A new unsteady three-dimensional convective-diffusive mathematical model for the transportation of macromolecules and water across the arterial wall was proposed . After the formation of leaky junctions due to the mit...A new unsteady three-dimensional convective-diffusive mathematical model for the transportation of macromolecules and water across the arterial wall was proposed . After the formation of leaky junctions due to the mitosis of endothelial cell of the arterial wall, the macromolecular transport happens surrounding the leaky cells. The arterial wall was divided into four layers: the endothelial layer, the subendothelial intima, the internal elastic lamina and the media for the convenience of research. The time-dependent concentration growth,the effect of the shape of endothelial cell and the effect of physiological parameters were analyzed. The analytical solution of velocity field and pressure field of water flow across the arterial wall were obtained; and concentration distribution of three macromolecules ; LDL,HRP and Albumin, were calculated with numerical simulation method. The new theory predicts, the maximum and distribution areas of time dependent concentration with round shape endothelial cell are both larger than that with ellipse-shape endothelial cell. The model also predicts the concentration growth is much alike that of a two-dimensional model and it shows that the concentration reaches its peak at the leaky junction where atherosclerotic formation frequently occurs and falls down rapidly in a limited area beginning from its earlier time growth to the state when macromolecular transfer approaches steadily. These predictions of the new model are in agreement with the experimental observation for the growth and concentration distribution of LDL and Albumin.展开更多
为了提高人脸姿态识别的识别精度,设计了一种增强边缘梯度二值卷积神经网络用于识别.首先,提出ROILBC(Region of Interest Local Binary Convolution)在人脸姿态图像上提取二值特征并归类,根据二值特征图谱和原像的对比情况选择人脸姿...为了提高人脸姿态识别的识别精度,设计了一种增强边缘梯度二值卷积神经网络用于识别.首先,提出ROILBC(Region of Interest Local Binary Convolution)在人脸姿态图像上提取二值特征并归类,根据二值特征图谱和原像的对比情况选择人脸姿态图像ROI(Region of Interest)以供后续网络学习.其次,提出DR-MGPC(Dimensionality Reduced Modified Gradient Pattern Convolution)提取图像边缘梯度二值特征,在此基础上,提出Enhanced DR-LDPC(Enhanced Dimensionality Reduced Local Directional Pattern Convolution)提取图像增强边缘梯度方向特征.网络采用直方图相似度、卡方检验、常态分布比对的巴氏距离法作为测量依据来进行识别;实验在FERET和CAS-PEAL-R1数据集上进行,相比其他人脸姿态识别方法,提出的二值模式卷积神经网络在识别精度和计算效率上更优异.展开更多
文摘A new unsteady three-dimensional convective-diffusive mathematical model for the transportation of macromolecules and water across the arterial wall was proposed . After the formation of leaky junctions due to the mitosis of endothelial cell of the arterial wall, the macromolecular transport happens surrounding the leaky cells. The arterial wall was divided into four layers: the endothelial layer, the subendothelial intima, the internal elastic lamina and the media for the convenience of research. The time-dependent concentration growth,the effect of the shape of endothelial cell and the effect of physiological parameters were analyzed. The analytical solution of velocity field and pressure field of water flow across the arterial wall were obtained; and concentration distribution of three macromolecules ; LDL,HRP and Albumin, were calculated with numerical simulation method. The new theory predicts, the maximum and distribution areas of time dependent concentration with round shape endothelial cell are both larger than that with ellipse-shape endothelial cell. The model also predicts the concentration growth is much alike that of a two-dimensional model and it shows that the concentration reaches its peak at the leaky junction where atherosclerotic formation frequently occurs and falls down rapidly in a limited area beginning from its earlier time growth to the state when macromolecular transfer approaches steadily. These predictions of the new model are in agreement with the experimental observation for the growth and concentration distribution of LDL and Albumin.
文摘为了提高人脸姿态识别的识别精度,设计了一种增强边缘梯度二值卷积神经网络用于识别.首先,提出ROILBC(Region of Interest Local Binary Convolution)在人脸姿态图像上提取二值特征并归类,根据二值特征图谱和原像的对比情况选择人脸姿态图像ROI(Region of Interest)以供后续网络学习.其次,提出DR-MGPC(Dimensionality Reduced Modified Gradient Pattern Convolution)提取图像边缘梯度二值特征,在此基础上,提出Enhanced DR-LDPC(Enhanced Dimensionality Reduced Local Directional Pattern Convolution)提取图像增强边缘梯度方向特征.网络采用直方图相似度、卡方检验、常态分布比对的巴氏距离法作为测量依据来进行识别;实验在FERET和CAS-PEAL-R1数据集上进行,相比其他人脸姿态识别方法,提出的二值模式卷积神经网络在识别精度和计算效率上更优异.
