SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly to Dst index but with much higher time-resolution. In this paper an artificial neural network (ANN) of N...SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly to Dst index but with much higher time-resolution. In this paper an artificial neural network (ANN) of Nonlinear Auto Regressive with eXogenous inputs (NARX) has been developed to predict SYM-H index from solar wind and IMF data. In comparison with usual BP and Elman network, the new NRAX model shows much better prediction capability. For 15 testing great storms including 5 super-storms of Min. SYM-H < -200 nT, the cross-correlation of SYM-H indices between NARX network predicted and really observed is 0.91 as a whole. For the 5 individual super-storms, the lowest coefficients is 0.91 relating to the super-storm of March 2001 with Min.SYM-H of -434 nT; while for the two super-storms with Min. SYM-H ranging from -300 nT to -400 nT, the correlations reach as high as 0.93 and 0.96 respectively. The remarkable improvement of the model performance can be attributed to such a key feedback from the network output of SYM-H with a suitable length (about 120 min) to the input, which implies that some information on the quasi real-time ring currents with a proper length of history does its work in the prediction. It tells us that, in addition to the direct driving by solar wind and IMF, the own status of the ring current plays an important role in its evolution especially for recovery phase and must properly be considered in storm-time SYM-H prediction by ANN. The neural network model of NARX developed in this paper provides an effective way to achieve it.展开更多
Multi-level spatial index techniques are always used in large spatial databases. After a general survey of R-tree relevant techniques, this paper presents a novel 2-level index structure, which is based on the schemas...Multi-level spatial index techniques are always used in large spatial databases. After a general survey of R-tree relevant techniques, this paper presents a novel 2-level index structure, which is based on the schemas of spatial grids, Hilbert R-tree and common R-tree. This structure is named H2R-tree, and it is specifically suitable for the indexing highly skewed, distributed, and large spatial database. Algorithms and a sample are given subsequently.展开更多
Recent time handling uncertainty and its measurement is considered as one of the major issues by data science and applied mathematics researchers. It becomes more complex when the dynamicity exists in data sets. One o...Recent time handling uncertainty and its measurement is considered as one of the major issues by data science and applied mathematics researchers. It becomes more complex when the dynamicity exists in data sets. One of the suitable examples is Scopus data sets which changes every time. In this case, precise measurement of consistency in document and citation publications is considered as one of the issues. It becomes more complex when the parameter like h-index and document count can be also manipulated over the period of time. To resolve this issue, a time-based index called as “t-index” is illustrated in this paper with an example. This method measures the randomness in document publication and citation using the average h-index and its entropy measurement.展开更多
Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide fiel...Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide field of view （WFV） camera, environment and disaster monitoring and forecasting satellite （H J-l） charge coupled device （CCD）, and Landsat-8 opera- tional land imager （OLI） data for estimating the leaf area index （LAI） of winter wheat via reflectance and vegetation indices （VIs）. The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations： （1） The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination （R2） between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors＇ images can all be used for cross calibration of the reflectance and VIs. （2） The four VIs from the three sensors are all demonstrated to be highly correlated with LAI （R2 between 0.703 and 0.849）. The linear models associated with the 2-band enhanced vegetation index （EVI2）, which feature the highest R2 （higher than 0.746） and the lowest root mean square errors （RMSE） （less than 0.21）, were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors （RE） of 2.18% and an RMSE of 0.13, while the H J-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. （3） The inversion errors in the different sensors＇ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. （4） The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.展开更多
The 5-parameter Morse potential （5-MP） of the interaction between H atom and Ag surfaces has been constructed. The adsorption and diffusion of H on Ag low-index surfaces are investigated with 5-MP in detail. The cha...The 5-parameter Morse potential （5-MP） of the interaction between H atom and Ag surfaces has been constructed. The adsorption and diffusion of H on Ag low-index surfaces are investigated with 5-MP in detail. The characteristics of critical points are obtained. The theoretical results show that H atom can only adsorb at the three-fold site on Ag（111 ）; the quasi-3-fold site and long-bridge site are stable adsorption sites on Ag（110） surface for the H atom, and at low coverage hydrogen predominantly occupies the quasi-3-fold site. This work predicts that the four-fold hollow site is the most stable adsorption state for H atom on Ag（100）. The results of this work are approved by the experimental and theoretical results.展开更多
