目的:通过分析针刺干预卒中后运动障碍的功能性磁共振(fMRI)临床研究结果,筛选针刺阳明经穴干预该病的中枢核心、稳定核团,为针刺治疗本病的中枢作用机制提供可靠证据。方法:检索PubMed、Web of Science、中国知网、万方和维普文献数据...目的:通过分析针刺干预卒中后运动障碍的功能性磁共振(fMRI)临床研究结果,筛选针刺阳明经穴干预该病的中枢核心、稳定核团,为针刺治疗本病的中枢作用机制提供可靠证据。方法:检索PubMed、Web of Science、中国知网、万方和维普文献数据库,收集从建库—2022年12月使用fMRI观察针刺阳明经穴治疗卒中后脑区变化情况的研究。使用Ginger-ALE 3.0.2软件计算脑区激活似然评估(ALE)分布,最后使用DPABI软件进行图像整合。结果:共有20篇文献纳入研究,包括356名患者和144名健康人。结果显示,与健康人比较,缺血性卒中患者存在异常的大脑功能活动模式,异常的脑区主要与额叶、颞叶、边缘系统以及小脑有关;针刺阳明经穴对缺血性卒中后运动障碍患者左侧顶叶和小脑后叶(具体包括:顶下小叶、缘上回、中央后回以及下半月小叶、蚓锥体和小脑扁桃体)功能活动有稳定的调制作用。结论:卒中发生后相关运动支配脑区存在损伤与功能重塑;针刺阳明经穴可以稳定调节缺血性卒中后运动障碍患者优势侧运动-感觉系统功能活动。展开更多
为了提高网络对局部邻域的特征提取能力,提升网络的降噪性能,现有的基于深度学习的点云降噪算法对每一邻域点计算一个权值刻画其与当前点位于同一几何结构的概率,通过筛选具有较大权值的点对邻域结构进行简化,提高网络特征提取的性能。...为了提高网络对局部邻域的特征提取能力,提升网络的降噪性能,现有的基于深度学习的点云降噪算法对每一邻域点计算一个权值刻画其与当前点位于同一几何结构的概率,通过筛选具有较大权值的点对邻域结构进行简化,提高网络特征提取的性能。但由于其未对权重学习进行有效的约束与引导,所学权重无法对邻域结构进行准确刻画。本文设计了权重引导,通过法向差异(法向权重引导)及欧几里得距离差异(距离权重引导)对邻域点与当前点位于同一几何结构的可能性进行预判,并将其用于约束邻域筛选网络的权重学习,提高权重学习和邻域筛选的准确性,进而提升整体点云降噪的质量。实验结果表明,本文的算法在降噪结果及法向估计方面均有提升,在不同噪声尺度下也更具鲁棒性。In order to improve the feature extraction ability of the network for local neighborhoods and improve the noise reduction performance of the network, the existing point cloud denoising algorithm based on deep learning calculates a weight for each neighborhood point to describe the probability that it is located in the same geometric structure as the current point, and simplifies the neighborhood structure by screening the points with larger weights to improve the performance of network feature extraction. However, due to the lack of effective constraints and guidance for weight learning, the learned weights cannot accurately describe the neighborhood structure. In this paper, we design a weight guidance to predict the probability that the neighborhood point is located in the same geometric structure as the current point through the normal difference (normal weight guidance) and the Euclidean distance difference (distance weight guidance), and uses it to constrain the weight learning of the neighborhood filtering network, so as to improve the accuracy of weight learning and neighbor-hood filtering, and then improve the quality of overall point cloud denoising. Experimental results show that the proposed algorithm is more robust in terms of noise reduction results and normal estimation, and is more robust at different noise scales.展开更多
在点云处理过程中,法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征,再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确,效果不...在点云处理过程中,法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征,再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确,效果不够理想。对此,本文先通过霍夫变换将法向空间与二维平面相对应,并将二维空间离散化获得所有潜在切平面,设计特征聚合将点特征转化为潜在切平面特征作为CNN输入来降低霍夫变换过程中信息的损失,从而提升卷积神经网络的输入,进而提升网络整体的法向估计质量。实验结果表明,由此产生的法向估计网络的整体性能有所提升,对于不同噪声尺度也更具鲁棒性。In the process of point cloud processing, normal estimation is a very important step. The existing normal estimation network of deep Hough transform obtains neighborhood features by performing Hough transform on the point cloud and then inputs them into a convolutional neural network to learn and estimate the normal. However, due to certain information loss in the Hough transform process, the finally obtained normal is inaccurate and the effect is not ideal. In response to this, this paper first corresponds the normal space to a two-dimensional plane through Hough transform, and discretizes the two-dimensional space to obtain all potential tangent planes. Feature aggregation is designed to transform point features into potential tangent plane features as the input of CNN to reduce the information loss in the Hough transform process, thereby enhancing the input of the convolutional neural network and further improving the overall normal estimation quality of the network. Experimental results show that the overall performance of the resulting normal estimation network is improved, and it is also more robust to different noise scales.展开更多
为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯...为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。展开更多
