The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatia...The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.展开更多
This paper reviews the development of f(R) gravity theory and Phantom and Quintessence fields. Specifically, we present a new general action of f(R) gravity and Phantom and Quintessence fields coupled to scalar curvat...This paper reviews the development of f(R) gravity theory and Phantom and Quintessence fields. Specifically, we present a new general action of f(R) gravity and Phantom and Quintessence fields coupled to scalar curvature. Then, we deduce Euler-Lagrange Equations of different fields, matter tensor and effective matter tensor. Additionally, this paper obtains the general pressure, density and speed sound of the new general field action, and investigates different cosmological evolutions with inflation. Further, this paper investigates a general f(R) gravity theory with a general matter action and obtains the different field equations, general matter tensor and effective matter tensor. Besides, this paper obtains the effective Strong Energy Condition (SEC) and effective Null Energy Condition (NEC). Then, we prove that when f(R) approaches to R, the effective SEC and the effective NEC approach to the usual SEC and the usual NEC, respectively. Finally, this paper presents a general action of f(R) gravity, Quintessence and Phantom fields and their applications.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42004030)Basic Scientific Fund for National Public Research Institutes of China(Grant No.2022S03)+1 种基金Science and Technology Innovation Project(LSKJ202205102)funded by Laoshan Laboratory,and the National Key Research and Development Program of China(2020YFB0505805).
文摘The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.
文摘This paper reviews the development of f(R) gravity theory and Phantom and Quintessence fields. Specifically, we present a new general action of f(R) gravity and Phantom and Quintessence fields coupled to scalar curvature. Then, we deduce Euler-Lagrange Equations of different fields, matter tensor and effective matter tensor. Additionally, this paper obtains the general pressure, density and speed sound of the new general field action, and investigates different cosmological evolutions with inflation. Further, this paper investigates a general f(R) gravity theory with a general matter action and obtains the different field equations, general matter tensor and effective matter tensor. Besides, this paper obtains the effective Strong Energy Condition (SEC) and effective Null Energy Condition (NEC). Then, we prove that when f(R) approaches to R, the effective SEC and the effective NEC approach to the usual SEC and the usual NEC, respectively. Finally, this paper presents a general action of f(R) gravity, Quintessence and Phantom fields and their applications.
文摘永磁同步电机(Permanent Magnet Synchronous Motor,PMSM)弱磁控制系统常用于电动汽车领域。电动汽车运行于低速时,PMSM需要输出大转矩,以响应快速起步、加速及爬坡需求;电动汽车运行于高速,且超过额定速度时,PMSM处于弱磁状态,需具备一定的带载能力,以满足高速行驶和超车工况。针对PMSM弱磁控制中的转速突变,文章设计了自抗扰控制器(Active Disturbances Rejection Controller,ADRC)替代速度外环PI控制器,对扰动项快速观测和补偿,减小速度突变对系统造成干扰,实现转速精准跟踪。针对转矩项干扰,结合转矩和磁链输出值设计有限集模型预测控制(Finite Control Set Model Predictive Control,FCS-MPC)以替代传统直接转矩控制(Direct Torque Control,DTC),构建令转矩和磁链脉动最小的价值函数,再通过价值函数的计算寻优,选取出最优空间矢量控制信号输送给逆变器。基于ADRC和FCS-MPC的优化作用,弱磁控制系统的抗扰能力、电流和转矩输出精度增强,试验验证了所设计系统的可行性和性能优势。
