一种基于FCOS神经网络的小建筑物目标检测方法北大核心

提出了一种基于FCOS神经网络的小建筑物目标检测算法,针对FCOS模型在特征提取阶段提取到的小建筑物目标特征较少问题,引入多尺度检测和可变形卷积方式,加强网络对小建筑物目标的特征提取能力,并通过改进后的SGE注意力机制降低特征图中的干扰噪声权重。改进后的网络可以提取到更多的小建筑物目标特征,对环境干扰噪声的鲁棒性更强。在自己搭建的数据集上进行了实验测试,结果表明,在相同环境下网络改进后建筑物的整体检测准确率提升了1.7%,其中对小建筑物目标提升了3.6%,减少了小建筑物目标漏检、误检的问题。     

基于磁偶极子的2阶磁场梯度张量缩并方法

针对磁场定位中2阶磁场梯度张量的理论尚不够完善的现状，提出基于磁偶极子的2阶磁 场梯度张量缩并方法。给出2阶磁场梯度张量的全局模量和局部模量计算公式，分析2阶磁场梯度张量的全局模量和局部模量及相关参数的三维空间分布规律，并给出相关参数k_H、k_Hxy和k_Hz的近似计算公式，比较1阶磁场梯度张量和2阶磁场梯度张量及其模量的分布规律及与距离的关系。计算结果表明：全局模量C_H及参数k_H值在0°≤≤90°时，随着增大而减小，在=0°时最大，在=90°时最小；局部模量C_Hxy和参数k_Hxy值在0°≤≤90°时，随先增加、后减少，当=35° 时最大，当=90°时最小；局部模量C_Hz和参数k_Hz随先减少、后增加，当=0°时最大，当=71°时最小；k_H、k_Hxy和k_Hz的拟合值与理论反演值高度吻合；在距离较近时，2阶磁场梯度张量及模量更敏感；在距离较远时，1阶磁场梯度张量及模量更敏感；在实际磁场定位的应用中，可结合1阶和2阶磁场梯度张量及全局模量进行使用。     

基于Massive MIMO及频谱叠加的5G SA网络上行优化方法

大规模多输入多输出(Multiple-Input Multiple-Output, MIMO)可以有效提升5G SA网络的上行链路数据传输速率以及可靠性。针对5G SA网络上行链路速率和覆盖不均衡的情况,提出了基于大规模MIMO的分组算法,将发送信号矢量进行分组,组内采用最大似然检测,组外采用基于正交三角分解(QR分解)的干扰消除检测,并且结合5G频谱的叠加策略,在降低算法复杂度的同时,有效提升网络覆盖和速率。通过5G SA现网实测,通过MIMO降低分组数量能够提升分组检测性能,结合上行低频段频谱叠加策略能够有效提升5G SA网络上行覆盖30%,提升5G SA网络上行平均速率40%~80%,特别是弱覆盖边缘的网络速率,最高可达600%。     

Simultaneous fractionation of multiple classes of polyphenols from honeybush tea using solid-phase extraction

The structural diversity of polyphenols and the inherent limitations of current extraction techniques pose a challenge to extract polyphenols using a simple and green method. Hence, in this study, a method was developed to simultaneously fractionate multiple classes of polyphenols by only varying ethanol-water solutions. Honeybush tea, which is rich in polyphenols, was selected as a model for this study. Solvent extraction followed by solid-phase extraction (SPE) was developed to obtain a polyphenol-rich fraction from six honeybush samples. Based on a gradient elution programme (10%, 30%, 50%, 70% and 90% (v/v) ethanol-water solution) of SPE, the Strata X cartridge showed a better recovery of most targeted polyphenols under 0.9 mL of the drying volume and 1 mL min⁻¹ of the dispensing speed. The elution programme for fractionating most polyphenols was as follows: single elution with 50% ethanol, followed by twice elution with 70% ethanol. The antioxidant capacity was used to analyse the differences among the polyphenol-rich fractions from six honeybush samples. Principal component analysis (PCA) revealed that unfermented C. genistoides (GG) has the greatest antioxidant capacity among the honeybush species studied. Additionally, mangiferin, isomangiferin and vicenin-2 were the main contributors to the antioxidant capacity in six honeybush fractions according to the correlation study.