Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diph...Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine （PBDPA） molecules were calculated. Then the quantitative structure-property relationships （QSPR） among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4：21：1, 4：24：1, and 4：24：1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of Sθ, AfHe and △fGθ- were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural pa- rameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs.展开更多
Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agricultu...Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture,based on unmanned aerial vehicle(UAV)remote sensing technology.In this study,the hyperspectral images were acquired by UAV and the leaf nitrogen content(LNC)and leaf nitrogen accumulation(LNA)were measured to estimate the N nutrition status of maize.24 vegetation indices(VIs)were constructed using hyperspectral images,and four prediction models were used to estimate the LNC and LNA of maize.The models include a single linear regression model,multivariable linear regression(MLR)model,random forest regression(RFR)model,and support vector regression(SVR)model.Moreover,the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields.The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll(NDchl)had the highest prediction accuracy for LNC(R^(2),RMSE,and RE were 0.72,0.21,and 12.19%,respectively)and LNA(R^(2),RMSE,and RE were 0.77,0.26,and 14.34%,respectively).And then,24 VIs were divided into 13 important VIs and 11 unimportant VIs.Three prediction models for LNC and LNA were constructed using 13 important VIs,and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model,in which RFR model had the highest prediction accuracy for the validation dataset of LNC(R^(2),RMSE,and RE were 0.78,0.16,and 8.83%,respectively)and LNA(R^(2),RMSE,and RE were 0.85,0.19,and 9.88%,respectively).This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.展开更多
针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建...针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。展开更多
文摘Based on the location of bromine substituents and conjugation matrix, a new substituent po- sition index ~X not only was defined, but also molecular shape indexes Km and electronega- tivity distance vectors Mm of diphenylamine and 209 kinds of polybrominated diphenylamine （PBDPA） molecules were calculated. Then the quantitative structure-property relationships （QSPR） among the thermodynamic properties of 210 organic pollutants and 0X, K3, M29, M36 were founded by Leaps-and-Bounds regression. Using the four structural parameters as input neurons of the artificial neural network, three satisfactory QSPR models with network structures of 4：21：1, 4：24：1, and 4：24：1 respectively, were achieved by the back-propagation algorithm. The total correlation coefficients R were 0.9999, 0.9997, and 0.9995 respectively and the standard errors S were 1.036, 1.469, and 1.510 respectively. The relative mean deviation between the predicted value and the experimental value of Sθ, AfHe and △fGθ- were 0.11%, 0.34% and 0.24% respectively, which indicated that the QSPR models had good stability and superior predictive ability. The results showed that there were good nonlinear correlations between the thermodynamic properties of PBDPAs and the four structural pa- rameters. Thus, it was concluded that the ANN models established by the new substituent position index were fully applicable to predict properties of PBDPAs.
基金financially supported by the Hainan Province Science and Technology Special Fund(Grant No.ZDYF2021GXJS038 and Grant No.ZDYF2024XDNY196)Hainan Provincial Natural Science Foundation of China(Grant No.320RC486)the National Natural Science Foundation of China(Grant No.42167011).
文摘Nitrogen(N)as a pivotal factor in influencing the growth,development,and yield of maize.Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture,based on unmanned aerial vehicle(UAV)remote sensing technology.In this study,the hyperspectral images were acquired by UAV and the leaf nitrogen content(LNC)and leaf nitrogen accumulation(LNA)were measured to estimate the N nutrition status of maize.24 vegetation indices(VIs)were constructed using hyperspectral images,and four prediction models were used to estimate the LNC and LNA of maize.The models include a single linear regression model,multivariable linear regression(MLR)model,random forest regression(RFR)model,and support vector regression(SVR)model.Moreover,the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields.The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll(NDchl)had the highest prediction accuracy for LNC(R^(2),RMSE,and RE were 0.72,0.21,and 12.19%,respectively)and LNA(R^(2),RMSE,and RE were 0.77,0.26,and 14.34%,respectively).And then,24 VIs were divided into 13 important VIs and 11 unimportant VIs.Three prediction models for LNC and LNA were constructed using 13 important VIs,and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model,in which RFR model had the highest prediction accuracy for the validation dataset of LNC(R^(2),RMSE,and RE were 0.78,0.16,and 8.83%,respectively)and LNA(R^(2),RMSE,and RE were 0.85,0.19,and 9.88%,respectively).This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture.
文摘针对具有多维状态变量、多种工作模式和故障模式的复杂工程系统,提出一种基于综合健康指数(synthesized health index,SHI)与相关向量机(relevance vector machine,RVM)的系统级失效预测方法。在离线训练阶段,先根据有限失效历史数据建立各工作模式下的健康评估模型,并据此获得各历史退化轨迹的SHI序列;然后再使用RVM对这些序列进行回归处理,进而辨识出与回归曲线最为匹配的函数模型。在线预测阶段,先运用健康评估模型计算当前设备的SHI序列并进行RVM回归,再拟合出离线阶段确定的函数模型并添加时变噪声;最后,外推预测出系统剩余使用寿命的概率密度分布。该方法成功应用到涡轮发动机的失效预测案例。
