在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项...在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项的振动信号进行自适应分解,生成一系列乘积函数(product functions, PF);随后,通过MR方法筛选出低频趋势项分量,去除这些成分并重构剩余信号,以校正基线漂移。仿真信号分析结果表明,与传统的最小二乘拟合法(ordinary least squares, OLS)和局部均值分解(local mean decomposition, LMD)相比,RLMD方法在提取趋势项方面具有更高的准确性和稳定性,有效避免了模态混叠现象。现场爆破振动监测试验结果显示,与远区振动信号相比,近区实测爆破振动信号受到低频趋势项的干扰更为严重。通过RLMD-MR方法进行基线校正后,信号波形能够有效恢复至基线中心附近,解决了基线漂移问题。展开更多
In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null d...In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null distribution follows a distribution, with the scale constant and the number of degree of freedom being independent of nuisance parameters or functions, which is called the wilks phenomenon. Both simulated and real data examples are given to illustrate the performance of the testing approach.展开更多
文摘在爆破振动监测过程中,为解决低频趋势成分干扰所引起的基线漂移问题,提出了一种基于鲁棒局部均值分解(robust local mean decomposition, RLMD)和均值判比(mean ratio, MR)方法的爆破振动信号基线校正方法。首先,利用RLMD对包含趋势项的振动信号进行自适应分解,生成一系列乘积函数(product functions, PF);随后,通过MR方法筛选出低频趋势项分量,去除这些成分并重构剩余信号,以校正基线漂移。仿真信号分析结果表明,与传统的最小二乘拟合法(ordinary least squares, OLS)和局部均值分解(local mean decomposition, LMD)相比,RLMD方法在提取趋势项方面具有更高的准确性和稳定性,有效避免了模态混叠现象。现场爆破振动监测试验结果显示,与远区振动信号相比,近区实测爆破振动信号受到低频趋势项的干扰更为严重。通过RLMD-MR方法进行基线校正后,信号波形能够有效恢复至基线中心附近,解决了基线漂移问题。
文摘In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null distribution follows a distribution, with the scale constant and the number of degree of freedom being independent of nuisance parameters or functions, which is called the wilks phenomenon. Both simulated and real data examples are given to illustrate the performance of the testing approach.
