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
Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics...Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield.展开更多
The scattering centers(SCs)of low-detectable targets(LDTs)have a low scattering intensity.It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs wi...The scattering centers(SCs)of low-detectable targets(LDTs)have a low scattering intensity.It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions.This paper presents an SC modeling approach to acquire the weak SCs of LDTs.We employ the induced currents at the LDT to search SCs,and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs.Particle swarm optimization(PSO)is applied to improve the estimation results of SCs.The accuracy of the SC model built by this approach is verified by a full-wave numerical method.The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images,such as high-resolution range profile and inverse synthetic aperture radar(ISAR)images.展开更多
The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. How...The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. However, the chirplet transform has not inherent inverse transform, and can not overcome the signal reconstructing problem. In this paper, we proposed the improved chirplet transform (ICT) and constructed the inverse ICT. Finally, by simulating the harmonic voltages, The power of the improved chirplet transform are illustrated for harmonic detection. The contours clearly showed the harmonic occurrence time and harmonic duration.展开更多
In this study, the performance of chirplet signal decomposition (CSD) and empirical mode decomposition (EMD) coupled with Hilbert spectrum have been evaluated and compared for ultrasonic imaging applications. Numerica...In this study, the performance of chirplet signal decomposition (CSD) and empirical mode decomposition (EMD) coupled with Hilbert spectrum have been evaluated and compared for ultrasonic imaging applications. Numerical and experimental results indicate that both the EMD and CSD are able to decompose sparsely distributed chirplets from noise. In case of signals consisting of multiple interfering chirplets, the CSD algorithm, based on successive search for estimating optimal chirplet parameters, outperforms the EMD algorithm which estimates a series of intrinsic mode functions (IMFs). In particular, we have utilized the EMD as a signal conditioning method for Hilbert time-frequency representation in order to estimate the arrival time and center frequency of chirplets in order to quantify the ultrasonic signals. Experimental results clearly exhibit that the combined EMD and CSD is an effective processing tools to analyze ultrasonic signals for target detection and pattern recognition.展开更多
In order to deal with aliasing distortions of Doppler frequencies shown in time-frequency representation( TFR) with aspect undersampling,an approach using adaptive segmental compressive sampling according to the aspec...In order to deal with aliasing distortions of Doppler frequencies shown in time-frequency representation( TFR) with aspect undersampling,an approach using adaptive segmental compressive sampling according to the aspect dependencies of the scattering centers is proposed. The random noise problem induced by compressive sampling is solved by employing a series of signal processing techniques of filtering,image transformation and Hough Transform. Three examples are presented to verify the effectiveness of this approach. The comparisons between the built models and the precise scattered fields computed by a well-validated full-wave numerical method are investigated,and the results showgood agreements between each other.展开更多
This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation(SST or simply the sparse S-Transf...This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation(SST or simply the sparse S-Transform).The average and differential current components are passed through a change detection filter,which senses the instant of fault inception and registers a change detection point(CDP).Subsequently,if CDP is registered for one or more phases,then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique,which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault.The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location.The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification,which is least effected from bias setting,has a faster relay trip response(less than one cycle from fault incipient)and a better accuracy in fault location.The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system,subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.展开更多
The linearity,time shifting,time scaling,and time inversion properties of FMmlet transform are proved,and the frequency shifting property of one of the subspaces of FMmlet transform,namely the chirplet transform is pr...The linearity,time shifting,time scaling,and time inversion properties of FMmlet transform are proved,and the frequency shifting property of one of the subspaces of FMmlet transform,namely the chirplet transform is presented.Moreover,it is proved that in the process of FMm let based atomic signal decomposition,the residual signals decay exponentially.展开更多
Electric load movement forecast is increasingly importance for the industry.This study addresses the load movement forecast modeling based on complex matrix interpolation of the S-transform(ST).In complex matrix of ti...Electric load movement forecast is increasingly importance for the industry.This study addresses the load movement forecast modeling based on complex matrix interpolation of the S-transform(ST).In complex matrix of time-frequency representation of the ST,each row follows conjugate symmetric property and each column appears a certain degree of similarity.Based on these characteristics,a complex matrix interpolation method for the time-frequency representation of the ST is proposed to interpolate each row of the complex matrix based on the conjugate symmetric property,and then to perform nearestneighbor interpolation on each column.Then with periodic extension for daily and yearly electric load movement,a forecast model employing the complex matrix interpolation of the ST is introduced.The forecast approach is applied to predict daily load movement of the European Network on Intelligent Technologies(EUNITE)load dataset and annual electric load movement of State Gird Corporation of China and its branches in 2005 and 2006.Result analysis indicates workability and effectiveness of the proposed method.展开更多
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.
基金supported by the National Defence Pre-research Foundation of China(30502010103).
