Multiple cropping index(MCI)is a very important indicator in crop production and agricultural intensification,which represents the utilizing degree of agriculture resources at time scale and the effective utilization ...Multiple cropping index(MCI)is a very important indicator in crop production and agricultural intensification,which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land.The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS(Moderate-Resolution Imaging Spectroradiometer)EVI(Enhanced Vegetation Index)after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index,and the method of no additional authentication data is independent and reliable.The result was accurate and stable,the slope of linear regression of the multiple cropping index between the statistical results and the remote sensing results was 1.0136(R2=0.779).The precision of sample areas validation was 97.91%.Suggesting that the time series MODIS-EVI which after Savitzky-Golay filtering processed,could provide an effective way to extract spatial information of multiple cropping index for management department of agriculture.展开更多
A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homo...A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.展开更多
In theoretical chemistry, the researchers use graph models to express the structure of molecular, and the Zagreb indices and multiplicative Zagreb indices defined on molecular graph G are applied to measure the chemic...In theoretical chemistry, the researchers use graph models to express the structure of molecular, and the Zagreb indices and multiplicative Zagreb indices defined on molecular graph G are applied to measure the chemical characteristics of compounds and drugs. In this paper, we present the exact expressions of multiplicative Zagreb indices for certain important chemical structures like nanotube, nanostar and polyomino chain.展开更多
Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accom...Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.展开更多
基金This work is supported by National Natural Science Foundation of China(Project No.518092509)Science and Technology Service Network Initiative(STS)of the Chinese Academy of Sciences(Project No.KFJ-STS-ZDTP-009)Open Foundation of The Ministry of Water Resources Key Laboratory of Soil and Water Loss Process and Control in the Loess Plateau(Project No.2017004).
文摘Multiple cropping index(MCI)is a very important indicator in crop production and agricultural intensification,which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land.The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS(Moderate-Resolution Imaging Spectroradiometer)EVI(Enhanced Vegetation Index)after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index,and the method of no additional authentication data is independent and reliable.The result was accurate and stable,the slope of linear regression of the multiple cropping index between the statistical results and the remote sensing results was 1.0136(R2=0.779).The precision of sample areas validation was 97.91%.Suggesting that the time series MODIS-EVI which after Savitzky-Golay filtering processed,could provide an effective way to extract spatial information of multiple cropping index for management department of agriculture.
基金supported by the National Natural Science Foundation of China(61102158)the China Postdoctoral Science Foundation(2011M500667)
文摘A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.
文摘In theoretical chemistry, the researchers use graph models to express the structure of molecular, and the Zagreb indices and multiplicative Zagreb indices defined on molecular graph G are applied to measure the chemical characteristics of compounds and drugs. In this paper, we present the exact expressions of multiplicative Zagreb indices for certain important chemical structures like nanotube, nanostar and polyomino chain.
文摘Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.
