Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyz...Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule.On this basis,EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items,and aggregate sub-items with similar attributes.Then,a prior basis function is set according to the aerodynamic acceleration stability rule,and the aggregated data are fitted by the basis function to predict its future state.After that,the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory.Finally,experiments verify the effectiveness of the method.In addition,the distribution of prediction errors in space is discussed,and the reasons are analyzed.展开更多
为提高采煤工作面涌水量预测准确度,收集大量工作面涌水量观测数据进行整理、统计、分析,将涌水量稳定性、周期性和季节性特征考虑在内,提出1种基于数据驱动的完全自适应模态分解算法(CEEMDAN)和改进的混合时间序列模型工作面涌水量预...为提高采煤工作面涌水量预测准确度,收集大量工作面涌水量观测数据进行整理、统计、分析,将涌水量稳定性、周期性和季节性特征考虑在内,提出1种基于数据驱动的完全自适应模态分解算法(CEEMDAN)和改进的混合时间序列模型工作面涌水量预测方法。该方法利用CEEMDAN处理涌水量数据,构建麻雀搜索算法(SSA)优化的长短期记忆网络(LSTM)和自回归移动平均模型(ARIMA)并行级联而成的混合时间序列模型对工作面涌水量进行预测。研究结果表明:该模型预测结果与真实数据相差更小,平均绝对误差为6.36 m 3/h,均方根误差为10.6 m 3/h,模型拟合系数为0.95,更适用于工作面涌水量预测。研究结果可为矿井工作面涌水量预测及防控提供参考。展开更多
噪声的包络调制检测(Detection of Envelope Modulation on Noise,DEMON)谱分析技术已被广泛应用于特征提取领域,但经典DEMON谱提取中高频信号频段的选取会影响DEMON谱的提取效果。针对这一问题,文中首先运用经验模态分解(Empirical Mod...噪声的包络调制检测(Detection of Envelope Modulation on Noise,DEMON)谱分析技术已被广泛应用于特征提取领域,但经典DEMON谱提取中高频信号频段的选取会影响DEMON谱的提取效果。针对这一问题,文中首先运用经验模态分解(Empirical Mode Decomposition,EMD)方法获得一系列固有模态函数(Intrinsic Mode Function,IMF),依据各阶模态函数与原信号的相关程度,筛选出更具代表性的几阶固有模态函数进行解调,再对解调的结果运用11/2维谱分析方法进行谱分析以抑制高斯噪声,通过这种方法获得的DEMON谱信噪比优于传统方法。实测湖试数据分析结果表明,该改进方法可以有效地进行特征提取,结果优于经典DEMON谱分析方法;该改进方法具有一定的实用性,有利于进行后续目标分类识别。展开更多
Empirical mode decomposition( EMD) is a powerful tool of time-frequency analysis. EMD decomposes a signal into a series of sub-signals,called Intrinsic mode functions( IMFs). Each IMF contains different frequency comp...Empirical mode decomposition( EMD) is a powerful tool of time-frequency analysis. EMD decomposes a signal into a series of sub-signals,called Intrinsic mode functions( IMFs). Each IMF contains different frequency components which can deal with the nonlinear and non-stationary of signal. Complete ensemble empirical mode decomposition( CEEMD) is an improved algorithm,which can provide an accurate reconstruction of the original signal and better spectral separation of the modes. The authors studied the decomposition result of a synthetic signal obtained from EMD and CEEMD. The result shows that the CEEMD has suitability in spectrum decomposition time-frequency analysis. Compared with traditional methods,a higher time-frequency resolution is obtained through verifying the method on both synthetic and real data.展开更多
The observations of in-situ spacecraft mission in the magnetosheath and a region of thermalized subsonic plasma behind the bow shock reveal a non-linear behaviour of plasma waves. The study of waves and optics in Phys...The observations of in-situ spacecraft mission in the magnetosheath and a region of thermalized subsonic plasma behind the bow shock reveal a non-linear behaviour of plasma waves. The study of waves and optics in Physics has given the understanding of the effect of many waves coming together to form a wave field or wave packet. The common aspect of such study shows that two or more waves can superimpose constructively or destructively. The sudden high magnetic field data in the magnetosheath displays such possibility of superposition of waves. In this paper, we use the empirical mode decomposition (EMD) and Hilbert transform (HT) techniques to determine the instantaneous frequencies of low frequency plasma waves in the magnetosheath. Our analysis has shown that the turbulent behavior of magnetic field in the magnetosheath within the selected period is due to superposition of waves.展开更多
基金supported by the National High-Tech R&D Program of China(2015AA70560452015AA8017032P)the Postgraduate Funding Project(JW2018A039)。
文摘Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule.On this basis,EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items,and aggregate sub-items with similar attributes.Then,a prior basis function is set according to the aerodynamic acceleration stability rule,and the aggregated data are fitted by the basis function to predict its future state.After that,the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory.Finally,experiments verify the effectiveness of the method.In addition,the distribution of prediction errors in space is discussed,and the reasons are analyzed.
