热致性液晶共聚酯的拉伸流动行为

采用入口收缩流动的实验方法研究了改性PET/ 80PHB液晶共聚酯LCP80的拉伸流动行为 ,考察了拉伸速率、温度等对其拉伸粘度、Trouton比的影响 .实验结果表明 ,LCP80的入口压降值很大 ,其中由拉伸引起的入口压降是主要的 .在该文实验条件下LCP80均表现出拉伸稀化现象 ,并且Trouton比值都远大于 3 .根据流动中液晶织态结构的变化解释了实验现象 ,并对入口收缩流动的实验数据处理方法作了改进 ,比Beery的方法更为合理 ,也具有更广的适用性 .     

Carbon-content dependent effect of magnetic field on austenitic decomposition of steels

The transformed microstructures of the high-purity Fe-0.12C alloy and Fe-0.36C alloy heat treated without and with a 12 T magnetic field have been investigated to explore the carbon-content dependent field effect on austenitic decomposition in steels. Results show that, the field-induced transformed morphology characteristics in different alloys differ from each other. In the Fe-0.12C alloy, the pearlite colonies are elongated along the field direction, and shaped by the chained and elongated proeutectoid ferrite grains in the field direction. However, in the Fe-0.36C alloy, the field mainly reduces the amount of Widmänstatten ferrite and elongates the formed proeutectoid ferrite grains in the field direction. No clear field direction alignment is obtained. The magnetic field also demonstrates carbon-content dependent effect on the texture of the formed ferrite. It clearly enhances the 〈001〉 fiber of the ferrite in the transverse field direction in the Fe-0.36C alloy. This field effect is related to the crystal lattice distortion induced by carbon solution and this impact becomes stronger with the increase of the carbon content. For the Fe-0.12C alloy, this field effect is greatly reduced due to the reduced carbon oversaturation in ferrite and elevated formation temperature. The orientation relationships (ORs) between the pearlitic ferrite and the pearlitic cementite in both alloys are less affected by the magnetic field. No obvious changes in the either type of the appearing ORs and their number of occurrences are detected.     

Texture analysis of bone marrow in knee MRI for classification of subjects with bone marrow lesion — Data from the Osteoarthritis Initiative

Visualization of bone marrow lesion (BML) can improve the diagnosis of many bone disorders that are associated with it. A quantitative approach in detecting BML could increase the accuracy and efficiency of diagnosing those bone disorders. In this paper, we investigated the feasibility of using magnetic resonance imaging (MRI)-based texture to (a) identify slices and (b) classify subjects with and without BML. A total of 58 subjects were studied; 29 of them were affected by BML. The ages of subjects ranged from 45 to 74 years with a mean age of 59. Texture parameters were calculated for the weight-bearing region of distal femur. The parameters were then analyzed using Mann–Whitney U test and individual feature selection methods to identify potentially discriminantive parameters. Forward feature selection was applied to select features subset for classification. Classification results from eight classifiers were studied. Results show that 98 of the 147 parameters studied are statistically significantly different between the normal and affected marrows: parameters based on co-occurrence matrix are ranked highest in their separability. The classification of subjects achieved an area under the receiver operating characteristic curve (AUC) of 0.914, and the classification of slices achieved an AUC of 0.780. The results show that MRI-texture-based classification can effectively classify subjects/slices with and without BML.     

Texture change through film thickness and off-axis accommodation of (0 0 2) planes

We present our recent experimental results on the formation of off-axis texture and crystallographic tilting of crystallites that take place in thin film of transition metal nitrides. For this purpose, the microstructural development of TiAlN film was studied, specially the change in texture with film thickness. Fiber texture was measured using θ-2θ and pole figure X-ray diffraction (XRD), while scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to observe the microstructure and changes in texture with thickness. The sin²ψ method was applied to determine the stresses on (1 1 1) and (0 0 2) plane. With deposition parameters chosen, the growth texture mechanism is discussed in three different stages of film growth. Surface energy minimization at low thickness leads to the development of (0 0 2) orientation. On the other hand, the competitive growth promotes the growth of (1 1 1) planes parallel to film surface at higher thickness. However, contrary to the prediction of growth models, the (0 0 2) grains are not completely overlapped by (1 1 1) grains at higher thickness. Rather the (0 0 2) grains still constitute the surface, but are tilted away from the substrate normal showing substantial in-plane alignment to allow the (1 1 1) planes remain parallel to film surface. Intrinsic stress along (1 1 1) and (0 0 2) shows a strong dependence with preferred orientation. The stress level in (0 0 2) grains which was compressive at low thickness changes to tensile at higher thickness. This change in the nature of stress allows the (0 0 2) planes to tilt away in order to promote the growth of 〈1 1 1〉 parallel to film normal and to minimize the overall energy of system due to high compressive stress stored in the (1 1 1) grains. The change in surface morphology with thickness was observed using SEM. An increase in surface roughness with film thickness was observed which indicates the development of (1 1 1) texture parallel to film surface. TEM observations support the XRD results regarding texture change. Film hardness was measured by nanoindentation and a correlation between (1 1 1) texture, stress and hardness is obtained. The results indicate that texture development is a complex interplay between thermodynamic and kinetic forces. An attempt is made to understand this phenomenon of off-axis accommodation of (0 0 2) at higher thicknesses, which is a new result not reported previously.     

