ID 原文 译文
19485 针对任意测站的对流层散射斜延迟估算时存在探空气象数据不易实时获取的不足，该文提出一种利用地面气象参数内插改进射线描迹计算公式的对流层散射斜延迟估计算法。 Considering the disadvantage of oblique delay estimation of tropospheric scattering at arbitrary stations, which is difficult to obtain real-time sounding meteorological data, an oblique delay estimation algorithm of tropospheric scattering based on improved ray tracing method with ground meteorological parameters is proposed.
19486 该算法利用中纬度大气气象参数公式推导了折射指数随地心距变化的关系式，并采用气象参数内插方法获取温度变化率和水汽压变化率，克服了射线描迹法对探空数据的依赖。 In order to get rid of the method’s dependence on radiosonde data, the algorithm infers the relationship between refractive index and altitude through the formula of meteorological parameters in the model of medium latitude atmosphere.
19487 根据亚洲地区6个国际GPS服务(IGS)测站2012年的实测气象数据，验证了该文算法解算天顶延迟年平均偏差的绝对值在1 cm以内； Meteorological data for 2012 from 6International GNSS Service (IGS) stations in Asia are selected to test the applicability of new method, the results suggest that precision is less than 1 cm.
19488 选取基线距离适宜的3个测站分成3组散射通信比对站，利用射线描迹法计算了其在0°～5°入射角下全年的斜延迟，结果表明，3组比对站进行单向传输的最大斜延迟为17.03～33.10 m； Then, the tropospheric slant delays of three parts observationstations under different angles of incidence (0°～5°) are calculated by the modified algorithm. The resultssuggest that the maximum delay is 17.03～33.10 m in a single way time transfer.
19489 进行双向时间比对相互抵消95%时，时间延迟为2.88～5.52 ns。 In two way time transfer,when the delay can counteract 95%, time delay is 2.88～5.52 ns.
19490 在偏标记学习中，示例的真实标记隐藏在由一组候选标记组成的标记集中。 In partial label learning, the true label of an instance is hidden in a label-set consisting of a group of candidate labels.
19491 现有的偏标记学习算法在衡量示例之间的相似度时，只基于示例的特征进行计算，缺乏对候选标记集信息的利用。 The existing partial label learning algorithm only measures the similarity between instances based on feature vectors and lacks the utilization of the candidate labelset information.
19492 该文提出一种候选标记感知的偏标记学习算法(CLAPLL)，在构建图的阶段有效地结合候选标记集信息来衡量示例之间的相似度。 In this paper, a Candidate Label-Aware Partial Label Learning (CLAPLL) method is proposed, which combines effectively candidate label information to measure the similarity between instances during the graph construction phase.
19493 首先，基于杰卡德距离和线性重构，计算出各个示例的标记集之间的相似度，然后结合示例相似度和标记集的相似度构建相似度图，并通过现有的基于图的偏标记学习算法进行学习和预测。 First, based on the jaccard distance and linear reconstruction, the similarity between the candidate labelsets of instances is calculated. Then, the similarity graph is constructed by combining the similarity of the instances and the label-sets, and then the existing graph-based partial label learning algorithm is presented for learning and prediction.
19494 3个合成数据集和6个真实数据集上实验结果表明，该文方法相比于基线算法消歧准确率提升了0.3%～16.5%，分类准确率提升了0.2%～2.8%。 The experimental results on 3 synthetic datasets and 6 real datasets show that disambiguation accuracy of the proposed method is 0.3%～16.5% higher than baseline algorithm, and the classification accuracy is increased by 0.2%～2.8%.