When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To o...When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.展开更多
We use submultiplicative companion matrix norms to provide new bounds for roots for a given polynomial <i>P</i>(<i>X</i>) over the field C[<i>X</i>]. From a <i>n</i>...We use submultiplicative companion matrix norms to provide new bounds for roots for a given polynomial <i>P</i>(<i>X</i>) over the field C[<i>X</i>]. From a <i>n</i>×<i>n</i> Fiedler companion matrix <i>C</i>, sparse companion matrices and triangular Hessenberg matrices are introduced. Then, we identify a special triangular Hessenberg matrix <i>L<sub>r</sub></i>, supposed to provide a good estimation of the roots. By application of Gershgorin’s theorems to this special matrix in case of submultiplicative matrix norms, some estimations of bounds for roots are made. The obtained bounds have been compared to known ones from the literature precisely Cauchy’s bounds, Montel’s bounds and Carmichel-Mason’s bounds. According to the starting formel of <i>L<sub>r</sub></i>, we see that the more we have coefficients closed to zero with a norm less than 1, the more the Sparse method is useful.展开更多
In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) ba...In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.展开更多
Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on fau...Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.展开更多
Sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time.This can effectively reduce the cost of signal storage,improve the efficiency of data transmission,...Sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time.This can effectively reduce the cost of signal storage,improve the efficiency of data transmission,and ultimately save the cost of equipment fault diagnosis in the aviation field.At present,the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms,which limits the adaptability of sparse decomposition.In this paper,a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform(TQWT)method to construct a self-adaptive complete dictionary.Finally,the sparse signal is obtained by the orthogonal matching pursuit(OMP)algorithm.The atoms obtained by this method are more flexible,and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals.Therefore,the sparse signal can better extract the fault characteristics.The simulation and experimental results show that the selfadaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries,such as discrete cosine transform(DCT),discrete Hartley transform(DHT)and discrete wavelet transform(DWT).In addition,the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals,and can more accurately extract the bearing fault feature frequency.展开更多
A new direct method for solving unsymmetrical sparse linear systems（USLS） arising from meshless methods was introduced. Computation of certain meshless methods such as meshless local Petrov-Galerkin （MLPG） method ...A new direct method for solving unsymmetrical sparse linear systems（USLS） arising from meshless methods was introduced. Computation of certain meshless methods such as meshless local Petrov-Galerkin （MLPG） method need to solve large USLS. The proposed solution method for unsymmetrical case performs factorization processes symmetrically on the upper and lower triangular portion of matrix, which differs from previous work based on general unsymmetrical process, and attains higher performance. It is shown that the solution algorithm for USLS can be simply derived from the existing approaches for the symmetrical case. The new matrix factorization algorithm in our method can be implemented easily by modifying a standard JKI symmetrical matrix factorization code. Multi-blocked out-of-core strategies were also developed to expand the solution scale. The approach convincingly increases the speed of the solution process, which is demonstrated with the numerical tests.展开更多
Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dicti...Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster.Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.展开更多
Sparse sums of Lorentzians can give good approximations to functions consisting of linear combination of piecewise continuous functions. To each Lorentzian, two parameters are as- signed： translation and scale. These...Sparse sums of Lorentzians can give good approximations to functions consisting of linear combination of piecewise continuous functions. To each Lorentzian, two parameters are as- signed： translation and scale. These parameters can be found by using a method for complex fre- quency detection in the frequency domain. This method is based on an alternating projection scheme between Hankel matrices and finite rank operators, and have the advantage that it can be done in weighted spaces. The weighted spaces can be used to partially revoke the effect of finite band-width filters. Apart from frequency extrapolation the method provides a way of estimating discontinuity locations.展开更多
In this paper, we present the construction of purely algebraic Daubechies wavelet based preconditioners for Krylov subspace iterative methods to solve linear sparse system of equations. Effective preconditioners are d...In this paper, we present the construction of purely algebraic Daubechies wavelet based preconditioners for Krylov subspace iterative methods to solve linear sparse system of equations. Effective preconditioners are designed with DWTPerMod algorithm by knowing size of the matrix and the order of Daubechies wavelet. A notable feature of this algorithm is that it enables wavelet level to be chosen automatically making it more robust than other wavelet based preconditioners and avoids user choosing a level of transform. We demonstrate the efficiency of these preconditioners by applying them to several matrices from Tim Davis collection of sparse matrices for restarted GMRES.展开更多
基金supported by National Natural Science Foundation of China (Grant No. 71271078)National Hi-tech Research and Development Program of China (863 Program, Grant No. 2009AA04Z414)Integration of Industry, Education and Research of Guangdong Province, and Ministry of Education of China (Grant No. 2009B090300312)
文摘When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.
