[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
用于交流输电线路故障测距的小波变换故障定位方法存在精度低和自适应差的缺陷。为此,提出一种VMD-TEO算法,利用变分模态分解(variational mode decomposition,VMD)算法分解故障行波信号,通过Teager能量算子(Teager energy operator,TEO...用于交流输电线路故障测距的小波变换故障定位方法存在精度低和自适应差的缺陷。为此,提出一种VMD-TEO算法,利用变分模态分解(variational mode decomposition,VMD)算法分解故障行波信号,通过Teager能量算子(Teager energy operator,TEO)提取分解后的高频模态分量的能量突变点,将能量峰值对应的时刻带入分布式故障测距算法,获取故障位置。算例仿真对比结果表明:VMD-TEO算法定位误差小于0.1%,小波变换法故障定位的误差约为VMD-TEO算法的10倍;VMD-TEO算法分解信号后能够有效去除噪声干扰,成功提取波头信号,大幅度提高故障定位的准确度。展开更多
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
文摘用于交流输电线路故障测距的小波变换故障定位方法存在精度低和自适应差的缺陷。为此,提出一种VMD-TEO算法,利用变分模态分解(variational mode decomposition,VMD)算法分解故障行波信号,通过Teager能量算子(Teager energy operator,TEO)提取分解后的高频模态分量的能量突变点,将能量峰值对应的时刻带入分布式故障测距算法,获取故障位置。算例仿真对比结果表明:VMD-TEO算法定位误差小于0.1%,小波变换法故障定位的误差约为VMD-TEO算法的10倍;VMD-TEO算法分解信号后能够有效去除噪声干扰,成功提取波头信号,大幅度提高故障定位的准确度。
