Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing metho...Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.展开更多
Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.Th...Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.展开更多
Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based v...Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.展开更多
In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decompositi...In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.展开更多
In recent study the bank of real square integrable functions that have nonlinear phases and admit a well-behaved Hilbert transform has been constructed for adaptive representation of nonlinear signals. We first s...In recent study the bank of real square integrable functions that have nonlinear phases and admit a well-behaved Hilbert transform has been constructed for adaptive representation of nonlinear signals. We first show in this paper that the available basic functions are adequate for establishing an ideal adaptive decomposition algorithm. However, we also point out that the best approximation algorithm, which is a common strategy in decomposing a function into a sum of functions in a prescribed class of basis functions, should not be considered as a candidate for the ideal algorithm.展开更多
In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture chan...In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.展开更多
Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key inno...Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis.展开更多
基金supported in part by the Science and Technology Major Project of Anhui Province(Grant No.17030901037)in part by the Humanities and Social Science Fund of Ministry of Education of China(Grant No.19YJAZH098)+2 种基金in part by the Program for Synergy Innovation in the Anhui Higher Education Institutions of China(Grant Nos.GXXT-2020-012,GXXT-2021-044)in part by Science and Technology Planning Project of Wuhu City,Anhui Province,China(Grant No.2021jc1-2)part by Research Start-Up Fund for Introducing Talents from Anhui Polytechnic University(Grant No.2021YQQ066).
文摘Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30.
文摘Noise interference critically impairs the stability and data accuracy of sensing systems.However,current suppression strategies fail to concurrently mitigate intrinsic system noise and extrinsic environmental noise.This study introduces a composite denoising approach to address this challenge.This method is based on the ameliorated ellipse fitting algorithm(AEFA)and adaptive successive variational mode decomposition(ASVMD).This algorithm employs AEFA to eliminate system noise tightly coupled with direct-current and alternating-current components in the interference signal,thereby obtaining a phase signal containing only environmental noise.The ASVMD technique adaptively extracts environmental noise components predominantly present in the phase signal.To achieve optimal decomposition results automatically,the permutation entropy criterion is employed to refine decomposition parameters.The correlation coefficient is utilized to differentiate effective components from noise components in the decomposition results.Experimental results indicate that the combined AEFA and ASVMD algorithm effectively suppresses both system and environmental noises.When applied to 50 Hz vibration signal processing,the proposed approach achieves a noise reduction of 17.81 dB and a phase resolution of 35.14μrad/√Hz.Given the excellent performance of the noise suppression,the proposed approach holds great application potential in high-performance interferometric sensing systems.
基金funded by National Key R&D Program of China(No.2022YFC3003403)Sichuan Science and Technology Program(No.2024NSFSC0072)+1 种基金Natural Science Foundation of Hebei Province(No.F2021201031)Geological Survey Project of China Geological Survey(No.DD20230442).
文摘Infrasound,known for its strong penetration and low attenuation,is extensively used in monitoring and warning systems for debris flows.Here,a debris-flow forecasting method was proposed by combining infrasound-based variational mode decomposition and Autoregressive Integrated Moving Average(ARIMA)model.High-precision infrasound sensor was utilized in experiments to record signals under twelve varying conditions of debris flow volume and velocity.Variational mode decomposition was performed on the detected raw signals,and the optimal decomposition scale and penalty factor were obtained through the sparrow search algorithm.The Hilbert transform,rescaled range analysis,power spectrum analysis,and Pearson correlation coefficients judgment criteria were employed to separate and reconstruct the signals.Based on the reconstructed infrasound signals,an ARIMA model was constructed to forecast the trend of debris flow infrasound signal.Results reveal that the Hilbert transform effectively separated noise,and the predictive model’s results fell within a 95%confidence interval.The Mean Absolute Percentage Error(MAPE)across four experiments were 4.87%,5.23%,5.32%and 4.47%,respectively,showing a satisfactory accuracy and providing an alternative for predicting debris flow by infrasound signals.
文摘In order to further analyze the micro-motion modulation signals generated by rotating components and extract micro-motion features,a modulation signal denoising algorithm based on improved variational mode decomposition(VMD)is proposed.To improve the time-frequency performance,this method decomposes the data into narrowband signals and analyzes the internal energy and frequency variations within the signal.Genetic algorithms are used to adaptively optimize the mode number and bandwidth control parameters in the process of VMD.This approach aims to obtain the optimal parameter combination and perform mode decomposition on the micro-motion modulation signal.The optimal mode number and quadratic penalty factor for VMD are determined.Based on the optimal values of the mode number and quadratic penalty factor,the original signal is decomposed using VMD,resulting in optimal mode number intrinsic mode function(IMF)components.The effective modes are then reconstructed with the denoised modes,achieving signal denoising.Through experimental data verification,the proposed algorithm demonstrates effective denoising of modulation signals.In simulation data validation,the algorithm achieves the highest signal-to-noise ratio(SNR)and exhibits the best performance.
基金The NSF(11426113)of Chinathe Science and Technology Research Project(2014161,2014163)of Jilin Provincial Department of Education of Chinathe Project(20160520110JH)of Science and Technology Development Plan for Jilin Provincial
文摘In recent study the bank of real square integrable functions that have nonlinear phases and admit a well-behaved Hilbert transform has been constructed for adaptive representation of nonlinear signals. We first show in this paper that the available basic functions are adequate for establishing an ideal adaptive decomposition algorithm. However, we also point out that the best approximation algorithm, which is a common strategy in decomposing a function into a sum of functions in a prescribed class of basis functions, should not be considered as a candidate for the ideal algorithm.
基金supported by the National Natural Science Foundation of China(Grant numbers 71988101,71901205).
文摘In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.
基金supported by Central Guidance on Local Science and Technology Development Fund of Hebei Province(Grant No.226Z1906G)funded by Science Research Project of Hebei Education Department(CXY2024038)+1 种基金funded by Basic Research Project of Shijiazhuang University in Hebei Province(241791157A)National Natural Science Foundation of China(52206224).
文摘Considering the noise problem of the acquisition signals frommechanical transmission systems,a novel denoising method is proposed that combines Variational Mode Decomposition(VMD)with wavelet thresholding.The key innovation of this method lies in the optimization of VMD parameters K and α using the improved Horned Lizard Optimization Algorithm(IHLOA).An inertia weight parameter is introduced into the random walk strategy of HLOA,and the related formula is improved.The acquisition signal can be adaptively decomposed into some Intrinsic Mode Functions(IMFs),and the high-noise IMFs are identified based on a correlation coefficient-variance method.Further noise reduction is achieved using wavelet thresholding.The proposed method is validated using simulated signals and experimental signals,and simulation results indicate that the proposed method surpasses original VMD,Empirical Mode Decomposition(EMD),and wavelet thresholding in terms of Signal-to-Noise Ratio(SNR)and Root Mean Square Error(RMSE),and experimental results indicate that the proposedmethod can effectively remove noise in terms of three evaluationmetrics.Furthermore,comparedwith FeatureModeDecomposition(FMD)andMultichannel Singular Spectrum Analysis(MSSA),this method has a better envelope spectrum.This method not only provides a solution for noise reduction in signal processing but also holds significant potential for applications in structural health monitoring and fault diagnosis.
