The harsh space radiation environment compromises the reliability of an on-board switching fabric by leading to cross-point and switching element(SE)faults.Different from traditional faulttolerant switching fabrics on...The harsh space radiation environment compromises the reliability of an on-board switching fabric by leading to cross-point and switching element(SE)faults.Different from traditional faulttolerant switching fabrics only taking crosspoint faults into account,a novel Input and Output Parallel Clos network,referred to as the(p_1,p_2)-IOPClos,is proposed to tolerate both cross-point and SE faults.In the(p_1,p_2)-IOPClos,there are p_1 and p_2 expanded parallel switching planes in the input and output stages,respectively.The multiple input/output switching planes are interconnected through the middle stage to provide multiple paths in each stage by which the network throughput can be increased remarkably.Furthermore,the network reliability of the(p_1,p_2)-IOPClos under the above both kinds of faults is analyzed.The corresponding implementation cost is also presented along with the network size.Both theoretical analysis and numerical results indicate that the(p_1,p_2)-IOPClos outperforms traditional Clos-type networks at reliability,while has less implementation cost than the multi-plane Clos network.展开更多
Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accura...Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model＇ s coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous （ARX） model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.展开更多
文摘利用多尺度特征策略进行特征提取的有效性不足是多模态医学图像融合领域存在的问题。为了增加融合结果的多尺结构信息,提出了一种基于残差多尺度网络(residual multi-scale network,Res2Net)、交错稠密网络和空间通道融合算法的多模态医学图像融合算法。Res2Net的编码器在提取多尺度特征时能保留更多语义信息;交错稠密网络减少了解码器和编码器之间的语义差异,丰富了融合图像的结构和细节信息;掩码鉴别器约束了脑瘤病灶区域,进一步提高了融合图像的质量;特征图通过空间通道融合算法融合减少了多模态图像之间的信息冗余。该算法在信息熵(entropy of information,EN)、互信息(mutual information,MI)、结构相似性(structure similarity index measure,SSIM)、多尺度结构相似性(multi scale structural similarity index measure,MI_SSIM)指标上拥有较高水平的性能表现,EN提高了6%,MI提高了3%。结果显示,所提出的算法在视觉感知和指标评估上达到了较高的融合质量。
基金supported by the National Natural Science Foundation of China(91338108,91438206)
文摘The harsh space radiation environment compromises the reliability of an on-board switching fabric by leading to cross-point and switching element(SE)faults.Different from traditional faulttolerant switching fabrics only taking crosspoint faults into account,a novel Input and Output Parallel Clos network,referred to as the(p_1,p_2)-IOPClos,is proposed to tolerate both cross-point and SE faults.In the(p_1,p_2)-IOPClos,there are p_1 and p_2 expanded parallel switching planes in the input and output stages,respectively.The multiple input/output switching planes are interconnected through the middle stage to provide multiple paths in each stage by which the network throughput can be increased remarkably.Furthermore,the network reliability of the(p_1,p_2)-IOPClos under the above both kinds of faults is analyzed.The corresponding implementation cost is also presented along with the network size.Both theoretical analysis and numerical results indicate that the(p_1,p_2)-IOPClos outperforms traditional Clos-type networks at reliability,while has less implementation cost than the multi-plane Clos network.
基金the National Basic Research Program of China(No.2003CB314806)China Next Generation Intemet Project(CNGI-04-6-2T)
文摘Predicting heartbeat message arrival time is crucial for the quality of failure detection service over intemet. However, intemet dynamic characteristics make it very difficult to understand message behavior and accurately predict heartbeat arrival time. To solve this problem, a novel black-box model is proposed to predict the next heartbeat arrival time. Heartbeat arrival time is modeled as auto-regressive process, heartbeat sending time is modeled as exogenous variable, the model＇ s coefficients are estimated based on the sliding window of observations and this result is used to predict the next heartbeat arrival time. Simulation shows that this adaptive auto-regressive exogenous （ARX） model can accurately capture heartbeat arrival dynamics and minimize prediction error in different network environments.
