By dint of the summer precipitation data from 21 stations in the Dongting Lake region during 1960-2008 and the sea surface temperature(SST) data from NOAA,the spatial and temporal distributions of summer precipitation...By dint of the summer precipitation data from 21 stations in the Dongting Lake region during 1960-2008 and the sea surface temperature(SST) data from NOAA,the spatial and temporal distributions of summer precipitation and their correlations with SST are analyzed.The coupling relationship between the anomalous distribution in summer precipitation and the variation of SST has between studied with the Singular Value Decomposition(SVD) analysis.The increase or decrease of summer precipitation in the Dongting Lake region is closely associated with the SST anomalies in three key regions.The variation of SST in the three key regions has been proved to be a significant previous signal to anomaly of summer rainfall in Dongting region.展开更多
This paper clarifies the essence of the significance test of singular value decomposition analysis （SVD）, and investigates four rules for testing the significance of coupled modes of SVD, including parallel analysis...This paper clarifies the essence of the significance test of singular value decomposition analysis （SVD）, and investigates four rules for testing the significance of coupled modes of SVD, including parallel analysis, nonparametric bootstrap, random-phase test, and a new rule named modified parallel analysis. A numerical experiment is conducted to quantitatively compare the performance of the four rules in judging whether a coupled mode of SVD is significant as parameters such as the sample size, the number of grid points, and the signal-to-noise ratio vary. The results show that the four rules perform better with lower ratio of the number of grid points to sample size. Modified parallel analysis and nonparametric bootstrap perform best to abandon the spurious coupled modes, but the latter is better than the former to retain the significant coupled modes when the sample size is not much larger than the number of grid points. Parallel analysis and random-phase test are robust to abandon the spurious coupled modes only when either （1） the observations at the grid points are spatially uncorrelated, or （2） the coupled signal is very strong for parallel analysis and is not weak for random-phase test. The reasons affecting the accuracy of the test rules are discussed.展开更多
A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directl...A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.展开更多
The generalized singular value decomposition(GSVD)of two matrices with the same number of columns is a very useful tool in many practical applications.However,the GSVD may suffer from heavy computational time and memo...The generalized singular value decomposition(GSVD)of two matrices with the same number of columns is a very useful tool in many practical applications.However,the GSVD may suffer from heavy computational time and memory requirement when the scale of the matrices is quite large.In this paper,we use random projections to capture the most of the action of the matrices and propose randomized algorithms for computing a low-rank approximation of the GSVD.Serval error bounds of the approximation are also presented for the proposed randomized algorithms.Finally,some experimental results show that the proposed randomized algorithms can achieve a good accuracy with less computational cost and storage requirement.展开更多
In order to construct Chinese ontology easily, an automated ontology learning technology for Chinese documents based on singular value decomposition (SVD) and conceptual clustering is proposed . First the system extra...In order to construct Chinese ontology easily, an automated ontology learning technology for Chinese documents based on singular value decomposition (SVD) and conceptual clustering is proposed . First the system extracts concepts from a set of domain-specific documents by using SVD technology, and then acquires subsumption relationships between the concepts by means of hierarchical conceptual clustering method. The system thus yields domain-related concept hierarchy.展开更多
Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR ...Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.展开更多
Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequ...Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequences such as the failure of the orbit feedback and the transverse feedback.A troubleshooting has been made to prevent the defective BPMs from affecting the accuracy and stability of the storage ring in Shanghai Synchrotron Radiation Facility(SSRF).Different types of malfunctions have been successfully identified by using the idea of principal component analysis(PCA).展开更多
With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction w...With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction with state estimation to assess system stability and event detection. However, these techniques require system topology and a large computational time. This paper presents a novel approach that uses real-time PMU data streams without the need of system connectivity or additional state estimation. The new development is based on the approximation of the eigenvalues related to the decoupled discreet-time power flow Jacobian matrix using direct openPDC data in real-time. Results are compared with other methods, such as Prony’s method, which can be too slow to handle big data. The newly developed Discreet-Time Jacobian Eigenvalue Approximation (DDJEA) method not only proves its accuracy, but also shows its effectiveness with minimal computational time: an essential element when considering situational awareness.展开更多
AIM:To present a content-based image retrieval(CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classificat...AIM:To present a content-based image retrieval(CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.METHODS:Breast density is characterized by image texture using singular value decomposition(SVD) and histograms.Pattern similarity is computed by a support vector machine(SVM) to separate the four BI-RADS tissue categories.The crucial number of remaining singular values is varied(SVD),and linear,radial,and polynomial kernels are investigated(SVM).The system is supported by a large reference database for training and evaluation.Experiments are based on 5-fold cross validation.RESULTS:Adopted from DDSM,MIAS,LLNL,and RWTH datasets,the reference database is composed of over 10000 various mammograms with unified and reliable ground truth.An average precision of 82.14% is obtained using 25 singular values(SVD),polynomial kernel and the one-against-one(SVM).CONCLUSION:Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.展开更多
