From the macroscopic point of view, expressions involving reservoir and operational parameters are established for investigating the stability of moving interface in piston- and non-piston-like displacements. In the c...From the macroscopic point of view, expressions involving reservoir and operational parameters are established for investigating the stability of moving interface in piston- and non-piston-like displacements. In the case of axisymmetrical piston-like displacement, the stability is related to the moving interface position and water to oil mobility ratio. The capillary effect on the stability of moving interface depends on whether or not the moving interface is already stable and correlates with the wettability of the reservoir rock. In the case of non-piston-like displacement, the stability of the front is governed by both the relative permeability and the mobility ratio.展开更多
In the present paper, a comparison of the performance between moving cutting data-rescaled range analysis (MC- R/S) and moving cutting data-rescaled variance analysis (MC-V/S) is made. The results clearly indicate...In the present paper, a comparison of the performance between moving cutting data-rescaled range analysis (MC- R/S) and moving cutting data-rescaled variance analysis (MC-V/S) is made. The results clearly indicate that the operating efficiency of the MC-R/S algorithm is higher than that of the MC-V/S algorithm. In our numerical test, the computer time consumed by MC-V/S is approximately 25 times that by MC-R/S for an identical window size in artificial data. Except for the difference in operating efficiency, there are no significant differences in performance between MC-R/S and MC-V/S for the abrupt dynamic change detection. Mc-R/s and MC-V/S both display some degree of anti-noise ability. However, it is important to consider the influences of strong noise on the detection results of MC-R/S and MC-V/S in practical application展开更多
International food trade has become a key driving force of agricultural land-use changes in trading countries, which has influenced food production and the global environment. Researchers have studied agricultural lan...International food trade has become a key driving force of agricultural land-use changes in trading countries, which has influenced food production and the global environment. Researchers have studied agricultural land-use changes and related environmental issues across multi-trading countries together, but most studies rely on statistic data without spatial attributes. However, agricultural land-use changes are spatially heterogeneous. Uncovering spatial attributes can reveal more critical information that is of scientific significance and has policy implications for enhancing food security and protecting the environment. Based on an integrated framework of telecoupling (socioeconomic and environmental interactions over distances), we studied spatial attributes of soybean land changes within and among trading countries at the same time. Three distant countries -- Brazil, China, and the United States -- constitute an excellent example of telecoupled systems through the process of soybean trade. Our results presented the spatial distribution of soybean land changes-- highlighting the hotspots of soybean gain and soybean loss, and indicated these changes were spatially clustered, different across multi-spatial scales, and varied among the trading countries. Assisted by the results, global challenges like food security and biodiversity loss within and among trading countries can be targeted and managed efficiently. Our work provides simul- taneously spatial information for understanding agricultural land-use changes caused by international food trade globally, highlights the needs of coordination among trading countries, and promotes global sustainability.展开更多
Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the g...Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.展开更多
Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak t...Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.展开更多
基金the National Basic Research Program of China (2005CB221300)the Innovative Project of Chinese Academy of Sciences (KJCX-SW-L08)
文摘From the macroscopic point of view, expressions involving reservoir and operational parameters are established for investigating the stability of moving interface in piston- and non-piston-like displacements. In the case of axisymmetrical piston-like displacement, the stability is related to the moving interface position and water to oil mobility ratio. The capillary effect on the stability of moving interface depends on whether or not the moving interface is already stable and correlates with the wettability of the reservoir rock. In the case of non-piston-like displacement, the stability of the front is governed by both the relative permeability and the mobility ratio.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB955902)the National Natural Science Foundation of China(Grant Nos.41275074,41475073,and 41175084)
文摘In the present paper, a comparison of the performance between moving cutting data-rescaled range analysis (MC- R/S) and moving cutting data-rescaled variance analysis (MC-V/S) is made. The results clearly indicate that the operating efficiency of the MC-R/S algorithm is higher than that of the MC-V/S algorithm. In our numerical test, the computer time consumed by MC-V/S is approximately 25 times that by MC-R/S for an identical window size in artificial data. Except for the difference in operating efficiency, there are no significant differences in performance between MC-R/S and MC-V/S for the abrupt dynamic change detection. Mc-R/s and MC-V/S both display some degree of anti-noise ability. However, it is important to consider the influences of strong noise on the detection results of MC-R/S and MC-V/S in practical application
基金financial support from the National Science FoundationMichigan State UniversityMichigan AgBio Research,United States
文摘International food trade has become a key driving force of agricultural land-use changes in trading countries, which has influenced food production and the global environment. Researchers have studied agricultural land-use changes and related environmental issues across multi-trading countries together, but most studies rely on statistic data without spatial attributes. However, agricultural land-use changes are spatially heterogeneous. Uncovering spatial attributes can reveal more critical information that is of scientific significance and has policy implications for enhancing food security and protecting the environment. Based on an integrated framework of telecoupling (socioeconomic and environmental interactions over distances), we studied spatial attributes of soybean land changes within and among trading countries at the same time. Three distant countries -- Brazil, China, and the United States -- constitute an excellent example of telecoupled systems through the process of soybean trade. Our results presented the spatial distribution of soybean land changes-- highlighting the hotspots of soybean gain and soybean loss, and indicated these changes were spatially clustered, different across multi-spatial scales, and varied among the trading countries. Assisted by the results, global challenges like food security and biodiversity loss within and among trading countries can be targeted and managed efficiently. Our work provides simul- taneously spatial information for understanding agricultural land-use changes caused by international food trade globally, highlights the needs of coordination among trading countries, and promotes global sustainability.
基金supported by General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China (2012IK169)National Natural Science Youth Foundation of China (21205053).
文摘Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.
文摘Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.