Excessive erosion caused by the continuous collision of sand-carrying annular flow with the gas well wellbore can lead to serious production accidents.This study combined the multifrequency response characteristics of...Excessive erosion caused by the continuous collision of sand-carrying annular flow with the gas well wellbore can lead to serious production accidents.This study combined the multifrequency response characteristics of sand particle-wall collision with a deep learning algorithm to improve the recognition accuracy of sand particle information in annular flow.The findings showed that sand-wall collision strength was closely related to the velocity,size,and number of sand particles and that the shielding effect generated by the collision behavior between multiple particles had a protective effect on the elbow.In addition,sand-wall collision strength increased with increases in gas velocity and particle size and decreased with an increase in liquid velocity.The shear effect,the secondary flow effect,and the liquid film buffering effect were shown to be key factors affecting the transportation behavior and spatial distribution of sand particles in annular flow.Furthermore,the fast Fourier transform(FFT)and short-time Fourier transform(STFT)analysis results showed that the multifrequency collision response characteristics of sand carrying annular flow were complex and that the main frequency response of sand-wall collision was concentrated in the high frequency range of 50e80 kHz.Moreover,the recognition accuracy results of convolutional neural network(CNN)models for particle size,gas velocity,and liquid velocity were 93.8%,91.7%,and 91%,respectively,which were significantly higher than the results for the long short-term memory(LSTM)model.The combination of multifrequency collision response and deep learning effectively characterized sand particle feature information in strong gas-liquid turbulence,providing a reference for the accurate monitoring of sand particle information in high-yield waterbearing gas wells.展开更多
The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks.Therefore,it is critical to find solut...The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks.Therefore,it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks.By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing,a multifrequency signal estimation approach based on HT-IpDFT-STWLS(HIpST)for distribution networks is provided.First,by introducing the Hilbert transform(HT),the influence of noise on the estimation algorithm is reduced.Second,signal frequency components are obtained on the basis of the calculated signal envelope spectrum,and the interpolated discrete Fourier transform(IpDFT)frequency coarse estimation results are used as the initial values of symmetric Taylor weighted least squares(STWLS)to achieve high-precision parameter estimation under the dynamic changes of the signal,and the method increases the number of discrete Fourier.Third,the accuracy of this proposed method is verified by simulation analysis.Data show that this proposed method can accurately achieve the parameter estimation of multifrequency signals in distribution networks.This approach provides a solution for the application of phasor measurement units in distribution networks.展开更多
A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar （PolSAR） images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix ...A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar （PolSAR） images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix Ω based on the eigenvalue decomposition of the coherency matrix of each band. A boxcar filter is then performed on the matrix Ω. The filtered data are put into a complex Wishart classifier. Finally, the effectiveness of the proposed method is demonstrated with JPL/AIRSAR multifrequency PolSAR data acquired over the Flevoland area.展开更多
Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exi...Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis （KPCA）. KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.展开更多
A numerical model which consists of the Korteweg-de Vries(KdV)equation,the action balance equation and the radar backscattering model is developed to simulate the frequency dependence of synthetic aperture radar(SAR)r...A numerical model which consists of the Korteweg-de Vries(KdV)equation,the action balance equation and the radar backscattering model is developed to simulate the frequency dependence of synthetic aperture radar(SAR)remote sensing of nonlinear ocean internal waves.Multifrequency data collected by NASA SIR-C SAR and NASA JPL AIRSAR are used as comparison.Case studies show that the results of simulation agree well with the results of SAR data.展开更多