A measuring profile was set up in both sides of the Qiongzhou strait to carry out the simultaneous observation of three component geomagnetic variation. The observed synchronous geomagnetic vertical variations of shor...A measuring profile was set up in both sides of the Qiongzhou strait to carry out the simultaneous observation of three component geomagnetic variation. The observed synchronous geomagnetic vertical variations of short periods were reversed on the both sides of the strait. It means that there is a abnormal concentration of electric current in the area. Spatial wave number domain analysis was performed by Sompi spectral analysis for the spatial distribution and the internal and the external parts of the geomagnetic variation field were separated. Inversion of the internal field was carried out by generalized inverse matrix inversion based on singular value decomposition and the distribution of undergrond current density was obtained. The discussion suggests that this abnormal current concentration comes from current channelling effect in the Quaternary sediment in this region.展开更多
Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted...Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.展开更多
Although the SSA （singular spectral analysis） is a potential tool for analysing time series of different physical processes, the processing of large geophysical data set requires more time and is found to be computa...Although the SSA （singular spectral analysis） is a potential tool for analysing time series of different physical processes, the processing of large geophysical data set requires more time and is found to be computationally expansive. In particular for the SVD （singular value decomposition） of large trajectory matrix, the processing units require huge memory and high performance computing system. In the present work, we propose an alternative scheme based on WSSA （windowed singular spectral analysis）, which is robust for analysing long data sets without losing any valuable low-frequency information contained in the data. The underlying scheme reduces the floating point operations in SVD computations as the size of the trajectory matrix is small in windowed processing. In order to test the efficiency, the authors applied the proposed method on two geophysical data sets i.e., the climatic record with 30,000 data points and seismic reflection trace with 8,000 data points. The authors have shown that without distorting any physical information, the low-frequency contents of the data are well preserved after the windowed processing in both the cases.展开更多
Recently,the tensor robust principal component analysis(TRPCA),aiming to recover the true low-rank tensor from noisy data,has attracted considerable attention.In this paper,we solve the TRPCA problem under the framewo...Recently,the tensor robust principal component analysis(TRPCA),aiming to recover the true low-rank tensor from noisy data,has attracted considerable attention.In this paper,we solve the TRPCA problem under the framework of the tensor singular value decomposition(t-SVD).Since the convex relaxation approaches have some limitations,we establish a new non-convex TRPCA model by introducing the non-convex tensor rank approximation based on the Laplace function via the weighted l_(p)-norm regularization.An efficient algorithm based on the alternating direction method of multipliers(ADMM)is developed to solve the proposed model.We further prove that the constructed sequence converges to the desirable Karush-Kuhn-Tucker point.Experimental results show that the proposed approach outperforms various latest approaches in the literature.展开更多
The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems.In this paper,a novel conceptua...The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems.In this paper,a novel conceptual framework for distributed monitoring of power system oscillations using multiblock principal component analysis(MB-PCA)and higher-order singular value decomposition(HOSVD)is proposed to understand,characterize,and visualize the global behavior of the power system.The proposed framework can be used to evaluate the influence of a given area or utility on the oscillatory behavior,uncover low-dimensional structures from high-dimensional data,and analyze the effects of heterogeneous data on the modal characteristics and interpretation of power system.The metrics are then investigated to examine the relationships between the dynamic patterns and participation of individual data blocks in the global behavior of the system.Practical application of these techniques is demonstrated by case studies of two systems:a 14-machine test system and a 5449-bus 635-generator equivalent model of a large power system.展开更多
Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value ...Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.展开更多
基金Supported by The Special Foundation of Chinese Meteorological Bureau Climate Changes Program(200920)The Special Foundation of Hunan Major Scientific and Technological Research Program(2008FJ1006)~~
文摘By dint of the summer precipitation data from 21 stations in the Dongting Lake region during 1960-2008 and the sea surface temperature(SST) data from NOAA,the spatial and temporal distributions of summer precipitation and their correlations with SST are analyzed.The coupling relationship between the anomalous distribution in summer precipitation and the variation of SST has between studied with the Singular Value Decomposition(SVD) analysis.The increase or decrease of summer precipitation in the Dongting Lake region is closely associated with the SST anomalies in three key regions.The variation of SST in the three key regions has been proved to be a significant previous signal to anomaly of summer rainfall in Dongting region.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 40233027.
