When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fa...When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.展开更多
In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power su...In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.展开更多
The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the va...The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the variational modal decomposition(VMD)method is introduced into the bolt detection signal analysis.On the basis of morphological filtering(MF)and the VMD method,a VMD?combined MF principle is established into a bolt detection signal analysis method(MF?VMD).MF?VMD is used to analyze the vibration and actual bolt detection signals of the simulation.Results show that MF?VMD effectively separates intrinsic mode function,even under strong interference.In comparison with conventional VMD method,the proposed method can remove noise interference.An intrinsic mode function of the field detection signal can be effectively identified by reflecting the signal at the bottom of the bolt.展开更多
Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking co...Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.展开更多
With the rapid development of the economy and industry and the improvement of pollution monitoring,how to accurately predict PM2.5 has become an issue of concern to the government and society.In the field of PM2.5 pol...With the rapid development of the economy and industry and the improvement of pollution monitoring,how to accurately predict PM2.5 has become an issue of concern to the government and society.In the field of PM2.5 pollution forecasting,a series of results have emerged so far.However,in the existing research field of PM2.5 prediction,most studies tend to predict short-term temporal series.Existing studies tend to ignore the temporal and spatial characteristics of PM2.5 transport,which leads to its poor performance in long-term prediction.In this paper,by optimizing previous PM2.5 deep learning prediction models,we propose a model GAT-EGRU.First,we add a spatial modular Graph Attention Network(GAT)and couple an Empirical Modal Decomposition algorithm(EMD),considering the temporal and spatial properties of PM2.5.Then,we use Gated Recurrent Unit(GRU)to filter spatio-temporal features for iterative rolling PM2.5 prediction.The experimental results show that the GAT-EGRU model has more advantages in predicting PM2.5 concentrations,especially for long time steps.This proves that the GAT-EGRU model outperforms other models for PM2.5 forecasting.After that,we verify the effectiveness of each module by distillation experiments.The experimental results show that each model module has an essential role in the final PM2.5 prediction results.The new model improves the ability to predict PM2.5 after a long time accurately and can be used as a practical tool for predicting PM2.5 concentrations.展开更多
A finite element / boundary element-modified modal decomposition method (FBMMD) is presented for predicting the vibration and sound radiation from submerged shell of revolution. Improvement has been made to accelerate...A finite element / boundary element-modified modal decomposition method (FBMMD) is presented for predicting the vibration and sound radiation from submerged shell of revolution. Improvement has been made to accelerate the convergence to FBMD method by means of introducing the residual modes which take into accaunt the quasi -state contributiort of all neglected modes. As an example, the vibration and sound radiation of a submerged spherical shell excited by axisymmetric force are studied in cases of ka=l,2,3 and 4. From the calculated results we see that the FBMMD method shows a significant improvement to the accuracy of surface sound pressure, normal displacement and directivity patterns of radiating sound, especially to the directivity patterns.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation.To solve the problems of low accuracy of the diagnostic model and unst...High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation.To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection,a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed.The first layer of the wide convolutional kernel deep convolutional neural network(WDCNN)is used to extract the local features of the signal and suppress the highfrequency noise.A Bidirectional Long Short-Term Memory Network(BILSTM)is used to obtain global time series features of the signal.Cross-attention combines the WDCNN layer and the BILSTM layer so that the model can recognize more comprehensive feature information of the signal.Meanwhile,to improve the accuracy,Variable Modal Decomposition(VMD)is used to decompose the signals and filter and reconstruct the signals using envelope entropy and kurtosis,which enables the pre-processing of the signals so that the data input to the neural network contains richer feature information.The feasibility of the model is tested and experimentally validated using publicly available datasets.The experimental results show that the accuracy of themodel proposed in this paper is significantly improved compared to the traditional WDCNN,BILSTM,and WDCNN-BILSTM models.展开更多
