Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environme...Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.展开更多
准确预测股价波动是金融投资关注的焦点问题。股价波动受多种因素影响,具有非线性特征,传统的线性预测方法往往难以奏效。文章选择A股5个代表性股指与5只大市值股票为样本,使用其2020—2023年的日收盘价数据,首先,借助相空间重构技术(Ph...准确预测股价波动是金融投资关注的焦点问题。股价波动受多种因素影响,具有非线性特征,传统的线性预测方法往往难以奏效。文章选择A股5个代表性股指与5只大市值股票为样本,使用其2020—2023年的日收盘价数据,首先,借助相空间重构技术(Phase Space Reconstruction,PSR)将股价时间序列映射到高维空间中,揭示其混沌特征;然后,基于门控循环单元(Gate Recurrent Unit,GRU)深度学习方法开发出PSR-GRU预测模型,生成股价预测结果;最后,将预测结果与经典预测模型所得结果进行对比。结果发现,股价波动具有混沌特性,PSR-GRU模型在股价预测上表现出更优异的性能。展开更多
On the streaming video of all typical flow pattern filmings on the experiment system by the high-speed video camera,the information of a single-frame was extracted and made into time-series.The series was analyzed wit...On the streaming video of all typical flow pattern filmings on the experiment system by the high-speed video camera,the information of a single-frame was extracted and made into time-series.The series was analyzed with the non-linear chaotic recurrence plot(RP),less used in the last years.It was combined with the average diagonal length and Shannon entropy of recursive features changed after the increase of gas superficial velocity.The results showed that the information entropy of flow image combined with RP could well characterize the evaluated tract of gas-liquid two-phase flow patterns.At the same time,the information well characterized the volume fraction of all kinds of gas-liquid two-phase flow patterns.The average diagonal length and recursive Shannon entropy of recursive features all increased first and then decreased with the increase of gas superficial velocity,and it reflected the transition of the mechanisms of five typical flow patterns from the recursive characteristics.展开更多
基金The National Natural Science Foundation of China under contract No.62275228the S&T Program of Hebei under contract Nos 19273901D and 20373301Dthe Hebei Natural Science Foundation under contract No.F2020203066.
文摘Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.
文摘准确预测股价波动是金融投资关注的焦点问题。股价波动受多种因素影响,具有非线性特征,传统的线性预测方法往往难以奏效。文章选择A股5个代表性股指与5只大市值股票为样本,使用其2020—2023年的日收盘价数据,首先,借助相空间重构技术(Phase Space Reconstruction,PSR)将股价时间序列映射到高维空间中,揭示其混沌特征;然后,基于门控循环单元(Gate Recurrent Unit,GRU)深度学习方法开发出PSR-GRU预测模型,生成股价预测结果;最后,将预测结果与经典预测模型所得结果进行对比。结果发现,股价波动具有混沌特性,PSR-GRU模型在股价预测上表现出更优异的性能。
文摘On the streaming video of all typical flow pattern filmings on the experiment system by the high-speed video camera,the information of a single-frame was extracted and made into time-series.The series was analyzed with the non-linear chaotic recurrence plot(RP),less used in the last years.It was combined with the average diagonal length and Shannon entropy of recursive features changed after the increase of gas superficial velocity.The results showed that the information entropy of flow image combined with RP could well characterize the evaluated tract of gas-liquid two-phase flow patterns.At the same time,the information well characterized the volume fraction of all kinds of gas-liquid two-phase flow patterns.The average diagonal length and recursive Shannon entropy of recursive features all increased first and then decreased with the increase of gas superficial velocity,and it reflected the transition of the mechanisms of five typical flow patterns from the recursive characteristics.
