小波变换结合LZW压缩算法探究
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摘要
为解决传统桥梁振动数据压缩方法存在的问题,在深入分析小波变换原理的基础上,提出了一种小波变换结合LZW的压缩算法。该算法针对桥梁振动数据的特点,根据小波变换多分辨率分析的特性,对分解后各级的高频分量采用Donoho阀值压缩算法进行量化处理。而后采用改进的LZW编码压缩变换后的系数,对实测数据进行了压缩实验,结果证明小波变换结合LZW的数据压缩算法可有效提高数据压缩比,能量恢复系数优于10-4,压缩比小于9.25%。
Based on the wavelet transform principle,a method of wavelet transform combined with Lempel-Ziv-Welch(LZW) coding has been proposed to solve the existing problems of traditional bridge vibration data compression.In accordance of the characteristics of the bridge vibration data and the properties of wavelet transform-based multi-resolution analysis,we adopted the Donoho threshold compression algorithm to quantify the high-frequency components that had been decomposed.Then with the improved LZW coding,we made further a depression of the bridge vibration data.To justify the proposed method,the measured data were simulated and the results indicated that the data compression based on the combination of LZW and wavelet transform can effectively improve the compression ratio,the energy recovery coefficient was smaller than 10-4,and the compression ratio was less than 9.25%.
Based on the wavelet transform principle,a method of wavelet transform combined with Lempel-Ziv-Welch(LZW) coding has been proposed to solve the existing problems of traditional bridge vibration data compression.In accordance of the characteristics of the bridge vibration data and the properties of wavelet transform-based multi-resolution analysis,we adopted the Donoho threshold compression algorithm to quantify the high-frequency components that had been decomposed.Then with the improved LZW coding,we made further a depression of the bridge vibration data.To justify the proposed method,the measured data were simulated and the results indicated that the data compression based on the combination of LZW and wavelet transform can effectively improve the compression ratio,the energy recovery coefficient was smaller than 10-4,and the compression ratio was less than 9.25%.
引文
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[14]张华,陈小宏,杨海燕.地震信号去噪的最优小波基选取方法[J].石油地球物理勘探,2011,46(1):70-75.Zhang H,Chen X H,Yang H Y.Optimistic wavelet ba-sis selection in seismic signal noise elimination[J].OilGeophysical Prospecting,2011,46(1):70-75.(inChinese)
[15]Donoho D L,Johnstone J M.Ideal spatial adaptation bywavelet shrinkage[J].Biometrika,1994,81(3):425-455.
[2]李兆霞,王滢,吴佰建,等.桥梁结构劣化与损伤过程的多尺度分析方法及其应用[J].固体力学学报,2010,31(6):731-756.Li Z X,Wang Y,Wu B J,et al.Multi-scale modelingand analyses on structural deterioration and damage inlong-span bridges and its application[J].Chinese Journalof Solid Mechanics,2010,31(6):731-756.(in Chi-nese)
[3]吴佰建,李兆霞,王滢,等.桥梁结构动态监测信息的分离与提取[J].东南大学学报:自然科学版,2008,38(5):767-773.Wu B J,Li Z X,Wang Y,et al.Separation and extrac-tion of bridge dynamic strain data[J].Journal of South-east University:Natural Science Edition,2008,38(5):767-773.(in Chinese)
[4]赵双龙,郝永生.LZW改进压缩算法的FPGA实现[J].现代电子技术,2011,34(3):110-114.Zhao S L,Hao Y S.Improved LZW compression algo-rithm implemented on FPGA[J].Modern ElectronicsTechnique,2011,34(3):110-114.(in Chinese)
[5]陈世海,裴东兴,张琦.FPGA实现滑动平均滤波算法和LZW压缩算法[J].电子设计工程,2010,18(2):67-69.Chen S H,Pei D X,Zhang Q.Implementation of mov-ing-average filter algorithm and LZW algorithm based onFPGA[J].Electronic Design Engineering,2010,18(2):67-69.(in Chinese)
[6]胡智宏,尹小正,路立平.一维信号小波压缩的能量动态自适应阈值算法[J].科学技术与工程,2012,12(18):4418-4421.Hu Z H,Yin X Z,Lu L P.Dynamic adaptive thresholdalgorithm for the compression of one-dimensional signalsusing wavelet transforms[J].Science Technology andEngineering,2012,12(18):4418-4421.(in Chinese)
[7]毕永成.多媒体数据处理中几种无损压缩算法的比较[J].今日科苑,2010,10(6):119-120.Bi Y C.The comparison of several nondestructive com-pression algorithms on multimedia data processing[J].Modern Science,2010,10(6):119-120.(in Chinese)
[8]孙妙平.电力系统故障录波数据压缩方法研究[J].电力自动化设备,2004,24(11):34-36.Sun M P.Research of power system fault data compres-sion[J].Electric Power Automation Equipment,2004,24(11):34-36.(in Chinese)
[9]刘亮,叶进.基于小波理论的动平衡信号滤波方法研究[J].机械制造与自动化,2011,46(1):132-137.Liu L,Ye J.Research on method of dynamic balancingsignal filtering based on wavelet theory[J].MachineBuilding&Automation,2011,46(1):132-137.(inChinese)
[10]陈俊,唐余亮,雷鹰.基于桥梁结构健康监测的无线传感系统自愈性研究[J].中国新通信,2010(4):69-71.Chen J,Tang Y L,Lei Y.Study on the self-repair ofbridge structural stability monitoring system based onwireless sensor system[J].China New Telecommunica-tions,2010(4):69-71.(in Chinese)
[11]Zhang F Y,Wen L L,Jia X L,et al.LZW algorithm re-search and amelioration based on pointer trace[J].Ap-plied Mechanics and Materials,2012,121/122/123/124/125/126:4498-4502.
[12]Zhang F Y,Li Z,Chen M L,et al.Implementation andoptimization of LZW compression algorithm based onbridge vibration data[J].Procedia Engineering,2011,15:1570-1574.
[13]秦毅,秦树人,毛永芳.小波变换中经验模态分解的基波检测及其在机械系统中的应用[J].机械工程学报,2008,44(3):135-142.Qin Y,Qin S R,Mao Y F.Fundamental wave detectionbased on wavelet transform and empirical mode decompo-sition with application in mechanical system[J].ChineseJournal of Mechanical Engineering,2008,44(3):135-142.(in Chinese)
[14]张华,陈小宏,杨海燕.地震信号去噪的最优小波基选取方法[J].石油地球物理勘探,2011,46(1):70-75.Zhang H,Chen X H,Yang H Y.Optimistic wavelet ba-sis selection in seismic signal noise elimination[J].OilGeophysical Prospecting,2011,46(1):70-75.(inChinese)
[15]Donoho D L,Johnstone J M.Ideal spatial adaptation bywavelet shrinkage[J].Biometrika,1994,81(3):425-455.
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