With the rapid economic development during the last 30 years in China, more and more disparities have emerged among different regions. It has been one of the hot topics in the fields of physical geography and economic...With the rapid economic development during the last 30 years in China, more and more disparities have emerged among different regions. It has been one of the hot topics in the fields of physical geography and economic geography, and also has been the task for Chinese government to handle. Nevertheless, to quantitatively assess the impacts of physio-geographical patterns （PGP） on the regional development disparity has been ignored for a long time. In this paper, a quantitative method was adopted to assess the marginal effects of the PGP on spatio-temporal disparity using the partial determination coefficients. The paper described the construction of the evaluation model step by step following its key scientific thinking. Total GDP, per capita GDP, primary industrial output value and secondary industrial output value were employed in this study as the indicators to reflect the impacts of PGP on the regional development disparity. Based on the evaluation methods built by researchers, this study firstly analyzed the temporal impacts of the PGP on spatio-temporal disparity of the regional development in China during the past 50 years, and then explained the spatial differences at each development stage. The results show that the spatio-temporal disparity in China is highly related to the PGP, and that the marginal contribution rate could be employed as an effective way to quantitatively assess the impact of the PGP on spatio-temporal disparity of the regional development.展开更多
With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network tr...With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network transmission has led to low data processing efficiency.Fortunately,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions faster.Therefore,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing environment.The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social welfare.The algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose.Through comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.展开更多
Matching is a classical problem in stereo vision. To solve the matching problem that components cannot continue growing on the occlusions region and repetitive patterns, an improved seed growth method is proposed. The...Matching is a classical problem in stereo vision. To solve the matching problem that components cannot continue growing on the occlusions region and repetitive patterns, an improved seed growth method is proposed. The method obtains a set of interesting points defined as initial seeds from a rectified image. Through global optimization the seeds and their neighbors can be selected in- to a match table. Finally the components grow with the matching points and create a semi-dense map under the maximum similar subset according to the principle of the unique constraint. Experimental results show that the proposed method in the grown process can rectify some errors in matching. The semi-dense map has a good performance in the occlusions region and repetitive patterns. This algorithm is faster and more accurate than the traditional seed growing method.展开更多
基金National Natural Science Foundation of China, No.40131010
文摘With the rapid economic development during the last 30 years in China, more and more disparities have emerged among different regions. It has been one of the hot topics in the fields of physical geography and economic geography, and also has been the task for Chinese government to handle. Nevertheless, to quantitatively assess the impacts of physio-geographical patterns （PGP） on the regional development disparity has been ignored for a long time. In this paper, a quantitative method was adopted to assess the marginal effects of the PGP on spatio-temporal disparity using the partial determination coefficients. The paper described the construction of the evaluation model step by step following its key scientific thinking. Total GDP, per capita GDP, primary industrial output value and secondary industrial output value were employed in this study as the indicators to reflect the impacts of PGP on the regional development disparity. Based on the evaluation methods built by researchers, this study firstly analyzed the temporal impacts of the PGP on spatio-temporal disparity of the regional development in China during the past 50 years, and then explained the spatial differences at each development stage. The results show that the spatio-temporal disparity in China is highly related to the PGP, and that the marginal contribution rate could be employed as an effective way to quantitatively assess the impact of the PGP on spatio-temporal disparity of the regional development.
基金supported in part by the National Natural Science Foundation of China under Grant 61822602,Grant 61772207,Grant 61802331,Grant 61572418,Grant 61602399,Grant 61702439 and Grant 61773331the China Postdoctoral Science Foundation under Grant 2019T120732 and Grant 2017M622691+1 种基金the National Science Foundation(NSF)under Grant 1704287,Grant 1252292 and Grant 1741277the Natural Science Foundation of Shandong Province under Grant ZR2016FM42.
文摘With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing tasks.However,the delay caused by network transmission has led to low data processing efficiency.Fortunately,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions faster.Therefore,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing environment.The proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular area.In this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social welfare.The algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to choose.Through comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.
基金Supported by State Key Laboratory of Explosion Science and Techno logy Foundation(YBKT11-7)
文摘Matching is a classical problem in stereo vision. To solve the matching problem that components cannot continue growing on the occlusions region and repetitive patterns, an improved seed growth method is proposed. The method obtains a set of interesting points defined as initial seeds from a rectified image. Through global optimization the seeds and their neighbors can be selected in- to a match table. Finally the components grow with the matching points and create a semi-dense map under the maximum similar subset according to the principle of the unique constraint. Experimental results show that the proposed method in the grown process can rectify some errors in matching. The semi-dense map has a good performance in the occlusions region and repetitive patterns. This algorithm is faster and more accurate than the traditional seed growing method.
