Petri网络在无控制交叉口延误分析中的应用

选用比较适合描述系统组织结构和动态行为的Petri网络模型对主路优先通行的无信号交叉口运行过程建立HSPN模型．在此基础上，结合间隙接受理论，对无控制交叉口进行平均延误分析探索，为交叉口仿真控制软件的研发奠定了算法基础。     

EXPONENTIAL STABILITY AND PERIODIC SOLUTION OF HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

In this paper, we study the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays. By ingeniously importing real parameters di > 0 (i = 1,2, …, n) which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions are established. Our results generalize and improve the related results in [9]. These conditions can be used both to design globally exponentially stable and periodical oscillatory hybrid bidirectional associative neural networks with discrete delays, and to enlarge the area of designing neural networks. Our work has important significance in related theory and its application.     

Discovering Frequent Subtrees from XML Data Using Neural Networks

By rapid progress of network and storage technologies, a huge amount of electronic data such as Web pages and XML has been available on Internet. In this paper, we study a data-mining problem of discovering frequent ordered sub-trees in a large collection of XML data, where both of the patterns and the data are modeled by labeled ordered trees. We present an efficient algorithm of Ordered Subtree Miner （OSTMiner） based on two- layer neural networks with Hebb rule, that computes all ordered sub-trees appearing in a collection of XML trees with frequent above a user-specified threshold using a special structure EM-tree. In this algo- rithm, EM-tree is used as an extended merging tree to supply scheme information for efficient pruning and mining frequent sub-trees. Experiments results showed that OSTMiner has good response time and scales well.     

Fuzzy Entropy： Axiomatic Definition and Neural Networks Model

The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model.     

BP人工神经网络用于除草剂活性预测

采用AMI方法研究了10种新型的磺酰脲类除草剂的电子结构，并以原子的Mulliken净电荷和除草剂在不同浓度(100，10mg／L)下对油菜、稗草两种作物的根、茎部位的抑制率为训练样本集。构造并训练得到具有活性预测能力的BP神经网络．结果表明，该BP网络不仅能对训练样本很好拟合。亦能对未知化合物的活性作出很好的预测．     

A Constitutive Model in Finite Viscoelasticity of Particle-reinforced Rubbers

Two series of tensile relaxation tests are performed on natural rubber filled with high abrasion furnace black. To fit observations, constitutive equations are derived for the nonlinear viscoelastic behavior of a particle-reinforced elastomer. A filled rubber is modeled as a composite medium, where inclusions with low concentrations of junctions are randomly distributed in the host matrix. The inclusions are treated as equivalent networks of macromolecules, where strands can separate from temporary junctions as they are thermally agitated. The bulk medium is thought of as a permanent network of chains. Unlike conventional concepts of transient networks, the concentration of strands in inclusions is assumed to be affected by mechanical factors: under active loading, inter-chain interactions weaken and some strands that were prevented from detachment from their junctions in a stress-free compound become free to separate from the junctions in a deformed medium. Unloading strengthens interactions between macromolecules, which results in an increase in the number of permanent strands. By using the laws of thermodynamics, stress–strain relations for a particle-reinforced rubber are developed. Adjustable parameters in the constitutive equations are found by fitting the experimental data. It is demonstrated that mechanical pre-loading and annealing of specimens at an elevated temperature noticeably affect concentrations of inclusions with various activation energies for rearrangement of strands.     

Hydrogel‐like elastic membrane consisting of semi‐interpenetrating polymer networks based on a phosphorylcholine polymer and a segmented polyurethane

To obtain a hydrogel‐like elastic membrane, we prepared semi‐interpenetrating polymer networks (IPNs) by the radical polymerization of methacrylates such as 2‐methacryloyloxyethyl phosphorylcholine (MPC), 2‐hydroxyethylmethacrylate, and triethyleneglycol dimethacrylate diffused into segmented polyurethane (SPU) membranes swollen with a monomer mixture. The values of Young's modulus for the hydrated semi‐IPN membranes were less than that for an SPU membrane because of higher hydration, but they were much higher than that for a hydrated MPC polymer gel (non‐SPU). According to a thermal analysis, the MPC polymer influenced the segment association of SPU. The diffusion coefficient of 8‐anilino‐1‐naphthalenesulfonic acid sodium salt from the semi‐IPN membrane could be controlled with different MPC unit concentrations in the membrane, and it was about 7 × 10² times higher than that of the SPU membrane. Fibroblast cell adhesion on the semi‐IPN membrane was effectively reduced by the MPC units. We concluded that semi‐IPNs composed of the MPC polymer and SPU may be novel polymer materials possessing attractive mechanical, diffusive‐release, and nonbiofouling properties. © 2002 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 41: 68–75, 2003     

层析成像的模糊-神经网络重建方法

考虑投影数据不足时，射线层析成像的离散重建算法难以取得较好效果的情况，利用模糊理论建立了表示物体衰减系数的连续函数，用神经网络学习算法确定该连续函数中的待定参数．提出了一种适于投影数据或投影方向不够时的CT图像重建算法．计算机仿真结果显示了该方法在投影数据严重缺少时的良好效果。     

Efficient and power-awareness query over sensor networks

Sensor networks consisted of low-cost, low-power, multifunctional miniature sensor devices have played an important role in our daily life. Light and humidity monitoring, seismic and animal activity detection, environment and habitat monitoring are the most common applications. However, due to the limited power supply, ordinary query methods and algorithms can not be applied on sensor networks. Queries over sensor networks should be power-aware to guarantee the maximum power savings. The minimal power consumption by avoiding the expensive communication of the redundant sensor nodes is concentrated on. A lot of work have been done to reduce the participated nodes, but none of them have considered the overlapping minimum bounded rectangle (MBR) of sensors which make them impossible to reach the optimization solution. The proposed OMSI-tree and OMR algorithm can efficiently solve this problem by executing a given query only on the sensors involved. Experiments show that there is an obvious improvement compared with TinyDB and other spatial index, adopting the proposed schema and algorithm.