| ID | 原文 | 译文 |
| 47116 | 提出一种车联网数据分发机制。 | A reliable VANET data dissemination method was proposed. |
| 47117 | 该方案利用车辆分布情况，求出各交通路段数据传输时延， | The method used the traffic information to esti-mate data transmission delay in each road. |
| 47118 | 并采用压缩感知方法构造交通网络各节点间的数据传输时延下界， | Compressive sensing method was used to deduce the lower bound of datatransmission delay among each intersection. |
| 47119 | 以此辅助车辆进行数据转发决策。 | These information could assist carrier to choose forward routing. |
| 47120 | 然后通过车辆和数据分组的路径匹配度选择下一跳转发车辆。 | In theprocess of data forward, the vehicle that its route more similar with the forwarding data will be chosen as the next carrier. |
| 47121 | 此外，通过马尔可夫模型推导出交通路口的数据分组转发概率。 | Furthermore, data forward probability at intersection was deduced based on Markov model. |
| 47122 | 仿真实验表明，与现有的数据转发方案相比，提出的方案具有较低的数据转发时延和较高的可靠性。 | The simulation results demon-strate that presented method achieves lower-delay and higher reliable performance than existed packet forward protocols. |
| 47123 | 针对由语种类内多样性引起的测试样本和训练模型不匹配的问题，提出一种基于局部距离离群因子准则（LDOF, local distance-based outlier factor）的自适应高斯后端语种识别方法。 | In order to alleviate the mismatch in model between training and testing samples caused by inter-languagevariations, adaptive Gaussian back-end based on LDOF criterion was proposed for language recognition. |
| 47124 | 定义 LDOF 准则，实现有效的参数寻优过程并动态地在多类语种训练集上挑选出与测试样本特性相近的训练样本， | The local dis-tance-based outlier factor (LDOF) criterion was defined to find the appropriate model parameters and dynamically selectthe training data subset similar to the testing samples from multiple class training sets. |
| 47125 | 调整原高斯后端，进而得到改进的语种识别方法。 | Then original back-end was ad-justed to obtain a more matched recognition model. |