Face recognition using elastic local reconstruction based on a single face image

In this paper, we propose a new face recognition algorithm based on a single frontal-view image for each face subject, which considers the effect of the face manifold structure. To compare two near-frontal face images, each face is considered a combination of a sequence of local image blocks. Each of the image blocks of one image can be reconstructed according to the corresponding local image block of the other face image. Then an elastic local reconstruction (ELR) method is proposed to measure the similarities between the image block pairs in order to measure the difference between the two face images. Our algorithm not only benefits from the face manifold structure, in terms of being robust to various image variations, but also is computationally simple because there is no need to build the face manifold. We evaluate the performance of our proposed face recognition algorithm with the use of different databases, which are produced under various conditions, e.g. lightings, expressions, perspectives, with/without glasses and occlusions. Consistent and promising experimental results were obtained, which show that our algorithm can greatly improve the recognition rates under all the different conditions.     

Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays

In this paper, we discuss average consensus problem in undirected networks of dynamic agents with fixed and switching topologies as well as multiple time-varying communication delays. By employing a linear matrix inequality method, we prove that all the nodes in the network achieve average consensus asymptotically for appropriate communication delays if the network topology is connected. Particularly, several feasible linear matrix inequalities are established to determine the maximal allowable upper bound of time-varying communication delays. Numerical examples are given to demonstrate the effectiveness and the sharpness of the theoretical results.     

Investigation of Mechanical and Electrical Characteristics for Cracked Conductive Particle in Anisotropic Conductive Adhesive (ACA) Assembly

In an anisotropic conductive adhesive (ACA) assembly, the electrical conduction is usually achieved with the conductive particles between the bumps of integrated circuit (IC) and corresponding conductive tracks on the glass substrate. Fully understanding of the mechanical and electrical characteristics of ACA particles can help to optimize the assembly process and improve the reliability of ACA interconnection. Most conductive particles used in the ACA assembly are with cracks in the metal coating of the particles after the ACA bonding. This paper introduced the fracture analysis by applying the cohesive elements in the numerical model of the nickel-coated polymer particle and further simulating the cracks initiation and propagation in the nickel coating during the ACA bonding. The simulation results showed that the stress distribution on the nickel-coated particle with cracks was significantly different from that on the nickel-coated particle without crack, indicating that the stress analysis by taking the crack into consideration is very important for the reliability assessment of the ACA interconnection. The stress analysis of cohesive elements indicated that the cracks initiated at the central area of the nickel coating and propagated to the polar area. Furthermore, by the introduction of a new parameter of the virtual resistance, a mathematical model was established to describe the electrical characteristics of the nickel-coated particle with cracks. The particle resistance of the nickel-coated particle with cracks was found to be much higher than that of the particle without crack in the optimized bonding pressure range, indicating that it is necessary to take the crack into consideration for the particle conduction analysis as well. Therefore, the fracture analysis on the conductive particle by taking the crack into consideration could accurately evaluate the reliability of ACA interconnection and avoid serious reliability issues.     

镁钴铝类水滑石催化合成安息香甲醚

采用共沉淀法制备了镁钴铝类水滑石化合物(MgCoAl-HTLcs),并用 X 射线衍射、扫描电子显微镜、NH_3程序升温脱附等方法对 MgCoAl-HTLcs 进行了表征,并以 MgCoAl-HTLcs 为催化剂催化苯甲醛与甲醇反应合成安息香甲醚,研究了n(Mg):n(Co):n(Al)、催化剂用量、原料配比、反应温度、反应时间对合成反应的影响。表征结果显示,MgCoAl-HTLcs 的晶相完整,表面主要为弱酸、弱碱性。催化合成安息香甲醚的适宜条件为:MgCoAl-HTLcs 催化剂用量0.10 g(约为原料总质量的0.23%),n(Mg):n(Co):n(Al)=0.4:1.6:1.0,V(苯甲醛):V(甲醇)=3:50,反应温度50℃,反应时间150 min。在此条件下,苯甲醛的平衡转化率达77.49%,安息香甲醚选择性接近100%。为洁净合成安息香甲醚开辟了一条新的途径。     

基于遗传算法的BP网络在导弹武器系统元器件贮存可靠性预测中的应用

针对目前BP神经网络在应用中,网络结构难以确定以及网络极易陷入局部解问题,用遗传算法优化神经网络的连接权和网络结构,并在遗传进化过程中引入交叉概率和变异概率与个体的适度值相联系,改进了操作算子,而且在交叉操作后又引入模拟退火机制,提高遗传算法的局部搜索能力.建立了基于改进遗传算法的BP网络贮存可靠性预测模型,并以某型导弹武器系统的元器件进行实例分析,计算结果表明,该方法预测的准确性高,且预测结果稳定.     

炼铁生产环境负荷分析和预测研究

利用寿命周期评价的思想，计算了炼铁生产过程中的环境负荷，并分析了各因素对环境负荷的影响，运用神经网络对炼铁生产过程环境负荷进行了预测。     

日本落叶松及其在造纸领域中的应用

日本落叶松是适合在北半球广泛种植的早期速生树种 ,在我国的生长适应范围比较广 ,如今在我国华北、西北、西南和华中亚热带山区的部分地区已有种植。它在造纸领域中的应用研究已经开始 ,而且已经证实是一种很有潜力的造纸木材     

GPRS业务模型和无线网络的研究

根据GPRS业务及业务发展情况，对GPRS业务模型进行深入研究，并对GPRS无线网络进行了更深入的探讨。