This paper addresses the problem of a viscoelastic Euler-Bernoulli beam under the influence of a constant velocity moving mass and different types of appendages.Four types of boundary conditions are considered:pinned-...This paper addresses the problem of a viscoelastic Euler-Bernoulli beam under the influence of a constant velocity moving mass and different types of appendages.Four types of boundary conditions are considered:pinned-pinned,fixed-pinned,fixed-free(or cantilever),and fixed-fixed.Appendages considered include lumped masses,dampers,and springs.The modal decomposition method is employed to derive the equation of motion of the beam,for which an analytical closed-form expression of the dynamic vibration response is generated.The proposed method enables the study of the effect of a single appendage or a combination of the three types of appendages on the non-dimensional dynamic response of the beam.Numerical examples are presented to illustrate the effects of these appendages and compare them to the reference cases of a beam with no appendages.The results demonstrate the importance of considering these parameters in the design of structures.The proposed method is compared to other techniques in the literature and found to be advantageous due to its direct approach.The method also offers a versatile tool for investigating various configurations,aiding in engineering design and structural analysis for which establishing a precise prediction of beam vibrations is crucial.展开更多
Cloud cavitation that forms around a two-dimensional hydrofoil may exhibit three-dimensional characteristics.This study investigates the spanwise variations of cloud-cavitating flows through comprehensive analyses of ...Cloud cavitation that forms around a two-dimensional hydrofoil may exhibit three-dimensional characteristics.This study investigates the spanwise variations of cloud-cavitating flows through comprehensive analyses of both numerical results and experimental snapshots.Experiments were conducted in the cavitation tunnel,utilizing high-speed cameras to record the evolution of cavitation,while the cavitating flow of the same configuration was simulated using detached eddy simulation(DES).The three-dimensional shedding phenomena of cloud cavitation,characterized by spanwise variations,are observed in both numerical and experimental results,impacting on the oscillation of hydrodynamic forces.According to the investigation on the spatial-temporal evolution of flow field,two distinct patterns in terms of spanwise shedding of cloud cavitation,namely complete and incomplete shedding,are identified.展开更多
Power prediction has been critical in large-scale wind power grid connections.However,traditional wind power prediction methods have long suffered from problems,for instance low prediction accuracy and poor reliabilit...Power prediction has been critical in large-scale wind power grid connections.However,traditional wind power prediction methods have long suffered from problems,for instance low prediction accuracy and poor reliability.For this purpose,a hybrid prediction model(VMD-LSTM-Attention)has been proposed,which integrates the variational modal decomposition(VMD),the long short-term memory(LSTM),and the attention mechanism(Attention),and has been optimized by improved dung beetle optimization algorithm(IDBO).Firstly,the algorithm's performance has been significantly enhanced through the implementation of three key strategies,namely the elite group strategy of the Logistic-Tent map,the nonlinear adjustment factor,and the adaptive T-distribution disturbance mechanism.Subsequently,IDBO has been applied to optimize the important parameters of VMD(decomposition layers and penalty factors)to ensure the best decomposition signal is obtained;Furthermore,the IDBO has been deployed to optimize the three key hyper-parameters of the LSTM,thereby improving its learning capability.Finally,an Attention mechanism has been incorporated to adaptively weight temporal features,thus increasing the model's ability to focus on key information.Comprehensive simulation experiments have demonstrated that the proposed model achieves higher prediction accuracy compared with VMD-LSTM,VMD-LSTM-Attention,and traditional prediction methods,and quantitative indexes verify the efectiveness of the algorithmic improvement as well as the excellence and precision of the model in wind power prediction.展开更多
In order to improve the detection accuracy of chaotic small signal prediction models under the background of sea clutter,a distributed sea clutter denoising algorithm is proposed,on the basis of variational modal deco...In order to improve the detection accuracy of chaotic small signal prediction models under the background of sea clutter,a distributed sea clutter denoising algorithm is proposed,on the basis of variational modal decomposition(VMD).The sea clutter signal is decomposed into variational modal functions(VMF)with different center bandwidths by means of VMD.By analyzing the autocorrelation characteristics of the deco mposed signal,we perform instantaneous half-period(IHP)and wavelet threshold denoising processing on the high-frequency and low-frequency components respectively,and regain the sea clutter signals.Based on LSSVM sea clutter prediction model,this research compares and analyzes the denoising effects of VMD.Experi ment results show that,the RMSE after denoising is reduced by two orders of magnitude,approximating 0.00034,with an apparently better denoising effect,compared with the root mean square error(RMSE)of the prediction before denoising.展开更多
In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-H...In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.展开更多
Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the mari...Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.展开更多
