The study on the conductivity of styrene-maleic anhydride copolymer electrolytes based on poly(ethylene glycol)400 and LiClO4

Polyelectrolytes, in this study were synthesized from styrene-maleic anhydride (SMA) copolymer, poly(ethylene glycol)400 (PEG400), and lithium perchlorate (LiClO₄). Fourier transform infrared spectroscopy (FTIR), and magic angle spinning (MAS) solid-state NMR were used to monitor the interaction between Li⁺ ions and polymer. The results of FTIR and MAS solid-state NMR indicate the Li⁺ ions are preferentially coordinated to the ether oxygen of PEG. The T_g of the PEG segments in polyelectrolyte increases with LiClO₄ concentration, as determined by differential scanning calorimetry (DSC), indicating that solubility of the Li⁺ ions in the host polymer increases with the PEG content. Impedance spectroscopy (IS) shows that the bulk conductivity of polyelectrolytes and the conductivity behavior obeys the Vogel-Tamman-Fulcher (VTF) equation.     

基于三维点云模型的特征线提取算法

针对以往算法存在无法区分尖锐和非尖锐特征点、提取的特征点与视角有关、特征点未连线等问题, 提出一种基于高斯映射和曲率值分析的三维点云模型尖锐特征线提取算法。该算法先进行点云数据点的离散高斯映射, 并将映射点集聚类; 然后使用自适应迭代过程得到两个或多个面的相交线上曲率值和法向量发生突变的尖锐特征点, 这些点与视角无关; 最后, 用改进的特征折线生长算法, 将特征点连接, 得到光顺特征线。实验证明, 该算法具有良好的自适应性、抗噪性和准确性, 是一种有效的三维模型特征线提取算法。     

图像小波阈值去噪方法研究

该文介绍了小波阈值去噪的基本原理,描述了图像小波阈值法去噪的过程,并通过仿真实验结果比较了小波阈值去噪方法在选取不同阈值函数下的去噪效果,证明了小波阈值去噪法是一种非常有效的变换域图像去噪方法。     

截面线特征直接提取的离散曲率法

特征提取是逆向工程的重要步骤,其中截面线特征点的弱化是需要解决的关键问题.提出用离散曲率法提取特征点,曲率表达式包含了高斯核函数曲线,选用了合适的离散尺度因子.根据一阶和二阶离散曲率曲线的局部极值点,确定出截面线特征点集,进行特征点的融合,输出最终的截面线特征点集.通过与文献算法的输出进行比较,表明本算法提取的截面线特征点不容易有漏检和误检,能最大限度地与原有形状特征元保持一致.仿真输出实例证实了本算法的适用性和有效性.     

基于四阶累积量的时延估计新方法

针对无线电定位中的时延估计问题,考虑接收信号为窄带信号以及背景噪声为高斯分布的情况下,将四阶累积量的一维切片作为高阶统计量,结合希尔伯特变换,给出一种新的时延估计方法.该方法对相关峰值进行了锐化处理,提高了估计精度.与广义互相关法相比,该算法可有效抑制空间相关高斯噪声的影响,计算量小,易于实现.仿真结果表明了该方法的有效性.     

基于图像邻域特性的高斯噪声去噪

针对传统高斯噪声去噪算法残余噪声较大的不足,根据噪声对图像视觉的影响,提出了基于像素邻域相关性的去噪算法.首先运用邻域像素的连续性判断像素点是否位于平滑区内;其次对非平滑区根据边缘和纹理的局部连续性运用形态学提取图像边缘和纹理进而定位噪声点;最后对平滑区内的噪声运用自适应邻域进行去噪处理,对非平滑区的噪声仅利用非平滑区的邻域进行平滑,实现了对高斯噪声先定位再去噪.经实验结果验证:与传统方法相比,该算法较好地抑制了图像平滑区内噪声,提高了去噪后图像的视觉效果.     

β衰变中共振跃迁高斯分布谱的研究

针对弱相互作用β衰变在恒星演化晚期的重要作用,讨论了晚期恒星状态下β衰变反应中Gamow-Teller(GT)共振跃迁强度的高斯分布,基于核的壳层模型着重计算了高斯分布函数的半宽度,分别给出了核56Fe,56Co,61Ni等元素的GT跃迁宽度,结果与部分核素实验的GT跃迁强度分布谱相近.采用高斯强度分布计算β衰变率较为简单,这对于β衰变率的计算和进一步精确研究恒星晚期演化有一定的意义.     

基于开关电流的Morlet小波变换研究

开关电流技术是一种新型的电流模式的模拟取样数据信号处理技术,对于实现小波变换具有很大的优势.Morlet小波在时域和频域都具有较好的局部性,能够提取信号中的幅值和相位信息.用开关电流技术实现Morlet小波变换的关键是高斯函数发生器.用Padé遥近获得高斯函数的有理分式逼近后,可以用开关电流一阶节和二阶节来实现高斯函数...     

基于窗口无关均值滤波的MSR图像增强

为解决多尺度Retinex方法在运算中要多次运用高斯卷积,尤其是采用大尺度高斯核进行卷积获取图像低频信息时,运算速度将大大下降这一问题,根据大尺度高斯卷积模板具有均值模板的特性,利用量化的均值滤波近似大尺度高斯卷积,将卷积模板简化成均值模板,并提出一种与滤波窗口无关的快速均值算法.该方法引入了一个数组s用于存储窗口横向移动时每一列的数值,从而减少重复计算,大大提高滤波速度.以640×640大小的彩色图像为例,其处理速度仅为0.0867s,相比原始的MSR算法速度提高了732倍.试验分析表明,该方法即保证实现良好的图像增强效果,同时极大的提高了运算效率.     

The Young's moduli prediction of random distributed short-fiber-reinforced polypropylene foams using finite element method

The elastic moduli of short-fiber-reinforced foams depend critically on the fiber content and fiber length, as well as on the fiber orientation distribution. Based on periodic tetrakaidecahedrons, the finite element models with short-fiber reinforcement were proposed in this paper to examine the effects of the fiber content and fiber length on Young's modulus. The fiber length distribution and fiber orientation distribution were also considered. The proposed models featured in a three-dimensional diorama with random short-fiber distribution within or on the surfaces of the walls and edges of the closed-cells of polypropylene (PP) foams. The fiber length/orientation distributions were modeled by Gaussian prob-ability density functions. Different fiber volume fractions, different lengths, and different distributions were investigated. The predicted Young's moduli of the PP foams with short-glass-fiber or short-carbon-fiber reinforcement were compared with other theoretic and experimental results, and the agreement was found to be satisfactory. The proposed finite element models were proved to be acceptable to predict the Young's moduli of the grafted closed-cell PP foams with short-fiber reinforcement.