针对航天测控领域中上行遥控业务的协议体系选择与可靠性设计问题,在对我国现行国军标技术指标要求与现有航天测控系统天地基遥控技术特点进行归纳梳理的基础上,基于空间段信息传输无线链路特点与CCSDS(Consultative Committee for Spac...针对航天测控领域中上行遥控业务的协议体系选择与可靠性设计问题,在对我国现行国军标技术指标要求与现有航天测控系统天地基遥控技术特点进行归纳梳理的基础上,基于空间段信息传输无线链路特点与CCSDS(Consultative Committee for Space Data Systems,空间数据系统咨询委员会)标准规范,研究给出了适用于我国航天测控任务的空间段遥控协议体系与可靠性措施,利用梳理统计方法对上行遥控体制进行了数学建模分析,并与CCSDS给出的应用算例进行了对比分析。分析结果表明,所涉及的上行遥控体制与CCSDS标准规范的工作效能基本相当,能够满足我国航天任务上行遥控任务使用需求。展开更多
Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weakne...Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classifi-cation accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the tradi-tional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.展开更多
文摘针对航天测控领域中上行遥控业务的协议体系选择与可靠性设计问题,在对我国现行国军标技术指标要求与现有航天测控系统天地基遥控技术特点进行归纳梳理的基础上,基于空间段信息传输无线链路特点与CCSDS(Consultative Committee for Space Data Systems,空间数据系统咨询委员会)标准规范,研究给出了适用于我国航天测控任务的空间段遥控协议体系与可靠性措施,利用梳理统计方法对上行遥控体制进行了数学建模分析,并与CCSDS给出的应用算例进行了对比分析。分析结果表明,所涉及的上行遥控体制与CCSDS标准规范的工作效能基本相当,能够满足我国航天任务上行遥控任务使用需求。
基金the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
文摘Classification is always the key point in the field of remote sensing. Fuzzy c-Means is a traditional clustering algorithm that has been widely used in fuzzy clustering. However, this algorithm usually has some weaknesses, such as the problems of falling into a local minimum, and it needs much time to accomplish the classification for a large number of data. In order to overcome these shortcomings and increase the classifi-cation accuracy, Gustafson-Kessel (GK) and Gath-Geva (GG) algorithms are proposed to improve the tradi-tional FCM algorithm which adopts Euclidean distance norm in this paper. The experimental result shows that these two methods are able to detect clusters of varying shapes, sizes and densities which FCM cannot do. Moreover, they can improve the classification accuracy of remote sensing images.
