This paper discusses the stability structure and the bifurcation of phase path characteristics of synoptic scale system.The analytic results show that the catastrophe of the synoptic scale disturbance may be caused by...This paper discusses the stability structure and the bifurcation of phase path characteristics of synoptic scale system.The analytic results show that the catastrophe of the synoptic scale disturbance may be caused by the nonlinear effects of barotropic and baroclinic instability and advection of ambient large-scale flow.Also, foregoing nonlinear effects on the speed of development and decay of the system are presented in the processes deviating from or approaching to equilibrium state.It has been found that there is a resonance phenomenon between the time-oscillation of heat source and the atmospheric disturbance.展开更多
提出了一种基于连续小波变换(continuous walelet transform,CWT)和奇异值分解(singular value decomposition,SVD)相结合的提升小波系数SVD辨识信号振荡频率和模式信息提取及信号去噪的新方法。克服了噪声较大或者密集模态时,小波脊线...提出了一种基于连续小波变换(continuous walelet transform,CWT)和奇异值分解(singular value decomposition,SVD)相结合的提升小波系数SVD辨识信号振荡频率和模式信息提取及信号去噪的新方法。克服了噪声较大或者密集模态时,小波脊线不清晰甚至会出现混叠和交叉难以提取频率的情况,根据提升的小波系数奇异值分解频率向量识别各阶振荡模式的频率。同时选用小波能量系数来识别主导振荡模式,用小波软阈值去噪和SVD分解后矩阵重构来进行信号去噪。CWT可以处理含时变振荡模式的低频振荡信号,且对模式参数具有较高的辨识精度。仿真算例验证了算法的有效性和适用性。展开更多
文摘This paper discusses the stability structure and the bifurcation of phase path characteristics of synoptic scale system.The analytic results show that the catastrophe of the synoptic scale disturbance may be caused by the nonlinear effects of barotropic and baroclinic instability and advection of ambient large-scale flow.Also, foregoing nonlinear effects on the speed of development and decay of the system are presented in the processes deviating from or approaching to equilibrium state.It has been found that there is a resonance phenomenon between the time-oscillation of heat source and the atmospheric disturbance.
文摘提出了一种基于连续小波变换(continuous walelet transform,CWT)和奇异值分解(singular value decomposition,SVD)相结合的提升小波系数SVD辨识信号振荡频率和模式信息提取及信号去噪的新方法。克服了噪声较大或者密集模态时,小波脊线不清晰甚至会出现混叠和交叉难以提取频率的情况,根据提升的小波系数奇异值分解频率向量识别各阶振荡模式的频率。同时选用小波能量系数来识别主导振荡模式,用小波软阈值去噪和SVD分解后矩阵重构来进行信号去噪。CWT可以处理含时变振荡模式的低频振荡信号,且对模式参数具有较高的辨识精度。仿真算例验证了算法的有效性和适用性。
