Structure and damping properties of polydimethylsiloxane and polymethacrylate sequential interpenetrating polymer networks

By using the technology of the sequential interpenetrating polymer network, a series of novel damping materials based on a polydimethylsiloxane (PDMS)/polyacrylate (PAC) matrix with polymethacrylate (PMAC) were synthesized. They have a controllable broad transition peak spanning the temperature range of 150–220°C and the medial value of loss factor with maximum of 0.35–0.60. Dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC), and atomic force microscopy (AFM) were applied to analyze and characterize the transition behavior and the microphase structure of the materials. It was found that the size and height of a transition peak at both the low‐ and the high‐temperature zones change as a function not only of the concentration of PMAC and PDMS but also of the kind of PMAC; simultaneously, the low‐ temperature behavior was also governed by the crystallization of PDMS. The content of the crosslinking agent exerts a significant influence on the configuration of the curves of the transition peaks. AFM shows a characteristic phase morphology of double‐phase continuity containing a transition layer and domain less than 1 μm, indicating that the interwoven multilayer networks are the key to incorporation of the immiscible components and form a broad damping functional region. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 545–551, 2002     

Predictive modeling of mercury speciation in combustion flue gases using GMDH-based abductive networks

Modeling mercury speciation is an important requirement for estimating harmful emissions from coal-fired power plants and developing strategies to reduce them. First-principle models based on chemical, kinetic, and thermodynamic aspects exist, but these are complex and difficult to develop. The use of modern data-based machine learning techniques has been recently introduced, including neural networks. Here we propose an alternative approach using abductive networks based on the group method of data handling (GMDH) algorithm, with the advantages of simplified and more automated model synthesis, automatic selection of significant inputs, and more transparent input–output model relationships. Models were developed for predicting three types of mercury speciation (elemental, oxidized, and particulate) using a small dataset containing six inputs parameters on the composition of the coal used and boiler operating conditions. Prediction performance compares favourably with neural network models developed using the same dataset, with correlation coefficients as high as 0.97 for training data. Network committees (ensembles) are proposed as a means of improving prediction accuracy, and suggestions are made for future work to further improve performance.     

IP网络资源调度与接纳控制技术研究

从IP QoS技术的研究现状出发,结合IETF的研究成果与电信网NGN的技术发展方向,阐述基于策略的端到端接纳控制框架的技术原理,在各大标准组织的技术框架内,给出一个具体的、切实可行的实现方法。     

On‐Line Detection Of State‐Of‐Charge In Lead Acid Battery Using Radial Basis Function Neural Network

To realize a stable supply of electric power in an automobile, an accurate and reliable detection method of SOC (state‐of‐charge) in a lead acid battery is required. However the dynamics of the battery is very complicated. The characteristics of the battery greatly change due to its degradation. Moreover a automobile has many driving patterns, which are unknown beforehand. Thus it is not easy to detect the SOC analytically. In this paper, to overcome this problem, a new on‐line SOC detection method with a radial basis function neural network is proposed. In order to increase the detection accuracy of degraded batteries, physical values related to the degradation degree are used as input signal in the neural network. The detection accuracies for different sized batteries and various degradation states are investigated.     

Tree-shaped flow structures designed by minimizing path lengths

This paper outlines a direct route to the construction of effective tree-shaped flow structures. Dendritic flow structures dominate the design of natural and engineered flow systems, especially in thermal and fluid systems. The starting point is the optimization of the shape of each elemental area or volume, such that the length of the flow path housed by the element is minimized. Proceeding toward larger and more complex structures - from elements, to first constructs, second constructs, etc. - the paper develops tree-shaped flow structures between one point and a straight line, one point and a plane, a circle and its center, and a point and many points distributed uniformly over an area. In the latter, the construction method is applied to a fluid flow configuration with laminar fully developed flow. The constructions reveal several features that are supported by empirical observations of natural tree-shaped flows: asymmetry, flow rate imbalance, pairing or bifurcation, angles between branches, and Y-shaped constructs that lie in a plane. It is shown that these basic features are necessary because of “packing”, i.e., assembling optimized elements into a fixed space, and filling the space completely. For the flow between an area and one point, the best elemental shape is the regular hexagon. It is shown that the emergence of string-shaped links that connect two or more elements are necessary features, which are also required by packing. Strings cover some of the inner zones of the tree network, particularly the inner zones of large and complex trees. Dichotomous Y-shaped constructs dominate the tree structure, especially the peripheral zones of the tree canopy. The practical importance of the simplified design method is discussed.     

手势语言识别的神经网络方法

提供了一种用于人机交互（HCI）的手势语言可视化识别方法。该方法包括用于几种控制命令的手势的探测、分割、特征提取及识别，第一步的处理都用到了神经网络方法，像肤色探测、主元分析（PCA）以及在编码识别。实验结果显示正确识别率高达94％。     

无线移动自组网络的动态源路由策略

文章介绍无线移动自组网中动态源路由策略的路由发现和路由维护,并提出改进思路和今后研究的方向。