摘要
针对煤矿井下干扰源会对煤岩受载破坏产生的电磁场监测造成较大影响,采用小波阈值函数进行信号前期预处理,采用粒子群优化算法进行优化,对加噪的信号进行去噪仿真,去噪效果对比硬、软阈值函数得到提高.对某矿工作面采集的电磁辐射信号利用改进小波算法进行去噪研究.研究结果表明:采用粒子群优化小波算法进行降噪重构,能够较好地去除信号中的尖峰噪声,并保留原始信号特征,信噪比得到显著提高.
In the light of the coal mine downhole interference source influencing electromagnetic monitor of load of coal and rock, the wavelet threshold function is used for signal preprocessing, and the particle swarm optimization algorithm is used to optimize the noise signal. Through the de-noising simulation study, the de-noising effect compared with hard, soft threshold function is improved. In a mine working face by collecting electromagnetic emission signal de-noising, the study results show that the algorithm of particle swarm optimization wavelet de-noising reconstruction can better remove the peak signal noise, retain the original signal, and improve the SNR significantly.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2015年第3期410-413,共4页
Journal of Liaoning Technical University (Natural Science)
基金
国家自然科学基金青年基金项目(51204087)
辽宁省大学生创新创业训练计划基金项目(201310147027)
辽宁工程技术大学基金项目(SCDY2013023
14-T-006)
关键词
电磁辐射
小波变换
粒子群算法(PSO)
阈值去噪
降噪
重构
electromagnetic emission
wavelet transform
particle swarm optimization（PSO）
threshold de-noising
de-noising
reconstruction
