The letter proposed a sound source localization method of digital hearing aids using wavelet based multivariate statistics with the Generalized Cross Correlation (GCC) algorithm. Haar wavelet is used to decompose GCC ...The letter proposed a sound source localization method of digital hearing aids using wavelet based multivariate statistics with the Generalized Cross Correlation (GCC) algorithm. Haar wavelet is used to decompose GCC sequences and extract four wavelet characteristics. And then, Hotelling T2 statistical method is used to fuse the four wavelet characteristics. The statistical value is used to judge the number of sound sources and obtain corresponding time delay estimation which is used to localize the position of sound source. The experimental results show that the proposed method has better robustness in an environment with severe noise and reverberation. Meanwhile, the complexity of al-gorithm is moderate, which is available for sound source localization of hearing aids.展开更多
With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(...With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.展开更多
多元自相关过程不满足现行多元质量控制理论的前提假设。该文探讨了两个随机变量相互独立,其中一个随机变量的观测值相互独立、另一随机变量服从一阶自回归模型的二元自相关过程。在参数已知的条件下,提出了二元自相关过程的残差T2控制...多元自相关过程不满足现行多元质量控制理论的前提假设。该文探讨了两个随机变量相互独立,其中一个随机变量的观测值相互独立、另一随机变量服从一阶自回归模型的二元自相关过程。在参数已知的条件下,提出了二元自相关过程的残差T2控制图。通过M on te C arlo模拟,得到了一族该二元自相关过程在不同偏移量下的平均链长。分析结果表明残差T2控制图的适用范围由自相关的强弱和偏移量的大小决定,可以有效监控大部分该类二元自相关过程。展开更多
The past two decades have witnessed the active development of a rich probability theory of Studentized statistics or self-normalized processes, typified by Student’s t-statistic as introduced by W. S. Gosset more tha...The past two decades have witnessed the active development of a rich probability theory of Studentized statistics or self-normalized processes, typified by Student’s t-statistic as introduced by W. S. Gosset more than a century ago, and their applications to statistical problems in high dimensions, including feature selection and ranking, large-scale multiple testing and sparse, high dimensional signal detection. Many of these applications rely on the robustness property of Studentization/self-normalization against heavy-tailed sampling distributions. This paper gives an overview of the salient progress of self-normalized limit theory, from Student’s t-statistic to more general Studentized nonlinear statistics. Prototypical examples include Studentized one- and two-sample U-statistics. Furthermore, we go beyond independence and glimpse some very recent advances in self-normalized moderate deviations under dependence.展开更多
多变量统计过程控制(Multivariate Statistical Process Control,MSPC)是对经典的单变量的统计过程控制(Statistical Process Control,SPC)的拓展,通过对多个质量特征参数联合分析,达到控制生产过程的目的。传统的MSPC方法都是基于数据...多变量统计过程控制(Multivariate Statistical Process Control,MSPC)是对经典的单变量的统计过程控制(Statistical Process Control,SPC)的拓展,通过对多个质量特征参数联合分析,达到控制生产过程的目的。传统的MSPC方法都是基于数据服从多元正态分布的假设,在实际生产过程中数据会呈现非正态特性。针对这一问题,提出基于高斯混合模型(Gaussian Mixture Model,GMM)的非参数T^(2)控制图,记为G-T^(2)控制图,通过基于密度初始化的GMM对原始数据进行多元正态转化,用计算得出的均值μ和协方差σ代替样本均值和样本协方差,并引入混合权重系数α对μ进行修正,计算得到样本的T^(2)统计量,并用T^(2)控制图判断过程是否处于统计控制状态下。通过蒙特卡洛(Monte Carlo)仿真方法测试控制图的性能,结果表明:G-T^(2)控制图相比于普通多元控制图在非正态情况下有更好的性能表现。展开更多
基金Supported by the National Natural Science Foundation of China (No. 60472058, No. 60975017)Jiangsu Provincial Natural Science Foundation (No. BK2008291)
文摘The letter proposed a sound source localization method of digital hearing aids using wavelet based multivariate statistics with the Generalized Cross Correlation (GCC) algorithm. Haar wavelet is used to decompose GCC sequences and extract four wavelet characteristics. And then, Hotelling T2 statistical method is used to fuse the four wavelet characteristics. The statistical value is used to judge the number of sound sources and obtain corresponding time delay estimation which is used to localize the position of sound source. The experimental results show that the proposed method has better robustness in an environment with severe noise and reverberation. Meanwhile, the complexity of al-gorithm is moderate, which is available for sound source localization of hearing aids.
基金supported by Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program(2023TSYCTD).
文摘With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal set.Therefore,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion detection.This method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after features.The network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between groups.For datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the outliers.Finally,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic data.The empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing techniques.The results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.
文摘多元自相关过程不满足现行多元质量控制理论的前提假设。该文探讨了两个随机变量相互独立,其中一个随机变量的观测值相互独立、另一随机变量服从一阶自回归模型的二元自相关过程。在参数已知的条件下,提出了二元自相关过程的残差T2控制图。通过M on te C arlo模拟,得到了一族该二元自相关过程在不同偏移量下的平均链长。分析结果表明残差T2控制图的适用范围由自相关的强弱和偏移量的大小决定,可以有效监控大部分该类二元自相关过程。
文摘The past two decades have witnessed the active development of a rich probability theory of Studentized statistics or self-normalized processes, typified by Student’s t-statistic as introduced by W. S. Gosset more than a century ago, and their applications to statistical problems in high dimensions, including feature selection and ranking, large-scale multiple testing and sparse, high dimensional signal detection. Many of these applications rely on the robustness property of Studentization/self-normalization against heavy-tailed sampling distributions. This paper gives an overview of the salient progress of self-normalized limit theory, from Student’s t-statistic to more general Studentized nonlinear statistics. Prototypical examples include Studentized one- and two-sample U-statistics. Furthermore, we go beyond independence and glimpse some very recent advances in self-normalized moderate deviations under dependence.
文摘多变量统计过程控制(Multivariate Statistical Process Control,MSPC)是对经典的单变量的统计过程控制(Statistical Process Control,SPC)的拓展,通过对多个质量特征参数联合分析,达到控制生产过程的目的。传统的MSPC方法都是基于数据服从多元正态分布的假设,在实际生产过程中数据会呈现非正态特性。针对这一问题,提出基于高斯混合模型(Gaussian Mixture Model,GMM)的非参数T^(2)控制图,记为G-T^(2)控制图,通过基于密度初始化的GMM对原始数据进行多元正态转化,用计算得出的均值μ和协方差σ代替样本均值和样本协方差,并引入混合权重系数α对μ进行修正,计算得到样本的T^(2)统计量,并用T^(2)控制图判断过程是否处于统计控制状态下。通过蒙特卡洛(Monte Carlo)仿真方法测试控制图的性能,结果表明:G-T^(2)控制图相比于普通多元控制图在非正态情况下有更好的性能表现。
