Reciprocal bi-directional generative adversarial network for cross-modal pedestrian re-identification

To improve the accuracy of cross-modal pedestrian re-identification,a reciprocal bi-directional generative adversarial network-based method is proposed.First,we build two generative adversarial networks to generate cross-modal heterogeneous images.Second,an associated loss is designed to pull close the distribution of features in latent space during the image translation between visible and infrared images so as to help the networks generate fake heterogeneous images that have high similarity with the real images.Finally,by concatenating the original and generated heterogeneous pedestrian images into the discriminative feature extraction network,images from different modalities can be unified into a common modality,thus suppressing the cross-modal gap.Representation learning and metric learning are utilized to achieve more discriminative pedestrian features.Comparative experiments are conducted on SYSU-MM01 and RegDB datasets to analyze the accuracy with different loss functions.Compared with other state-of-the-art cross-modal pedestrian re-identification methods,the proposed method achieves a higher accuracy and stronger robustness.     

Method for discrimination of false targets in multistation radar systems based on the deep neural network

For the existing jamming discrimination methods for multistation radar systems,only the single feature of target echo space correlation is utilized as the metric,which leads to insufficient comprehensiveness of feature extraction,so that effectiveness and universality are insufficient for the discrimination algorithm.In this paper,an identification method in multistatic radar systems based on the deep neural network is proposed.This method combines the characteristics of multistatic radar systems cooperative detection technology,which has many available resources and strong scheduling ability in space,time and frequency domain,with the strong model learning and feature representation ability in the process of information processing on the deep neural network,so that it can effectively apply to the field of anti-deception jamming.Full use is made of unknown information about echo data to obtain more multi-dimensional,more comprehensive,more complete and deeper feature differences besides correlation,so as to achieve a better jamming discrimination effect.Simulation results show that the proposed method can effectively reduce the influence of noise and pulse number on the jamming discrimination performance.At the same time,the limitation of the target echo correlation coefficient on anti-jamming technology under nonideal conditions is alleviated,which broadens the boundary conditions of the application process.     

Fast network intrusion detection system using adaptive binning feature selection

Aiming at the problems of the low detection rate of traditional intrusion detection systems and the long training and detection time of intrusion detection systems based on deep learning,an adaptive binning feature selection algorithm using the information gain is proposed,which is combined with LightGBM to design a fast network intrusion detection system.First,the original data set is preprocessed to standardize the data;then the redundant features and noise in the original data are removed through the adaptive binning feature selection algorithm,and the original high-dimensional data are reduced to the low-dimensional data,thereby improving the accuracy of the system and reducing the training and detection time;finally,LightGBM is used for model training on the training set selected by the characteristics to train an intrusion detection system that can detect attack traffic.Through verification on the NSL-KDD data set,the proposed feature selection algorithm only takes 27.35 seconds in feature selection,which is 96.68% lower than that by the traditional algorithm.The designed intrusion detection system has an accuracy rate of 93.32% on the test set,and its training time is low.Compared with the existing network intrusion detection system,the accuracy rate of the proposed system is higher,and its model training speed is faster.     

一种面向弱纹理图像的特征点描述子

现有的大多数特征点提取算法适用于处理纹理丰富的图像,而对于弱纹理图像则无法提取有效的特征点. 对此,提出了多邻域结构张量特征(MNSTF)算法. 基于一系列固定的邻域和图像结构张量,通过表达局部图像的结构和纹理信息,解决了弱纹理和无纹理场景下特征点提取和匹配等相关问题;同时,通过计算邻域之间的相对方向,实现了MNSTF算法特征描述子的旋转不变性. 实验结果表明,MNSTF算法在经过旋转的弱纹理图像测试集上的特征点匹配准确率达到了99.9%以上,验证了其良好的适用性、旋转不变性和鲁棒性.     

