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GCRA-FWVAE:Anomaly detection for IIoT univariate time series using time-frequency domain analysis 认领 引用 被引量：1
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作者 Xiaoling Tao Haowei Liu +3 位作者 Wenbo Zhao Weikun Li Yaqi Nie Jingqi Fu 《Digital Communications and Networks》 SCIE EI CSCD 2026年第3期405-416,共12页
With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT ... With the rapid development of Artificial Intelligence of Things(AIoT)technologies,the security of Industrial Internet of Things(IIoT)data faces increasing challenges,particularly in time series anomaly detection.IIoT data are typically scarce in abnormal samples and noisy,making unsupervised learning a common solution.The security challenges of IIoT data in AIoT environments require robust unsupervised anomaly detection methods.While Variational Autoencoders(VAEs)excel in noise resilience,they face two critical challenges in IIoT data:difficulties in single-variable time-series modeling and conflicts between static prior assumptions and dynamic temporal features.To address these challenges,we propose the Greater Cane Rat Algorithm-enhanced FourierWavelet Conditional Variational Autoencoder(GCRA-FWVAE).Our method introduces a time-frequency dualbranch architecture that synergistically combines wavelet transforms for localized transient feature extraction and Fourier transforms for global spectral characterization.These complementary representations jointly regulate the Conditional Variational Autoencoder(CVAE)reconstruction process,effectively preserving critical anomaly signatures while suppressing noise interference.The architecture is further optimized through bioinspired Greater Cane Rat Algorithm(GCRA)to improve adaptive learning capabilities.Extensive validation on the Yahoo benchmark indicates state-of-the-art performance,achieving an F1-score of 93.6%(an improvement of 4.5% over baseline VAEs)and a precision of 95.1%.These improvements significantly increase anomaly detection accuracy and robustness,particularly in the AIoT environment,where it effectively handles more complex and dynamic industrial data. 展开更多
关键词 Anomaly detection Time series GCRA CVAE Time-frequency domain
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Multimodal Signal Processing of ECG Signals with Time-Frequency Representations for Arrhythmia Classification 认领 引用 被引量：1
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作者 Yu Zhou Jiawei Tian Kyungtae Kang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期990-1017,共28页
Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin... Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification. 展开更多
关键词 Electrocardiogram arrhythmia classification multimodal time-frequency representation
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Advances in time-frequency based geopotential determination 认领 引用
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作者 Heping Sun Wenbin Shen +5 位作者 Kelin Gao Yuping Gao Mingqiang Hou Lifeng Bao Pengfei Zhang Ziyu Shen 《Geodesy and Geodynamics》 EI CSCD 2026年第1期12-24,共13页
The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,sate... The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals. 展开更多
关键词 General relativity Geopotential Time-frequency signal transmission techniques Orthometric height Optical clock
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Dominant frequency response and dynamic mechanism of rock slopes under blasting loads:A machine learning-driven time-frequency analysis 认领 引用
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作者 MA Ke PENG Yilin +2 位作者 LIAO Zhiyi LUO Longlong HUANG Yinglu 《Journal of Mountain Science》 SCIE CSCD 2026年第3期1334-1354,共21页
Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predic... Understanding how rock slopes respond to blasting loads is crucial for maintaining excavation safety and slope stability.Nevertheless,the spatiotemporal evolution,nonlinear dependence on blasting parameters,and predictive behavior of dominant frequency responses in slope vibrations remain insufficiently understood and quantified.This study combines time-frequency analysis with machine learning to explore how the dominant frequency(fd)evolves in slopes under blasting.Continuous Wavelet Transform(CWT)was employed to characterize the temporal-frequency evolution of vibration signals,revealing that the dominant frequency exhibits strong spatial dependence and nonlinear variability influenced by blasting parameters and rock mass structures.Three machine learning models,namely Back Propagation Neural Network(BP),Support Vector Machine(SVM),and Random Forest(RF),were developed to predict fd based on 1,000 monitoring samples obtained from numerical and field simulations.Among them,the RF model achieved the highest prediction accuracy,with mean absolute percentage errors(MAPE)below 15%,demonstrating strong robustness and generalization capability.Our analysis shows that external excitation factors,especially the loading frequency(fd),mainly control the frequency response,while internal controlling factors,such as spatial position,lithological variation,and mechanical heterogeneity,modulate localized frequency amplification and energy redistribution.The results reveal that fd tends to decrease with elevation and distance from the blasting source,whereas structural planes and weathered zones induce high-frequency amplification due to scattering and modal coupling effects.This study offers a new framework combining time-frequency analysis and machine learning to measure the nonlinear interaction between blasting and rock mass response,offering new insights for dynamic stability evaluation and hazard mitigation in complex rock slope systems. 展开更多
