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使用改进K-SVD的网络多媒体业务QoS类识别

王再见 董育宁 汤萍萍 杨凌云 张晖

王再见, 董育宁, 汤萍萍, 杨凌云, 张晖. 使用改进K-SVD的网络多媒体业务QoS类识别[J]. 电子与信息学报, 2017, 39(12): 3023-3029. doi: 10.11999/JEIT170133
引用本文: 王再见, 董育宁, 汤萍萍, 杨凌云, 张晖. 使用改进K-SVD的网络多媒体业务QoS类识别[J]. 电子与信息学报, 2017, 39(12): 3023-3029. doi: 10.11999/JEIT170133
WANG Zaijian, DONG Yuning, TANG Pingping, YANG Lingyun, ZHANG Hui. Network Multimedia QoS Class Recognition Based on Improved K-SVD[J]. Journal of Electronics and Information Technology, 2017, 39(12): 3023-3029. doi: 10.11999/JEIT170133
Citation: WANG Zaijian, DONG Yuning, TANG Pingping, YANG Lingyun, ZHANG Hui. Network Multimedia QoS Class Recognition Based on Improved K-SVD[J]. Journal of Electronics and Information Technology, 2017, 39(12): 3023-3029. doi: 10.11999/JEIT170133

使用改进K-SVD的网络多媒体业务QoS类识别

doi: 10.11999/JEIT170133
基金项目: 

国家自然科学基金(61401004, 61271233, 61471203), 2016年安徽省高校领军人才引进与培育计划项目(gxfxZD2016013),安徽师范大学博士科研启动基金(2016XJJ129)

Network Multimedia QoS Class Recognition Based on Improved K-SVD

Funds: 

The National Natural Science Foundation of China (61401004, 61271233, 61471203), The Plan of Introduction and Cultivation of University Leading Talents in Anhui (gxfxZD2016013), The Startup Project of Anhui Normal University Doctor Scientific Research (2016XJJ129)

  • 摘要: 该文基于网络多媒体业务QoS(Quality of Service)特征特点,提出网络业务QoS类识别算法。探索了新的多媒体业务QoS类划分模式,在QoS分类的基础上,可以通过将具有相同或相似QoS需求特征的业务流聚集生成聚集流。聚集流划分使用较少的QoS特征,借助聚集流可以在合理的粒度上区分多媒体业务。该文从QoS特征出发分析了聚集流识别的特点,利用网络多媒体业务典型QoS特征的稀疏性,使用改进K-SVD(Kernel Singular Value Decomposition)进行字典学习,实现网络多媒体业务QoS类识别。实验结果表明,该文算法比现有方法具有更高的QoS类识别准确性。
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出版历程
  • 收稿日期:  2017-02-17
  • 修回日期:  2017-10-01
  • 刊出日期:  2017-12-19

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