实时专家智能控制系统与外部控制过程的数据通信

本文介绍一个实时专家智能控制系统EICS.文中着重简述了专家系统与控制过程的数据通信方法、程序接口.     

清洗机器人自主清洗方法研究

为提高清洗机器人运行的自主性,提出了针对具有六自由度清洗机器人的自主清洗方法;首先给出了清洗机器人的机械及电气结构,建立了机器人的运动学模型,分析了机器人的运动学及逆运动学问题,实时计算出污物的位置;在此基础上,为实现机器人的自主清洗,接着设计了机器人的控制系统结构,采用了RBF模糊神经网络对机器人进行智能控制;最后通过实验表明所提出的方法能满足清洗机器人精确定位到任意目标位置的要求,其动态性能和稳定性都达到了清洗要求。     

油-气管道检测机器人技术现状及展望

为提高油-气管道检测的效果,结合国内外典型管道机器人的特点,综合分析了管道机器人运动控制技术、定位技术和实时检测技术3个方面的现状,并指出了核心技术的不足;针对石油化工行业高温带压管道环境,提出管道检测机器人的研究重点为分层模糊神经网络运动控制技术、CCD视觉定位技术、低频电磁定位技术和分层图像分割的优化方法等。为进一步提高化工油-气管道内检测机器人的检测效果提供了理论依据。     

Preparation and properties of PET/PA6 copolymer/montmorillonite hybrid nanocomposite

From in situ polycondensation, a poly(ethylene terephthalate)/Polyamide 6 copolymer/montmorillonite nanocomposite was prepared, after the treatment of montmorillonite (MMT) with a water soluble polymer. The resulting nanocomposites were characterized by X‐ray diffraction (XRD), differential scanning calorimeter (DSC), nuclear magnetic resonance (NMR), dynamic mechanical analysis (DMA), and transmission electron microscopy (TEM). The results of DSC, ¹H NMR, and DMA proved that the nanocomposite synthesized was PET/PA6 copolymer/MMT nanocomposite, not the PET/PA6 blend/MMT nanocomposite. The results of XRD and TEM proved that the dispersion of MMT was improved observably after the introduction of PA6 molecular chain into PET. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2512–2517, 2006     

超重型切削过程硬质合金刀具高温力学特性

超重型切削过程中,刀具的高温失效是硬质合金刀具破损的主要形式之一。首先,通过理论分析、切削仿真及数学模型进行切削热产生过程分析,并研究切削温度对硬质合金刀具变形抗力的影响;然后,进行切削温度及材料高温硬度测量实验,实验结果验证模型有效,硬质合金和工件材料试块的硬度在1000℃时分别下降了28%和60%左右;最后,从刀具几何结构优化设计和复合涂层技术两方面提出超重型切削专用硬质合金刀具抗高温性能的提效措施,在5组切削参数下进行刀具寿命对比实验,优化设计的XF8刀具寿命是YT15刀具的2~5倍。研究结果可以为重型高效切削刀具技术的研究及推广提供借鉴。     

基于极坐标Log Gabor小波的纹理分析方法

提出一种基于极坐标Log Gabor小波的纹理分析方法,该方法突破了Gabor小波的带宽限制,仅采用较少的极坐标Log Gabor滤波器,即实现了准确地纹理分割.实验表明,在频谱覆盖相同的情况下,极坐标Log Gabor小波具有较Gabor小波更好的性能和更高的效率;此外,实验中针对多通道滤波方法对最小距离判别方法的改进,也取得了较好的效果,这为基于小波分解的分类问题提供了一个新的思路.     

A three-dimensional probabilistic fuzzy control system for network queue management

A novel probabilistic fuzzy control system is proposed to treat the congestion avoidance problem in transmission control protocol （TCP） networks. Studies on traffic measurement of TCP networks have shown that the packet traffic exhibits long range dependent properties called self-similarity, which degrades the network performance greatly. The probabilistic fuzzy control （PFC） system is used to handle the complex stochastic features of self-similar traffic and the modeling uncertainties in the network system. A three-dimensional （3-D） membership function （MF） is embedded in the PFC to express and describe the stochastic feature of network traffic. The 3-D MF has extended the traditional fuzzy planar mapping and further provides a spatial mapping among ＂fuzziness-randomness-state＂. The additional stochastic expression of 3-D MF provides the PFC an additional freedom to handle the stochastic features of self-similar traffic. Simulation experiments show that the proposed control method achieves superior performance compared to traditional control schemes in a stochastic environment.     

A new ensemble feature selection and its application to pattern classification

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.     

基于神经网络的遥感图像分类

针对传统的遥感图像分类方法分类精度低的缺点,提出了一种基于神经网络的分类方法。实验结果表明,这种基于神经网络的分类器经过训练后,可应用于遥感图像的分类,其分类精度明显高于传统的最大似然分类法。