Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

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.

Generalized load graphical forecasting method based on modal decomposition

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.

Anchoring Bolt Detection Based on Morphological Filtering and Variational Modal Decomposition

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.

Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

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.

GAT-EGRU:A Deep Learning Prediction Model for PM2.5 Coupled with Empirical Modal Decomposition Algorithm

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.

A FINITE ELEMENT/BOUNDARY ELEMENT——MODIFIED MODAL DECOMPOSITION METHOD FOR VIBRATION AND SOUND RADIATION FROM SUBMERGED SHELL OF REVOLUTION

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.

Distributed Sea Clutter Denoising Algorithm Based on Variational Mode Decomposition

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.

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

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.

Three-Dimensional Sound Source Location Algorithm for Subsea Leakage Using Hydrophone

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.

Combined filter method for weakening GNSS multipath error

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.

Cross-shelf variation of internal tides west of the Dongsha Plateau in the northern South China Sea

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.

Coherence entropy during propagation through complex media

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.