基于改进MobileNet v2的垃圾图像分类算法

针对现有的垃圾图像分类模型实时性能差和分类精度低的问题,提出基于改进MobileNet v2的垃圾图像分类方法,构建以MobileNet v2为核心的轻量级特征提取网络. 通过调整宽度因子降低模型的参数量;在模型中嵌入通道和空间注意力模块,增强网络对特征的细化能力;设计多尺度特征融合结构,增强网络对尺度的适应性;利用迁移学习的方式优化模型参数,进一步提高模型精度. 实验结果表明,算法在自建数据集上的平均准确率为94.6%,分别高于MobileNet v2、VGG16、GoogleNet、ResNet50、ResNet101模型2.0%、3.4%、3.2%、2.3%、1.2%;所提算法在2种公共图像分类数据集CIFAR-100和tiny-ImageNet中均取得不错表现;模型参数量仅为0.83 M,体积约为基础模型的2/5,在边缘设备JETSON TX2上的单次推理耗时68 ms,实现了推理速度和预测准确率的提升.     

带特征指标约束描述的设计模式分类挖掘

为解决设计模式挖掘正确率较低的问题, 提出一种带特征指标约束描述的设计模式分类挖掘方法。归纳了47种特征指标, 给出了设计模式约束定义, 对设计模式特征进行了描述, 以Adapter模式、Command模式、Factory Method模式为例, 分结构型、行为型与创建型3类挖掘设计模式, 设计了3个基准系统与4个经典系统的设计模式挖掘试验。试验结果表明, 本研究方法对基准系统的Adapter模式、Command模式、Factory Method模式挖掘正确率为96.13%、91.67%、72.23%, 对经典系统挖掘正确率分别为84.3%、81.26%、73.17%, 与传统方法比较, 本研究方法取得了较好的效果。     

基于CCA的人耳和侧面人脸特征融合的身份识别*

鉴于人耳和人脸特殊的生理位置关系,从非打扰识别的角度出发,提出仅采集侧面人脸图像,利用典型相关分析的思想提取人耳和侧面人脸的关联特征,进行人耳和侧面人脸在特征层的融合.实验结果表明,此方法与单一的人耳或侧面人脸特征识别比较,识别率得到提高.     

基于截面特征相容的曲面约束重构

为了从点云中重建高品质的曲面模型,提出一种基于截面特征相容的曲面重构方法.该方法将由点云切片获取的截面曲线分层次表达为直线、圆弧、B样条等基本曲线元和特征点的集合,并基于曲线微分几何属性,建立截面曲线特征点对应的数学模型,采用动态规划法求解并确定其初始对应关系,以曲线元特征匹配准则优化对应,逐步确立截面曲线间的特征对应关系,将特征对应关系映射为参数对应即可利用成熟算法逼近生成蒙皮曲面.实例表明,该算法获取的曲面模型不但满足精度和光顺性要求,而且忠实地反映了原始物理样件所蕴含的特征信息.     

桥梁健康监测中损伤特征提取的小波包方法

针对桥梁健康监测中结构损伤识别的特点,从模式识别的角度提出和分析了损伤特征提取问题.阐述了基于小波包分析的两种节点能量特征提取的方法.为了研究小波包系数节点能量和小波包信号成分节点能量对损伤信息进行特征提取的差异,通过对随机荷载激励下的连续梁进行数值模拟,得到了结构未损和损伤状态下的加速度时程信号.应用小波包变换,对不同结构状态下的加速度信号分别提取了两种小波包节点能量特征,并对它们作为结构损伤特征指标的敏感性进行了比较.认为两种特征指标的敏感性相差很小,而小波包系数节点能量特征指标的计算效率更高,更适合桥梁健康监测中损伤特征提取的要求.     

基于PCC-RBF网络的风电功率短期预测方法

风电功率预测对风电场安全平稳运行、电网调度具有重要意义。针对风电功率短期预测指标选择不合理、预测精确度偏低的问题,提出一种基于皮尔逊相关系数(PCC)和径向基函数(RBF)神经网络的风电功率短期预测方法。该方法利用PCC筛选出与风电功率密切相关的3个指标,即电流、温度、风速,然后以这3个指标作为预测模型的输入对风电功率进行RBF样本训练与短期预测。试验结果表明,所提的预测模型预测误差更小,预测精度更高,能够满足风电功率短期预测的要求,具有广泛的应用前景。