网络应用,如网络状态跟踪、服务等级协议保障和网络故障定位等,依赖于完整准确的吞吐量测量数据.由于测量代价大,网络监控系统通常难以获得全网吞吐量测量数据.稀疏网络测量技术基于采样的方式降低测量代价,通过张量填充等算法挖掘数据...网络应用,如网络状态跟踪、服务等级协议保障和网络故障定位等,依赖于完整准确的吞吐量测量数据.由于测量代价大,网络监控系统通常难以获得全网吞吐量测量数据.稀疏网络测量技术基于采样的方式降低测量代价,通过张量填充等算法挖掘数据内部的时空相关性,从部分网络测量数据恢复缺失数据.然而,现有研究仅考虑了单个性能指标,忽略了多个指标之间的关联信息,导致恢复精度受限且整体测量代价依然很大.本文提出了一个面向大规模网络测量的数据恢复算法——基于关联学习的张量填充(Association Learning based Tensor Completion,ALTC).为了捕获网络性能指标之间的复杂关系,设计了一个关联学习模型,使用低测量开销的往返时延推测高测量开销的吞吐量,降低网络测量代价.在此基础上设计了一个张量填充模型,同时学习吞吐量测量数据内部的时空相关性和来自往返时延的外部辅助关联信息,最终以更高的恢复精度获取全网吞吐量数据.实验表明,在相同的吞吐量测量代价下,本文所提算法的恢复误差比目前主流方法的恢复误差降低了13%,达到了更好的恢复效果.展开更多
Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resol...Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.展开更多
文摘网络应用,如网络状态跟踪、服务等级协议保障和网络故障定位等,依赖于完整准确的吞吐量测量数据.由于测量代价大,网络监控系统通常难以获得全网吞吐量测量数据.稀疏网络测量技术基于采样的方式降低测量代价,通过张量填充等算法挖掘数据内部的时空相关性,从部分网络测量数据恢复缺失数据.然而,现有研究仅考虑了单个性能指标,忽略了多个指标之间的关联信息,导致恢复精度受限且整体测量代价依然很大.本文提出了一个面向大规模网络测量的数据恢复算法——基于关联学习的张量填充(Association Learning based Tensor Completion,ALTC).为了捕获网络性能指标之间的复杂关系,设计了一个关联学习模型,使用低测量开销的往返时延推测高测量开销的吞吐量,降低网络测量代价.在此基础上设计了一个张量填充模型,同时学习吞吐量测量数据内部的时空相关性和来自往返时延的外部辅助关联信息,最终以更高的恢复精度获取全网吞吐量数据.实验表明,在相同的吞吐量测量代价下,本文所提算法的恢复误差比目前主流方法的恢复误差降低了13%,达到了更好的恢复效果.
基金Project supported by the National Natural Science Foundation of China
文摘Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.
