A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa...A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.展开更多
A new method of fault analysis and detection by signal classification inrotating machines is presented. The Local Wave time-frequency spectrum which is a new method forprocessing a non-stationary signal is used to pro...A new method of fault analysis and detection by signal classification inrotating machines is presented. The Local Wave time-frequency spectrum which is a new method forprocessing a non-stationary signal is used to produce the representation of the signal. This methodallows the decomposition of one-dimensional signals into intrinsic mode functions (IMFs) usingempirical mode decomposition and the calculation of a meaningful multi-component instantaneousfrequency. Applied to fault signals , it provides new time-frequency attributes. Then the momentsand margins of the time-frequency spectrum are calculated as the feature vectors. The probabilisticneural network is used to classify different fault modes. The accuracy and robustness of theproposed methods is investigated on signals obtained during the different fault modes (early rub,loose, misalignment of the rotor).展开更多
Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional...Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.展开更多
A novel method of EEG time-frequency analysis and representation based on a wavelet network is presented. The wavelet network model can represent the EEG data effectively. Based on the wavelet network model, a novel t...A novel method of EEG time-frequency analysis and representation based on a wavelet network is presented. The wavelet network model can represent the EEG data effectively. Based on the wavelet network model, a novel time-frequency energy distribution function is obtained, which has the same time-frequency resolution as Wigner-Ville distribution and is free of cross-term interference. There is a great potential for the use of the novel time-frequency representation of nonstationary biosignal based on a wavelet network in the field of the electrophysiological signal processing and time-frequency analysis.展开更多
The Space-Air-Ground-Sea Integrated Networks(SAGSIN)significantly enhance global communication by merging satellite,aviation,terrestrial,and marine networks.Crucial to SAGSIN’s functionality and security is spectrum ...The Space-Air-Ground-Sea Integrated Networks(SAGSIN)significantly enhance global communication by merging satellite,aviation,terrestrial,and marine networks.Crucial to SAGSIN’s functionality and security is spectrum monitoring using deep learning-based Automatic Modulation Classification(AMC),essential for processing and classifying complex modulation signals.However,these AMC models are susceptible to adversarial attacks.Thus,we introduce the Deep Time-Frequency Denoising Transformation(DTFDT)defense method to mitigate the impact of adversarial attacks.The DTFDT method is comprised of a deep denoising module and a transformation module.The denoising module maps signals into the time-frequency domain,amplifying the differences between benign and adversarial examples,aiding in the elimination of adversarial perturbations.Concurrently,the transformation module develops a learnable network,generating example-specific transformation matrices suited for signal data,which diminishes the effectiveness of attacks.Extensive evaluations on two datasets,RML2016.10a and DMRadio09.real,demonstrate the superior defense capabilities of DTFDT against various attacks.展开更多
In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended spee...In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.展开更多
The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this...The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.展开更多
An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformat...An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.展开更多
The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals...The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.展开更多
Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling me...Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.展开更多
Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks,challenging conventional monitoring.This study probes the coupled acoustic-electrical ...Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks,challenging conventional monitoring.This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multiparameter,multi-level dynamic integrated early-warning model.Using a true-triaxial Split Hopkinson Pressure Bar(SHPB) system,we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts.Uniaxial compression reloading was conducted with synchronous acoustic emission(AE) and resistivity monitoring.Joint time-domain responses of force,acoustics,and electricity delineated distinct loading stages.Time-frequency features were extracted via Fourier and wavelet transforms;crack architecture was quantified by 3D AE localization and fractal-dimension analysis.Initial damage markedly reduced load-bearing capacity.Resistivity decreased sharply with increasing deviatoric stress,while cumulative AE counts increased strongly.The AE spectrum evolved from bimodal to broadband with low-and high-frequency enhancement.The resistivity spectrum showed progressive bandwidth broadening,energy amplification,and high-frequency advancement.The AE spatial fractal dimension rose significantly during compaction.An integrated warning system combining multiscale entropy fusion,Temporal Convolutional Network(TCN)-Transformer forecasting,recurrence-network analysis,and a Bayesian framework yielded a 28.4 s lead time,offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.展开更多
With the introduction of underwater bionic camouflage covert communication,conventional communication signal recognition methods can no longer meet the needs of current underwater military confrontations.However,the r...With the introduction of underwater bionic camouflage covert communication,conventional communication signal recognition methods can no longer meet the needs of current underwater military confrontations.However,the research on bionic communication signal recognition is still not comprehensive.This paper takes underwater communication signals that mimic dolphin whistles through phase-shifting modulation as the research object,and proposes a recognition method based on a convolutional neural network.A time-frequency contour(TFC)masking filtering method is designed,which uses image technology to obtain the TFC mask of whistles and extracts whistles from the obtained mask.Spatial diversity combining is used to suppress the signal fading in multipath channels.The phase derivative spectrum image is obtained by Hilbert transform and continuous wavelet transform,and is then used as the basis for recognition.Finally,the effectiveness of the proposed method is verified by simulations and lake experiments.In the simulations,a recognition accuracy of 90%is achieved at a signal-to-noise ratio(SNR)of 0 dB in multipath channels.In the real underwater communication environment,a recognition accuracy of 81%is achieved at a symbol width of 50 ms and an SNR of 6.36 dB.展开更多
基金supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337)Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069)。
文摘A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.