文摘目的:通过分析针刺干预卒中后运动障碍的功能性磁共振(fMRI)临床研究结果,筛选针刺阳明经穴干预该病的中枢核心、稳定核团,为针刺治疗本病的中枢作用机制提供可靠证据。方法:检索PubMed、Web of Science、中国知网、万方和维普文献数据库,收集从建库—2022年12月使用fMRI观察针刺阳明经穴治疗卒中后脑区变化情况的研究。使用Ginger-ALE 3.0.2软件计算脑区激活似然评估(ALE)分布,最后使用DPABI软件进行图像整合。结果:共有20篇文献纳入研究,包括356名患者和144名健康人。结果显示,与健康人比较,缺血性卒中患者存在异常的大脑功能活动模式,异常的脑区主要与额叶、颞叶、边缘系统以及小脑有关;针刺阳明经穴对缺血性卒中后运动障碍患者左侧顶叶和小脑后叶(具体包括:顶下小叶、缘上回、中央后回以及下半月小叶、蚓锥体和小脑扁桃体)功能活动有稳定的调制作用。结论:卒中发生后相关运动支配脑区存在损伤与功能重塑;针刺阳明经穴可以稳定调节缺血性卒中后运动障碍患者优势侧运动-感觉系统功能活动。
文摘为了提高网络对局部邻域的特征提取能力,提升网络的降噪性能,现有的基于深度学习的点云降噪算法对每一邻域点计算一个权值刻画其与当前点位于同一几何结构的概率,通过筛选具有较大权值的点对邻域结构进行简化,提高网络特征提取的性能。但由于其未对权重学习进行有效的约束与引导,所学权重无法对邻域结构进行准确刻画。本文设计了权重引导,通过法向差异(法向权重引导)及欧几里得距离差异(距离权重引导)对邻域点与当前点位于同一几何结构的可能性进行预判,并将其用于约束邻域筛选网络的权重学习,提高权重学习和邻域筛选的准确性,进而提升整体点云降噪的质量。实验结果表明,本文的算法在降噪结果及法向估计方面均有提升,在不同噪声尺度下也更具鲁棒性。In order to improve the feature extraction ability of the network for local neighborhoods and improve the noise reduction performance of the network, the existing point cloud denoising algorithm based on deep learning calculates a weight for each neighborhood point to describe the probability that it is located in the same geometric structure as the current point, and simplifies the neighborhood structure by screening the points with larger weights to improve the performance of network feature extraction. However, due to the lack of effective constraints and guidance for weight learning, the learned weights cannot accurately describe the neighborhood structure. In this paper, we design a weight guidance to predict the probability that the neighborhood point is located in the same geometric structure as the current point through the normal difference (normal weight guidance) and the Euclidean distance difference (distance weight guidance), and uses it to constrain the weight learning of the neighborhood filtering network, so as to improve the accuracy of weight learning and neighbor-hood filtering, and then improve the quality of overall point cloud denoising. Experimental results show that the proposed algorithm is more robust in terms of noise reduction results and normal estimation, and is more robust at different noise scales.
文摘在点云处理过程中,法向估计是非常重要的一步。现有的深度霍夫变换的法向估计网络通过对点云进行霍夫变换得到邻域特征,再将其输入至卷积神经网络中学习估计法向。但由于霍夫变换过程中存在一定信息损失导致最后所得法向不准确,效果不够理想。对此,本文先通过霍夫变换将法向空间与二维平面相对应,并将二维空间离散化获得所有潜在切平面,设计特征聚合将点特征转化为潜在切平面特征作为CNN输入来降低霍夫变换过程中信息的损失,从而提升卷积神经网络的输入,进而提升网络整体的法向估计质量。实验结果表明,由此产生的法向估计网络的整体性能有所提升,对于不同噪声尺度也更具鲁棒性。In the process of point cloud processing, normal estimation is a very important step. The existing normal estimation network of deep Hough transform obtains neighborhood features by performing Hough transform on the point cloud and then inputs them into a convolutional neural network to learn and estimate the normal. However, due to certain information loss in the Hough transform process, the finally obtained normal is inaccurate and the effect is not ideal. In response to this, this paper first corresponds the normal space to a two-dimensional plane through Hough transform, and discretizes the two-dimensional space to obtain all potential tangent planes. Feature aggregation is designed to transform point features into potential tangent plane features as the input of CNN to reduce the information loss in the Hough transform process, thereby enhancing the input of the convolutional neural network and further improving the overall normal estimation quality of the network. Experimental results show that the overall performance of the resulting normal estimation network is improved, and it is also more robust to different noise scales.
文摘为实现储能电池全生命周期下的电池状态动态评估,提高复杂工况下锂离子电池模型的自适应性与状态估计的准确性,提出基于改进逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)-模糊贝叶斯网络的电池荷电状态(state of charge,SOC)和健康状态(state of health,SOH)联合估计方法。应用多阶电阻-电容电路(resistor-capacitance circuit,RC)模型、使用节点-支路框架构建电池的等效电路模型,通过基尔霍夫定律与欧姆定律对二阶RC电池等效电路模型中的并联回路进行电气特性分析,构建空间状态方程及等效输出方程;对构建的状态方程进行离散化处理,分别定义并联独立回路离散化零输入响应、零状态响应,分析离散化电池模型状态空间方程;将专家打分法引入TOPSIS算法中进行电池SOC量化估计,结合融入模糊尺度的贝叶斯网络,在相同时间分布尺度下通过电池SOH值计算电池观测样本中对应的SOC值,实现电池SOH与SOC联合估计。实验结果表明:所提方法可有效估计不同离散空间尺度下的电池SOC和SOH结果,估计方法具有良好的准确性与较高的精度。
