This paper introduces how to automatically derive a minimum set of viewpoints for maximum coverage over a large scale of digital terrain data.This is a typical data and computation-intensive research covering a series...This paper introduces how to automatically derive a minimum set of viewpoints for maximum coverage over a large scale of digital terrain data.This is a typical data and computation-intensive research covering a series of geocomputation tasks that have not been implemented efficiently or optimally in prior works.This paper introduces a three-step computational solution to resolve the problem.For any given digital elevation model(DEM)data,automatic generation of control viewpoints is the first step through map algebra calculation and hydrological modeling approaches.For each viewpoint,the viewshed calculation then has to be implemented.The combined viewshed derived from the viewshed of all viewpoints establishes the maximum viewshed coverage of the given DEM.Finally,detecting the minimum set of viewpoints for the maximum coverage is a Non-deterministic Polynomial-time hard problem.The outcome of the computation has broader societal impacts since the research questions and solutions can be adapted into realworld application and decision-making practice,such as the distribution,optimization and management of telecommunication infrastructure and wildfire observation towers,and military tactics and operations dependent upon landscape and terrain features.展开更多
The spatial organization of the Chinese petrochemical industry was optimized according to the status of development of the industry employing linear programming and ArcGIS spatial analysis tools. We first identified t...The spatial organization of the Chinese petrochemical industry was optimized according to the status of development of the industry employing linear programming and ArcGIS spatial analysis tools. We first identified the indexes of the spatial organization of the petrochemical industry and established a comprehensive evaluation index system that in- cludes four major categories and 11 indicators. The weight of each index was then deter- mined by the analytical hierarchy process. Afterward, taking the 337 Chinese prefecture-level administrations as basic units and scientifically evaluating the potential comprehensive layout coefficients of the cities, 151 prefecture-level administrative units were selected as the basis for the choice of optimization sites with a linear programming model. Secondly, using the 151 prefecture-level administrative units and the maximum-coverage model, the optimal number and spatial distribution of refineries were identified for service radii of 100, 200 and 300 km. Thirdly, considering the actual distribution of China＇s refineries, general rules for the number of refinery layout points and objective values were summarized, and 52 refinery layout points were selected for China. Finally, with ArcGIS spatial analysis tools, the spatial effect of the 52 optimal refinery layout points was simulated for the service scope and socioeconomic factors respectively, and the GDP and population data for each refinery layout point were then ex- tracted within the service scope. On this basis and with estimation of the intensity of crude-oil consumption, final results were obtained for the optimal spatial organization of the Chinese refining capacity and ethylene production capacity.展开更多
In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k ini...In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated groups.The GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider groups.Focusing on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes remain.Although the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of groups.Moreover,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM.Finally,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.展开更多
Green macroalgae bloom(GMB),with the dominant species of Ulva prolifera,has regularly occurred since 2007 along the China coast.Although disaster prevention and control achieved favorable results in 2020,the satellite...Green macroalgae bloom(GMB),with the dominant species of Ulva prolifera,has regularly occurred since 2007 along the China coast.Although disaster prevention and control achieved favorable results in 2020,the satellite-observed GMB annual maximum coverage(AMC)rebounded sharply in 2021 to an unprecedented level.The reasons for this rebound and the significant interannual variability over past 15 years are still open questions.Here,by using long-term time-series(2007-2022)optical and Synthetic Aperture Radar satellite observations(1000+scenes),meteorological data and water quality statistics,the mechanism analysis was performed by exploring effects from natural factors and human activities.Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area(the Subei Shoal)during a critical period(from April to May 20)and the nutrient availability.Furthermore,by using these two parameters,a novel model for AMC prediction(R^(2)=0.87,p<0.01)is proposed and independently validated,which can reasonably explain the significant interannual variability(2014-2021)and agree well with the latest observation in 2022(percentage difference 12%).Finally,suggestions are proposed for future disaster prevention and alleviation.This work may aid future bloom prediction and management measure optimization.展开更多
基金the National Science Foundation[NSF SMA-1416509].
