CNN for object detection based on small samples

Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of military objects, as in these instances, a large number of samples is hard to obtain. In order to solve this problem, this paper proposes the use of Gabor-CNN for object detection based on a small number of samples. First of all, a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed, and the optimal Gabor convolution kernel group is obtained by means of training and screening, which is convolved with the input image to obtain feature information of objects with strong auxiliary function. Then, the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes, which improves the quality of the regional proposals. We call this regional proposal process the Gabor-assisted Region Proposal Network (Gabor-assisted RPN). Finally, the Deeply-Utilized Feature Pyramid Network (DU-FPN) method is proposed to strengthen the feature expression of objects in the image. A bottom-up and a top-down feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales. Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate, and thus has a strong application prospect.