关键词 Blasting vibration Time-frequency domain analysis Machine learning Dominant frequency
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Robust connectivity in time-frequency dual fading channels for the 6G satellite IoT:chirp and FRFT 认领 引用
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作者 Wenliang Lin Xinghao Cao +6 位作者 Ke Wang Zhongliang Deng Yishan He Kejia Cui Yaohua Deng Da Wan Zhiwei He 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期375-387,共13页
With the development of Sixth-Generation(6G)mobile communication technologies,Low Earth Orbit(LEO)satellite communication systems have become extremely important in mobile communications owing to their large coverage,... With the development of Sixth-Generation(6G)mobile communication technologies,Low Earth Orbit(LEO)satellite communication systems have become extremely important in mobile communications owing to their large coverage,high efficiency,and low cost.However,the high dynamic LEO satellite channels cause serious time-frequency dual selective fading,significantly impairing the performance of conventional single time or frequency domain synchronization algorithms and limiting their applicability.To address these challenges,this paper proposes a synchronization algorithm based on Linear Frequency Modulation(LFM)signals and the Fractional Fourier Transform(FRFT).Exploiting the inherent robustness of LFM signals against frequency deviations and multipath effects,coupled with their energy concentration property in the optimal fractional Fourier domain,the proposed algorithm enables efficient synchronization with enhanced resilience to time-frequency variations.Furthermore,LFM preamble sequences are optimally designed for diverse channel conditions.This work presents a theoretical analysis of the time-frequency nonstationary characteristics of LEO satellite channels and discusses the performance limitations of traditional synchronization algorithms.The proposed integrated FRFTLFM synchronization framework and sequence optimization scheme are rigorously evaluated via comprehensive simulations.The results demonstrate substantial improvements in synchronization accuracy and computational efficiency compared with conventional methods,particularly under time-frequency dual selective fading LEO satellite channels.The algorithm provides a robust and reliable solution for time-frequency synchronization in LEO satellite communication systems,thereby enhancing overall system performance and reliability. 展开更多
关键词 Time-frequency synchronization Linear frequency modulation Fractional fourier transform Low earth orbit satellite communications Nonstationary channel
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A Method for Detecting Spatio-Temporal Correlation Anomalies of WSN Nodes Based on Topological Information Enhancement and Time-Frequency Feature Extraction 认领 引用
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作者 Miao Ye Ziheng Wang +4 位作者 Qiuxiang Jiang Xingsi Xue Wenxi Liu Yu Ning Cheng Zhu 《Computers, Materials & Continua》 SCIE EI 2026年第8期1840-1870,共31页
In recent years,anomaly detection in Wireless Sensor Networks(WSNs)has been widely studied using Graph Neural Networks and Transformer-based methods.However,in multi-node and multi-modal data scenarios,these approache... In recent years,anomaly detection in Wireless Sensor Networks(WSNs)has been widely studied using Graph Neural Networks and Transformer-based methods.However,in multi-node and multi-modal data scenarios,these approaches still face challenges such as insufficient extraction of spatiotemporal correlation features,limited modeling capabilities when relying solely on either time-domain or frequency-domain information,and high computational overhead.To address these issues,this work aims to develop an anomaly detection model that balances detection performance with computational efficiency,enabling effective identification of complex anomaly patterns.Specifically,we propose a time–frequency feature extraction method with topological information enhancement,topology-enhanced multi-modal spatio-temporal anomaly detection(TE-MSTAD).Building upon the Receptance Weighted Key Value(RWKV)model with linear complexity,a cross-modal feature extraction module is introduced to strengthen the modeling of multi-modal correlations.Meanwhile,adaptive adjacency matrices are constructed by integrating time–frequency features and combining outputs from different Graph Neural Networks,thereby enhancing topological information.Furthermore,a dual-branch structure is designed to jointly model time-domain and frequency-domain features,improving the extraction of complex anomaly characteristics.Experiments on both publicly available datasets and real-world collected data demonstrate that the proposed method achieves F1-scores of 92.52%and 93.28%,respectively,outperforming existing methods in detection performance and generalization capability. 展开更多
关键词 Wireless sensor networks anomaly detection time-frequency domain fusion graph neural networks information enhancement
