The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the moder...The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow’s SVD method.展开更多
To avoid drawbacks of classic discrete Fourier transform(DFT)method,modern spectral estimation theory was introduced into harmonics and inter-harmonics analysis in electric power system.Idea of the subspace-based root...To avoid drawbacks of classic discrete Fourier transform(DFT)method,modern spectral estimation theory was introduced into harmonics and inter-harmonics analysis in electric power system.Idea of the subspace-based root-min-norm algorithm was described,but it is susceptive to noises with unstable performance in different SNRs.So the modified root-min-norm algorithm based on cross-spectral estimation was proposed,utilizing cross-correlation matrix and independence of different Gaussian noise series.Lots of simulation experiments were carried out to test performance of the algorithm in different conditions,and its statistical characteristics was presented.Simulation results show that the modified algorithm can efficiently suppress influence of the noises,and has high frequency resolution,high precision and high stability,and it is much superior to the classic DFT method.展开更多
Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamformin...Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.展开更多
针对高光谱图像分类任务中小样本引起分类精度不高的问题,提出了一种基于动态图-谱特征提取的高光谱分类方法,提高全局建模和局部信息提取能力,实现跨域空间特征和光谱相似性特征的互补融合。首先,提出动态轴滑动建图策略,建立高效、有...针对高光谱图像分类任务中小样本引起分类精度不高的问题,提出了一种基于动态图-谱特征提取的高光谱分类方法,提高全局建模和局部信息提取能力,实现跨域空间特征和光谱相似性特征的互补融合。首先,提出动态轴滑动建图策略,建立高效、有代表性的图结构。其次,基于动态图结构设计动态图特征提取网络,采用特征卷积层、动态空间卷积模块和动态图卷积模块以捕捉局部特征并整合不同尺度的跨域空间特征。然后,区域-全局光谱特征网络通过多层光谱特征卷积模块,融合局部信息并跨层融合编码器,深入挖掘局部和全局光谱特征的序列属性。最后,交叉注意力建立动态关联以融合空间和光谱信息,完成分类。实验结果表明,该方法在Indian Pines、University of Pavia和Salinas三个高光谱数据集上取得了优于现有方法的分类性能,为处理高光谱图像复杂空间和光谱信息提供了一种有效的深度学习框架。展开更多
以NACA 65(12)–10独立基准叶片为对象,使用线性传声器阵列和SODIX(SOurce DIrectivity modeling in the cross-spectral matriX)方法对基准叶片前缘噪声指向性分布特征及波浪前缘对叶片前缘噪声的影响进行了实验研究。开发了SODIX数据...以NACA 65(12)–10独立基准叶片为对象,使用线性传声器阵列和SODIX(SOurce DIrectivity modeling in the cross-spectral matriX)方法对基准叶片前缘噪声指向性分布特征及波浪前缘对叶片前缘噪声的影响进行了实验研究。开发了SODIX数据处理程序并进行了数值仿真验证,结果表明:不同指向角下计算结果的最大误差不超过0.26 dB。在半消声室内,利用由31个传声器组成的非均匀分布优化阵列,对NACA 65(12)–10独立基准叶片和仿生学叶片的前缘噪声开展了参数化声学实验。结果表明:在40°~142°指向角测量范围内,基准叶片前缘噪声指向性符合典型偶极子声源特征,峰值在130°指向角附近;随着频率升高,基准叶片前缘噪声指向性产生了显著的“波瓣”现象,频率越高,“波瓣”越多。进一步研究表明:不同波长和幅值的前缘构型都可以有效降低指向角测量范围内的前缘噪声;与波浪前缘的波长相比,波浪前缘的幅值对前缘噪声的影响更为显著,特别是在90°~120°指向角范围内,A30W20叶型的降噪量可达7.71 dB。展开更多
The study of large-scale atmospheric turbulence and transport processes is of vital importance in the general circulation of the atmosphere. The governing equations of the power and cross-spectra for the atmospheric m...The study of large-scale atmospheric turbulence and transport processes is of vital importance in the general circulation of the atmosphere. The governing equations of the power and cross-spectra for the atmospheric motion and transports in the domain of wave number frequency space have been derived. The contributions of the nonlinear interactions of the atmospheric waves in velocity and temperature fields to the conversion of kinetic and potential energies and to the meridional transports of angular momentum and sensible heat in the atmosphere have been discussed.展开更多
基金Supported by Doctoral Fund of the State Education Commission of China
文摘The cross-spectral estimation methods are efficient in estimating the parameters of sinusoidal signals embedded in colored noise. But up to now, only FPT and cross-periodogram methods are used in this field, the modern auto-spectral estimation method is introduced into cross-spectral estimation in this paper, meanwhile the cross-correlation based Yule-Walker equation is proposed theoretically and the moment and singular-value decomposition (SVD)) algorithms for cross-spectral estimation have been developed. Finally, a numerical example is given for comparing the presented methods with the well-known Cadzow’s SVD method.
基金Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘To avoid drawbacks of classic discrete Fourier transform(DFT)method,modern spectral estimation theory was introduced into harmonics and inter-harmonics analysis in electric power system.Idea of the subspace-based root-min-norm algorithm was described,but it is susceptive to noises with unstable performance in different SNRs.So the modified root-min-norm algorithm based on cross-spectral estimation was proposed,utilizing cross-correlation matrix and independence of different Gaussian noise series.Lots of simulation experiments were carried out to test performance of the algorithm in different conditions,and its statistical characteristics was presented.Simulation results show that the modified algorithm can efficiently suppress influence of the noises,and has high frequency resolution,high precision and high stability,and it is much superior to the classic DFT method.
基金supported by the National Key Research and Development Plan of China(No.2023YFB3406500)the National Natural Science Foundation of China(No.52475132)+2 种基金the Aeronautical Science Foundation of China(No.20200015053001)the Shaanxi Key Research Program Project,China(No.2024GX-ZDCYL-01–16)the Xi’an Key Industrial Chain Technology Research Project,China(No.2023JH-RGZNGG-0033)。
文摘Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.
文摘针对高光谱图像分类任务中小样本引起分类精度不高的问题,提出了一种基于动态图-谱特征提取的高光谱分类方法,提高全局建模和局部信息提取能力,实现跨域空间特征和光谱相似性特征的互补融合。首先,提出动态轴滑动建图策略,建立高效、有代表性的图结构。其次,基于动态图结构设计动态图特征提取网络,采用特征卷积层、动态空间卷积模块和动态图卷积模块以捕捉局部特征并整合不同尺度的跨域空间特征。然后,区域-全局光谱特征网络通过多层光谱特征卷积模块,融合局部信息并跨层融合编码器,深入挖掘局部和全局光谱特征的序列属性。最后,交叉注意力建立动态关联以融合空间和光谱信息,完成分类。实验结果表明,该方法在Indian Pines、University of Pavia和Salinas三个高光谱数据集上取得了优于现有方法的分类性能,为处理高光谱图像复杂空间和光谱信息提供了一种有效的深度学习框架。
文摘The study of large-scale atmospheric turbulence and transport processes is of vital importance in the general circulation of the atmosphere. The governing equations of the power and cross-spectra for the atmospheric motion and transports in the domain of wave number frequency space have been derived. The contributions of the nonlinear interactions of the atmospheric waves in velocity and temperature fields to the conversion of kinetic and potential energies and to the meridional transports of angular momentum and sensible heat in the atmosphere have been discussed.