Characterization and development of an evolutionary yield function for the superconducting niobium sheet

Superconducting radio frequency (SRF) niobium cavities are widely used in high-energy physics to accelerate particle beams in particle accelerators. The performance of SRF cavities is affected by the microstructure and purity of the niobium sheet, surface quality, geometry, etc. Following optimum strain paths in the forming of these cavities can significantly control these parameters. To select these strain paths, however, information about the mechanical behavior, microstructure, and formability of the niobium sheet is required. Due to the lack of information, first an extensive experimental study was carried out to characterize the formability of the niobium sheet, followed by examining the suitability of Hill’s anisotropic yield function to model its plastic behavior. Results from this study showed that, due to intrinsic behavior, it is necessary to evolve the anisotropic coefficients of Hill’s yield function in order to properly model the plastic behavior of the niobium sheet. The accuracy of the newly developed evolutionary yield function was verified by applying it to the modeling of the hydrostatic bulging of the niobium sheet.     

Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle

The traditional Local Binary Pattern (LBP) algorithm can analyze the center pixel and neighboring pixels of the gray relationship, using in facial expression recognition, but you cannot consider the eyes, mouth, forehead and other areas in the expression feature different trends in the gradient direction. Firstly, we propose the Local Gradient Coding (LGC) algorithm, though the binary encoding to the horizontal, vertical and diagonal gradients respectively, to produce the fusion characteristic, then this can fully describe the facial muscles texture, wrinkles and other local deformation of contains the expression information. On the other hand, in order to reduce the computational complexity, and to remove the redundant, while not lose the main information contained in the face texture expression. This paper proposes and optimizes a new LGC operator based on horizontal and diagonal gradient prior principle (LGC-HD). The experimental results from JAFFE database show that, LGC-HD algorithm is more quickly and effectively to extract facial expression feature than LGC algorithm. Comparing to the traditional LBP algorithm, LBP uniform pattern and Gabor filtering, this LGC-HD algorithm has a significant advantage in the recognition accuracy and run time.     

复杂背景下钢索图像的纹理分割与边界识别

 针对复杂背景下钢索图像难以准确分割的问题,提出一种基于纹理分析的钢索图像分割与边界识别方法.采用基于模糊Hough变换的纹理方向检测方法确定钢索走向,利用边缘方向密度直方图作为纹理特征,对与钢索纹理方向相应的边缘方向赋予不同权重,抑制纹理分割中背景的干扰,对钢丝绳图像进行聚类分割,采用检测平行直线的方法确定其边界,并根据算法参量对边界进行修正.在实验中,对比了边缘方向密度直方图特征与灰度共生矩阵、局部二值模式在钢索图像纹理分割中的结果与计算时间,结果表明边缘方向密度直方图特征计算速度快、受背景干扰小,分割准确率高.本文方法无须预先训练,受背景干扰小,可以准确地识别出钢索并确定其边界,能满足钢丝绳视觉检测的要求.     

多尺度无监督彩色图像分割

非采样Contourlet变换是一种新的多尺度多分辨率分析工具.本文提出了一种基于非采样Contourlet变换的彩色图像无监督分割算法.首先利用非采样Contourlet变换的平移不变性在其变换域应用梯度向量法提取图像多尺度边缘|然后在Contourlet变换域的低频子带和高频子带中分别提取局部低频能量纹理特征与高频多尺度Zernike矩纹理特征,并将二种纹理特征融合.最后在边缘图像中映射种子像素点,利用纹理和颜色特征欧氏距离,对彩色图像采用区域生长和区域合并的方法进行分割.实验结果证明:该算法将图像空间域的颜色特征与非采样Contourlet变换域的多尺度边缘和纹理特征恰当结合在一起实现彩色图像无监督自动分割,与传统算法相比有更高的准确性和鲁棒性.     

不同分辨率无人机数码影像的马铃薯地上生物量估算研究

地上生物量（AGB）是表征作物生命活动的重要参数,对作物长势监测和产量预测尤为关键。因此,快速准确地获取AGB信息,对于监测作物生长状况、指导农业管理和提高产量具有重要的意义。以无人机为平台搭载数码相机传感器,因机动性强、价格低、空间分辨率高的优势,能够及时准确的估算作物AGB,已成为遥感估算研究的热点之一。由于无人机不同飞行高度及其对应不同分辨率数码影像的AGB估算模型精度不同,因此,尝试在马铃薯的块茎增长期,通过设置10,20,30,40和50 m共5种无人机飞行高度,获取不同分辨率的数码影像,探究其对以光谱信息、纹理特征和光谱信息+纹理特征构建AGB模型精度的影响。首先,基于无人机数码影像,分别提取光谱信息和纹理特征,通过光谱信息构建的植被指数和纹理特征,分别结合地面试验获取的实测地上部生物量数据进行相关性分析,分别筛选了相关系数绝对值较大的前10个影像指数和前8个纹理特征。然后,分别以3种输入变量整合方差膨胀因子（VIF）进行主成分分析（PCA）降维处理,获得最佳主成分后以多元线性回归（MLR）构建AGB估算模型。最后,对比不同分辨率的数码影像以3种变量和同种分辨率下以同种变量构建的AGB估测模型效果。结果发现：（1）获得的影像分辨率在0.43~2.05 cm之间变化时,纹理特征与马铃薯AGB相关性弱于植被指数,但都达到极显著相关水平（p<0.01）,随着数码影像分辨率降低,二者相关性差异明显。（2）同种分辨率影像下,光谱信息+纹理特征估算AGB的效果最优,其次为单一纹理特征模型,而单一光谱模型表现效果最差。（3）随着数码影像分辨率提高,光谱信息、纹理信息以及光谱+纹理信息估算AGB的精度逐渐变好。