Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity ...Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.展开更多
针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分...针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分解,获取模态分量。接着,将原始信号与这些模态分量分割为多个时间局部片段,并计算它们对应时间局部片段的相关熵值。然后,通过LWT算法处理弱相关分量,最后重构剩余分量以完成去噪过程。实验结果表明:在5 km的传感距离和10 m的空间分辨率的条件下,系统的信噪比达到了54.36 d B,同时均方根误差降低至0.091。展开更多
为解决风力发电直接并网所产生的功率波动问题,提出了一种基于改进阿基米德优化算法融合自适应噪声完全集合经验模态分解(complete ensemble EMD with adaptive noise,CEEMDAN)的容量配置方法。采用由限幅与滑动平均结合的加权滤波算法...为解决风力发电直接并网所产生的功率波动问题,提出了一种基于改进阿基米德优化算法融合自适应噪声完全集合经验模态分解(complete ensemble EMD with adaptive noise,CEEMDAN)的容量配置方法。采用由限幅与滑动平均结合的加权滤波算法平滑风电出力,同时减小平滑结果的滞后性,得到风电并网功率和混合储能系统(hybrid energy storage system,HESS)参考功率。为了合理分配HESS的内部功率,借助CEEMDAN分解HESS的参考功率,得到高低频分量。综合考虑HESS功率和容量、荷电状态(state of charge,SOC)与负荷缺点率等因素,构建以年综合成本最小为目标的容量优化配置模型并采用改进阿基米德优化算法求解。基于实际算例进行仿真分析,结果表明,与原始风电并网相比,HESS配置方案将波动率减少了13.538%,平滑度提高了16.057%。相较于传统单一储能平抑效果更加明显,减少了容量配置。同时,对比传统阿基米德优化算法节省了15.325%的投资成本。展开更多
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China(No.11574250).
文摘Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.
文摘针对分布式光纤声传感系统信号信噪比过低的问题,提出一种基于时域局部广义最大互相关熵(TLGMCC)准则联合自适应噪声完备集合经验模态分解(CEEMDAN)与提升小波变换(LWT)的优化降噪方法。首先,使用自适应噪声完备CEEMDAN对原始信号进行分解,获取模态分量。接着,将原始信号与这些模态分量分割为多个时间局部片段,并计算它们对应时间局部片段的相关熵值。然后,通过LWT算法处理弱相关分量,最后重构剩余分量以完成去噪过程。实验结果表明:在5 km的传感距离和10 m的空间分辨率的条件下,系统的信噪比达到了54.36 d B,同时均方根误差降低至0.091。
文摘为解决风力发电直接并网所产生的功率波动问题,提出了一种基于改进阿基米德优化算法融合自适应噪声完全集合经验模态分解(complete ensemble EMD with adaptive noise,CEEMDAN)的容量配置方法。采用由限幅与滑动平均结合的加权滤波算法平滑风电出力,同时减小平滑结果的滞后性,得到风电并网功率和混合储能系统(hybrid energy storage system,HESS)参考功率。为了合理分配HESS的内部功率,借助CEEMDAN分解HESS的参考功率,得到高低频分量。综合考虑HESS功率和容量、荷电状态(state of charge,SOC)与负荷缺点率等因素,构建以年综合成本最小为目标的容量优化配置模型并采用改进阿基米德优化算法求解。基于实际算例进行仿真分析,结果表明,与原始风电并网相比,HESS配置方案将波动率减少了13.538%,平滑度提高了16.057%。相较于传统单一储能平抑效果更加明显,减少了容量配置。同时,对比传统阿基米德优化算法节省了15.325%的投资成本。
