ID 原文 译文
3823 首先，考虑发射功率门限和最小收集能量约束，构建了发射功率、传输时间、反射系数和收集能量分配系数联合优化的多变量非线性资源分配模型。 Firstly, a multivariable and nonlinear resource allocation modelwas formulated to jointly optimize transmit power, transmission time, reflection coefficient, and energy-harvesting allo-cation coefficient, where the maximum transmit power constraint of the power station and the minimum harvested energy constraint at the backscatter device were considered.
3824 然后，基于 Dinkelbach 方法和变量替换法，将原非凸资源分配问题转化为凸优化问题。 Then, the original non-convex optimization problem was trans-formed into a convex one which was solved by using Dinkelbach's method and the variable substitution approach.
3825 同时，利用拉格朗日对偶理论获得解析解。 Fur-thermore, the analytical solution of the resource allocation problem was obtained based on Lagrange dual theory.
3826 仿真结果表明，与纯反向散射算法和纯能量收集算法相比，所提算法具有较好的能效。 Simula-tion results verify that the proposed algorithm has better EE by comparing it with the existing algorithm under purebackscatter mode and algorithm under the harvested-then-transmit mode.
3827 针对动态社交网络中节点存在的时序关系，提出了基于时序关系的社交网络影响最大化问题，即在时序社交网络上寻找 k 个节点使信息传播最大化。 For the time sequential relationship between nodes in a dynamic social network, social network influencemaximization based on time sequential relationship was proved. The problem was to find k nodes on a time sequentialsocial network to maximize the spread of information.
3828 首先，通过改进度估计算法来计算节点间的传播概率； Firstly, the propagation probability between nodes was calculatedby the improved degree estimation algorithm.
3829 其次，针对静态社交网络的 WCM 传播模型无法适用于时序社交网络的问题，提出了 IWCM 传播模型，并以此为基础提出了 TIM算法， Secondly, in order to solve the problem that WCM models based on staticsocial networks could not be applied to time sequential social networks, an IWCM propagation model was proposed andbased on this, a two-stage time sequential social network influence maximization algorithm was proposed.
3830 该算法分别利用时序启发阶段和时序贪心阶段，选择影响力估计值 inf(u)最大的备选节点和影响力最大的种子节点； The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largestinfluence estimated value inf (u) and the most influential seeds.
3831 最后，通过实验验证了 TIM 算法的高效性和准确度。 At last, the efficiency and accuracy of the TIM algorithmwere proved by experiments.
3832 此外，所提算法结合了启发式算法和贪心算法的优点，将边际收益的计算范围由网络中所有节点缩减到了备选节点，在保证精度的前提下大大缩短了程序的运行时间。 In addition, the algorithm combines the advantages of the heuristic algorithm and the greedyalgorithm, reducing the calculation range of the marginal revenue from all nodes in the network to the candidate nodes, and greatly shortens the running time of the program while ensuring accuracy.