Application of SVD EMD SG Combination Algorithm in TDLAS Rice Seed Respiration Detection

Jun Yuan, Liangquan Jia, Lu Gao, Hengnian Qi, Xu Huang
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引用次数: 1

Abstract

A SVD, EMD, SG combined algorithm was proposed to denoise the signal of TDLAS rice seed respiration detection, for seed respiration was a nonstationary procedure and sensitive to environmental changes. Firstly, the amplitude signal of the second harmonic was denoised by singular value decomposition (SVD) adaptively. Subsequently, the denoising signal was decomposed by Empirical mode decomposition (EMD). The dispersion entropy (DE) of each IMF and the correlation coefficients between each IMF and the denoising signal were calculated as the common coefficient, which was used to determine the effective component. Finally, Savitzky- Golay (SG) filter was used to smooth the signal, the polynomial order and window size of SG filters were obtained by improving particle swarm optimization (PSO) algorithm. A simulated signal and experiment data acquired from the TDLAS system were processed, the denoising results were compared with those from the traditional algorithms, this method showed better performance with the higher signal-to- noise ratio and correlation coefficient. The SVD, EMD, SG combination algorithm was suitable for TDLAS rice seed respiration detection
SVD - EMD - SG组合算法在TDLAS水稻种子呼吸检测中的应用
针对水稻种子呼吸是非平稳过程且对环境变化敏感的特点,提出了一种SVD、EMD、SG组合算法对TDLAS种子呼吸检测信号进行降噪。首先,利用奇异值分解(SVD)自适应去噪二次谐波振幅信号;然后,对去噪信号进行经验模态分解(EMD)。计算各IMF的色散熵(DE)以及各IMF与去噪信号之间的相关系数作为共同系数,用于确定有效分量。最后,采用Savitzky- Golay (SG)滤波器对信号进行平滑处理,通过改进粒子群优化(PSO)算法得到SG滤波器的多项式阶数和窗口大小。对TDLAS系统的仿真信号和实验数据进行了处理,并与传统算法的去噪结果进行了比较,结果表明,该方法具有较高的信噪比和相关系数,具有较好的降噪效果。SVD、EMD、SG组合算法适用于TDLAS水稻种子呼吸检测
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