The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition...The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.展开更多
针对现有的工业控制系统异常检测分类方法大多无法有效处理类不平衡和重叠耦合的问题,提出了一种基于干扰样本分布优化的工控异常检测改进SVM模型(Improved SVM Model Based on Adaptive Differential Evolution with Sphere, SJADE_SV...针对现有的工业控制系统异常检测分类方法大多无法有效处理类不平衡和重叠耦合的问题,提出了一种基于干扰样本分布优化的工控异常检测改进SVM模型(Improved SVM Model Based on Adaptive Differential Evolution with Sphere, SJADE_SVM),该模型将基于超球体覆盖的自适应差分进化过采样技术与支持向量机相结合。首先,通过改进超球体覆盖算法和构建概率公式,来识别和排除干扰样本;然后,改进合成少数派过采样技术,通过对安全样本采样,缓解类不平衡和重叠耦合问题;最后,使用自适应差分进化算法优化样本的位置和属性,同时使用SVM进行分类。在6个真实工控数据集和4个UCI公开数据集上共设计3组实验,包括与逻辑回归和高斯朴素贝叶斯等异常检测分类算法的性能对比、改善样本分布方法的实验对比以及算法的运行时间对比。实验结果表明,该模型在F-score和G-mean评价指标上分别提高了38.29%和10.54%,分类效果稳居前三,且在α=0.05的非参数双侧Wilcoxon符号秩检验和Friedman检验等统计实验中表现出显著的性能优势。展开更多
基金This work was supported by the National Science Fund for Distinguished Young Scholars(62325104).
文摘The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.
文摘针对现有的工业控制系统异常检测分类方法大多无法有效处理类不平衡和重叠耦合的问题,提出了一种基于干扰样本分布优化的工控异常检测改进SVM模型(Improved SVM Model Based on Adaptive Differential Evolution with Sphere, SJADE_SVM),该模型将基于超球体覆盖的自适应差分进化过采样技术与支持向量机相结合。首先,通过改进超球体覆盖算法和构建概率公式,来识别和排除干扰样本;然后,改进合成少数派过采样技术,通过对安全样本采样,缓解类不平衡和重叠耦合问题;最后,使用自适应差分进化算法优化样本的位置和属性,同时使用SVM进行分类。在6个真实工控数据集和4个UCI公开数据集上共设计3组实验,包括与逻辑回归和高斯朴素贝叶斯等异常检测分类算法的性能对比、改善样本分布方法的实验对比以及算法的运行时间对比。实验结果表明,该模型在F-score和G-mean评价指标上分别提高了38.29%和10.54%,分类效果稳居前三,且在α=0.05的非参数双侧Wilcoxon符号秩检验和Friedman检验等统计实验中表现出显著的性能优势。
