用小波变换提高散斑能见度光谱测量法的灵敏度
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摘要
在分析被动层颗粒温度含噪特点的基础上模拟了低信噪比的方波信号,根据变化规律,采用Mallat快速算法分析低信噪比的方波信号,并根据噪声分布特性设计了用于抑制被动层颗粒温度中干扰噪声的算法。对所设计算法进行仿真实验,结果表明,该算法可以最大限度地滤除信号中的噪声。通过搭建滚筒实验装置,测量滚筒被动层的颗粒温度,对测量数据进行分析,有效地测出了内部颗粒温度状态变化,表明了小波变换能有效提高测量被动层颗粒温度的信噪比。
On the basis of analyzing the noise characteristics of passive layer particles, a squarewave signal with low SNR was simulated. Based on the variation law, a Mallat fast algorithm was used to analyze the wavelet signals with low SNR and analyzed the characteristics of the noise. An algorithm designed to suppress interference noise in the passive layer particle temperatures. The simulation on the designed algorithm shows that the algorithm can filter the noise in the signal. By constructing a roller experiment device,the particle temperature of the passive layer of the roller is measured, and the measurement data is analyzed. The change of internal particle temperature state is effectively measured, and the practicality of denoising the passive layer particle temperature signal using wavelet transform is proved.
On the basis of analyzing the noise characteristics of passive layer particles, a squarewave signal with low SNR was simulated. Based on the variation law, a Mallat fast algorithm was used to analyze the wavelet signals with low SNR and analyzed the characteristics of the noise. An algorithm designed to suppress interference noise in the passive layer particle temperatures. The simulation on the designed algorithm shows that the algorithm can filter the noise in the signal. By constructing a roller experiment device,the particle temperature of the passive layer of the roller is measured, and the measurement data is analyzed. The change of internal particle temperature state is effectively measured, and the practicality of denoising the passive layer particle temperature signal using wavelet transform is proved.
引文
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[3]陆珉,李禹,黄春琳,等.基于小波变换的探地雷达随机RFI抑制方法[J].国防科技大学学报,2009,31(3):65-69.
[4]刘清君,叶伟伟,杜立萍,等.小波变换在神经细胞传感器信号去噪中的应用[J].传感技术学报,2009,22(11):1586-1590.
[5]卢竟蔓,蓝兴英,徐春明,等.颗粒温度计算方法对FCC颗粒湍动床数值模拟的影响[J].化工进展,2011,30(增刊1):38-41.
[6]张北帆,李然,杨晖.散斑可见度光谱法中的相干因子[J].光学仪器,2017,39(6):38-45.
[7]盛旭波,杨晖,李然,等.基于激光在线测量滚筒内颗粒流崩塌角新方法[J].激光技术,2016,40(3):344-348.
[8]LI R,YANG H,ZHENG G,et al.Double specklevisibility spectroscopy for the dynamics of a passive layer in a rotating drum[J].Powder Technology,2016,295:167-174.
[9]邹文,罗金香.小波变换在医学图像处理中的应用[J].中国医疗设备,2008,23(4):58-60.
[10]柳刚,李术才,薛翊国,等.基于小波变换的雷达低信噪比信号处理技术及应用研究[J].工程勘察,2009(9):85-90.
[11]滕海文,许树峰,李秀芬,等.结构裂缝损伤识别的连续小波分析方法[J].振动与冲击,2008,27(8):155-157.
[12]王香云.基于FPGA的小波去噪的硬件实现[J].激光技术,2013,37(6):786-790.
[13]周朋,奚日辉,宋玲玲,等.基于小波变换的医学图像融合技术的实现[J].中国图象图形学报,2006,11(11):1720-1723.
[14]吴卓智,高晓蓉,郭建强.基于小波变换的轨道超声检测数据压缩研究[J].信息技术,2016(3):108-110,120.
[15]李合生,毛剑琴,韩宇.小波变换在大地回波噪声处理中的应用[J].北京航空航天大学学报,2001,27(2):221-225.
[16]徐全,龚彬,韦新平.基于ARM&WinCE的刀具状态在线监测系统[J].西华大学学报(自然科学版),2011,30(4):58-61.
[17]张健,邓志勇.基于小波变换的列车广播声压自适应调整算法[J].电声技术,2012,36(5):85-88.
[18]周铁军,顾晓辉,吕艳新.基于BP神经网络的地震动信号识别[J].现代电子技术,2012,35(10):100-102.
[19]黄小毛,张永刚.小波变换在雷达回波信号消噪处理中的应用[J].现代雷达,2001,23(5):34-37.
[20]YANG H,JIANG G L,SAW H Y,et al.Granular dynamics of cohesive powders in a rotating drum as revealed by speckle visibility spectroscopy and synchronous measurement of forces due to avalanching[J].Chemical Engineering Science,2016,146:1-9.
[21]YANG H,ZHANG B F,LI R,et al.Particle dynamics in avalanche flow of irregular sand particles in the slumping regime of a rotating drum[J].Powder Technology,2017,311:439-448.
[22]严静,刘瑞军,丁玲,等.基于小波变换的车窗防夹系统电流降噪与特性分析[J].广西大学学报(自然科学版),2017,42(2):602-610.