文摘A new method of fault analysis and detection by signal classification inrotating machines is presented. The Local Wave time-frequency spectrum which is a new method forprocessing a non-stationary signal is used to produce the representation of the signal. This methodallows the decomposition of one-dimensional signals into intrinsic mode functions (IMFs) usingempirical mode decomposition and the calculation of a meaningful multi-component instantaneousfrequency. Applied to fault signals , it provides new time-frequency attributes. Then the momentsand margins of the time-frequency spectrum are calculated as the feature vectors. The probabilisticneural network is used to classify different fault modes. The accuracy and robustness of theproposed methods is investigated on signals obtained during the different fault modes (early rub,loose, misalignment of the rotor).
基金Aeronautical Science Foundation of China (20071551016)
文摘Predicting the time-varying auto-spectral density of a spacecraft in high-altitude orbits requires an accurate model for the non-stationary random vibration signals with densely spaced modal frequency. The traditional time-varying algorithm limits prediction accuracy, thus affecting a number of operational decisions. To solve this problem, a time-varying auto regressive （TVAR） model based on the process neural network （PNN） and the empirical mode decomposition （EMD） is proposed. The time-varying system is tracked on-line by establishing a time-varying parameter model, and then the relevant parameter spectrum is obtained. Firstly, the EMD method is utilized to decompose the signal into several intrinsic mode functions （IMFs）. Then for each IMF, the PNN is established and the time-varying auto-spectral density is obtained. Finally, the time-frequency distribution of the signals can be reconstructed by linear superposition. The simulation and the analytical results from an example demonstrate that this approach possesses simplicity, effectiveness, and feasibility, as well as higher frequency resolution.
基金This work is Funded in part by the Science Foundation of Shandong Province (No.Y2000C25 and No.Y2001C02)
文摘A novel method of EEG time-frequency analysis and representation based on a wavelet network is presented. The wavelet network model can represent the EEG data effectively. Based on the wavelet network model, a novel time-frequency energy distribution function is obtained, which has the same time-frequency resolution as Wigner-Ville distribution and is free of cross-term interference. There is a great potential for the use of the novel time-frequency representation of nonstationary biosignal based on a wavelet network in the field of the electrophysiological signal processing and time-frequency analysis.
文摘The Space-Air-Ground-Sea Integrated Networks(SAGSIN)significantly enhance global communication by merging satellite,aviation,terrestrial,and marine networks.Crucial to SAGSIN’s functionality and security is spectrum monitoring using deep learning-based Automatic Modulation Classification(AMC),essential for processing and classifying complex modulation signals.However,these AMC models are susceptible to adversarial attacks.Thus,we introduce the Deep Time-Frequency Denoising Transformation(DTFDT)defense method to mitigate the impact of adversarial attacks.The DTFDT method is comprised of a deep denoising module and a transformation module.The denoising module maps signals into the time-frequency domain,amplifying the differences between benign and adversarial examples,aiding in the elimination of adversarial perturbations.Concurrently,the transformation module develops a learnable network,generating example-specific transformation matrices suited for signal data,which diminishes the effectiveness of attacks.Extensive evaluations on two datasets,RML2016.10a and DMRadio09.real,demonstrate the superior defense capabilities of DTFDT against various attacks.
文摘In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.
基金supported by the National Key Research and De-velopment Program of China(No.2020YFB0505601).
