基于群延时信息的超声信号处理方法

充分利用超声信号的群延时信息，结合短时傅里叶变换，给出了群延时对频率导数在时一频域中的分布，以此来反映时一频域中局部信噪比的强弱。在此基础上，进一步利用超声信号的群延时信息，极大地增强了缺陷信号。     

200MW汽轮机焊接隔板检查与加固技术

阐述了200MW汽轮机焊接隔板变形这一在国内电站中多次出现的问题,以及由于隔板变形而对机组带来的危害.强调了在隔板发生严重变形时对隔板进行焊接加固的必要性,概述了隔板检查方法,包括隔板表面缺陷、内部缺陷、通流间隙、挠曲变形等的检查,以及隔板加固的工艺要点.     

基于小波变换的超声信号自适应消噪方法研究

超声检测中，特定的缺陷回波信号一般都有一定的相关性，因而可利用自适应噪声抵消来增强缺陷回波信号。针对最小均方（LMS）算法自适应噪声抵消的缺点，提出了基于小波变换的自适应噪声抵消方法，通过Matlab软件将该算法应用于超声缺陷信号的仿真处理。结果表明，该算法大大提高了缺陷回波信号的信噪比，且具有较高的缺陷定位精度和纵向分辨率。     

Thermal degradation behaviors of polypropylene with novel silicon‐containing intumescent flame retardant

The N‐[3‐(dimethoxy‐methyl‐silanyl)‐propyl]‐N′‐ (9‐methyl‐3,9‐dioxo‐2,4,8,10‐tetraoxa‐3,9‐diphospha‐spiro[5.5]undec‐3‐yl)‐ethane‐1,2‐diamine/dimethoxy dimethyl silane copolymer (PSiN II), which simultaneously contains silicon, phosphorus, and nitrogen, is synthesized and incorporated into polypropylene (PP). The flame retardancy is evaluated by the limiting oxygen index value, which is enhanced to 29.5 from 17.4 with 20% total loading of PSiN II. The thermal degradation behavior of PP/PSiN II is investigated by thermogravimetric analysis under N₂ and air. The PP/PSiN II sample degrades at 400°C for different amounts of time, and the process of degradation is studied by Fourier transform IR. The morphology of the char formed at 400°C for 10 min is investigated by scanning electron microscopy. The swollen inner structure, close, and smooth outer surface provide a much better barrier for the transfer of heat and mass during fire and good flame retardancy. The thermal stability of PP is improved by incorporation of PSiN II. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 2487–2492, 2005     

傅立叶变换红外光谱法在橡胶分析中的应用与进展

简介了用橡胶分析的傅立叶变换红外光谱透射法，漫反射ＦＴＩＲ法，衰全反射ＦＴＩＲ法，ＦＴＩＲ－光声光谱法，ＦＴＩＲ仪与各种仪器的联用，以及计算机技术在ＦＴＩＲ分析中的应用与进展。     

参数化的Deslauriers-Dubuc插值小波——构造及图像编码性能分析

研究了采用提升构造具有任意偶数阶消失矩, 满足对称性, 且仅用一个自由参数表达的 Deslauriers-Dubuc (D-D)双正交插值小波. 首先,采用多相矩阵理论推导出了此类小波存在的条件; 然后,给出了对应小波滤波器和插值小波变换的构造算法. 采用算法具体构造了分别具有消失矩对(4, 2)、(4, 4)、(6, 2)以及(6, 4) 等4类一参数表达的D-D插值小波; 最后, 以自由参数为自变量, 根据编码增益准则, 优化设计了4种用于图像编码的插值小波, 其滤波器系数全为二进制分数, 可实现非乘法运算的离散小波变换(Discrete wavelet transform, DWT). 系统分析表明, 两种小波的压缩性能超过CDF-9/7小波, 对于纹理图像, PSNR增益达到0.44.dB, 并且计算复杂度可降低17%以上. 实验同时表明, 新小波的重构图像具有更好的主观可视质量.     

一种改进的超宽条带噪声消除算法

针对高光谱遥感图像中的超宽条带噪声干扰现象,在深入研究高光谱图像特点和条带噪声产生机理的基础上,提出了一种新的基于最小序列值、小波变换和矩匹配相结合的滤波算法（OWM算法）。该算法主要包括灰度对比度处理、最小序列值处理、小波变换系数归零处理和矩匹配处理等四个步骤。用实际的高光谱图像进行了一系列的验证比较实验,获得了好的实验效果。实验结果表明OWM算法不仅能够有效滤除高光谱图像中的超宽条带噪声,而且还具有较好的普适性。     

基于小波变换和Retinex算法的显著性检测

近些年来,关于图像显著性检测的研究越来越热门。基于之前提出的很多算法产生的显著图都存在背景信息杂乱、干扰噪声多、细节丢失等问题,本文提出了一种基于小波变换和Retinex算法的显著性检测算法来解决以上问题。首先,利用Retinex算法对图像进行前期处理;然后,对前期处理过的图像进行SLIC超像素分割,对超像素进行小波变换,分别生成原始图像低频部分和高频部分的特征图,并进行适当的双边滤波降噪,生成对应的显著图;最后,通过加权组合这两种显著图,得到最终的显著性图。实验结果表明,本文提出的算法生成的显著图具有受背景影响小、噪声少以及细节突出等优势。     

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

面向旋翼无人机的高压输电线在线检测方法

面向旋翼无人机电力巡线的高压输电线在线检测对算法实时性、鲁棒性要求高的问题,提出了一种基于边界搜索和Radon变换(BSRT)的高压输电线识别算法.分析了高压输电线在图像中的边界贯穿特征,提出了以图像四条边界作为起始点的搜索策略,给出了边界约束下的直线特征Radon变换能量函数和求解方法.复杂度分析结果表明本算法与经典的Radon算法相比,复杂度降低了一个数量级.以人工合成图像和无人机实际航拍图像,对本算法、Radon算法和LSD算法在实时性和有效性方面进行了实验对比分析,实验结果表明,本算法的处理速度较Radon算法有很大的提高,与LSD算法的处理速度基本处于同一量级,但本算法的高压输电线检测精度大幅优于传统的Radon和LSD算法.理论分析及实验结果证明,提出的BSRT方法有效地解决了经典Radon算法的高复杂度和LSD算法的复杂背景高敏感性的问题,具有较好的应用价值.