End-to-end delay is one of the most important characteristics of Internet end-to-end packet dynamics, which can be applied to quality of services （OoS） management, service level agreement （SLA） management, congest...End-to-end delay is one of the most important characteristics of Internet end-to-end packet dynamics, which can be applied to quality of services （OoS） management, service level agreement （SLA） management, congestion control algorithm development, etc. Nonstationarity and nonlinearity are found by the analysis of various delay series measured from different links. The fact that different types of links have different degree of Self-Similarity is also obtained. By constructing appropriate network architecture and neural functions, functional networks can be used to model the Internet end-to-end nonlinear delay time series. Furthermore, by using adaptive parameter studying algorithm, the nonstationarity can also be well modeled. The numerical results show that the provided functional network architecture and adaptive algorithm can precisely characterize the Internet end-to-end delay dynamics.展开更多
This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold ...This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.展开更多
基金This project was supported by the National Natural Science Foundation of China (60132030 60572147)
文摘End-to-end delay is one of the most important characteristics of Internet end-to-end packet dynamics, which can be applied to quality of services （OoS） management, service level agreement （SLA） management, congestion control algorithm development, etc. Nonstationarity and nonlinearity are found by the analysis of various delay series measured from different links. The fact that different types of links have different degree of Self-Similarity is also obtained. By constructing appropriate network architecture and neural functions, functional networks can be used to model the Internet end-to-end nonlinear delay time series. Furthermore, by using adaptive parameter studying algorithm, the nonstationarity can also be well modeled. The numerical results show that the provided functional network architecture and adaptive algorithm can precisely characterize the Internet end-to-end delay dynamics.
基金Supported by the National Natural Science Foundation of China under Grant No.60573126 (国家自然科学基金) the National High-Tech Research and Development Plan of China under Grant Nos.2003AA115440, 2005AA112030 (国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.2002CB312005 (国家重点基础研究发展计划(973))
文摘当前普遍采用的复制技术和事务处理技术都无法满足应用的End-to-End可靠性需求,前者通过前向错误恢复来保证应用操作的存活性,后者通过后向错误恢复来保证应用数据的安全性.如何融合这两种技术以实现End-to-End可靠性保证,成为目前研究的热点问题.然而,已有的方法都是基于简单事务模式的假设,即只有中间层应用服务器上的容器发起事务,而很少考虑应用中普遍存在的复杂事务模式,如客户事务和嵌套事务.为了解决这个问题,首先识别出了几种典型的事务模式.针对这些事务模式,基于状态同步点概念提出了一种能够统一提供End-to-End可靠性保证的Web应用服务器复制机制RSCTP(replication scheme for complex transaction pattern).RSCTP机制采取primary-backup方式来复制EJB组件以保证业务逻辑的高可用性,同时采取primary-backup方式复制事务协调者来消除分布式事务处理中两阶段提交协议可能出现的阻塞问题.通过在不同事务模式下的失效分析,说明了该机制的有效性.已经实现了RSCTP机制并集成到了遵循J2EE规范的Web应用服务器OnceAS中.性能评价显示,该机制带来的系统开销较小.
基金This was work supported by the National Natural Science Foundation of China(U19B2031).
文摘This paper focuses on the sea-surface weak target detection based on memory-fully convolutional network(M-FCN)in strong sea clutter.Firstly,the constant false alarm rate(CFAR)detection method utilizes a low threshold with high probability of false alarm to detect sea-surface weak targets after non-coherent integration.Reducing the detection threshold can generate a large number of false alarms while increasing the detection rate,and how to suppress a large number of false alarms is the key to improve the performance of weak target detection.Then,the detection result of the low threshold is operated to construct the target matrix suitable for the size of fully convolutional networks and the convolution operator form.Finally,the M-FCN architecture is designed to learn the different accumulation characteristics of the target and the sea clutter between different frames.For improving the detection performance,the historical multi-frame information is memorized by the network,and the end-to-end structure is established to detect sea-surface weak target automatically.Experimental results on measured data demonstrate that the M-FCN method outperforms the traditional track before detection(TBD)method and reduces false alarm tracks by 35.1%,which greatly improves the track quality.
