Researching the dynamic distribution characteristics and trend evolution of agricultural carbon emissions is of considerable significance in formulating an effective agricultural carbon reduction policy.Based on measu...Researching the dynamic distribution characteristics and trend evolution of agricultural carbon emissions is of considerable significance in formulating an effective agricultural carbon reduction policy.Based on measurement of agricultural carbon emissions of 31 provinces over the period 2002-2011,the study observed regional differences and the dynamic evolution of distribution of agricultural carbon emissions using agricultural carbon intensity as the indicator,accompanied by Gini coefficients and the kernel density estimation method.The results demonstrate first that agricultural carbon emissions for China show an obvious nonequilibrium nature in regard to spatial distribution.According to the differences in agricultural carbon emissions dynamic trends,we divided the 31 regions into four types- continuous decline,fluctuating decline,continuous increase,and fluctuating increase.Further,agricultural carbon emissions intensity showed a downward trend with significant differences in the research areas.Second,the gap in spatial distribution of national agricultural carbon emissions is gradually expanding based on the results calculated by Gini coefficient.From the perception of regional differences in agricultural carbon emissions,the eastern region showed an average level,the gap was more obvious in the central region,while western region showed a trend of fluctuating downward.Third,according to estimation by kernel density,the regional disparity in agricultural carbon emissions had a downward,but limited,trend.In regard to agricultural carbon emissions over the three areas,the regional gap not only tended to decrease but also showed a "four way" differentiation phenomenon in the eastern region.The difference in the central region difference was narrower.On the whole,the gap for the western region reduced steadily over a small range.展开更多
Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(...Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.展开更多
This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and ...This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field(MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes(ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.展开更多
基金funded by the National Natural Science Foundation of China[grant number 71273105]the Fundamental Research Funds for the Central Universities[grant number 2013YB12]
文摘Researching the dynamic distribution characteristics and trend evolution of agricultural carbon emissions is of considerable significance in formulating an effective agricultural carbon reduction policy.Based on measurement of agricultural carbon emissions of 31 provinces over the period 2002-2011,the study observed regional differences and the dynamic evolution of distribution of agricultural carbon emissions using agricultural carbon intensity as the indicator,accompanied by Gini coefficients and the kernel density estimation method.The results demonstrate first that agricultural carbon emissions for China show an obvious nonequilibrium nature in regard to spatial distribution.According to the differences in agricultural carbon emissions dynamic trends,we divided the 31 regions into four types- continuous decline,fluctuating decline,continuous increase,and fluctuating increase.Further,agricultural carbon emissions intensity showed a downward trend with significant differences in the research areas.Second,the gap in spatial distribution of national agricultural carbon emissions is gradually expanding based on the results calculated by Gini coefficient.From the perception of regional differences in agricultural carbon emissions,the eastern region showed an average level,the gap was more obvious in the central region,while western region showed a trend of fluctuating downward.Third,according to estimation by kernel density,the regional disparity in agricultural carbon emissions had a downward,but limited,trend.In regard to agricultural carbon emissions over the three areas,the regional gap not only tended to decrease but also showed a "four way" differentiation phenomenon in the eastern region.The difference in the central region difference was narrower.On the whole,the gap for the western region reduced steadily over a small range.
基金financial support from the National Natural Science Foundation of China (21706220)
文摘Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.
基金Argentinean National Council for Scientific Research (CONICET)the National University of San Juan (UNSJ) of ArgentinaNVIDIA Corporation for their support
文摘This article proposes a simultaneous localization and mapping(SLAM) version with continuous probabilistic mapping(CPSLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field(MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes(ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.
