In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on m...In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.展开更多
锂离子电池健康状态(state of health, SOH)估计对于保证锂离子电池管理系统的安全稳定运行至关重要。然而,由于锂离子电池在放电过程中存在容量再生现象,SOH的准确估计一直是一个挑战。为了提高估计精度,提出了一种基于变分模态分解(va...锂离子电池健康状态(state of health, SOH)估计对于保证锂离子电池管理系统的安全稳定运行至关重要。然而,由于锂离子电池在放电过程中存在容量再生现象,SOH的准确估计一直是一个挑战。为了提高估计精度,提出了一种基于变分模态分解(variational mode decomposition, VMD)和双向长短期记忆网络-注意力机制(bidirectional long short term memory-attention, BiLSTM-ATT)的混合模型估计方法。首先,采用VMD分解算法对锂电池容量进行分解,得到一组相对稳定的子序列。为了降低后续的计算规模,通过引入了排列熵的方法对各个子序列进行重构。然后,将重构后的序列输入到BiLSTM-ATT模型中,利用注意力机制来分配隐藏层的特征权重,并通过双向长短期记忆网络(bidirectional long short term memory, BiLSTM)模型对SOH值进行训练和估计。最后,将所有估计值进行相加得到完整的SOH估计结果。通过在CALCE锂电池数据集上的CS2_36、CS2_38和CX2_35进行验证,实验结果表明所提出算法的均方根误差始终保持在0.6%以内,平均绝对误差始终保持在0.4%以内,相比其他估计模型表现出更高的精度和性能。展开更多
Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of ...Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.展开更多
基金Project(61301095)supported by the National Natural Science Foundation of ChinaProject(QC2012C070)supported by Heilongjiang Provincial Natural Science Foundation for the Youth,ChinaProjects(HEUCF130807,HEUCFZ1129)supported by the Fundamental Research Funds for the Central Universities of China
文摘In modern electromagnetic environment, radar emitter signal recognition is an important research topic. On the basis of multi-resolution wavelet analysis, an adaptive radar emitter signal recognition method based on multi-scale wavelet entropy feature extraction and feature weighting was proposed. With the only priori knowledge of signal to noise ratio(SNR), the method of extracting multi-scale wavelet entropy features of wavelet coefficients from different received signals were combined with calculating uneven weight factor and stability weight factor of the extracted multi-dimensional characteristics. Radar emitter signals of different modulation types and different parameters modulated were recognized through feature weighting and feature fusion. Theoretical analysis and simulation results show that the presented algorithm has a high recognition rate. Additionally, when the SNR is greater than-4 d B, the correct recognition rate is higher than 93%. Hence, the proposed algorithm has great application value.
文摘锂离子电池健康状态(state of health, SOH)估计对于保证锂离子电池管理系统的安全稳定运行至关重要。然而,由于锂离子电池在放电过程中存在容量再生现象,SOH的准确估计一直是一个挑战。为了提高估计精度,提出了一种基于变分模态分解(variational mode decomposition, VMD)和双向长短期记忆网络-注意力机制(bidirectional long short term memory-attention, BiLSTM-ATT)的混合模型估计方法。首先,采用VMD分解算法对锂电池容量进行分解,得到一组相对稳定的子序列。为了降低后续的计算规模,通过引入了排列熵的方法对各个子序列进行重构。然后,将重构后的序列输入到BiLSTM-ATT模型中,利用注意力机制来分配隐藏层的特征权重,并通过双向长短期记忆网络(bidirectional long short term memory, BiLSTM)模型对SOH值进行训练和估计。最后,将所有估计值进行相加得到完整的SOH估计结果。通过在CALCE锂电池数据集上的CS2_36、CS2_38和CX2_35进行验证,实验结果表明所提出算法的均方根误差始终保持在0.6%以内,平均绝对误差始终保持在0.4%以内,相比其他估计模型表现出更高的精度和性能。
基金the supported by National Natural Science Foundation of China(No.61871318 and 11574250)Scientific Research Plan Projects of Shaanxi Education Department(No.19JK0568).
文摘Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.
