ID	原文	译文
57188	分析结果表明，基于几何的随机信道模型具有通用性，可通过调整信道参数，用于各种城市环境中的通信场景建模.	In addition，the random channel model based on geometry has enough generality and can be used to model communication scenes in various urban environments by adjusting channel parameters.
57189	为解决全球定位系统( GPS) 和北斗卫星导航系统( BDS) 等传统定位方式在信号被遮挡的区域定位范围受限以及精度不足的问题，基于第 5 代移动通信系统和车联网技术提出了一种多径信号辅助的网联车辆无线协作定位方法.	In order to solve the less-accuracy and insufficient-coverage problem of the traditional localiza?tion methods like global positioning system ( GPS) and BeiDou navigation satellite system ( BDS) in theareas where the signals are blocked，a multi-path assisted cooperative radio-based localization method isproposed for connected vehicles based on the fifth generation of mobile communications system and vehi?cle to everything.
57190	首先将车联网中接收端与发射端之间多径信号成分携带的定位信息提取成多径定位线段，并基于最小二乘法估计出接收端与发射端之间的相对位置;	In detail，the localization information carried by multi-path components from the trans?mitter to the receiver in vehicle network is modeled into multi-path positioning segments，thus their rela?tive position is calculated based on least squared algorithm.
57191	然后基于置信传播算法，将相对位置信息以及部分覆盖的 GPS/BDS 信息进行协作融合，得出整个车联网中各个车辆的位置.	Then a belief propagation based algorithm ispresented to calculate the position of each vehicle by fusing the relative positioning information and thepartial covered GPS /BDS information.
57192	仿真结果表明，在不同的车辆密度和 GPS/BDS 可用概率下，新算法能提升网联车辆的定位精度，有效解决车辆 GPS/BDS 覆盖不充分的问题，为网联车辆提供高精度的定位.	Simulations show that the algorithm can improve the localizationaccuracy over different vehicle densities and different GPS /BDS available probabilities，which further in?dicates that the algorithm can solve the GPS /BDS insufficient-coverage problem so as to provide high-ac?curacy localization for connected vehicles.
57193	为提高差分隐私保护下推荐算法的准确性，提出了一种考虑差分隐私保护的基于 Bhattacharyya 系数( BC) 的聚类推荐算法.	To improve the accuracy of recommendation algorithm under differential privacy protection，aprivacy preservation recommendation algorithm is proposed based on a clustering method with Bhatta?charyya coefficient( BC) . In the proposed algorithm，the Bhattacharyya coefficient is used as the standardof measuring item similarity.
57194	以 BC 作为项目相似性度量的标准，根据 BC 相似性对项目进行 K-medoids 聚类，并在聚类簇中进行私有项目邻居选择.	Based on the BC similarity，the items are clustered by K-medoids，and theprivate neighbors of the items are selected from the clusters.
57195	最后，根据最近邻居集信息，对用户的评分进行预测和 Top-n 推荐.	Finally，according to the selected nearestneighbor set，the user＇s rating is predicted and the Top-n recommendations are output.
57196	提出的方案有效地克服了已有方法中存在的相似性度量依赖于共同评分的问题，提高了相似性度量的准确性，有效避免了因隐私保护而造成的最近邻居集质量下降的问题.	The proposed al?gorithm effectively overcomes the problem that the calculation of similarity must depend on the commonrated ratings，improves the accuracy of the similarity measurement，and also avoid the problem of qualitydegradation of the nearest neighbor set due to privacy protection.
57197	理论分析和实验测试的结果表明，该方法在实现隐私保护的同时还能有效保证推荐的高质量，较好地实现了隐私保护和数据效用之间的平衡，具有良好的应用潜力.	It is shown that the proposed algorithmnot only achieves privacy preservation but also guarantees the high quality of recommendation. Therefore，the proposed algorithm effectively balances the privacy preservation and the data utility，which has goodapplication potential in the recommendation system.