Different ocean features usually appear in synthetic aperture radar(SAR)images simultaneously.This makes the image complicated and hard to understand.Because of lower signal-to-noise rate,it is much more difficult to ...Different ocean features usually appear in synthetic aperture radar(SAR)images simultaneously.This makes the image complicated and hard to understand.Because of lower signal-to-noise rate,it is much more difficult to separate different ocean features than to separate different land features.A completely novel method is presented to separate ocean features from multifrequency polarimetric SAR imagery.AIRSAR data from Jet Propulsion Laboratory(JPL),National Aeronautics and Space Administration(NASA)are used in the case studies and good results are achieved.展开更多
This paper describes the design and implementation of a three-axis acceleration control autopilot for an asymmetric tail-controlled,skid-to-turn tactical missile.In an earlier flight test,degraded autopilot performanc...This paper describes the design and implementation of a three-axis acceleration control autopilot for an asymmetric tail-controlled,skid-to-turn tactical missile.In an earlier flight test,degraded autopilot performance was attributed to multiple disturbances and uncertainties and the presence of hidden coupling terms,giving rise to a miss distance of greater than 20 m.To address these issues,the missile dynamics are decomposed into the angular rate dynamics as fast and the acceleration dynamics as slow subsystem using the singular perturbation theory to analyze a multi-time-scale property.Multifrequency extended state observers are then incorporated into the gain scheduling technique to attenuate disturbances,thus enhancing the control performance significantly.In the proposed engineering/practical design framework for missile autopilot,simple,conventional,and explicit tuning rules are provided.And the proposed control scheme can achieve input-to-state stability across the entire flight envelope under unknown but bounded disturbances.The advantages of the method over existing benchmark approaches are shown through nonlinear numerical simulations.This is supported by evidence from a new flight test result with a miss distance of only 2 m.展开更多
This paper introduces the characteristics of TD-SCDMA, and analyzes some networking schemes and methods of multifrequency. For the 5 MHz frequency bandwidth, a frequency planning scheme containing three frequencies is...This paper introduces the characteristics of TD-SCDMA, and analyzes some networking schemes and methods of multifrequency. For the 5 MHz frequency bandwidth, a frequency planning scheme containing three frequencies is examined, and a simulation model is built to validate the performance of this scheme. Finally, this paper analyzes the advantages and disadvantages of the scheme, and proposes some directions for the future study of networking planning.展开更多
A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products b...A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented.展开更多
Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ...Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.展开更多
基金supported by the National Natural Science Foundation of China(52104015)the Natural Science Foundation of Shandong Province(ZR2021ME001)the Innovation Fund Project for graduate students of China University of Petroleum(East China)supported by the Fundamental Research Funds for the Central Universities(24CX04040A).
文摘Excessive erosion caused by the continuous collision of sand-carrying annular flow with the gas well wellbore can lead to serious production accidents.This study combined the multifrequency response characteristics of sand particle-wall collision with a deep learning algorithm to improve the recognition accuracy of sand particle information in annular flow.The findings showed that sand-wall collision strength was closely related to the velocity,size,and number of sand particles and that the shielding effect generated by the collision behavior between multiple particles had a protective effect on the elbow.In addition,sand-wall collision strength increased with increases in gas velocity and particle size and decreased with an increase in liquid velocity.The shear effect,the secondary flow effect,and the liquid film buffering effect were shown to be key factors affecting the transportation behavior and spatial distribution of sand particles in annular flow.Furthermore,the fast Fourier transform(FFT)and short-time Fourier transform(STFT)analysis results showed that the multifrequency collision response characteristics of sand carrying annular flow were complex and that the main frequency response of sand-wall collision was concentrated in the high frequency range of 50e80 kHz.Moreover,the recognition accuracy results of convolutional neural network(CNN)models for particle size,gas velocity,and liquid velocity were 93.8%,91.7%,and 91%,respectively,which were significantly higher than the results for the long short-term memory(LSTM)model.The combination of multifrequency collision response and deep learning effectively characterized sand particle feature information in strong gas-liquid turbulence,providing a reference for the accurate monitoring of sand particle information in high-yield waterbearing gas wells.
基金supported by the State Grid Corporation of China Headquarters Management Science and Technology Project(No.526620200008).
