Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine

It is well recognized that the impact-acoustic emissions contain information that can indicate the presence of the adhesive defects in the bonding structures. In our previous papers, artificial neural network (ANN) was adopted to assess the bonding integrity of the tile–walls with the feature extracted from the power spectral density (PSD) of the impact-acoustic signals acting as the input of classifier. However, in addition to the inconvenience posed by the general drawbacks such as long training time and large number of training samples needed, the performance of the classic ANN classifier is deteriorated by the similar spectral characteristics between different bonding status caused by abnormal impacts. In this paper our previous works was developed by the employment of the least-squares support vector machine (LS-SVM) classifier instead of the ANN to derive a bonding integrity recognition approach with better reliability and enhanced immunity to surface roughness. With the help of the specially designed artificial sample slabs, experiments results obtained with the proposed method are provided and compared with that using the ANN classifier, demonstrating the effectiveness of the present strategy.     

基于混沌系统和极化恒等式的数字图像加密算法

混沌系统具有初值敏感性,参数敏感性和类随机性等特性。将图像分成大小为8 8的图像子块,利用经典的Logistic方程产生的混沌序列并对其排序生成匹配模板来改变图像子块的位置,然后利用极化恒等式内积分解加密算法改变图像像素的值。因此,基于混沌系统和极化恒等式的数字图像加密算法使图像的像素的位置和值都发生了变化,实现了图像的混淆和扩散。仿真实验表明该方法进行图像加密具有可行性和安全性。     

模糊综合评判理论在研究性学习系统中的研究及应用

介绍了工程数学中的模糊综合评判理论以及相应树型向量推理算法,并将模糊综合推理机制运用到研究性学习系统中进行研究型应用。该系统中运用模糊综合评判对学习效果进行合理评判,得出综合成绩,以其作为改进研究性学习模式的参考评证,从而使研究性学习过程良性发展。     

Conservative and aggressive rough SVR modeling

Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use ?-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.     

基于矢量合成的平衡机最小可达剩余不平衡度测试方法的研究

通过对国标规定的平衡机最小可达剩余不平衡度评定方法的分析和研究，指出了该方法在原理和方法上所存在的问题，并且提出了一种全新的评定方法。这种基于矢量分析的新的评定方法能够快速、准确地计算出转子的最小可达剩余不平衡度，提高平衡机测试的准确性和现场测试效率，为更加客观地评价被测平衡机的综合性能提供了依据。     

Pseudo-Stokes Vector Correlation From Complex Signal Representation of a Speckle Pattern and Its Applications to Micro-Displacement Measurement

Abstract:  As an improvement of the intensity correlation used widely in conventional electronic speckle photography, we propose a new technique for displacement measurement based on correlating Stokes-like parameters derivatives for transformed speckle patterns. The method is based on a Riesz transform of the intensity speckle pattern, which converts the original real-valued signal into a complex signal. In closest analogy to the polarisation of a vector wave, the Stokes-like vector constructed from the spatial derivative of the generated complex signal has been applied for correlation. Experimental results are presented that demonstrate the validity and advantage of the proposed pseudo-Stokes vector correlation technique over conventional intensity correlation technique.     

混合式步进电动机的矢量控制分析

混合式步进电动机与传统的异步、同步电动机相比,由于定子磁势、转子磁钢的非线性分布以及磁饱和等因素的存在而没有简单的线性化数学模型.根据已有数学模型,利用电动机矢量控制原理,在d-q坐标系下推导出混合式步进电动机电磁转矩与电流关系、静态控制与动态控制的数学模型,以及电动机定子电流细分算法,最后通过搭建实验样机验证其控制方法的有效性.     

基于支持向量机与神经网络的间谐波测量混合算法

针对目前缺乏国家标准规范间谐波对电能质量的影响,也缺乏有效的手段监测间谐波的问题,用支持向量机理论对其进行了探索。支持向量机理论将间谐波这个非线性问题变换到更高维的空间中进行线性回归,它基于迭代权调整最小二乘法,引入反向传播神经网络算法整定算法参数,通过反馈现场采集、分析信号,并将教师信号与计算信号的差值作为调整参数的驱动信号,在此基础上通过反向遗传算法(BPNN)的反向传播能力对参数进行基于负反馈的调整,提高了算法的收敛速度。通过数值实验对比,该算法比没有使用差动信号的混合算法效率提高约13%。     

J-K旋转坐标轴定向的矢量控制策略

考虑到传统矢量控制在空载或轻载时励磁电流损耗较大会导致运行效率降低,研究了一种基于j-k坐标轴的新型矢量控制方法。j-k坐标轴的定向角度超前传统的d-q坐标轴45°。在空载或轻载运行时,控制j轴一个方向的电流大小即可达到控制磁链和转矩的目的。励磁电流不再一直维持在额定值,而是随输出转矩的变化而变化。实验结果表明,可以减小励磁电流损耗,具有控制简单,动态性能良好的优点。     

基于矢量量化的数字视频压缩技术研究

数字视频作为一种重要的信息载体,其应用十分广泛。但是到目前为止,这种大数据量信号的存储和传输仍然受到硬件技术和网络技术发展现状的制约,解决这一问题最现实的方法是设计数字视频压缩算法。矢量量化作为一种十分有效的有损压缩编码技术,具有解码简单、压缩比大等特点,特别适用于电视广播等领域中一次编码多次解码的情况。对各种基于矢量量化器的视频压缩算法进行综述,分析其优缺点,并提出其未来发展研究方向。