Development of grain boundary allotriomorphic ferrite/granular bainite duplex steel

A new hot-rolled low alloy high strength steel with grain boundary allotriomorphic ferrite/granular bainite duplex micro-structure has been developed through novel microstructure and alloying designs without any noble metal elements such as nickel and molybdenum. Its as-rolled microstructure and mechanical properties, fatigue crack propagation behavior compared with single granular bainitic steel as well as continuous cooling transformation, were investigated in detail. The measured result of CCT (continuous cooling transformation) curve shows that such duplex microstructure can be easily obtained within a wide air-cooling rate range. More importantly, this duplex microstructure has much better combination of toughness and strength than the single granular bainite microstructure. It is found that the grain boundary allotriomorphic ferrite in this duplex microstructure can blunt the rni-crocrack tip, cause fatigue crack propagation route branching and curving, and thus it increases the resistance to fati     

Modification of High Performances of Polymer Cement Concrete

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iButton技术在安防系统中的应用

本文介绍了一种新型的智能化信息载体iButton以及iButton在安防系统上的应用举例，着重介绍了其硬件、软件设计及工作原理。     

工业热电偶、热电阻自动检定装置

将传统的手动热电偶、热电阻检定装置改进为自动检定装置，实现了自动连续检定。     

用InTouch、MATLAB和VC++开发工业监控系统软件

本文针对工业自动控制的需要开发了基于InTouch、MATULB以及VC 三者混合编程的具有普通分散控制系统不具备的较完善控制功能(例如人工神经网络控制)的控制系统监控软件。本系统可通过通信连入控制网络。开发时充分利用了三者各自的优点。使本系统具有较高的开发效率和应用价值。     

网络嗅探技术

嗅探(Sniffer)技术是网络安全攻防技术中很重要的一种。通过它可以获取网络中大量的信息。文中首先分析了嗅探的原理和危害，并介绍了几种常见的嗅探器，最后提出一些解决嗅探的对策。     

面向对象的柔性设计与控制理论(IV)

将可视化的面向对象技术应用到计算机控制领域,借助COM(CompoundObjectModel)思想,实现了计算机控制的可视化和模块化的应用平台,用户可根据自己需要通过已有的模块随意搭建各种计算机控制系统,实现了控制系统的“设计灵活、简洁、方便;控制实时、智能、鲁棒”。     

基于判断矩阵的软件评价指标权重分配方法

本文主要讨论判断矩阵的性质和特点，阐明了利用判断矩阵为软件质量评价指标分配权重的原理，对一致性较差的判断矩阵给出了修正方法，最后给出了权重分配方法。     

仿人抽象逻辑推理控制器的研究

人能够通过观察被控量变化的速度、大小、方向,完成对一个复杂系统的控制。文中定义了被控量偏差变化的大小、方向、速度三个特征量,运用LVQ神经网络做控制器,充分模拟人的控制经验与技巧,取得了较好的控制效果。     

A dual neural network for redundancy resolution of kinematically redundant manipulators subject to joint limits and joint velocity limits

In this paper, a recurrent neural network called the dual neural network is proposed for online redundancy resolution of kinematically redundant manipulators. Physical constraints such as joint limits and joint velocity limits, together with the drift-free criterion as a secondary task, are incorporated into the problem formulation of redundancy resolution. Compared to other recurrent neural networks, the dual neural network is piecewise linear and has much simpler architecture with only one layer of neurons. The dual neural network is shown to be globally (exponentially) convergent to optimal solutions. The dual neural network is simulated to control the PA10 robot manipulator with effectiveness demonstrated.