Dynamic spreading behavior of homogeneous and heterogeneous networks

The detailed investigation of the dynamic epidemic spreading on homogeneous and heterogeneous networks was carried out. After the analysis of the basic epidemic models, the susceptible-infected-susceptible (SIS) model on homogenous and heterogeneous networks is established, and the dynamical evolution of the density of the infected individuals in these two different kinds of networks is analyzed theoretically. It indicates that heterogeneous networks are easier to propagate for the epidemics and the leading spreading behavior is dictated by the exponential increasing in the initial outbreaks. Large-scale simulations display that the infection is much faster on heterogeneous networks than that on homogeneous ones. It means that the network topology can have a significant effect on the epidemics taking place on complex networks. Some containment strategies of epidemic outbreaks are presented according to the theoretical analyses and numerical simulations.     

Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.     

MnOx-M/AC(M=La,Ce,Co,Ni)氧还原催化材料的制备及性能

用热解法制备了掺杂La、Ce、Co、Ni等元素的氧还原催化材料MnOx/AC,并采用XRD和电化学方法对所制备材料的性质和电化学反应性能进行了表征。结果表明,掺杂适量的La、Ce、Co和Ni等元素可改善电极的氧还原反应性能。掺杂La和Co元素的MnOx/AC氧催化还原性能最好,其中,在50mA/cm2的电流条件下,使用这两种掺杂材料所制备的模拟锌-空气电池的放电电压分别为1.1236V和1.1246V。     

Comparative Study on Composite Electrodes for Medium Temperature PEFCs

1 Results The phenomena that affect the membranes could be found in the electrodes when they operate at medium temperature,due to the utilisation of Nafion as an ionomer in the catalytic layer.An approach to improve the mechanical properties of the ionomer in the catalytic layer was used: different inorganic compounds (zeolite,titania and zirconia),having different chemical-physical properties,were selected as inorganic fillers due to their water retention capacity and to act as mechanical reinforce by ...     

双向联想记忆神经网络的全局指数稳定性

在研究双向联想记忆神经网络时,通常都假设输出响应函数是光滑的增函数,但实际应用中遇到的大多数函数都是非光滑函数。因此,本文将双向联想记忆神经网络的输出响应函数连续可微的假设削弱为满足Lipschitz条件,通过引入Lyapunov函数,利用不等式的方法,证明了双向联想记忆神经网络全局指数稳定性的一个定理。     

PDMDAAC-PFS复合絮凝剂处理硅藻土悬浊液的絮体分形特征

采用扫描电镜考察了聚二甲基二烯丙基氯化铵-聚合硫酸铁复合絮凝剂(PDMDAAC-PFS)处理硅藻土悬浊液的絮体形貌,并与单独使用PDMDAAC和PFS产生的絮体进行比较.运用密度-密度相关函数计算了絮体的分维,探讨了絮体的结构、分维与沉降速度的关系.结果表明,使用PDMDAAC-PFS产生的硅藻土絮体分维为1.89,比单独使用PFS和PDMDAAC产生的絮体分维大(分维分别为1.67和1.55),其沉降速度比使用PFS产生的絮体快;絮体结构为内部有较多内孔的网状结构,内孔的可渗透性可降低沉降阻力,提高沉降速度.图5,参9.     

时滞细胞神经网络的指数稳定性

研究了时滞细胞神经网络(DCNNs)平衡点的全局渐近稳定性,去掉了有关文献中关于激活函数的有界性、单调性和可微性的条件,运用拓扑度理论和M-矩阵理论给出了更弱的判定平衡点的存在唯一性及全局指数稳定性的条件.     

Al/Pb-WC-ZrO2复合电极材料的电化学性能研究

 研究了电镀液中固体微粒质量浓度及工艺条件对Al/Pb-WC-ZrO₂复合电极材料的表面化学组成及稳态析氧极化曲线的影响规律,对上述工艺条件进行了优化,优化后的工艺条件如下:WC:50 g/L,ZrO₂:40g/L,温度:20℃,电流密度:1.5 A/dm²,电沉积时间:2 h.所获得Al/Pb-WC-ZrO₂复合电极材料的析氧动力学参数为:a=771 mV,b=140 mV,i⁰=3.11×10^-6A·cm^-2,并且电极的析氧过电位为η=1 009 mV,复合镀层中WC和ZrO₂质量分数分别为10.02%和3.55%.     

Al-SiCp复合材料磨损表面分形维数的研究

旨在通过对SiCP增强Al基复合材料磨损表面的分形研究,更深入研究亚微米SiCP增强Al基复合材料的摩擦磨损特性与分形维数的关系。按照结构函数法,用MATLAB软件进行编程,得到复合材料双对数坐标图。研究标明,亚微米SiCP增强Al基复合材料的具有分形特性,且其分形维数与材料的磨损量有关,随着磨损量的增加,分形维数亦趋于增大。     

Feature-point-extracting-based automatically mosaic for composite microscopic images

Image mosaic is a crucial step in the three-dimensional reconstruction of composite materials to align the serial images. A novel method is adopted to mosaic two SiC/Al microscopic images with an amplification coefficient of 1000. The two images are denoised by Gaussian model, and feature points are then extracted by using Harris corner detector. The feature points are filtered through Canny edge detector. A 40×40 feature template is chosen by sowing a seed in an overlapped area of the reference image, and the homologous region in floating image is acquired automatically by the way of correlation analysis. The feature points in matched templates are used as feature point-sets. Using the transformational parameters acquired by SVD-ICP method, the two images are transformed into the universal coordinates and merged to the final mosaic image.