文摘This paper clarifies the essence of the significance test of singular value decomposition analysis （SVD）, and investigates four rules for testing the significance of coupled modes of SVD, including parallel analysis, nonparametric bootstrap, random-phase test, and a new rule named modified parallel analysis. A numerical experiment is conducted to quantitatively compare the performance of the four rules in judging whether a coupled mode of SVD is significant as parameters such as the sample size, the number of grid points, and the signal-to-noise ratio vary. The results show that the four rules perform better with lower ratio of the number of grid points to sample size. Modified parallel analysis and nonparametric bootstrap perform best to abandon the spurious coupled modes, but the latter is better than the former to retain the significant coupled modes when the sample size is not much larger than the number of grid points. Parallel analysis and random-phase test are robust to abandon the spurious coupled modes only when either （1） the observations at the grid points are spatially uncorrelated, or （2） the coupled signal is very strong for parallel analysis and is not weak for random-phase test. The reasons affecting the accuracy of the test rules are discussed.
基金The National Natural Science Foundation of China (No.61374194)
文摘A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.
基金supported by the National Natural Science Foundation of China under Grant nos.11701409 and 11571171the Natural Science Foundation of Jiangsu Province of China under Grant BK20170591the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant 17KJB110018.
文摘The generalized singular value decomposition(GSVD)of two matrices with the same number of columns is a very useful tool in many practical applications.However,the GSVD may suffer from heavy computational time and memory requirement when the scale of the matrices is quite large.In this paper,we use random projections to capture the most of the action of the matrices and propose randomized algorithms for computing a low-rank approximation of the GSVD.Serval error bounds of the approximation are also presented for the proposed randomized algorithms.Finally,some experimental results show that the proposed randomized algorithms can achieve a good accuracy with less computational cost and storage requirement.
文摘In order to construct Chinese ontology easily, an automated ontology learning technology for Chinese documents based on singular value decomposition (SVD) and conceptual clustering is proposed . First the system extracts concepts from a set of domain-specific documents by using SVD technology, and then acquires subsumption relationships between the concepts by means of hierarchical conceptual clustering method. The system thus yields domain-related concept hierarchy.
基金This research was funded by the National Natural Science Foundation of China(Nos.71762010,62262019,62162025,61966013,12162012)the Hainan Provincial Natural Science Foundation of China(Nos.823RC488,623RC481,620RC603,621QN241,620RC602,121RC536)+1 种基金the Haikou Science and Technology Plan Project of China(No.2022-016)the Project supported by the Education Department of Hainan Province,No.Hnky2021-23.
文摘Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.
基金Supported by National Natural Science Foundation of China(No.11075198)
文摘Beam position monitors(BPMs)have been widely used in all kinds of measurement systems,feedback systems and other areas in particle accelerator field these days.The malfunction of a single BPM can cause serious consequences such as the failure of the orbit feedback and the transverse feedback.A troubleshooting has been made to prevent the defective BPMs from affecting the accuracy and stability of the storage ring in Shanghai Synchrotron Radiation Facility(SSRF).Different types of malfunctions have been successfully identified by using the idea of principal component analysis(PCA).
文摘With the increased number of PMUs in the power grid, effective high speed, realtime methods to ascertain relevant data for situational awareness are needed. Several techniques have used data from PMUs in conjunction with state estimation to assess system stability and event detection. However, these techniques require system topology and a large computational time. This paper presents a novel approach that uses real-time PMU data streams without the need of system connectivity or additional state estimation. The new development is based on the approximation of the eigenvalues related to the decoupled discreet-time power flow Jacobian matrix using direct openPDC data in real-time. Results are compared with other methods, such as Prony’s method, which can be too slow to handle big data. The newly developed Discreet-Time Jacobian Eigenvalue Approximation (DDJEA) method not only proves its accuracy, but also shows its effectiveness with minimal computational time: an essential element when considering situational awareness.
基金Supported by CNPq-Brazil,Grants 306193/2007-8,471518/ 2007-7,307373/2006-1 and 484893/2007-6,by FAPEMIG,Grant PPM 347/08,and by CAPESThe IRMA project is funded by the German Research Foundation(DFG),Le 1108/4 and Le 1108/9
文摘AIM:To present a content-based image retrieval(CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.METHODS:Breast density is characterized by image texture using singular value decomposition(SVD) and histograms.Pattern similarity is computed by a support vector machine(SVM) to separate the four BI-RADS tissue categories.The crucial number of remaining singular values is varied(SVD),and linear,radial,and polynomial kernels are investigated(SVM).The system is supported by a large reference database for training and evaluation.Experiments are based on 5-fold cross validation.RESULTS:Adopted from DDSM,MIAS,LLNL,and RWTH datasets,the reference database is composed of over 10000 various mammograms with unified and reliable ground truth.An average precision of 82.14% is obtained using 25 singular values(SVD),polynomial kernel and the one-against-one(SVM).CONCLUSION:Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.
