China experienced a decline of water use intensity in the 11th Five Year Plan,but the water use intensity actually increased in 2009.To the best of our knowledge,the index decomposition analysis method was rarely used...China experienced a decline of water use intensity in the 11th Five Year Plan,but the water use intensity actually increased in 2009.To the best of our knowledge,the index decomposition analysis method was rarely used to analyze changes in water use,and no decomposition analysis has investigated the role of regional economy in the decline of water use intensity.In this paper,we use logarithmic mean Divisia index(LMDI)techniques to decompose the change of water use intensity in the period 2006-2010.We find that the change of industrial water use intensity is confirmed as the dominant contributor to the decline in the overall water use intensity;the regional structure effect and the industrial structure effect is positive to the decline of overall water use intensity;the decline of China's water use intensity is mainly attributed to the effect of developed eastern provinces;meanwhile,the effect of central and undeveloped western is also positive to the decline of overall water use intensity;at least one out of three effects is positive to the decline of water use intensity in the different provinces;the intensity effect is positive and the industrial structure effect is positive to the declines of China's water use intensity based on chaining approach except the period 2008-2009,individually;and the deviation of regional structure effect and industrial structure effect between with regional economy and without regional economy in LMDI is 0.9 and2.3 m^3/10~4 RMB,respectively.展开更多
We have derived and tested several relations between geoid （N） and quasi-geoid （~） with model validation. The elevation correction consists of the first-term （Bouguer anomaly） and second-term （vertical gradient...We have derived and tested several relations between geoid （N） and quasi-geoid （~） with model validation. The elevation correction consists of the first-term （Bouguer anomaly） and second-term （vertical gradient of gravity anomaly）. The vertical gradient was obtained from direct measurement and terrain calcula- tion. The test results demonstrated that the precision of geoid can reach centimeter-level in mountains less than 5000 meters high.展开更多
Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an...Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.展开更多
基金subsidized by the Central Project of Water Resource Fees[grant number 1261320212020]
文摘China experienced a decline of water use intensity in the 11th Five Year Plan,but the water use intensity actually increased in 2009.To the best of our knowledge,the index decomposition analysis method was rarely used to analyze changes in water use,and no decomposition analysis has investigated the role of regional economy in the decline of water use intensity.In this paper,we use logarithmic mean Divisia index(LMDI)techniques to decompose the change of water use intensity in the period 2006-2010.We find that the change of industrial water use intensity is confirmed as the dominant contributor to the decline in the overall water use intensity;the regional structure effect and the industrial structure effect is positive to the decline of overall water use intensity;the decline of China's water use intensity is mainly attributed to the effect of developed eastern provinces;meanwhile,the effect of central and undeveloped western is also positive to the decline of overall water use intensity;at least one out of three effects is positive to the decline of water use intensity in the different provinces;the intensity effect is positive and the industrial structure effect is positive to the declines of China's water use intensity based on chaining approach except the period 2008-2009,individually;and the deviation of regional structure effect and industrial structure effect between with regional economy and without regional economy in LMDI is 0.9 and2.3 m^3/10~4 RMB,respectively.
文摘We have derived and tested several relations between geoid （N） and quasi-geoid （~） with model validation. The elevation correction consists of the first-term （Bouguer anomaly） and second-term （vertical gradient of gravity anomaly）. The vertical gradient was obtained from direct measurement and terrain calcula- tion. The test results demonstrated that the precision of geoid can reach centimeter-level in mountains less than 5000 meters high.
基金funded by Huanggang Normal University,China,Self-type Project of 2021(No.30120210103)and 2022(No.2042021008).
文摘Object detection in images has been identified as a critical area of research in computer vision image processing.Research has developed several novel methods for determining an object’s location and category from an image.However,there is still room for improvement in terms of detection effi-ciency.This study aims to develop a technique for detecting objects in images.To enhance overall detection performance,we considered object detection a two-fold problem,including localization and classification.The proposed method generates class-independent,high-quality,and precise proposals using an agglomerative clustering technique.We then combine these proposals with the relevant input image to train our network on convolutional features.Next,a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals.Finally,revised candidate proposals are sent into the network’s detection process to determine the object type.The algorithm’s performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007(VOC2007),VOC2012,and Microsoft Common Objects in Context(MS-COCO)datasets.Using only 100 proposals per image at intersection over union((IoU)=0.5 and 0.7),the proposed method attains Detection Recall(DR)rates of(93.17%and 79.35%)and(69.4%and 58.35%),and Mean Average Best Overlap(MABO)values of(79.25%and 62.65%),for the VOC2007 and MS-COCO datasets,respectively.Besides,it achieves a Mean Average Precision(mAP)of(84.7%and 81.5%)on both VOC datasets.The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance,proving its effectiveness.
