In this paper, we study the joint limit distributions of point processes of exceedances and partial sums of multivariate Gaussian sequences and show that the point processes and partial sums are asymptotically indepen...In this paper, we study the joint limit distributions of point processes of exceedances and partial sums of multivariate Gaussian sequences and show that the point processes and partial sums are asymptotically independent under some mild conditions. As a result, for a sequence of standardized stationary Gaussian vectors, we obtain that the point process of exceedances formed by the sequence （centered at the sample mean） converges in distribution to a Poisson process and it is asymptotically independent of the partial sums. The asymptotic joint limit distributions of order statistics and partial sums are also investigated under different conditions.展开更多
The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledg...The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.展开更多
1研究背景波速比是特征化描述岩石成分和流体饱和度的重要参数,可用来反映地下介质性质改变以及间接反映断层活动情况。波速比研究是地震学领域重要的研究方向,常用多台或者多震方法计算平均波速比(黎明晓等,2004;张小涛等,2012;王林瑛...1研究背景波速比是特征化描述岩石成分和流体饱和度的重要参数,可用来反映地下介质性质改变以及间接反映断层活动情况。波速比研究是地震学领域重要的研究方向,常用多台或者多震方法计算平均波速比(黎明晓等,2004;张小涛等,2012;王林瑛等,2014)。但在震群活动过程中,震中较为集中,传统波速比计算方法不能准确反映震源区地下介质情况。Lin等(2007)提出,基于P波、S波的双差思想,将距离同一台站相近的2个地震组成一组,通过扣除这2个地震射线的相同路径,获得震群震源区波速比。该方法首次讨论和检验了应用2次差分来计算震源区波速比的可行性,随后诸多学者做了相关研究(Dahmet al,2014;Bachura et al,2016;贾漯昭等,2017;郑建常等,2018)。双差波速比方法是,基于误差分布和概率密度,借鉴双差定位程序中2次差分技术,使用台站S与P震相到时差和台站对的2次差分,扣除地震射线相同路径,实现震群活动波速比值求解。展开更多
In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determine...In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.11171275)the Program for Excellent Talents in Chongqing Higher Education Institutions(Grant No.120060-20600204)supported by the Swiss National Science Foundation Project(Grant No.200021-134785)
文摘In this paper, we study the joint limit distributions of point processes of exceedances and partial sums of multivariate Gaussian sequences and show that the point processes and partial sums are asymptotically independent under some mild conditions. As a result, for a sequence of standardized stationary Gaussian vectors, we obtain that the point process of exceedances formed by the sequence （centered at the sample mean） converges in distribution to a Poisson process and it is asymptotically independent of the partial sums. The asymptotic joint limit distributions of order statistics and partial sums are also investigated under different conditions.
基金supported by the National Natural Science Foundation of China (Nos. 61305017, 61304264)the Natural Science Foundation of Jiangsu Province (No. BK20130154)
文摘The probability hypothesis density(PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation(APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter(PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.
文摘1研究背景波速比是特征化描述岩石成分和流体饱和度的重要参数,可用来反映地下介质性质改变以及间接反映断层活动情况。波速比研究是地震学领域重要的研究方向,常用多台或者多震方法计算平均波速比(黎明晓等,2004;张小涛等,2012;王林瑛等,2014)。但在震群活动过程中,震中较为集中,传统波速比计算方法不能准确反映震源区地下介质情况。Lin等(2007)提出,基于P波、S波的双差思想,将距离同一台站相近的2个地震组成一组,通过扣除这2个地震射线的相同路径,获得震群震源区波速比。该方法首次讨论和检验了应用2次差分来计算震源区波速比的可行性,随后诸多学者做了相关研究(Dahmet al,2014;Bachura et al,2016;贾漯昭等,2017;郑建常等,2018)。双差波速比方法是,基于误差分布和概率密度,借鉴双差定位程序中2次差分技术,使用台站S与P震相到时差和台站对的2次差分,扣除地震射线相同路径,实现震群活动波速比值求解。
基金This work was supported by grant PM484400 PM41500 from"High-Tech Port Research Program"founded by Ministry of Maritime Affairs and Fisheries of Korean Government.
文摘In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.