文摘The complicated electromagnetic environment of the BeiDou satellites introduces vari-ous types of external jamming to communication links,in which recognition of jamming signals with uncertainties is essential.In this work,the jamming recognition framework proposed consists of fea-ture fusion and a convolutional neural network(CNN).Firstly,the recognition inputs are obtained by prepossessing procedure,in which the 1-D power spectrum and 2-D time-frequency image are ac-cessed through the Welch algorithm and short-time Fourier transform(STFT),respectively.Then,the 1D-CNN and residual neural network(ResNet)are introduced to extract the deep features of the two prepossessing inputs,respectively.Finally,the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer.Results show the proposed method could reduce the impacts of potential feature loss,therefore improving the generalization ability on dealing with uncertainties.
基金Supported by the National Natural Science Foundation of China(64601500)
文摘An signal noise ratio（ SNR） adaptive sorting algorithm using the time-frequency（ TF）sparsity of frequency-hopping（ FH） signal is proposed in this paper. Firstly,the Gabor transformation is used as TF transformation in the system and a sorting model is established under undetermined condition; then the SNR adaptive pivot threshold setting method is used to find the TF single source. The mixed matrix is estimated according to the TF matrix of single source. Lastly,signal sorting is realized through improved subspace projection combined with relative power deviation of source. Theoretical analysis and simulation results showthat this algorithm has good effectiveness and performance.
基金supported by the National Natural Science Foundation of China(61872126)the Key Scientific Research Project Plan of Colleges and Universities in Henan Province(19A520004)。
文摘The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional states.Aiming at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was proposed.Specifically,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral cortex.Then,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional state.Spatiotemporal emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification tasks.The experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.
基金supported in part by the National Key Research Program of China(2016YFB0900100)Key Project of Shanghai Science and Technology Committee(18DZ1100303).
文摘Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.
基金supported by the National Key Scientific Instruments and Equipment Development Projects of China (No.52227901)the National Key R&D Program of China (No.2022YFC3004705)+2 种基金the Graduate Innovation Program of China University of Mining and Technology (No.2024WLKXJ153)the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX24_2926)the Special Funding for the Jiangsu Provincial Science and Technology Plan (No.BM2022013)。
文摘Initial damage from engineering disturbances in deep coal mining degrades mechanical properties and heightens dynamic-hazard risks,challenging conventional monitoring.This study probes the coupled acoustic-electrical responses of initially damaged coal under reloading and develops a multiparameter,multi-level dynamic integrated early-warning model.Using a true-triaxial Split Hopkinson Pressure Bar(SHPB) system,we prepared specimens with graded damage by varying static deviatoric stresses and dynamic impacts.Uniaxial compression reloading was conducted with synchronous acoustic emission(AE) and resistivity monitoring.Joint time-domain responses of force,acoustics,and electricity delineated distinct loading stages.Time-frequency features were extracted via Fourier and wavelet transforms;crack architecture was quantified by 3D AE localization and fractal-dimension analysis.Initial damage markedly reduced load-bearing capacity.Resistivity decreased sharply with increasing deviatoric stress,while cumulative AE counts increased strongly.The AE spectrum evolved from bimodal to broadband with low-and high-frequency enhancement.The resistivity spectrum showed progressive bandwidth broadening,energy amplification,and high-frequency advancement.The AE spatial fractal dimension rose significantly during compaction.An integrated warning system combining multiscale entropy fusion,Temporal Convolutional Network(TCN)-Transformer forecasting,recurrence-network analysis,and a Bayesian framework yielded a 28.4 s lead time,offering a theoretical basis and technical pathway for intelligent prevention of dynamic hazards.
基金Project supported by the National Natural Science Foundation of China(No.62231011)the Tianjin Outstanding Young Scientists Fund Project(No.24JCJQJC00240)。
文摘With the introduction of underwater bionic camouflage covert communication,conventional communication signal recognition methods can no longer meet the needs of current underwater military confrontations.However,the research on bionic communication signal recognition is still not comprehensive.This paper takes underwater communication signals that mimic dolphin whistles through phase-shifting modulation as the research object,and proposes a recognition method based on a convolutional neural network.A time-frequency contour(TFC)masking filtering method is designed,which uses image technology to obtain the TFC mask of whistles and extracts whistles from the obtained mask.Spatial diversity combining is used to suppress the signal fading in multipath channels.The phase derivative spectrum image is obtained by Hilbert transform and continuous wavelet transform,and is then used as the basis for recognition.Finally,the effectiveness of the proposed method is verified by simulations and lake experiments.In the simulations,a recognition accuracy of 90%is achieved at a signal-to-noise ratio(SNR)of 0 dB in multipath channels.In the real underwater communication environment,a recognition accuracy of 81%is achieved at a symbol width of 50 ms and an SNR of 6.36 dB.