文摘A measuring profile was set up in both sides of the Qiongzhou strait to carry out the simultaneous observation of three component geomagnetic variation. The observed synchronous geomagnetic vertical variations of short periods were reversed on the both sides of the strait. It means that there is a abnormal concentration of electric current in the area. Spatial wave number domain analysis was performed by Sompi spectral analysis for the spatial distribution and the internal and the external parts of the geomagnetic variation field were separated. Inversion of the internal field was carried out by generalized inverse matrix inversion based on singular value decomposition and the distribution of undergrond current density was obtained. The discussion suggests that this abnormal current concentration comes from current channelling effect in the Quaternary sediment in this region.
基金supported by MOST under Grant No.104-2221-E-468-007
文摘Images are generally corrupted by impulse noise during acquisition and transmission.Noise deteriorates the quality of images.To remove corruption noise,we propose a hybrid approach to restoring a random noisecorrupted image,including a block matching 3D(BM3D)method,an adaptive non-local mean(ANLM)scheme,and the K-singular value decomposition(K-SVD)algorithm.In the proposed method,we employ the morphological component analysis(MCA)to decompose an image into the texture,structure,and edge parts.Then,the BM3D method,ANLM scheme,and K-SVD algorithm are utilized to eliminate noise in the texture,structure,and edge parts of the image,respectively.Experimental results show that the proposed approach can effectively remove interference random noise in different parts;meanwhile,the deteriorated image is able to be reconstructed well.
文摘Although the SSA （singular spectral analysis） is a potential tool for analysing time series of different physical processes, the processing of large geophysical data set requires more time and is found to be computationally expansive. In particular for the SVD （singular value decomposition） of large trajectory matrix, the processing units require huge memory and high performance computing system. In the present work, we propose an alternative scheme based on WSSA （windowed singular spectral analysis）, which is robust for analysing long data sets without losing any valuable low-frequency information contained in the data. The underlying scheme reduces the floating point operations in SVD computations as the size of the trajectory matrix is small in windowed processing. In order to test the efficiency, the authors applied the proposed method on two geophysical data sets i.e., the climatic record with 30,000 data points and seismic reflection trace with 8,000 data points. The authors have shown that without distorting any physical information, the low-frequency contents of the data are well preserved after the windowed processing in both the cases.
基金the editor and the anonymous referees for their constructive comments and suggestions,which greatly improved the paper.
文摘Recently,the tensor robust principal component analysis(TRPCA),aiming to recover the true low-rank tensor from noisy data,has attracted considerable attention.In this paper,we solve the TRPCA problem under the framework of the tensor singular value decomposition(t-SVD).Since the convex relaxation approaches have some limitations,we establish a new non-convex TRPCA model by introducing the non-convex tensor rank approximation based on the Laplace function via the weighted l_(p)-norm regularization.An efficient algorithm based on the alternating direction method of multipliers(ADMM)is developed to solve the proposed model.We further prove that the constructed sequence converges to the desirable Karush-Kuhn-Tucker point.Experimental results show that the proposed approach outperforms various latest approaches in the literature.
文摘The primary goal in the analysis of hierarchical distributed monitoring and control architectures is to study the spatiotemporal patterns of the interactions between areas or subsystems.In this paper,a novel conceptual framework for distributed monitoring of power system oscillations using multiblock principal component analysis(MB-PCA)and higher-order singular value decomposition(HOSVD)is proposed to understand,characterize,and visualize the global behavior of the power system.The proposed framework can be used to evaluate the influence of a given area or utility on the oscillatory behavior,uncover low-dimensional structures from high-dimensional data,and analyze the effects of heterogeneous data on the modal characteristics and interpretation of power system.The metrics are then investigated to examine the relationships between the dynamic patterns and participation of individual data blocks in the global behavior of the system.Practical application of these techniques is demonstrated by case studies of two systems:a 14-machine test system and a 5449-bus 635-generator equivalent model of a large power system.
文摘Aimed at studying normali zed radial basis function network (NRBFN), this paper introduces the subtractiv e clustering based on a mountain function to construct the initial structure of NR BFN, adopts singular value decomposition (SVD) to analyze the relationship betwe en neural nodes of the hidden layer and singular values, cumulative contribution ratio, index vector, and optimizes the structure of NRBFN. Finally, simulation and performance comparison show that the algorithm is feasible and effective.
