| ID | 原文 | 译文 |
| 18355 | 采用EM算法对模型参数和多个目标的距离、速度进行估计，并同时拟合多条时间-距离直线。 | EM algorithm isapplied to estimate the parameters of the model and the range and velocity of multiple targets. |
| 18356 | 直线斜率对应目标速度，直线纵轴截距对应目标初始距离。 | Also, multipletime-range lines are fitted at the same time, and the slope of the line corresponds to the velocity of the target,as well as, the vertical intercept of the line corresponds to the initial range of the target, separately. |
| 18357 | 最终，分别分析了信噪比(SNR)对检测概率以及目标速度对相对估计误差的影响。 | Finally, the influence of the Signal-to-Noise Ratio (SNR) on detection probability and the target velocity on relative error of estimation are analyzed, respectively. |
| 18358 | 仿真实验验证了所提算法的有效性。 | Simulations are provided to verify the effectiveness of the proposal. |
| 18359 | 生存性虚拟光网络映射是提高光网络应对灾难故障的重要技术保障措施。 | Survivable virtual optical network mapping is an important technology to improve the opticalnetwork response to disaster failures. |
| 18360 | 为解决灾难性多区域故障导致弹性光网络的带宽容量损失问题，该文提出基于灾难预测故障模型的蚁群优化虚拟光网络映射 (DFM-ACO-VNM)算法。 | In order to solve the problem of bandwidth capacity loss caused by multi-area faults resulted from disasters in Elastic Optical Networks (EONs), a multi-area disaster fault model ofsurvivable virtual network based on risk assessment is established, and a Disaster Fault Model based AntColony Optimization for Virtual Network Mapping (DFM-ACO-VNM) algorithm is proposed in the paper. |
| 18361 | 在该算法中，设计基于光节点资源和相邻链路的全局潜在故障概率的光节点排序映射准则，并设计启发式信息公式实现多区域故障下最小带宽容量损失的虚拟节点和虚拟链路协同映射。 | Anoptical node ranking mapping criterion based on node resources and global potential failure probability ofadjacent links in EONs is designed. Then, a heuristic information formula is designed to realize cooperativemapping of virtual nodes and virtual links with minimum bandwidth capacity loss under multi-area faults. |
| 18362 | 仿真结果表明，该文所提算法在多区域故障时能降低带宽容量损失，减少带宽阻塞率和提高频谱利用率。 | The simulation results show that the proposed algorithm can decrease the bandwidth capacity loss, reduce the bandwidth blocking probability and improve the spectrum utilization in multi-area faults. |
| 18363 | 噪声是影响聚类结果的最重要的因素之一，现有的模糊聚类算法主要通过对隶属度约束进行松弛的方式来降低噪声样本的影响。 | Noise is one of the most important influences for clustering. Existing fuzzy clustering methods try toreduce the impact of noise by relaxing the constraint condition of membership. |
| 18364 | 这种方式仍然存在两个基本问题需要解决：第一，如何评估一个样本是噪声的可能性； | But there are still two basicproblems to be solved. The first is how to evaluate the probability that a sample point is a noise. |