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A high-resolution time-frequency analysis tool for fault diagnosis of rotating machinery 认领 引用
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作者 Gang Yu Zhenghao Cui 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期145-154,共10页
Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-syn... Fault features in mechanical systems often manifest as transient impulses,which can be effectively analyzed using time-frequency analysis(TFA)methods.Recently,a new TFA technique known as the time-reassigned multi-synchrosqueezing transform(TMssT)was proposed to capture these transient impulses for fault diagnosis.However,the TMSST,which is based on the short-time Fourier transform(STFT),suffers from unclear high-frequency re-presentations owing to the fixed sliding window used in the STFT.To address this limitation,the current study combined TMSST with the S-transform and a local maximum method to enhance the time-frequency representation for improved signal analysis.Furthermore,an extractive reconstruction algorithm that binds the maximum value of the spectral envelope is proposed for spectral decomposition.To validate the proposed technique,a simulated noise-added signal and four experimental bearing defect datasets were used.The results demonstrate that the proposed technique can effectively and accurately extract fault features from bearing signals regardless of whether the bearings operate under constant or varying speed conditions.This study offers a novel and efficient approach for fault diagnosis in mechanical systems with complex dynamic behaviors. 展开更多
关键词 Time-frequency analysis S-transform Extractive reconstruction algorithm Mechanical systems
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Advanced High-Order Graph Convolutional Networks With Assorted Time-Frequency Transforms 认领 引用
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作者 Ling Wang Ye Yuan Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第2期394-408,共15页
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
关键词 Dynamic graph(DG)learning ensemble graph representation learning high-order graph convolution network(HGCN) time-frequency transform tensor product
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A Comparative Study Between SDP-CNN and Time-Frequency-CNN-Based Approaches for Fault Detection 认领 引用
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作者 Mario Spirto Francesco Melluso +4 位作者 Armando Nicolella Pierangelo Malfi Chiara Cosenza Sergio Savino Vincenzo Niola 《Journal of Dynamics, Monitoring and Diagnostics》 2026年第1期25-37,共13页
The image-based approach is widely used in fault detection(FD)algorithms of mechanical systems.The images are derived from the vibrational signals transformed from the time to time–frequency domain,and they are used ... The image-based approach is widely used in fault detection(FD)algorithms of mechanical systems.The images are derived from the vibrational signals transformed from the time to time–frequency domain,and they are used to develop a convolutional neural network(CNN)to automate the FD process.Nowadays,images are also obtained from the transformation of vibrational signals from the time domain to symmetrized dot pattern(SDP)coordinates,achieving high CNN testing accuracy.This paper shows a comparison of image-CNN approaches for FD using images obtained from time–frequency transforms and those obtained from the SDP transform as input.The comparison was conducted using experimental data from two publicly available bearing datasets,examining both the accuracy of the CNNs and the computational time required for the vibrational signal transformations.The results show that the SDP-CNN approach achieves the same accuracy as spectrogram-CNN approaches but with a significantly reduced computational time.These results support the future real-time implementation of the SDPCNN approach for FD in mechanical systems such as bearings. 展开更多
关键词 ball bearings CNN fault detection symmetrized dot pattern time-frequency analysis
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Abnormal Signal Recognition with Time-Frequency Spectrogram:A Deep Learning Approach 认领 引用
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作者 Kuang Tingyan Chen Huichao +3 位作者 Han Lu He Rong Wang Wei Ding Guoru 《China Communications》 SCIE EI CSCD 2025年第11期305-319,共15页
With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communicat... With the increasingly complex and changeable electromagnetic environment,wireless communication systems are facing jamming and abnormal signal injection,which significantly affects the normal operation of a communication system.In particular,the abnormal signals may emulate the normal signals,which makes it very challenging for abnormal signal recognition.In this paper,we propose a new abnormal signal recognition scheme,which combines time-frequency analysis with deep learning to effectively identify synthetic abnormal communication signals.Firstly,we emulate synthetic abnormal communication signals including seven jamming patterns.Then,we model an abnormal communication signals recognition system based on the communication protocol between the transmitter and the receiver.To improve the performance,we convert the original signal into the time-frequency spectrogram to develop an image classification algorithm.Simulation results demonstrate that the proposed method can effectively recognize the abnormal signals under various parameter configurations,even under low signal-to-noise ratio(SNR)and low jamming-to-signal ratio(JSR)conditions. 展开更多