A filter method that combines ensemble empirical modal decomposition(EEMD)and wavelet analysis methods was proposed to separate and correct the global navigation satellite system(GNSS)multipath error more effectively....A filter method that combines ensemble empirical modal decomposition(EEMD)and wavelet analysis methods was proposed to separate and correct the global navigation satellite system(GNSS)multipath error more effectively.In this method,the GNSS signal is first decomposed into several intrinsic mode functions(IMFs)and a residual through EEMD.Then,the IMFs and residual are classified into noise terms,mixed terms,and useful terms according to a combined classification criterion.Finally,the mixed term denoised by wavelet and the useful term are reconstructed to obtain the multipath error and thus enable an error correction model to be built.The measurement data provided by the Curtin GNSS Research Center were used for processing and analysis.Results show that the proposed method can separate multipath error from GNSS data to a great extent,thereby effectively addressing the defects of EEMD and wavelet methods on multipath error weakening.The error correction model established with the separated multipath error has a higher accuracy and provides a certain reference value for research on related signal processing.展开更多
We examine the cross-shelf variation of internal tides(ITs)west of the Dongsha Plateau in the northern South China Sea based on observations from 4 moorings deployed between August 2017 and September 2018.On the slope...We examine the cross-shelf variation of internal tides(ITs)west of the Dongsha Plateau in the northern South China Sea based on observations from 4 moorings deployed between August 2017 and September 2018.On the slope,the amplitude of diurnal baroclinic current ellipses are 5 times larger than that of barotropic currents.The baroclinic energy quickly dissipates during cross-shelf propagation,and barotropic currents become dominant on the shelf outside of the Zhujiang River Estuary,with the amplitude of semidiurnal barotropic current ellipses being 10 times larger than that of the baroclinic ones.Dynamic modal decomposition indicates the first baroclinic mode is dominant for both diurnal and semidiurnal ITs.The total horizontal kinetic energy(HKE)of the first three baroclinic modes shows spatiotemporal differences among the 4 moorings.On the slope,the HKE for diurnal ITs is stronger in summer and winter,but weaker in spring and autumn;for semidiurnal ITs there is a similar seasonal variation,but the HKE in winter is even stronger than that in summer.On the shallow shelf,both diurnal and semidiurnal ITs maintain a certain intensity in summer but almost disappear in winter.Further analysis shows that only the upper water column is affected by seasonal variation of stratification on the slope,variation of diurnal ITs is thus controlled by the semi-annual cycle of barotropic energy input from the Luzon Strait,while the incoherent baroclinic currents make a major contribution to the temporal variation of semidiurnal ITs.For the shelf region,the water column is well mixed in winter,and the baroclinic energy largely dissipates when ITs propagate to the shelf zone despite of a strong barotropic energy input from the Luzon Strait.展开更多
An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challe...An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.展开更多
The deformation,flicker,and drift of a light field owing to complex media such as a turbulent atmosphere have limited its practical applications.Thus,research on invariants in randomly fluctuated light fields has garn...The deformation,flicker,and drift of a light field owing to complex media such as a turbulent atmosphere have limited its practical applications.Thus,research on invariants in randomly fluctuated light fields has garnered considerable attention in recent years.Coherence is a statistical property of light,while its full and quantitative characterization is challenging.Herein,we successfully realize the orthogonal modal decomposition of partially coherent beams and introduce the application of coherence entropy as a global coherence characteristic of such randomly fluctuated light fields.It is demonstrated that coherence entropy remains consistent during propagation in a unitary system by unraveling complex channels.As representative examples,we study the robustness of coherence entropy for partially coherent beams as they propagate through deformed optical systems and turbulent media.Coherence entropy is anticipated to serve as a key metric for evaluating the propagation of partially coherent beams in complex channels.This study paves the way for a broader application scope of a customized low-coherence light field through nonideal optical systems and complex media.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.51109158,U2106223)the Science and Technology Development Plan Program of Tianjin Municipal Transportation Commission(Grant No.2022-48)。
文摘When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.
基金supported by the National Natural Science Foundation of China(Grant No.62063016).
文摘In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.
基金supported by the Key Project of the National Natural Science Foundation of China (No.51739006)the Open Research Fund of the Fundamental Science on Radioactive Geology and Exploration Technology Laboratory (No.RGET1502)+1 种基金the Open Research Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering (No.2017SDSJ05)the Project of the Hubei Foundation for Innovative Research Groups (No.2015CFA025)
文摘The pull test is a damaging detection method that fails to measure the actual length of a bolt.Thus,the ultrasonic echo is an important non?destructive testing method for bolt quality detection.In this research,the variational modal decomposition(VMD)method is introduced into the bolt detection signal analysis.On the basis of morphological filtering(MF)and the VMD method,a VMD?combined MF principle is established into a bolt detection signal analysis method(MF?VMD).MF?VMD is used to analyze the vibration and actual bolt detection signals of the simulation.Results show that MF?VMD effectively separates intrinsic mode function,even under strong interference.In comparison with conventional VMD method,the proposed method can remove noise interference.An intrinsic mode function of the field detection signal can be effectively identified by reflecting the signal at the bottom of the bolt.
