The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve mod...The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve model in consideration of the shortage of current surface subsidence time functions. By analyzing the characteristics of the new time function, we found that it could meet the dynamic process, the velocity change process and the acceleration change process during surface subsidence. Then its rationality had been verified through project cases. The results show that the proposed time function model can give a good reflection of the regularity of surface subsidence in mined-out area and can accurately predict surface subsidence. And the prediction data of the model are a little greater than measured data on condition of proper measured data quantity, which is safety in the engineering. This model provides a new method for the analysis of surface subsidence in mined-out area and reference for future prediction, and it is valuable to engineering application.展开更多
We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functi...We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.展开更多
It is shown theoretically that the viscoelasticity of polymer melts is determined by three combining factorst they are the primary molecular weight and its distribution, the number of entanglement sites on polymer cha...It is shown theoretically that the viscoelasticity of polymer melts is determined by three combining factorst they are the primary molecular weight and its distribution, the number of entanglement sites on polymer chain and the sequence distribution of constituent chains in entanglement spacings. A unified quantity for the three combing factors is the average constrained dimensional number of constituent chains in the long entanglement spacings (v). A new relation of v to the primary molecular weight and the number of testing polymers were derived from the multiple entanglement and reptation model, and a new method for determining v was proposed. The dependences of linear viscoelastic functions on the primary molecular weight and its distribution were derived by the statistical method. When Mn=6Me to 18 Me, the values of (v) can range from 3.33 to 3.70. Their values are in a good agreement with the experiment data, and it can slightjy vary with the different species of polymers and the different ranges of molecular weight of polymers展开更多
Very short-term prediction of ship motion is critically important in many scenarios such as carrier aircraft landings and marine engineering operations.This paper introduces the newly developed functional deep learnin...Very short-term prediction of ship motion is critically important in many scenarios such as carrier aircraft landings and marine engineering operations.This paper introduces the newly developed functional deep learning model,named as Deep Operator networks neural network(DeepOnet)to predict very short-term ship motion in waves.It takes wave height as input and predicts ship motion as output,employing a cause-to-effect prediction approach.The modeling data for this study is derived from publicly available experimental data at the Iowa Institute of Hydraulic Research.Initially,the tuning of the hyperparameters within the neural network system was conducted to identify the optimal parameter combination.Subsequently,the DeepOnet model for wave height and multi-degree-of-freedom motion was established,and the impact of increasing time steps on prediction accuracy was analyzed.Lastly,a comparative analysis was performed between the DeepOnet model and the classical time series model,long short-term memory(LSTM).It was observed that the DeepOnet model exhibited a tenfold improvement in accuracy for roll and heave motions.Furthermore,as the forecast duration increased,the advantage of the DeepOnet model showed a trend of strengthening.As a functional prediction model,DeepOnet offers a novel and promising tool for very short-term ship motion prediction.展开更多
基金supported by the Key Program of the National Natural Science Foundation of China (No. 50334060)
文摘The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve model in consideration of the shortage of current surface subsidence time functions. By analyzing the characteristics of the new time function, we found that it could meet the dynamic process, the velocity change process and the acceleration change process during surface subsidence. Then its rationality had been verified through project cases. The results show that the proposed time function model can give a good reflection of the regularity of surface subsidence in mined-out area and can accurately predict surface subsidence. And the prediction data of the model are a little greater than measured data on condition of proper measured data quantity, which is safety in the engineering. This model provides a new method for the analysis of surface subsidence in mined-out area and reference for future prediction, and it is valuable to engineering application.
基金supported by National Natural Science Foundation of China (Grant No. 11271080)
文摘We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.
文摘It is shown theoretically that the viscoelasticity of polymer melts is determined by three combining factorst they are the primary molecular weight and its distribution, the number of entanglement sites on polymer chain and the sequence distribution of constituent chains in entanglement spacings. A unified quantity for the three combing factors is the average constrained dimensional number of constituent chains in the long entanglement spacings (v). A new relation of v to the primary molecular weight and the number of testing polymers were derived from the multiple entanglement and reptation model, and a new method for determining v was proposed. The dependences of linear viscoelastic functions on the primary molecular weight and its distribution were derived by the statistical method. When Mn=6Me to 18 Me, the values of (v) can range from 3.33 to 3.70. Their values are in a good agreement with the experiment data, and it can slightjy vary with the different species of polymers and the different ranges of molecular weight of polymers
基金Project supported by the National Natural Science Foundation of China(Grant No.51679021)supported by the Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)(Grant Nos.GML20240001,GML2024009).
文摘Very short-term prediction of ship motion is critically important in many scenarios such as carrier aircraft landings and marine engineering operations.This paper introduces the newly developed functional deep learning model,named as Deep Operator networks neural network(DeepOnet)to predict very short-term ship motion in waves.It takes wave height as input and predicts ship motion as output,employing a cause-to-effect prediction approach.The modeling data for this study is derived from publicly available experimental data at the Iowa Institute of Hydraulic Research.Initially,the tuning of the hyperparameters within the neural network system was conducted to identify the optimal parameter combination.Subsequently,the DeepOnet model for wave height and multi-degree-of-freedom motion was established,and the impact of increasing time steps on prediction accuracy was analyzed.Lastly,a comparative analysis was performed between the DeepOnet model and the classical time series model,long short-term memory(LSTM).It was observed that the DeepOnet model exhibited a tenfold improvement in accuracy for roll and heave motions.Furthermore,as the forecast duration increased,the advantage of the DeepOnet model showed a trend of strengthening.As a functional prediction model,DeepOnet offers a novel and promising tool for very short-term ship motion prediction.