关键词 abnormal signal recognition deep learning time-frequency analysis
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Time-Frequency Signal Processing for Gas-Liquid Two Phase Flow Through a Horizontal Venturi Based on Adaptive Optimal-Kernel Theory 认领 引用 被引量：12
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作者 孙斌 王二朋 +2 位作者 丁洋 白宏震 黄咏梅 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2011年第2期243-252,共10页
A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal o... A time-frequency signal processing method for two-phase flow through a horizontal Venturi based on adaptive optimal-kernel （AOK） was presented in this paper.First,the collected dynamic differential pressure signal of gas-liquid two-phase flow was preprocessed,and then the AOK theory was used to analyze the dynamic differ-ential pressure signal.The mechanism of two-phase flow was discussed through the time-frequency spectrum.On the condition of steady water flow rate,with the increasing of gas flow rate,the flow pattern changes from bubbly flow to slug flow,then to plug flow,meanwhile,the energy distribution of signal fluctuations show significant change that energy transfer from 15-35 Hz band to 0-8 Hz band;moreover,when the flow pattern is slug flow,there are two wave peaks showed in the time-frequency spectrum.Finally,a number of characteristic variables were defined by using the time-frequency spectrum and the ridge of AOK.When the characteristic variables were visu-ally analyzed,the relationship between different combination of characteristic variables and flow patterns would be gotten.The results show that,this method can explain the law of flow in different flow patterns.And characteristic variables,defined by this method,can get a clear description of the flow information.This method provides a new way for the flow pattern identification,and the percentage of correct prediction is up to 91.11%. 展开更多
关键词 adaptive optimal-kernel two-phase flow time-frequency spectrum time-frequency ridge flow pattern identification
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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning 认领 引用 被引量：5
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作者 Yun-Peng He Hai-Bo Cheng +4 位作者 Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong 《Petroleum Science》 SCIE EI CAS CSCD 2024年第1期641-653,共13页
High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an eff... High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well operations.Deep learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis method.However,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort.Additionally,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different moments.Thus,there is heterogeneity in field data,which can dramatically impair the diagnostic accuracy.To solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this paper.First,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC data.Subsequently,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity.Finally,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the 4S-TFSM.Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational cost.To the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS. 展开更多
关键词 Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix
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Application of sparse time-frequency decomposition to seismic data 认领 引用 被引量：4
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作者 王雄文 王华忠 《Applied Geophysics》 SCIE CSCD 2014年第4期447-458,510,共12页
The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time... The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results. 展开更多
关键词 Time-frequency analysis sparse time-frequency decomposition nonstationary signal resolution
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Parametric adaptive time-frequency representation based on time-sheared Gabor atoms 认领 引用 被引量：2
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作者 Ma Shiwei Zhu Xiaojin Chen Guanghua Wang Jian Cao Jialin 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第1期1-7,共7页
A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization ... A localized parametric time-sheared Gabor atom is derived by convolving a linear frequency modulated factor, modulating in frequency and translating in time to a dilated Gaussian function, which is the generalization of Gabor atom and is more delicate for matching most of the signals encountered in practice, especially for those having frequency dispersion characteristics. The time-frequency distribution of this atom concentrates in its time center and frequency center along energy curve, with the curve being oblique to a certain extent along the time axis. A novel parametric adaptive time-frequency distribution based on a set of the derived atoms is then proposed using a adaptive signal subspace decomposition method in frequency domain, which is non-negative time-frequency energy distribution and free of cross-term interference for multicomponent signals. The results of numerical simulation manifest the effectiveness of the approach in time-frequency representation and signal de-noising processing. 展开更多
关键词 Time-frequency analysis Gabor atom Time-shear Adaptive signal decomposition Time-frequency distribution.