文摘Modern radar signals mostly use low probability of intercept(LPI)waveforms,which have short pulses in the time domain,multicomponent properties,frequency hopping,combined modulation waveforms and other characteristics,making the detection and estimation of LPI radar signals extremely difficult,and leading to highly required significant research on perception technology in the battlefield environment.This paper proposes a visibility graphs(VG)-based multicomponent signals detection method and a modulation waveforms parameter estimation algorithm based on the time-frequency representation(TFR).On the one hand,the frequency domain VG is used to set the dynamic threshold for detecting the multicomponent LPI radar waveforms.On the other hand,the signal is projected into the time and frequency domains by the TFR method for estimating its symbol width and instantaneous frequency(IF).Simulation performance shows that,compared with the most advanced methods,the algorithm proposed in this paper has a valuable advantage.Meanwhile,the calculation cost of the algorithm is quite low,and it is achievable in the future battlefield.
基金This work was supported by the National Key R&D Program of China(2017YFB0202500)the National Natural Science Foundation of China(61771052).
文摘The scattering centers(SCs)of low-detectable targets(LDTs)have a low scattering intensity.It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions.This paper presents an SC modeling approach to acquire the weak SCs of LDTs.We employ the induced currents at the LDT to search SCs,and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs.Particle swarm optimization(PSO)is applied to improve the estimation results of SCs.The accuracy of the SC model built by this approach is verified by a full-wave numerical method.The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images,such as high-resolution range profile and inverse synthetic aperture radar(ISAR)images.
文摘The chirplet transform is the generalization form of fast Fourier transform , short-time Fourier transform, and wavelet transform. It has the most flexible time frequency window and successfully used in practices. However, the chirplet transform has not inherent inverse transform, and can not overcome the signal reconstructing problem. In this paper, we proposed the improved chirplet transform (ICT) and constructed the inverse ICT. Finally, by simulating the harmonic voltages, The power of the improved chirplet transform are illustrated for harmonic detection. The contours clearly showed the harmonic occurrence time and harmonic duration.
文摘In this study, the performance of chirplet signal decomposition (CSD) and empirical mode decomposition (EMD) coupled with Hilbert spectrum have been evaluated and compared for ultrasonic imaging applications. Numerical and experimental results indicate that both the EMD and CSD are able to decompose sparsely distributed chirplets from noise. In case of signals consisting of multiple interfering chirplets, the CSD algorithm, based on successive search for estimating optimal chirplet parameters, outperforms the EMD algorithm which estimates a series of intrinsic mode functions (IMFs). In particular, we have utilized the EMD as a signal conditioning method for Hilbert time-frequency representation in order to estimate the arrival time and center frequency of chirplets in order to quantify the ultrasonic signals. Experimental results clearly exhibit that the combined EMD and CSD is an effective processing tools to analyze ultrasonic signals for target detection and pattern recognition.
基金Supported by the National Natural Science Foundation of China(61421001,61471041,61671059)
文摘In order to deal with aliasing distortions of Doppler frequencies shown in time-frequency representation( TFR) with aspect undersampling,an approach using adaptive segmental compressive sampling according to the aspect dependencies of the scattering centers is proposed. The random noise problem induced by compressive sampling is solved by employing a series of signal processing techniques of filtering,image transformation and Hough Transform. Three examples are presented to verify the effectiveness of this approach. The comparisons between the built models and the precise scattered fields computed by a well-validated full-wave numerical method are investigated,and the results showgood agreements between each other.
文摘This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation(SST or simply the sparse S-Transform).The average and differential current components are passed through a change detection filter,which senses the instant of fault inception and registers a change detection point(CDP).Subsequently,if CDP is registered for one or more phases,then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique,which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault.The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location.The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification,which is least effected from bias setting,has a faster relay trip response(less than one cycle from fault incipient)and a better accuracy in fault location.The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system,subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.
基金This work was supported in part by the National Natural Science Foundation of China (Grant No.60172026) by the Basic Research Foundation of Tsinghua University (Grant No. JC2001028) and by the Scientific Innovation Foundation of Ph. D. candidates
文摘The linearity,time shifting,time scaling,and time inversion properties of FMmlet transform are proved,and the frequency shifting property of one of the subspaces of FMmlet transform,namely the chirplet transform is presented.Moreover,it is proved that in the process of FMm let based atomic signal decomposition,the residual signals decay exponentially.
基金supported by the Scientific Research Fund of Hunan Provin-cial Science and Technology Department(2013GK3090)the research fund of Hunan University of Science and Technology(E50811)。
文摘Electric load movement forecast is increasingly importance for the industry.This study addresses the load movement forecast modeling based on complex matrix interpolation of the S-transform(ST).In complex matrix of time-frequency representation of the ST,each row follows conjugate symmetric property and each column appears a certain degree of similarity.Based on these characteristics,a complex matrix interpolation method for the time-frequency representation of the ST is proposed to interpolate each row of the complex matrix based on the conjugate symmetric property,and then to perform nearestneighbor interpolation on each column.Then with periodic extension for daily and yearly electric load movement,a forecast model employing the complex matrix interpolation of the ST is introduced.The forecast approach is applied to predict daily load movement of the European Network on Intelligent Technologies(EUNITE)load dataset and annual electric load movement of State Gird Corporation of China and its branches in 2005 and 2006.Result analysis indicates workability and effectiveness of the proposed method.