文摘The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks.Therefore,it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks.By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing,a multifrequency signal estimation approach based on HT-IpDFT-STWLS(HIpST)for distribution networks is provided.First,by introducing the Hilbert transform(HT),the influence of noise on the estimation algorithm is reduced.Second,signal frequency components are obtained on the basis of the calculated signal envelope spectrum,and the interpolated discrete Fourier transform(IpDFT)frequency coarse estimation results are used as the initial values of symmetric Taylor weighted least squares(STWLS)to achieve high-precision parameter estimation under the dynamic changes of the signal,and the method increases the number of discrete Fourier.Third,the accuracy of this proposed method is verified by simulation analysis.Data show that this proposed method can accurately achieve the parameter estimation of multifrequency signals in distribution networks.This approach provides a solution for the application of phasor measurement units in distribution networks.
基金supported in part by the National Natural Science Fundation of China(4117131761132008+1 种基金61490693)Aeronautical Science Foundation of China(20132058003)
文摘A novel method is proposed for the supervised classification of multifrequency polarimetric synthetic aperture radar （PolSAR） images. The coherency matrices in P-, L-, and C-bands are mapped onto a 9×9 matrix Ω based on the eigenvalue decomposition of the coherency matrix of each band. A boxcar filter is then performed on the matrix Ω. The filtered data are put into a complex Wishart classifier. Finally, the effectiveness of the proposed method is demonstrated with JPL/AIRSAR multifrequency PolSAR data acquired over the Flevoland area.
基金the Specialized Research Found for the Doctoral Program of Higher Education (20070699013)the Natural Science Foundation of Shaanxi Province (2006F05)the Aeronautical Science Foundation (05I53076).
文摘Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis （KPCA）. KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.
基金the National Natural Science Foundation of China under contract Nos 40206023 and 40776099.
文摘A numerical model which consists of the Korteweg-de Vries(KdV)equation,the action balance equation and the radar backscattering model is developed to simulate the frequency dependence of synthetic aperture radar(SAR)remote sensing of nonlinear ocean internal waves.Multifrequency data collected by NASA SIR-C SAR and NASA JPL AIRSAR are used as comparison.Case studies show that the results of simulation agree well with the results of SAR data.
基金National Natural Science Foundation of China under contract Nos 40206023 and 40776099.
文摘Different ocean features usually appear in synthetic aperture radar(SAR)images simultaneously.This makes the image complicated and hard to understand.Because of lower signal-to-noise rate,it is much more difficult to separate different ocean features than to separate different land features.A completely novel method is presented to separate ocean features from multifrequency polarimetric SAR imagery.AIRSAR data from Jet Propulsion Laboratory(JPL),National Aeronautics and Space Administration(NASA)are used in the case studies and good results are achieved.
基金the support of the National Natural Science Foundation of China(No.U21B6003)。
文摘This paper describes the design and implementation of a three-axis acceleration control autopilot for an asymmetric tail-controlled,skid-to-turn tactical missile.In an earlier flight test,degraded autopilot performance was attributed to multiple disturbances and uncertainties and the presence of hidden coupling terms,giving rise to a miss distance of greater than 20 m.To address these issues,the missile dynamics are decomposed into the angular rate dynamics as fast and the acceleration dynamics as slow subsystem using the singular perturbation theory to analyze a multi-time-scale property.Multifrequency extended state observers are then incorporated into the gain scheduling technique to attenuate disturbances,thus enhancing the control performance significantly.In the proposed engineering/practical design framework for missile autopilot,simple,conventional,and explicit tuning rules are provided.And the proposed control scheme can achieve input-to-state stability across the entire flight envelope under unknown but bounded disturbances.The advantages of the method over existing benchmark approaches are shown through nonlinear numerical simulations.This is supported by evidence from a new flight test result with a miss distance of only 2 m.
文摘This paper introduces the characteristics of TD-SCDMA, and analyzes some networking schemes and methods of multifrequency. For the 5 MHz frequency bandwidth, a frequency planning scheme containing three frequencies is examined, and a simulation model is built to validate the performance of this scheme. Finally, this paper analyzes the advantages and disadvantages of the scheme, and proposes some directions for the future study of networking planning.
文摘A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented.
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.