基金Supported by Doctoral Fund of Ministry of Education of China (Grant No. 200804860012)
文摘SYM-H is one of the important indices for space weather. It indicates the intensity of magnetic storm, similarly to Dst index but with much higher time-resolution. In this paper an artificial neural network (ANN) of Nonlinear Auto Regressive with eXogenous inputs (NARX) has been developed to predict SYM-H index from solar wind and IMF data. In comparison with usual BP and Elman network, the new NRAX model shows much better prediction capability. For 15 testing great storms including 5 super-storms of Min. SYM-H < -200 nT, the cross-correlation of SYM-H indices between NARX network predicted and really observed is 0.91 as a whole. For the 5 individual super-storms, the lowest coefficients is 0.91 relating to the super-storm of March 2001 with Min.SYM-H of -434 nT; while for the two super-storms with Min. SYM-H ranging from -300 nT to -400 nT, the correlations reach as high as 0.93 and 0.96 respectively. The remarkable improvement of the model performance can be attributed to such a key feedback from the network output of SYM-H with a suitable length (about 120 min) to the input, which implies that some information on the quasi real-time ring currents with a proper length of history does its work in the prediction. It tells us that, in addition to the direct driving by solar wind and IMF, the own status of the ring current plays an important role in its evolution especially for recovery phase and must properly be considered in storm-time SYM-H prediction by ANN. The neural network model of NARX developed in this paper provides an effective way to achieve it.
文摘Multi-level spatial index techniques are always used in large spatial databases. After a general survey of R-tree relevant techniques, this paper presents a novel 2-level index structure, which is based on the schemas of spatial grids, Hilbert R-tree and common R-tree. This structure is named H2R-tree, and it is specifically suitable for the indexing highly skewed, distributed, and large spatial database. Algorithms and a sample are given subsequently.
文摘Recent time handling uncertainty and its measurement is considered as one of the major issues by data science and applied mathematics researchers. It becomes more complex when the dynamicity exists in data sets. One of the suitable examples is Scopus data sets which changes every time. In this case, precise measurement of consistency in document and citation publications is considered as one of the issues. It becomes more complex when the parameter like h-index and document count can be also manipulated over the period of time. To resolve this issue, a time-based index called as “t-index” is illustrated in this paper with an example. This method measures the randomness in document publication and citation using the average h-index and its entropy measurement.
基金supported by the National Natural Science Foundation of China (41371396,41401491 and 41471364)the Introduction of International Advanced Agricultural Science and Technology,Ministry of Agriculture,China (948 Program,2011-G6)the Agricultural Scientific Research Fund of Outstanding Talents and the Open Fund for the Key Laboratory of Agri-informatics,Ministry of Agriculture,China (2013009)
文摘Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide field of view （WFV） camera, environment and disaster monitoring and forecasting satellite （H J-l） charge coupled device （CCD）, and Landsat-8 opera- tional land imager （OLI） data for estimating the leaf area index （LAI） of winter wheat via reflectance and vegetation indices （VIs）. The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations： （1） The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination （R2） between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors＇ images can all be used for cross calibration of the reflectance and VIs. （2） The four VIs from the three sensors are all demonstrated to be highly correlated with LAI （R2 between 0.703 and 0.849）. The linear models associated with the 2-band enhanced vegetation index （EVI2）, which feature the highest R2 （higher than 0.746） and the lowest root mean square errors （RMSE） （less than 0.21）, were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors （RE） of 2.18% and an RMSE of 0.13, while the H J-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. （3） The inversion errors in the different sensors＇ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. （4） The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.
基金The project was supported by NSF of Shandong Province (Y2002B09)
文摘The 5-parameter Morse potential （5-MP） of the interaction between H atom and Ag surfaces has been constructed. The adsorption and diffusion of H on Ag low-index surfaces are investigated with 5-MP in detail. The characteristics of critical points are obtained. The theoretical results show that H atom can only adsorb at the three-fold site on Ag（111 ）; the quasi-3-fold site and long-bridge site are stable adsorption sites on Ag（110） surface for the H atom, and at low coverage hydrogen predominantly occupies the quasi-3-fold site. This work predicts that the four-fold hollow site is the most stable adsorption state for H atom on Ag（100）. The results of this work are approved by the experimental and theoretical results.
