ID 原文 译文
18995 通过双向参考集挖掘出困难样本进行特征描述，从而得到准确的外观差异描述。 With hard samples which are mined by the BRS to represent feature descriptors, accurate appearance difference representations could be obtained.
18996 最后利用该特征描述进行更有效的矩阵度量学习。 Finally, these representations are utilized to conduct more effective matrix metric learning.
18997 在多个公开数据集上的实验结果证明了该算法比现有算法具有更好的行人再识别性能。 Experimental results on several public datasets demonstrate the superiority of the proposed method.
18998 针对低速率语音编码问题，该文提出基于G.723.1编码标准的信息隐藏算法。 For the low-rate speech encoding problem, an information hidden algorithm based on the G.723.1coding standard is proposed.
18999 在基音预测编码过程中，通过控制闭环基音周期(自适应码本)的搜索范围，该文结合随机位置选择方法(RPS)和矩阵编码方法(MCM)，实现秘密信息的嵌入，在语音编码过程中实现了信息的隐藏。 In the pitch prediction coding process, by controlling the search range of the closed-loop pitch period (adaptive codebook), combined with the Random Position Selection (RPS) method and the Matrix Coding Method (MCM), the secret information is embedded, which is implemented in the speech coding process.
19000 RPS方法的采用降低了载体码字之间的关联性，MCM方法的采用降低了载体的改变率。 The adoption of the RPS method reduces the correlation between the carrier code-words, andthe adoption of the MCM method reduces the rate of change of the carrier.
19001 实验结果证明，该文算法下PESQ恶化率平均值最大为1.63%，隐蔽性良好。 The experimental results show that the average PESQ (Perceptual Evaluation of Speech Quality) deterioration rate under the algorithm is 1.63%, and the concealment is good.
19002 卫星健康状况监测是卫星安全保障的重要基础，而卫星遥测数据又是卫星健康状况分析的唯一数据来源。 Satellite health monitoring is an important concern for satellite security, for which satellite telemetry data is the only source of data.
19003 因此，卫星遥测缺失数据的准确预测是卫星健康分析的重要前瞻性手段。 Therefore, accurate prediction of missing data of satellite telemetry is animportant forward-looking approach for satellite health diagnosis.
19004 针对极轨卫星多组成系统、多仪器载荷以及多监测指标形成的高维数据特点，该文提出一种基于张量分解的卫星遥测缺失数据预测算法(TFP)，以解决当前数据预测方法大多面向低维数据或只能针对特定维度的不足。 For the high-dimensional structure formed by the satellite multi-component system, multi-instrument and multi-monitoring index, the Tensor Factorization based Prediction (TFP) algorithm for missing telemetry data is proposed. The proposed algorithm surpasses most existing methods, which can only be applied to low-dimensional data or specific dimension.