文摘This paper introduces how to automatically derive a minimum set of viewpoints for maximum coverage over a large scale of digital terrain data.This is a typical data and computation-intensive research covering a series of geocomputation tasks that have not been implemented efficiently or optimally in prior works.This paper introduces a three-step computational solution to resolve the problem.For any given digital elevation model(DEM)data,automatic generation of control viewpoints is the first step through map algebra calculation and hydrological modeling approaches.For each viewpoint,the viewshed calculation then has to be implemented.The combined viewshed derived from the viewshed of all viewpoints establishes the maximum viewshed coverage of the given DEM.Finally,detecting the minimum set of viewpoints for the maximum coverage is a Non-deterministic Polynomial-time hard problem.The outcome of the computation has broader societal impacts since the research questions and solutions can be adapted into realworld application and decision-making practice,such as the distribution,optimization and management of telecommunication infrastructure and wildfire observation towers,and military tactics and operations dependent upon landscape and terrain features.
基金China Postdoctoral Science Foundation, No.2011M500375 National Natural Science Foundation of China, No.40635026 Knowledge Innovation Program of the Chinese Academy of Sciences, No.KZCXZ-YW-Q10-4
文摘The spatial organization of the Chinese petrochemical industry was optimized according to the status of development of the industry employing linear programming and ArcGIS spatial analysis tools. We first identified the indexes of the spatial organization of the petrochemical industry and established a comprehensive evaluation index system that in- cludes four major categories and 11 indicators. The weight of each index was then deter- mined by the analytical hierarchy process. Afterward, taking the 337 Chinese prefecture-level administrations as basic units and scientifically evaluating the potential comprehensive layout coefficients of the cities, 151 prefecture-level administrative units were selected as the basis for the choice of optimization sites with a linear programming model. Secondly, using the 151 prefecture-level administrative units and the maximum-coverage model, the optimal number and spatial distribution of refineries were identified for service radii of 100, 200 and 300 km. Thirdly, considering the actual distribution of China＇s refineries, general rules for the number of refinery layout points and objective values were summarized, and 52 refinery layout points were selected for China. Finally, with ArcGIS spatial analysis tools, the spatial effect of the 52 optimal refinery layout points was simulated for the service scope and socioeconomic factors respectively, and the GDP and population data for each refinery layout point were then ex- tracted within the service scope. On this basis and with estimation of the intensity of crude-oil consumption, final results were obtained for the optimal spatial organization of the Chinese refining capacity and ethylene production capacity.
基金supported by the Natural Science Foundation of Fujian Province (No. 2020J01845)the Educational Research Project for Young and MiddleAged Teachers of Fujian Provincial Department of Education (No. JAT190613)+1 种基金the National Natural Science Foundation of China (Nos. 61772005 and 92067108)the Outstanding Youth Innovation Team Project for Universities of Shandong Province (No. 2020KJN008)。
文摘In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated groups.The GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider groups.Focusing on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes remain.Although the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of groups.Moreover,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM.Finally,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.
基金supported in part by the National Natural Science Foundation of China[grant number 42088101]in part by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2021SP313]+1 种基金in part by the China-Korea Joint Ocean Research Center,China[grant number PI-2022-1]in part by the Fundamental Research Funds for the Central Universities,Sun Yat-sen University[grant number 23xkjc019].
文摘Green macroalgae bloom(GMB),with the dominant species of Ulva prolifera,has regularly occurred since 2007 along the China coast.Although disaster prevention and control achieved favorable results in 2020,the satellite-observed GMB annual maximum coverage(AMC)rebounded sharply in 2021 to an unprecedented level.The reasons for this rebound and the significant interannual variability over past 15 years are still open questions.Here,by using long-term time-series(2007-2022)optical and Synthetic Aperture Radar satellite observations(1000+scenes),meteorological data and water quality statistics,the mechanism analysis was performed by exploring effects from natural factors and human activities.Two key determinants for AMC are successfully identified from numerous potential factors which are the macroalgae distribution in a key area(the Subei Shoal)during a critical period(from April to May 20)and the nutrient availability.Furthermore,by using these two parameters,a novel model for AMC prediction(R^(2)=0.87,p<0.01)is proposed and independently validated,which can reasonably explain the significant interannual variability(2014-2021)and agree well with the latest observation in 2022(percentage difference 12%).Finally,suggestions are proposed for future disaster prevention and alleviation.This work may aid future bloom prediction and management measure optimization.