文摘为提高采煤工作面涌水量预测准确度,收集大量工作面涌水量观测数据进行整理、统计、分析,将涌水量稳定性、周期性和季节性特征考虑在内,提出1种基于数据驱动的完全自适应模态分解算法(CEEMDAN)和改进的混合时间序列模型工作面涌水量预测方法。该方法利用CEEMDAN处理涌水量数据,构建麻雀搜索算法(SSA)优化的长短期记忆网络(LSTM)和自回归移动平均模型(ARIMA)并行级联而成的混合时间序列模型对工作面涌水量进行预测。研究结果表明:该模型预测结果与真实数据相差更小,平均绝对误差为6.36 m 3/h,均方根误差为10.6 m 3/h,模型拟合系数为0.95,更适用于工作面涌水量预测。研究结果可为矿井工作面涌水量预测及防控提供参考。
文摘噪声的包络调制检测(Detection of Envelope Modulation on Noise,DEMON)谱分析技术已被广泛应用于特征提取领域,但经典DEMON谱提取中高频信号频段的选取会影响DEMON谱的提取效果。针对这一问题,文中首先运用经验模态分解(Empirical Mode Decomposition,EMD)方法获得一系列固有模态函数(Intrinsic Mode Function,IMF),依据各阶模态函数与原信号的相关程度,筛选出更具代表性的几阶固有模态函数进行解调,再对解调的结果运用11/2维谱分析方法进行谱分析以抑制高斯噪声,通过这种方法获得的DEMON谱信噪比优于传统方法。实测湖试数据分析结果表明,该改进方法可以有效地进行特征提取,结果优于经典DEMON谱分析方法;该改进方法具有一定的实用性,有利于进行后续目标分类识别。
文摘Empirical mode decomposition( EMD) is a powerful tool of time-frequency analysis. EMD decomposes a signal into a series of sub-signals,called Intrinsic mode functions( IMFs). Each IMF contains different frequency components which can deal with the nonlinear and non-stationary of signal. Complete ensemble empirical mode decomposition( CEEMD) is an improved algorithm,which can provide an accurate reconstruction of the original signal and better spectral separation of the modes. The authors studied the decomposition result of a synthetic signal obtained from EMD and CEEMD. The result shows that the CEEMD has suitability in spectrum decomposition time-frequency analysis. Compared with traditional methods,a higher time-frequency resolution is obtained through verifying the method on both synthetic and real data.
文摘The observations of in-situ spacecraft mission in the magnetosheath and a region of thermalized subsonic plasma behind the bow shock reveal a non-linear behaviour of plasma waves. The study of waves and optics in Physics has given the understanding of the effect of many waves coming together to form a wave field or wave packet. The common aspect of such study shows that two or more waves can superimpose constructively or destructively. The sudden high magnetic field data in the magnetosheath displays such possibility of superposition of waves. In this paper, we use the empirical mode decomposition (EMD) and Hilbert transform (HT) techniques to determine the instantaneous frequencies of low frequency plasma waves in the magnetosheath. Our analysis has shown that the turbulent behavior of magnetic field in the magnetosheath within the selected period is due to superposition of waves.
