6804.2 20012361sEMG 信号分析及其应用研究进展=Some advancesin the research of sEMG signal analysis and itsapplication[刊,中,A]/王健∥体育科学.-2000.-20(4).-56-60 参 25(TY)肌电图∥肌肉收缩∥肌纤维∥分析∥肌肉∥神经表面...6804.2 20012361sEMG 信号分析及其应用研究进展=Some advancesin the research of sEMG signal analysis and itsapplication[刊,中,A]/王健∥体育科学.-2000.-20(4).-56-60 参 25(TY)肌电图∥肌肉收缩∥肌纤维∥分析∥肌肉∥神经表面肌电信号是从肌肉表面通过电极引导、记录下来的神经肌肉系统活动时的一维时间序列信号,其变化与参与活动的运动单位数量、运动单位活动模式和代谢状态等因素有关,能够实时地、准确地和在非损伤状态下反映肌肉活动状态和功能状态。本文拟就 sEMG 信号分析及其应用研究进展进行系统回顾。展开更多
In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based...In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.展开更多
文摘6804.2 20012361sEMG 信号分析及其应用研究进展=Some advancesin the research of sEMG signal analysis and itsapplication[刊,中,A]/王健∥体育科学.-2000.-20(4).-56-60 参 25(TY)肌电图∥肌肉收缩∥肌纤维∥分析∥肌肉∥神经表面肌电信号是从肌肉表面通过电极引导、记录下来的神经肌肉系统活动时的一维时间序列信号,其变化与参与活动的运动单位数量、运动单位活动模式和代谢状态等因素有关,能够实时地、准确地和在非损伤状态下反映肌肉活动状态和功能状态。本文拟就 sEMG 信号分析及其应用研究进展进行系统回顾。
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.
