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.     

一种传感器网络时空融合模型

针对多传感器间存在时空相关性和先验知识未知的情况,提出了一种基于核偏最小二乘最优加权的传感器网络时空融合模型。首先用核偏最小二乘法对每个传感器在不同时刻的测量值进行融合,然后将各个传感器同时刻的估计值进行空间最优融合,从而得到被测参量的最终融合估计。该模型还能排除那些取值远离样本点集合平均水平的奇异点。理论分析和融合实验表明,该模型能取得较高的融合精度,并能显著节省网络能量。     

麒麟操作系统层次式内核设计技术

麒麟操作系统是高性能、高安全的国产服务器操作系统,自主设计了层次式内核结构,由基本内核层和系统服务层组成.基本内核层负责硬件初始化,并提供基本的存储管理和任务管理,系统服务层基于FreeBSD改进,提供UFS2文件系统和BSD的网络协议.详细阐述了麒麟操作系统层次式内核的结构,然后采用标准的Benchmark对麒麟操作系统进行了基本性能测试,测试结果表明层次式内核结构的麒麟操作系统与宏内核结构的UNIX类操作系统性能相当,最后探讨了麒麟操作系统层次式内核结构的特点和下一步发展思路.     

Kernel estimation of quantile sensitivities

Quantiles, also known as value‐at‐risks in the financial industry, are important measures of random performances. Quantile sensitivities provide information on how changes in input parameters affect output quantiles. They are very useful in risk management. In this article, we study the estimation of quantile sensitivities using stochastic simulation. We propose a kernel estimator and prove that it is consistent and asymptotically normally distributed for outputs from both terminating and steady‐state simulations. The theoretical analysis and numerical experiments both show that the kernel estimator is more efficient than the batching estimator of Hong 9 . © 2009 Wiley Periodicals, Inc. Naval Research Logistics 2009     

基于离线文本独立的笔迹鉴别系统设计

笔迹鉴别是通过分析手写字符的书写风格来判断书写人身份的一门技术，利用改进型多通道Gabor滤波器来提取笔迹图像的纹理特征，用最邻近分类器进行分类。本设计中采集了12个人的不同笔迹，取得了较好的结果。     

基于深度学习的MPSK信号调制识别

为了有效实现信号调制方式的智能识别,提出基于深度学习的多进制相移键控(Multiple Phase Shift Keying, MPSK)信号调制识别方法。分析接收MPSK信号的循环谱,并通过提取MPSK信号循环谱的等高图获得二维特征信息,利用深度学习中的卷积神经网络对二维特征进行训练,使用测试样本对所设计的调制识别方法的有效性进行验证。仿真结果表明,所提方法具有良好的识别性能。     

基于改进支持向量机的航空装备维修安全评估

传统航空装备维修安全评估大多采用定性评估方法,针对传统评估中存在主观性、随意性过强,实践中缺乏可操作性等问题,提出了基于核密度估计的改进支持向量机。构建了航空装备维修安全评估指标体系,在利用层次分析法对其权重进行计算的基础上,通过改进支持向量机对某单位航空装备维修状况进行了全面评估,为航空装备维修安全评估提供了科学的方法,对确保航空装备维修安全具有较强的理论和现实意义。