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.     

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.     

GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.     

光学全交叉网络与SW榕树（F＝S＝2）网络拓扑等价的多样性

本文对有关互连网络拓扑等价的分析方法作了简单评述。并采用互连网络拓扑等价的图分析法分析了自由空间光学互连全交叉网络与ＳＷ榕树网络的拓扑等价及其多样性，拓展了全交叉网络在数字光计算、光子交换及多处理机系统的潜在应用。     

Energy minimization and design for testability

The problem of fault detection in general combinational circuits is NP-complete. The only previous result on identifying easily testable circuits is due to Fujiwara who gave a polynomial time algorithm for detecting any single stuck fault inK-bounded circuits. Such circuits may only contain logic blocks with no more thanK input lines and the blocks are so connected that there is no reconvergent fanout among them. We introduce a new class of combinational circuits called the (k, K)-circuits and present a polynomial time algorithm to detect any single or multiple stuck fault in such circuits. We represent the circuit as an undirected graphG with a vertex for each gate and an edge between a pair of vertices whenever the corresponding gates have a connection. For a (k, K)-circuit,G is a subgraph of ak-tree, which, by definition, cannot have a clique of size greater thank+1. Basically, this is a restriction on gate interconnections rather than on the function of gates comprising the circuit. The (k, K)-circuits are a generalization of Fujiwara'sK-bounded circuits. Using the bidirectional neural network model of the circuit and the energy function minimization formulation of the fault detection problem, we present a test generation algorithm for single and multiple faults in (k, K)-circuits. This polynomial time aggorithm minimizes the energy function by recursively eliminating the variables.     

BP网络过拟合问题研究及应用

通过类比信息传递过程中的一般测不准关系式,研究BP网络出现过拟合现象时,代表网络学习能力的权值改变量与反映网络推广能力的未参与训练的检验样本的辨识误差之间的关系.在该关系式中引入一个过拟合参数P,通过对岷江流域紫坪埔站的流量预报,说明在判断网络是否出现过拟合现象时,计算过拟合参数P是一种有研究意义的参考.     

Analytical general results for handoff dwell time distribution in wireless networks

1 Introduction The calling dwell time characteristic is critical for the user network planning and deployment, e.g., global system for mobile communications (GSM), as well as the next generation wireless multimedia networks, such as, the currently standar…