Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time se...Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.展开更多
The cyclic cross-correlation between a stationary process and a cyclostationary process at cycle frequency α(≠ 0)is identically zero under an ideal condition, which indicates that a cyclic cross-correlation method p...The cyclic cross-correlation between a stationary process and a cyclostationary process at cycle frequency α(≠ 0)is identically zero under an ideal condition, which indicates that a cyclic cross-correlation method performs much better than the conventional cross-correlation method in suppressing the stationary noise or interference. But unfortunately, the cyclic cross-correlation will not really approach zero due to the limited data length in some real conditions. In this paper, the quantitative relation between the data length and the estimated cyclic cross-correlation is deduced, and some useful conclusions are drawn, which are proven by some computer simulations. The conclusion in this paper is really useful for the practical application of cyclostationary signal processing.展开更多
Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum...Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.展开更多
Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to th...Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.展开更多
It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting ...It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.展开更多
Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy ...Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy metal pollution.Spectral reflectance implies information of pollution impacts on vegetation;estimation of lead pollution degree based on the spectral reflectance is equivalent to extraction of weak information.This study puts forward a new feature extraction method based展开更多
Carrying out experiments and researches on tool bre ak age and undercut of work-piece of free-form surface by using wavelet analysis, both the fault features can be extracted in a special frequency segment of wave let...Carrying out experiments and researches on tool bre ak age and undercut of work-piece of free-form surface by using wavelet analysis, both the fault features can be extracted in a special frequency segment of wave let decompose. According to the feature of transient fault, the author proposes for the first time the automatic determination technology of the threshold by us e of the adaptive filter characteristic of wavelet transform. Based on profound researches on steady fault feature, this dissertation makes an effective token o f steady fault feature by using wavelet energy method, and proposes the new idea to identify cut-in case and cut-out case, thereby successfully gives an uniqu e description quantitatively on the characterization of the variation of fault a nd cutting condition in the monitoring system.展开更多
Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals i...Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform （FRIT） deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.展开更多
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.
文摘The cyclic cross-correlation between a stationary process and a cyclostationary process at cycle frequency α(≠ 0)is identically zero under an ideal condition, which indicates that a cyclic cross-correlation method performs much better than the conventional cross-correlation method in suppressing the stationary noise or interference. But unfortunately, the cyclic cross-correlation will not really approach zero due to the limited data length in some real conditions. In this paper, the quantitative relation between the data length and the estimated cyclic cross-correlation is deduced, and some useful conclusions are drawn, which are proven by some computer simulations. The conclusion in this paper is really useful for the practical application of cyclostationary signal processing.
文摘Wavelet-fractal based SAR （synthetic aperture radar） image processing is one of the advanced technologies in image processing. The main concept of analysis is that after wavelet transformation, multifractal spectrum of the signal is different from that of noise. This difference is used to alleviate the noise produced by SAR image.The method to denoise SAR image using the process based on wavelet-fractai analysis is discussed in detail. Essentially, the present method focuses on adjusting the Hoelder exponent α of multifractal spectrum. After simulation, α should be adjusted to 1.72-1.73. The more the value of α exceeds 1.73, the less distinctive the edges of SAR image become. According to the authors denoising is optimal at α=1.72-1.73. In other words, when α =1.72-1.73, a smooth and denoised SAR image is produced.
基金Projects(51678071,51278071)supported by the National Natural Science Foundation of ChinaProjects(14KC06,CX2015BS02)supported by Changsha University of Science&Technology,China
文摘Due to the disturbances arising from the coherence of reflected waves and from echo noise,problems such as limitations,instability and poor accuracy exist with the current quantitative analysis methods.According to the intrinsic features of GPR signals and wavelet time–frequency analysis,an optimal wavelet basis named GPR3.3 wavelet is constructed via an improved biorthogonal wavelet construction method to quantitatively analyse the GPR signal.A new quantitative analysis method based on the biorthogonal wavelet(the QAGBW method)is proposed and applied in the analysis of analogue and measured signals.The results show that compared with the Bayesian frequency-domain blind deconvolution and with existing wavelet bases,the QAGBW method based on optimal wavelet can limit the disturbance from factors such as the coherence of reflected waves and echo noise,improve the quantitative analytical precision of the GPR signal,and match the minimum thickness for quantitative analysis with the vertical resolution of GPR detection.
基金Project(50374079) supported by the National Natural Science Foundation of China
文摘It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.
文摘Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy metal pollution.Spectral reflectance implies information of pollution impacts on vegetation;estimation of lead pollution degree based on the spectral reflectance is equivalent to extraction of weak information.This study puts forward a new feature extraction method based
文摘Carrying out experiments and researches on tool bre ak age and undercut of work-piece of free-form surface by using wavelet analysis, both the fault features can be extracted in a special frequency segment of wave let decompose. According to the feature of transient fault, the author proposes for the first time the automatic determination technology of the threshold by us e of the adaptive filter characteristic of wavelet transform. Based on profound researches on steady fault feature, this dissertation makes an effective token o f steady fault feature by using wavelet energy method, and proposes the new idea to identify cut-in case and cut-out case, thereby successfully gives an uniqu e description quantitatively on the characterization of the variation of fault a nd cutting condition in the monitoring system.
基金supported by the National Natural Science Foundation of China(61571088)the State High-Tech Development Plan(the 863 Program)(2015AA7031093B2015AA8098088B)
基金This project was supported by the National Natural Science Foundation of China (60672034)the Research Fund for the Doctoral Program of Higher Education(20060217021)the Natural Science Foundation of Heilongjiang Province of China (ZJG0606-01)
文摘Sonar images have complex background, low contrast, and deteriorative edges; these characteristics make it difficult for researchers to dispose the sonar objects. The multi-resolution analysis represents the signals in different scales efficiently, which is widely used in image processing. Wavelets are successful in disposing point discontinuities in one dimension, but not in two dimensions. The finite Ridgelet transform （FRIT） deals efficiently with the singularity in high dimension. It presents three improved denoising approaches, which are based on FRIT and used in the sonar image disposal technique. By experiment and comparison with traditional methods, these approaches not only suppress the artifacts, but also obtain good effect in edge keeping and SNR of the sonar image denoising.