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Digital modulation classification using multi-layer perceptron and time-frequency features 认领 引用
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作者 Yuan Ye Mei Wenbo 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第2期249-254,共6页
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributio... Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals： The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio （SNR） variation. According to good classification by the correct rate of a neural network classifier, a multilayer perceptron （MLP） classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed. Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier. 展开更多
关键词 Digital modulation classification Time-frequency feature Time-frequency distribution Multi-layer perceptron.
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NON-STATIONARY SIGNAL DENOISING USING TIME-FREQUENCY CURVE SURFACE FITTING 认领 引用
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作者 Liu Xiaofeng Qin Shuren Bo Lin 《Journal of Electronics(China)》 2007年第6期776-781,共6页
Based on the theory of adaptive time-frequency decomposition and Time-Frequency Dis- tribution Series (TFDS), this paper presents a novel denoising method for non-stationary signal. Ac- cording to the input signal fea... Based on the theory of adaptive time-frequency decomposition and Time-Frequency Dis- tribution Series (TFDS), this paper presents a novel denoising method for non-stationary signal. Ac- cording to the input signal features, an appropriate kind of elementary functions with great concen- tration in the Time-Frequency (TF) plane is selected. Then the input signal is decomposed into a linear combination of these functions. The elementary function parameters are determined by using ele- mentary function TF curve surface to fit the input signal’s TFDS. The process of curved surface fitting corresponds to the signal structure matching process. The input signal’s dominating component whose structure has the resemblance with elementary function is fitted out firstly. Repeating the fitting process, the residue can be regarded as noises, which are greatly different from the function. Selecting the functions fitted out initially for reconstruction, the denoised signal is obtained. The performance of the proposed method is assessed by means of several tests on an emulated signal and a gearbox vi- brating signal. 展开更多
关键词 Time-frequency decomposition Elementary function Time-Frequency Distribution Series （TFDS） Curve surface fitting Noise suppressing
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Adaptive target and jamming recognition for the pulse doppler radar fuze based on a time-frequency joint feature and an online-updated naive bayesian classifier with minimal risk 认领 引用 被引量：16
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作者 Jian Dai Xin-hong Hao +2 位作者 Ze Li Ping Li Xiao-peng Yan 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2022年第3期457-466,共10页
This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed... This paper considers the problem of target and jamming recognition for the pulse Doppler radar fuze(PDRF).To solve the problem,the matched filter outputs of the PDRF under the action of target and jamming are analyzed.Then,the frequency entropy and peak-to-peak ratio are extracted from the matched filter output of the PDRF,and the time-frequency joint feature is constructed.Based on the time-frequency joint feature,the naive Bayesian classifier(NBC)with minimal risk is established for target and jamming recognition.To improve the adaptability of the proposed method in complex environments,an online update process that adaptively modifies the classifier in the duration of the work of the PDRF is proposed.The experiments show that the PDRF can maintain high recognition accuracy when the signal-to-noise ratio(SNR)decreases and the jamming-to-signal ratio(JSR)increases.Moreover,the applicable analysis shows that he ONBCMR method has low computational complexity and can fully meet the real-time requirements of PDRF. 展开更多
关键词 Pulse Doppler radar fuze(PDRF) Target and jamming recognition Time-frequency joint feature Online-update naive Bayesian classifier minimal risk(ONBCMR)