基金The National Natural Science Foundation of China (No.62262011)The Natural Science Foundation of Guangxi (No.2021JJA170130).
文摘Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.42071273,71671024 and 71874021Fundamental Research Funds for the Central Universities under Grant Nos.DUT20JC38,DUT20RW301 and DUT21YG119.
文摘With the rapid development of the economy and industry and the improvement of pollution monitoring,how to accurately predict PM2.5 has become an issue of concern to the government and society.In the field of PM2.5 pollution forecasting,a series of results have emerged so far.However,in the existing research field of PM2.5 prediction,most studies tend to predict short-term temporal series.Existing studies tend to ignore the temporal and spatial characteristics of PM2.5 transport,which leads to its poor performance in long-term prediction.In this paper,by optimizing previous PM2.5 deep learning prediction models,we propose a model GAT-EGRU.First,we add a spatial modular Graph Attention Network(GAT)and couple an Empirical Modal Decomposition algorithm(EMD),considering the temporal and spatial properties of PM2.5.Then,we use Gated Recurrent Unit(GRU)to filter spatio-temporal features for iterative rolling PM2.5 prediction.The experimental results show that the GAT-EGRU model has more advantages in predicting PM2.5 concentrations,especially for long time steps.This proves that the GAT-EGRU model outperforms other models for PM2.5 forecasting.After that,we verify the effectiveness of each module by distillation experiments.The experimental results show that each model module has an essential role in the final PM2.5 prediction results.The new model improves the ability to predict PM2.5 after a long time accurately and can be used as a practical tool for predicting PM2.5 concentrations.
文摘A finite element / boundary element-modified modal decomposition method (FBMMD) is presented for predicting the vibration and sound radiation from submerged shell of revolution. Improvement has been made to accelerate the convergence to FBMD method by means of introducing the residual modes which take into accaunt the quasi -state contributiort of all neglected modes. As an example, the vibration and sound radiation of a submerged spherical shell excited by axisymmetric force are studied in cases of ka=l,2,3 and 4. From the calculated results we see that the FBMMD method shows a significant improvement to the accuracy of surface sound pressure, normal displacement and directivity patterns of radiating sound, especially to the directivity patterns.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC。
文摘High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation.To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection,a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed.The first layer of the wide convolutional kernel deep convolutional neural network(WDCNN)is used to extract the local features of the signal and suppress the highfrequency noise.A Bidirectional Long Short-Term Memory Network(BILSTM)is used to obtain global time series features of the signal.Cross-attention combines the WDCNN layer and the BILSTM layer so that the model can recognize more comprehensive feature information of the signal.Meanwhile,to improve the accuracy,Variable Modal Decomposition(VMD)is used to decompose the signals and filter and reconstruct the signals using envelope entropy and kurtosis,which enables the pre-processing of the signals so that the data input to the neural network contains richer feature information.The feasibility of the model is tested and experimentally validated using publicly available datasets.The experimental results show that the accuracy of themodel proposed in this paper is significantly improved compared to the traditional WDCNN,BILSTM,and WDCNN-BILSTM models.
文摘This paper addresses the problem of a viscoelastic Euler-Bernoulli beam under the influence of a constant velocity moving mass and different types of appendages.Four types of boundary conditions are considered:pinned-pinned,fixed-pinned,fixed-free(or cantilever),and fixed-fixed.Appendages considered include lumped masses,dampers,and springs.The modal decomposition method is employed to derive the equation of motion of the beam,for which an analytical closed-form expression of the dynamic vibration response is generated.The proposed method enables the study of the effect of a single appendage or a combination of the three types of appendages on the non-dimensional dynamic response of the beam.Numerical examples are presented to illustrate the effects of these appendages and compare them to the reference cases of a beam with no appendages.The results demonstrate the importance of considering these parameters in the design of structures.The proposed method is compared to other techniques in the literature and found to be advantageous due to its direct approach.The method also offers a versatile tool for investigating various configurations,aiding in engineering design and structural analysis for which establishing a precise prediction of beam vibrations is crucial.
基金Project supported by the Fundamental Research Project(GrantNo.9091203)the National Natural Science Foundation of China(Grant Nos.92252205,12402286 and 12172219).
