针对谷物产量分布研究中自主开发的系统或软件对于用户来说操作繁琐,及对向日葵测产研究较少的问题,文中采用HTML5+CSS+JavaScript、ArcGIS API for JavaScript、EChart、Access数据库等技术,设计一种基于WebGIS的向日葵产量远程监测平...针对谷物产量分布研究中自主开发的系统或软件对于用户来说操作繁琐,及对向日葵测产研究较少的问题,文中采用HTML5+CSS+JavaScript、ArcGIS API for JavaScript、EChart、Access数据库等技术,设计一种基于WebGIS的向日葵产量远程监测平台。首先,采用ASP.NET框架对平台前端可视界面进行总体样式设计,运用jQuery布局组件实现数据的可视化展示,并通过调用天地图API在线显示底图;其次,设计所需产量数据库表并进行后端编程,使前端界面与后端数据库进行数据交互。基于该设计平台,用户只需在通用浏览器内输入网址即可查询某个日期地块的向日葵产量分布图,实现远程在线查询的功能。最后,以内蒙古自治区红泥井村向日葵测产试验为例,对开发平台的功能进行应用。结果表明所开发平台简单、易操作,能够满足产量分布查询的实时性和便捷性要求,可为精准农业科学决策提供参考。展开更多
The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approa...The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.展开更多
文摘针对谷物产量分布研究中自主开发的系统或软件对于用户来说操作繁琐,及对向日葵测产研究较少的问题,文中采用HTML5+CSS+JavaScript、ArcGIS API for JavaScript、EChart、Access数据库等技术,设计一种基于WebGIS的向日葵产量远程监测平台。首先,采用ASP.NET框架对平台前端可视界面进行总体样式设计,运用jQuery布局组件实现数据的可视化展示,并通过调用天地图API在线显示底图;其次,设计所需产量数据库表并进行后端编程,使前端界面与后端数据库进行数据交互。基于该设计平台,用户只需在通用浏览器内输入网址即可查询某个日期地块的向日葵产量分布图,实现远程在线查询的功能。最后,以内蒙古自治区红泥井村向日葵测产试验为例,对开发平台的功能进行应用。结果表明所开发平台简单、易操作,能够满足产量分布查询的实时性和便捷性要求,可为精准农业科学决策提供参考。
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1A5A1021944 and 2021R1I1A3048013)Additionally,the research was supported by Kyungpook National University Research Fund,2020.
文摘The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.
