Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of hea...Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.展开更多
The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity fr...The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.展开更多
A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural n...A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.展开更多
Brain arteriovenous malformation(BAVM) is frequently described as vascular malformation. Although computer tomography(CT), magnetic resonance imaging(MRI) and angiography can clearly detect lesions, there are no diagn...Brain arteriovenous malformation(BAVM) is frequently described as vascular malformation. Although computer tomography(CT), magnetic resonance imaging(MRI) and angiography can clearly detect lesions, there are no diagnostic biological markers of BAVM available. Current study demonstrated that micro RNA(mi RNA)showed a feasible marker for vascular disease. To find key correlations between these mi RNAs and the onset of BAVM, we carried out chip analysis of serum mi RNAs by identifying 18 potential markers of BAVM. We then constructed a principle component analysis and logistic regression(PCA-LR) model to analyze the 18 mi RNAs collected from 77 patients. Another 9 independent samples were used to test the resulting model. The results showed that mi RNAs hsa-mir-126-3p and hsa-mir-140 are important protective factors, while hsa-mir-338 is a dominating risk factor, all of which have stronger correlation with BAVM than others. We also compared the testing results using PCA-LR model with those using LR model. The comparison revealed that PCA-LR model is better in predicting the disease.展开更多
基金the Hi-Tech Research and Development Program (863) of China (No. 2006AA10Z203)the National Scienceand Technology Task Force Project (No. 2006BAD10A01), China
文摘Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.
基金supported by the National Natural Science Foundation of China under project 41275013the National High-Tech Research and development program of China under project 2013AA09A506-4the National Basic Research Program under project 2009CB723903
文摘The sea surface salinity(SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity(SMOS) satellite data. Based on the principal component regression(PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea(in the area of 4?–25?N, 105?–125?E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu(practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.
文摘A comprehensive risk based security assessment which includes low voltage, line overload and voltage collapse was presented using a relatively new neural network technique called as the generalized regression neural network (GRNN) with incorporation of feature extraction method using principle component analysis. In the risk based security assessment formulation, the failure rate associated to weather condition of each line was used to compute the probability of line outage for a given weather condition and the extent of security violation was represented by a severity function. For low voltage and line overload, continuous severity function was considered due to its ability to zoom in into the effect of near violating contingency. New severity function for voltage collapse using the voltage collapse prediction index was proposed. To reduce the computational burden, a new contingency screening method was proposed using the risk factor so as to select the critical line outages. The risk based security assessment method using GRNN was implemented on a large scale 87-bus power system and the results show that the risk prediction results obtained using GRNN with the incorporation of principal component analysis give better performance in terms of accuracy.
文摘Brain arteriovenous malformation(BAVM) is frequently described as vascular malformation. Although computer tomography(CT), magnetic resonance imaging(MRI) and angiography can clearly detect lesions, there are no diagnostic biological markers of BAVM available. Current study demonstrated that micro RNA(mi RNA)showed a feasible marker for vascular disease. To find key correlations between these mi RNAs and the onset of BAVM, we carried out chip analysis of serum mi RNAs by identifying 18 potential markers of BAVM. We then constructed a principle component analysis and logistic regression(PCA-LR) model to analyze the 18 mi RNAs collected from 77 patients. Another 9 independent samples were used to test the resulting model. The results showed that mi RNAs hsa-mir-126-3p and hsa-mir-140 are important protective factors, while hsa-mir-338 is a dominating risk factor, all of which have stronger correlation with BAVM than others. We also compared the testing results using PCA-LR model with those using LR model. The comparison revealed that PCA-LR model is better in predicting the disease.