文摘Cloud cavitation that forms around a two-dimensional hydrofoil may exhibit three-dimensional characteristics.This study investigates the spanwise variations of cloud-cavitating flows through comprehensive analyses of both numerical results and experimental snapshots.Experiments were conducted in the cavitation tunnel,utilizing high-speed cameras to record the evolution of cavitation,while the cavitating flow of the same configuration was simulated using detached eddy simulation(DES).The three-dimensional shedding phenomena of cloud cavitation,characterized by spanwise variations,are observed in both numerical and experimental results,impacting on the oscillation of hydrodynamic forces.According to the investigation on the spatial-temporal evolution of flow field,two distinct patterns in terms of spanwise shedding of cloud cavitation,namely complete and incomplete shedding,are identified.
基金the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Saving(Project Number:Guike Energy 17-J-21-3).
文摘Power prediction has been critical in large-scale wind power grid connections.However,traditional wind power prediction methods have long suffered from problems,for instance low prediction accuracy and poor reliability.For this purpose,a hybrid prediction model(VMD-LSTM-Attention)has been proposed,which integrates the variational modal decomposition(VMD),the long short-term memory(LSTM),and the attention mechanism(Attention),and has been optimized by improved dung beetle optimization algorithm(IDBO).Firstly,the algorithm's performance has been significantly enhanced through the implementation of three key strategies,namely the elite group strategy of the Logistic-Tent map,the nonlinear adjustment factor,and the adaptive T-distribution disturbance mechanism.Subsequently,IDBO has been applied to optimize the important parameters of VMD(decomposition layers and penalty factors)to ensure the best decomposition signal is obtained;Furthermore,the IDBO has been deployed to optimize the three key hyper-parameters of the LSTM,thereby improving its learning capability.Finally,an Attention mechanism has been incorporated to adaptively weight temporal features,thus increasing the model's ability to focus on key information.Comprehensive simulation experiments have demonstrated that the proposed model achieves higher prediction accuracy compared with VMD-LSTM,VMD-LSTM-Attention,and traditional prediction methods,and quantitative indexes verify the efectiveness of the algorithmic improvement as well as the excellence and precision of the model in wind power prediction.
文摘In order to improve the detection accuracy of chaotic small signal prediction models under the background of sea clutter,a distributed sea clutter denoising algorithm is proposed,on the basis of variational modal decomposition(VMD).The sea clutter signal is decomposed into variational modal functions(VMF)with different center bandwidths by means of VMD.By analyzing the autocorrelation characteristics of the deco mposed signal,we perform instantaneous half-period(IHP)and wavelet threshold denoising processing on the high-frequency and low-frequency components respectively,and regain the sea clutter signals.Based on LSSVM sea clutter prediction model,this research compares and analyzes the denoising effects of VMD.Experi ment results show that,the RMSE after denoising is reduced by two orders of magnitude,approximating 0.00034,with an apparently better denoising effect,compared with the root mean square error(RMSE)of the prediction before denoising.
文摘In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC2806102)the National Natural Science Foundation of China(Grant Nos.52171287,52325107)+2 种基金High Tech Ship Research Project of Ministry of Industry and Information Technology(Grant Nos.2023GXB01-05-004-03,GXBZH2022-293)the Science Foundation for Distinguished Young Scholars of Shandong Province(Grant No.ZR2022JQ25)the Taishan Scholars Project(Grant No.tsqn201909063)。
文摘Leakages from subsea oil and gas equipment cause substantial economic losses and damage to marine ecosystem,so it is essential to locate the source of the leak.However,due to the complexity and variability of the marine environment,the signals collected by hydrophone contain a variety of noises,which makes it challenging to extract useful signals for localization.To solve this problem,a hydrophone denoising algorithm is proposed based on variational modal decomposition(VMD)with grey wolf optimization.First,the average envelope entropy is used as the fitness function of the grey wolf optimizer to find the optimal solution for the parameters K andα.Afterward,the VMD algorithm decomposes the original signal parameters to obtain the intrinsic mode functions(IMFs).Subsequently,the number of interrelationships between each IMF and the original signal was calculated,the threshold value was set,and the noise signal was removed to calculate the time difference using the valid signal obtained by reconstruction.Finally,the arrival time difference is used to locate the origin of the leak.The localization accuracy of the method in finding leaks is investigated experimentally by constructing a simulated leak test rig,and the effectiveness and feasibility of the method are verified.
基金The National Natural Science Foundation of China(No.41974030)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX17_0150).
