ID 原文 译文
57818 最后构建完整的 PM2.5 浓度集成预测模型. With standard arithmetic mean aggregation method，the article calculates the influence degree of each feature on the increment of PM2. 5 concentration，and provides the impact ranking from strong to weak.
57819 使用北京市 2015— 2016 年的污染物浓度和气象条件观测值 2 个数据集，对模型进行了预测仿真实验. Two datasets，the pollutant concentration data and meteorological observation da- ta of Beijing from 2015 to 2016，are used in the prediction model proposed.
57820 对比实验结果表明，所提出的EnsembleTrees-GBDT 预测模型相比于决策树、随机森林、支持向量机等模型，具有更低的平均绝对误差和均方根误差，同时具有更好的泛化能力，能够更准确地预测 PM2.5浓度，并实现对 PM2.5浓度影响因素的有效分析. Compared with standard models such as decision tree，random forest and support vector machine，the ensemble trees-GBDT model is found to be lower mean absolute errors，lower root mean square errors and better generalization ability.
57821 为了解决 SiamFC 在目标快速移动、背景与前景相似、光照强烈等复杂场景下鲁棒性低的问题，提出了一种新的基于语义和外观双分支孪生网络的跟踪方法 SA-Siam + + ，包括通过沙漏－通道注意力机制提取语义信息的语义分支和通过 SiamFC 提取外观信息的外观分支. To deal with the problem of low robustness of SiamFC in complex scenarios in which the object is moving fast，the background is similar to the foreground，and the illumination is strong，a new tracking method called SA-Siam + + was proposed based on two-branch siamese network，including semantic branch which is used to extract semantic information through the hourglass-channel attention mechanism and the appearance branch which is used to extract appearance information through SiamFC.
57822 此外，将 AlexNet 网络更换为经过改进的 VGG-16 网络能显著增加特征提取能力. In addition， replacing the AlexNet network with an improved VGG-16 network can significantly increase the feature extraction capabilities.
57823 在 OTB-2013、OTB-2015、UAV123 和 VOT2018 等目标跟踪标准数据集上进行了实验. Finally，experiments were carried out on OTB-2013，OTB-2015，UAV123 and VOT2018 which are standard object tracking datasets.
57824 实验结果表明，所提算法获得的测试结果相比现有主流算法有较大提高，平均帧率为 49 帧/s，满足实时性要求. It is shown show that the obtained with the pro- posed algorithm are greatly improved compared with the existing mainstream algorithms，and the average frame rate reaches 49 FPS，that can meet the real-time requirements.
57825 针对卷积神经网络在行人识别过程中错误率较高的问题，提出了一种基于深度胶囊模型的行人再识别方法. Convolutional neural networks produce higher probability of error for person re-identifications. To overcome the shortcomings，a new deep learning method based on capsule networks model for person re-identification was proposed.
57826 首先利用标准卷积层学习区分度较高的特征; First，the standard convolutional layers are used to learn discriminative features.
57827 然后将不同卷积层中的若干特征划分为一组，生成一个具有丰富语义特征的主胶囊. Then，several features in different layers are grouped together to form the primary capsules which represent a rich semantic features.