文摘We use submultiplicative companion matrix norms to provide new bounds for roots for a given polynomial <i>P</i>(<i>X</i>) over the field C[<i>X</i>]. From a <i>n</i>×<i>n</i> Fiedler companion matrix <i>C</i>, sparse companion matrices and triangular Hessenberg matrices are introduced. Then, we identify a special triangular Hessenberg matrix <i>L<sub>r</sub></i>, supposed to provide a good estimation of the roots. By application of Gershgorin’s theorems to this special matrix in case of submultiplicative matrix norms, some estimations of bounds for roots are made. The obtained bounds have been compared to known ones from the literature precisely Cauchy’s bounds, Montel’s bounds and Carmichel-Mason’s bounds. According to the starting formel of <i>L<sub>r</sub></i>, we see that the more we have coefficients closed to zero with a norm less than 1, the more the Sparse method is useful.
基金supported by the National Natural Science Foundation of China (62271255,61871218)the Fundamental Research Funds for the Central University (3082019NC2019002)+1 种基金the Aeronautical Science Foundation (ASFC-201920007002)the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements。
文摘In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection(MSFKSPP) based on the maximum margin criterion(MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile(HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.
文摘Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply.Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible.However,these approaches may easily result in overfitting and high computational cost,which cannot meet requirements for detection accuracy and efficiency in practical applications.This paper proposes an innovative waveform encoding method and details a simple convolutional neural network(CNN)with one layer of convolution used for identification,which seeks to improve detection accuracy and efficiency simultaneously.First,sparse characteristics of waveforms are utilized to encode into compact vectors,and a waveform-vector matrix is generated.Second,to deduce waveform-vector matrix,a simple CNN with multi-scale filters and one layer of convolution is established.Finally,a methodology for faulty-feeder detection is proposed,and both detection accuracy and efficiency are considerably enhanced.Comparative studies have confirmed clear superiority of the developed method,which outperforms existing approaches in both detection accuracy and efficiency,thus highlighting its significant potential for application.
基金This work was supported by the National Key R&D Program of China(Grant No.2018YFB1503103).
文摘Sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time.This can effectively reduce the cost of signal storage,improve the efficiency of data transmission,and ultimately save the cost of equipment fault diagnosis in the aviation field.At present,the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms,which limits the adaptability of sparse decomposition.In this paper,a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform(TQWT)method to construct a self-adaptive complete dictionary.Finally,the sparse signal is obtained by the orthogonal matching pursuit(OMP)algorithm.The atoms obtained by this method are more flexible,and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals.Therefore,the sparse signal can better extract the fault characteristics.The simulation and experimental results show that the selfadaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries,such as discrete cosine transform(DCT),discrete Hartley transform(DHT)and discrete wavelet transform(DWT).In addition,the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals,and can more accurately extract the bearing fault feature frequency.
基金Project supported by the National Natural Science Foundation of China (Nos. 10232040, 10572002 and 10572003)
文摘A new direct method for solving unsymmetrical sparse linear systems（USLS） arising from meshless methods was introduced. Computation of certain meshless methods such as meshless local Petrov-Galerkin （MLPG） method need to solve large USLS. The proposed solution method for unsymmetrical case performs factorization processes symmetrically on the upper and lower triangular portion of matrix, which differs from previous work based on general unsymmetrical process, and attains higher performance. It is shown that the solution algorithm for USLS can be simply derived from the existing approaches for the symmetrical case. The new matrix factorization algorithm in our method can be implemented easily by modifying a standard JKI symmetrical matrix factorization code. Multi-blocked out-of-core strategies were also developed to expand the solution scale. The approach convincingly increases the speed of the solution process, which is demonstrated with the numerical tests.
基金supported by the National Natural Science Foundation of China(61573219,61402203,61401209,61701192,61671274)the Opening Fund of Shandong Provincial Key Laboratory of Network Based Intelligent Computing+2 种基金the Fostering Project of Dominant DisciplineTalent Team of Shandong Province Higher Education InstitutionsFostering Project of Dominant Discipline and Talent Team of SDUFE
文摘Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each cluster.Then a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.
文摘Sparse sums of Lorentzians can give good approximations to functions consisting of linear combination of piecewise continuous functions. To each Lorentzian, two parameters are as- signed： translation and scale. These parameters can be found by using a method for complex fre- quency detection in the frequency domain. This method is based on an alternating projection scheme between Hankel matrices and finite rank operators, and have the advantage that it can be done in weighted spaces. The weighted spaces can be used to partially revoke the effect of finite band-width filters. Apart from frequency extrapolation the method provides a way of estimating discontinuity locations.
文摘In this paper, we present the construction of purely algebraic Daubechies wavelet based preconditioners for Krylov subspace iterative methods to solve linear sparse system of equations. Effective preconditioners are designed with DWTPerMod algorithm by knowing size of the matrix and the order of Daubechies wavelet. A notable feature of this algorithm is that it enables wavelet level to be chosen automatically making it more robust than other wavelet based preconditioners and avoids user choosing a level of transform. We demonstrate the efficiency of these preconditioners by applying them to several matrices from Tim Davis collection of sparse matrices for restarted GMRES.
