ID 原文 译文
50687 提出一种新的实现规则变量节点度LT的编码方法，利用数组的赋值和清空操作来实现信息符号度值规则化，降低了现有方法的编码复杂度， Propose a new implementation rules LT coding method of variable node degree, using an array of assignments and empty operation to realize the information symbol value of regulation, reduces the coding complexity of existing methods,
50688 并利用对度分布的修正来改善解码瀑布区; and use the modified to improve the decoding of degree distribution falls area;
50689 将该编码方法应用到协作通信系统中，并分析了误符号率性能。 The coding method is applied to the cooperative communication system, and analyzes the error symbol can spontaneously.
50690 实验结果表明，此方法能节省系统编解码时间，有效降低误符号率差错平台，减少成功解码所需的平均传输开销。 The experimental results show that this method can save system decoding time, effectively reduce the symbol error rate errors platform, reduce the average transmission overhead required for the successful decoding.
50691 针对目前有向传感网中覆盖增强和冗余节点休眠调度算法存在的问题，提出虚拟势场结合学习自动机的覆盖控制算法。 According to the present have to coverage enhancement and redundant nodes in sensor network problems of sleep scheduling algorithm, proposed the virtual potential field coverage control algorithm combined with learning automata.
50692 引入基于质心距离和重复感知率的虚拟力改进模型，综合考虑虚拟向心力和切向力对感知角度调整的影响，建立微观虚拟力与转动角度的关系模型， Based on centroid distance and repeated awareness rate was improved virtual force model, considering the virtual centripetal force and tangential force influence on cognitive Angle adjustment, to establish the micro virtual force and rotation Angle relation model,
50693 并根据网络整体覆盖率增长率对节点调整幅度进行宏观控制，合理调整节点感知方向。 and according to the network overall coverage rate of node macro control and adjustment, reasonably adjust the nodes sense of direction.
50694 在此基础上，根据节点重复感知率和能量因素建立学习自动机与环境信息的交互机制，学习最优的冗余节点休眠调度策略。 On this basis, according to node repeated awareness rate and energy factors to establish learning automata and interaction mechanism of environmental information, study the optimal redundant nodes sleep scheduling strategy.
50695 仿真实验结果表明，该算法能够显著增强网络覆盖，并有效地控制网络覆盖冗余。 The simulation results show that the proposed algorithm can significantly enhance the network coverage, and effectively control network coverage redundancy.
50696 针对传统超视距空战威胁评估不能根据各类威胁因素的变化动态调整其对应权值的问题，引入前向反馈(back propagation，BP)神经网络， In view of the traditional beyond visual range air combat threat assessment based on the dynamic changes of various types of threats to the problem of the corresponding weights of the introduction of the forward feedback back propagation of BP neural network,