ID 原文 译文
25605 同时，节点大部分时间处于睡眠状态，仅在少部分时间内苏醒工作，造成数据备份的通信延迟过大。 At the same time, each node is sleeping in most of its time, and wakes upto work in only a small portion of time． This sleeping /working mode results in excessive communication delay for data back-up．
25606 提出一种快速的低能耗数据保存机制。 A fast data storage mechanism with low energy consumption is proposed．
25607 首先，源节点基于连续时间序列对感知数据进行分段线性拟合压缩; First, each source node performs piecewise lin-ear fitting compression on its sensing data based on continuous time series．
25608 接着，节点根据预估故障概率和存储空间大小，计算出合理的压缩数据备份数量。 Then, the node calculates a reasonable number of compressed data backups based on an estimated failure probability and the size of its storage space．
25609 在此基础上，设计一种动态自适应传输协议。 On this basis, a dynamic adaptive transmission protocol is designed．
25610 实验仿真表明，与已有存储算法比较，该机制具有更低的传输能耗和通信延迟。 Experimental simulations show that this mechanism has lower energy consumption of transmission and lower communication delay compared with existing storage algorithms．
25611 多标记学习用于处理一个示例同时与多个类别标记相关的问题。 Multi-label learning deals with the problem where each instance has a set of class labels simultaneously．
25612 在多标记学习中，标记相关性能够显著提升学习算法的性能。 In multi-label learning, label correlations have shown promising strength in improving multi-label learning．
25613 大多数现有的多标记学习算法在利用标记的相关性时，要么只使用被所有示例所共享的全局标记相关性，要么就使用局部标记相关性，它们认为不同簇中的示例应该存在不同的标记相关性。 Most of the existing multi-label learning algorithms exploited either global label correlations shared among all instances, or local label correlations varied across different clusters of instances．
25614 本文中，我们提出了一种同时利用全局和局部标记相关性的多标记学习算法，从而为学习进程提供更全面的标记信息。 In this study, we propose a novel multi-label learning method by simultaneously taking into account both the global and local label correlations to capture more comprehensive label information during thelearning process．