文摘A filter method that combines ensemble empirical modal decomposition(EEMD)and wavelet analysis methods was proposed to separate and correct the global navigation satellite system(GNSS)multipath error more effectively.In this method,the GNSS signal is first decomposed into several intrinsic mode functions(IMFs)and a residual through EEMD.Then,the IMFs and residual are classified into noise terms,mixed terms,and useful terms according to a combined classification criterion.Finally,the mixed term denoised by wavelet and the useful term are reconstructed to obtain the multipath error and thus enable an error correction model to be built.The measurement data provided by the Curtin GNSS Research Center were used for processing and analysis.Results show that the proposed method can separate multipath error from GNSS data to a great extent,thereby effectively addressing the defects of EEMD and wavelet methods on multipath error weakening.The error correction model established with the separated multipath error has a higher accuracy and provides a certain reference value for research on related signal processing.
基金The Key-Area Research and Development Project of Guangdong Province under contract No.2020B1111020003the Science and Technology Plan Projects of Guangdong Province under contract No.2021B1212050025the Science and Technology Development Fund of the South China Sea Bureau,Ministry of Natural Resources under contract No.202205.
文摘We examine the cross-shelf variation of internal tides(ITs)west of the Dongsha Plateau in the northern South China Sea based on observations from 4 moorings deployed between August 2017 and September 2018.On the slope,the amplitude of diurnal baroclinic current ellipses are 5 times larger than that of barotropic currents.The baroclinic energy quickly dissipates during cross-shelf propagation,and barotropic currents become dominant on the shelf outside of the Zhujiang River Estuary,with the amplitude of semidiurnal barotropic current ellipses being 10 times larger than that of the baroclinic ones.Dynamic modal decomposition indicates the first baroclinic mode is dominant for both diurnal and semidiurnal ITs.The total horizontal kinetic energy(HKE)of the first three baroclinic modes shows spatiotemporal differences among the 4 moorings.On the slope,the HKE for diurnal ITs is stronger in summer and winter,but weaker in spring and autumn;for semidiurnal ITs there is a similar seasonal variation,but the HKE in winter is even stronger than that in summer.On the shallow shelf,both diurnal and semidiurnal ITs maintain a certain intensity in summer but almost disappear in winter.Further analysis shows that only the upper water column is affected by seasonal variation of stratification on the slope,variation of diurnal ITs is thus controlled by the semi-annual cycle of barotropic energy input from the Luzon Strait,while the incoherent baroclinic currents make a major contribution to the temporal variation of semidiurnal ITs.For the shelf region,the water column is well mixed in winter,and the baroclinic energy largely dissipates when ITs propagate to the shelf zone despite of a strong barotropic energy input from the Luzon Strait.
基金supported by the Action Programme for Cultivation of Young and Middle-aged Teachers in Universities in Anhui Province(YQYB2023030),Chinathe Supporting Programme for Outstanding Young Talents in Colleges and Universities of Anhui Provincial Department of Education(gxyq2022068),China+1 种基金the Huainan Normal University Scientific Research Project(2023XJZD016),Chinathe Key Projects of Huainan Normal University(2024XJZD012),China.
文摘An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.
基金supported by the National Key Research and Development Program of China(Grant Nos.2022YFA1404800 and 2019YFA0705000)the National Natural Science Foundation of China(Grant Nos.12174280,12204340,12192254,92250304,and 92050202)+2 种基金the China Postdoctoral Science Foundation(Grant No.2022M722325)the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Key Lab of Modern Optical Technologies of Jiangsu Province(Grant No.KJS2138)
文摘The deformation,flicker,and drift of a light field owing to complex media such as a turbulent atmosphere have limited its practical applications.Thus,research on invariants in randomly fluctuated light fields has garnered considerable attention in recent years.Coherence is a statistical property of light,while its full and quantitative characterization is challenging.Herein,we successfully realize the orthogonal modal decomposition of partially coherent beams and introduce the application of coherence entropy as a global coherence characteristic of such randomly fluctuated light fields.It is demonstrated that coherence entropy remains consistent during propagation in a unitary system by unraveling complex channels.As representative examples,we study the robustness of coherence entropy for partially coherent beams as they propagate through deformed optical systems and turbulent media.Coherence entropy is anticipated to serve as a key metric for evaluating the propagation of partially coherent beams in complex channels.This study paves the way for a broader application scope of a customized low-coherence light field through nonideal optical systems and complex media.
