粘接结构超声信号的小波分解与特征提取

粘接结构因其独特的物理、机械和声学特性在诸多领域具有非常广泛的应用，对于铝合金-橡胶这样一种金属-非金属粘接结构的超声检测信号，本文利用了小波变换的多分辨率分析(时频分析)的方法，有效地提取出了反映多层材料粘接状况的信号特征，该特征提取方法可用在粘接结构的无损检测中。     

海洋平台振动控制研究综述

对海洋平台结构振动的控制研究进行了综述。主要介绍了TMD、TLD以及粘弹性阻尼器，并相应地介绍了被动控制、半主动控制和主动控制技术。平台结构的自身减振设计也进行了介绍。最后对海洋平台结构振动控制问题进行了探讨，提出了一些看法。     

乙烯裂解炉对流段局部结构设计改进

介绍了乙烯裂解炉对流段的结构功能,各部件局部结构的改善设计。     

110kV变电站深埋接地极降阻的设计与应用

110kV变电站接地网，由于年久老化，存在接地电阻值高的缺陷，根据设备的分布及现场地形情况，设计采用安装深埋接地极的方法施工，取得了良好的效果，为一些年久老化接地电阻值偏高需要改造的老接地网积累了经验。     

基于小波除噪和神经网络理论的气固循环流化床颗粒浓度预测

提出了一种将混沌的相空间重构、小波包分析和神经网络相结合的新方法用于预测气-固循环流化床的颗粒浓度。首先利用小波包进行数据的消噪,然后用混沌方法重构相空间吸引子,用径向基神经网络拟合吸引子上的全局整体映射,构成混合预测模型。实验结果表明,将此混合模型用于预测气-固循环流化床的颗粒浓度,能达到较好的预测效果,预测精度比奇异值分解和傅里叶变换除噪高。     

钻井工程实时监测与井场信息系统开发

介绍了钻井工程实时监测与井场信息系统的开发背景、组成及功能。该系统实现了对钻井信息的实时采集、实时传输、现场施工情况的实时分析、工程事故与复杂情况的实时警示与处理，实现了对钻井施工过程的有效监测。现场测试与应用表明，该系统计算精度较高，能满足工程施工要求，而且稳定性好，操作简单，具有很好的推广应用前景。     

Memory of Distributed Computer Systems: Distinctive Features of Their Architectural-Structural Organization and Use

Distinctive features of architectural-structural organization and use of memory networks are considered with the aim of choosing an optimal configuration in designing distributed computer systems.     

Passivity Analysis of Dynamic Neural Networks with Different Time-scales

Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.     

Improving Neural Networks for Mechanism Kinematic Chain Isomorphism Identification

Detection of isomorphism among kinematic chains is essential in mechanical design, but difficult and computationally expensive. It has been shown that both traditional methods and previously presented neural networks still have a lot to be desired in aspects such as simplifying procedure of identification and adapting automatic computation. Therefore, a new algorithm based on a competitive Hopfield network is developed for automatic computation in the kinematic chain isomorphism problem. The neural approach provides directly interpretable solutions and does not demand tuning of parameters. We have tested the algorithm by solving problems reported in the recent mechanical literature. Simulation results show the effectiveness of the network that rapidly identifies isomorphic kinematic chains.     

虚拟企业模型并行仿真研究

随着各种全新的制造理念,特别是虚拟企业的出现,企业模型的规模不断扩大,使目前存在的大多数企业模型仿真系统的效率大大下降。该文针对此问题,通过深入分析虚拟企业模型结构,并在已有的构件化企业建模体系CEMS及其仿真系统的基础上,结合异步仿真技术和多线程技术提出了一种分层式企业模型仿真体系结构,为虚拟企业模型的快速高效的仿真提供了一种有效的方法。