As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the ...As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the complex relationships.In this paper,we propose a novel method for constructing independence tests for random elements in Hilbert spaces,which includes functional data as a special case.Our approach is using distance covariance of random projections to build a test statistic that is computationally efficient and exhibits strong power performance.We prove the equivalence between testing for independence expressed on the original and the projected covariates,bridging the gap between measures of testing independence in Euclidean spaces and Hilbert spaces.Implementation of the test involves calibration by permutation and combining several p-values from different projections using the false discovery rate method.Simulation studies and real data examples illustrate the finite sample properties of the proposed method under a variety of scenarios.展开更多
The prediction and compensation control of marine ship motion is crucial for ensuring the safety of offshore wind turbine loading and unloading operations.However,the accuracy of prediction and control is significantl...The prediction and compensation control of marine ship motion is crucial for ensuring the safety of offshore wind turbine loading and unloading operations.However,the accuracy of prediction and control is significantly affected by the hysteresis phenomenon in the wave compensation system.To address this issue,a ship heave motion prediction is proposed in this paper on the basis of the Gauss-DeepAR(AR stands for autoregressive recurrent)model and the Hilbert−Huang time-delay compensation control strategy.Initially,the zero upward traveling wave period of the level 4−6 sea state ship heave motion is analyzed,which serves as the input sliding window for the Gauss-DeepAR prediction model,and probability predictions at different wave direction angles are conducted.Next,considering the hysteresis characteristics of the ship heave motion compensation platform,the Hilbert−Huang transform is employed to analyze and calculate the hysteresis delay of the compensation platform.After the optimal control action value is subsequently calculated,simulations and hardware platform tests are conducted.The simulation results demonstrated that the Gauss-DeepAR model outperforms autoregressive integrated moving average model(ARIMA),support vector machine(SVM),and longshort-term memory(LSTM)in predicting non-independent identically distributed datasets at a 90°wave direction angle in the level 4−6 sea states.Furthermore,the model has good predictive performance and generalizability for non-independent and non-uniformly distributed datasets at a 180°wave direction angle.The hardware platform compensation test results revealed that the Hilbert–Huang method has an outstanding effect on determining the hysteretic delay and selecting the optimal control action value,and the compensation efficiency was higher than 90%in the level 4−6 sea states.展开更多
在图像处理,机器学习和工程学等应用领域中,通常需要处理一些定义在Hilbert空间中的大规模算子方程。为求解这类算子方程及最值问题,构造了一类增量型不精确Broyden方法并证明了该算法的线性收敛和局部超线性收敛性。该算法降低了在处...在图像处理,机器学习和工程学等应用领域中,通常需要处理一些定义在Hilbert空间中的大规模算子方程。为求解这类算子方程及最值问题,构造了一类增量型不精确Broyden方法并证明了该算法的线性收敛和局部超线性收敛性。该算法降低了在处理大规模问题中所产生的储存成本,并通过应用证明了该算法的有效性。In application fields such as image processing, machine learning, and engineering, it is often necessary to solve large-scale operator equations defined in Hilbert spaces. To address such operator equations and optimization problems, a class of incremental inexact Broyden methods has been developed, and the linear convergence as well as local superlinear convergence of this algorithm has been proven. This algorithm reduces the storage costs associated with handling large-scale problems, and its effectiveness has been demonstrated through practical applications.展开更多
基金Supported by the Grant of National Science Foundation of China(11971433)Zhejiang Gongshang University“Digital+”Disciplinary Construction Management Project(SZJ2022B004)+1 种基金Institute for International People-to-People Exchange in Artificial Intelligence and Advanced Manufacturing(CCIPERGZN202439)the Development Fund for Zhejiang College of Shanghai University of Finance and Economics(2023FZJJ15).
文摘As data becomes increasingly complex,measuring dependence among variables is of great interest.However,most existing measures of dependence are limited to the Euclidean setting and cannot effectively characterize the complex relationships.In this paper,we propose a novel method for constructing independence tests for random elements in Hilbert spaces,which includes functional data as a special case.Our approach is using distance covariance of random projections to build a test statistic that is computationally efficient and exhibits strong power performance.We prove the equivalence between testing for independence expressed on the original and the projected covariates,bridging the gap between measures of testing independence in Euclidean spaces and Hilbert spaces.Implementation of the test involves calibration by permutation and combining several p-values from different projections using the false discovery rate method.Simulation studies and real data examples illustrate the finite sample properties of the proposed method under a variety of scenarios.
基金supported by the National Natural Science Foundation of China(Grant No.52105466).
文摘The prediction and compensation control of marine ship motion is crucial for ensuring the safety of offshore wind turbine loading and unloading operations.However,the accuracy of prediction and control is significantly affected by the hysteresis phenomenon in the wave compensation system.To address this issue,a ship heave motion prediction is proposed in this paper on the basis of the Gauss-DeepAR(AR stands for autoregressive recurrent)model and the Hilbert−Huang time-delay compensation control strategy.Initially,the zero upward traveling wave period of the level 4−6 sea state ship heave motion is analyzed,which serves as the input sliding window for the Gauss-DeepAR prediction model,and probability predictions at different wave direction angles are conducted.Next,considering the hysteresis characteristics of the ship heave motion compensation platform,the Hilbert−Huang transform is employed to analyze and calculate the hysteresis delay of the compensation platform.After the optimal control action value is subsequently calculated,simulations and hardware platform tests are conducted.The simulation results demonstrated that the Gauss-DeepAR model outperforms autoregressive integrated moving average model(ARIMA),support vector machine(SVM),and longshort-term memory(LSTM)in predicting non-independent identically distributed datasets at a 90°wave direction angle in the level 4−6 sea states.Furthermore,the model has good predictive performance and generalizability for non-independent and non-uniformly distributed datasets at a 180°wave direction angle.The hardware platform compensation test results revealed that the Hilbert–Huang method has an outstanding effect on determining the hysteretic delay and selecting the optimal control action value,and the compensation efficiency was higher than 90%in the level 4−6 sea states.
文摘在图像处理,机器学习和工程学等应用领域中,通常需要处理一些定义在Hilbert空间中的大规模算子方程。为求解这类算子方程及最值问题,构造了一类增量型不精确Broyden方法并证明了该算法的线性收敛和局部超线性收敛性。该算法降低了在处理大规模问题中所产生的储存成本,并通过应用证明了该算法的有效性。In application fields such as image processing, machine learning, and engineering, it is often necessary to solve large-scale operator equations defined in Hilbert spaces. To address such operator equations and optimization problems, a class of incremental inexact Broyden methods has been developed, and the linear convergence as well as local superlinear convergence of this algorithm has been proven. This algorithm reduces the storage costs associated with handling large-scale problems, and its effectiveness has been demonstrated through practical applications.
