Discrimination of Acori Tatarinowii Rhizoma from two habitats based on GC-MS fingerprinting and LASSO-PLS-DA

This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma （ATR） samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry （GC-MS） was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index （RI）. Furthermore, least absolute shrinkage and selection operator （LASSO）, a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis （PLS-DA）. After variable selection by LASSO, three chemical markers,β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.

A robust vector tracking loop structure based on potential bias analysis

This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly,the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis,a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases;meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.

基于Bayesian Bootstrap抽样的高维线性回归模型

研究小样本下高维线性回归模型中的变量选择问题和模型预测能力。当自变量维数p远大于样本量n时,提出基于Bayesian bootstrap抽样的SCAD(smoothly clipped absolute deviation)压缩方法。仿真和实证分析表明,与SCAD和LASSO(least absolute shrinkage and selection operator)两种传统回归压缩方法相比,本算法受随机干扰影响较小。当样本量较小时,本算法的变量压缩结果更好,变量选择能力更强,模型的标准均方误差值也最小,且模型预测能力提升明显。