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Multi-Objective Deep Reinforcement Learning Based Time-Frequency Resource Allocation for Multi-Beam Satellite Communications 认领 引用 被引量：15
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作者 Yuanzhi He Biao Sheng +2 位作者 Hao Yin Di Yan Yingchao Zhang 《China Communications》 SCIE CSCD 2022年第1期77-91,共15页
Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requiremen... Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO). 展开更多
关键词 multi-beam satellite communications time-frequency resource allocation multi-objective optimization deep reinforcement learning
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ECCM scheme against interrupted sampling repeater jammer based on time-frequency analysis 认领 引用 被引量：45
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作者 Shixian Gong Xizhang Wei Xiang Li 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第6期996-1003,共8页
The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure... The interrupted sampling repeater jamming（ISRJ） is an effective deception jamming method for coherent radar, especially for the wideband linear frequency modulation（LFM） radar. An electronic counter-countermeasure（ECCM） scheme is proposed to remove the ISRJ-based false targets from the pulse compression result of the de-chirping radar. Through the time-frequency（TF） analysis of the radar echo signal, it can be found that the TF characteristics of the ISRJ signal are discontinuous in the pulse duration because the ISRJ jammer needs short durations to receive the radar signal. Based on the discontinuous characteristics a particular band-pass filter can be generated by two alternative approaches to retain the true target signal and suppress the ISRJ signal. The simulation results prove the validity of the proposed ECCM scheme for the ISRJ. 展开更多
关键词 interrupted sampling repeater jamming（ISRJ） de-chirping radar time-frequency（TF） electronic counter-countermeasure（ECCM）
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Time-Frequency Characteristics of the Relationships Between Tropical Indo-Pacific SSTs 认领 引用 被引量：9
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作者 Song YANG 丁晓利 +1 位作者 郑大伟 Soo-Hyun YOO 《Advances in Atmospheric Sciences》 SCIE CAS 2007年第3期343-359,共17页
In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By a... In this study, several advanced analysis methods are applied to understand the relationships between the Nino-3.4 sea surface temperatures （SST） and the SSTs related to the tropical Indian Ocean Dipole （IOD）. By analyzing a long data record, the authors focus on the time-frequency characteristics of these relationships, and of the structure of IOD. They also focus on the seasonal dependence of those characteristics in both time and frequency domains. Among the Nino-3.4 SST, IOD, and SSTs over the tropical western Indian Ocean （WIO） and eastern Indian Ocean （EIO）, the WIO SST has the strongest annual and semiannual oscillations. While the Nino-3.4 SST has large inter-annual variability that is only second to its annual variability, the IOD is characterized by the largest semiannual oscillation, which is even stronger than its annual oscillation. The IOD is strongly and stably related to the EIO SST in a wide range of frequency bands and in all seasons. However, it is less significantly related to the WIO SST in the boreal winter and spring. There exists a generally weak and unstable relationship between the WIO and EIO SSTs, especially in the biennial and higher frequency bands. The relationship is especially weak in summer and fall, when IOD is apparent, but appears highly positive in winter and spring, when the IOD is unimportantly weak and even disappears. This feature reflects a caution in the definition and application of IOD. The Nino-3.4 SST has a strong positive relationship with the WIO SST in all seasons, mainly in the biennial and longer frequency bands. However, it shows no significant relationship with the EIO SST in summer and fall, and with IOD in winter and spring. 展开更多
关键词 Indian Ocean dipole ENSO time-frequency relationship coherence analysis
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