ID 原文 译文
24735 仿真结果表明分层路由方案相对于现有单层逐跳式路由方案能够提高大约 77.6% 密钥资源利用率，同时缩短一半密钥服务延时． Based on the evaluation of the cost and power consumption required for embedding virtual nodes and links, the cost function of VNE was formulated. Under the constraints of resource requirements, the VNE problem was formulated as cost function minimization problem.
24736 继而针对特定时间窗内的 VNR，将其转换为虚拟节点映射子问题和虚拟链路映射子问题，并应用启发式算法对两个子问题分别进行求解，从而确定 VNR映射策略。 A time window-based batch embedding strategy was proposed to dynamically process online VNRs。 The VNR in certain time window was transformed into virtual node embedding subproblem and virtual link embedding subproblem, and the corresponding heuristic algorithms were proposed, respectively.
24737 仿真结果表明，所提算法能显著减少VNE成本及功耗，提高VNR接受率。 Simulation results showed that the proposed algorithm reduced the cost and power consumption of VNRs, and improved the acceptance ratio of VNRs.
24738 动态蛋白质网络的构建和复合物挖掘问题是目前研究的热点。 Dynamic protein network construction and complex mining problem is a hot topic.
24739 针对现有的算法在解决前述问题上的不足，文中考虑了蛋白质的活性周期和连接强度，首先提出了一种基于动态图的蛋白质网络构建算法。 In view of the shortcomings of existing algorithms in solving the above problems, a protein network construction algorithm based on dynamic graph is firstly proposed by considering the active period and the connection strength of proteins in this paper.
24740 然后基于密度聚类设计了一种在动态蛋白质网络上挖掘复合物的算法(PCMA)。 Then, a protein complex mining algorithm (PCMA) on dynamic protein network is designed based on the density clustering.
24741 整个挖掘过程包含三个步骤：基于 DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法的蛋白质复合物生成； The whole mining process consists of three steps: the generation of protein complex based on DBSCAN (density-based spatial clustering of applications with noise) algorithm;
24742 基于合并增益的蛋白质复合物合并和基于归属度的复合物调整。 the combination of protein complex based on the combination gain and the adjustment of protein complex based on the degree of membership.
24743 在多个公开的生物数据集上进行了实验，实验结果表明，所提算法在查全率、查准率和F-measure方面的性能都要优于现有的算法，且对输入参数不敏感。 Experiments are carried out on several open biological datasets. The experimental results show that the performance of the proposed algorithm is better than that of the existing algorithms in terms of recall, precision and F-measure, and it is not sensitive to the input parameters.
24744 在保证蛋白质复合物挖掘准确性的前提下，算法的时间复杂度处于一个合理的范围之内。 On the premise of ensuring the accuracy of protein complex mining, the time complexity of the proposed algorithm is in a reasonable range.