ID	原文	译文
16395	以吊悬线区域代替吊弦结构整体区域送入分类网络进行训练，通过所建立的多尺度吊弦状态检测模型，实现吊弦状态的精确识别。	Then the surrounding area of the dropper line is extractedwith a ralated twiddle factor. Those extracted areas, replacing the results of detection net, are fed intoclassification network for training the final dropper state analysis mode.
16396	实验结果表明，吊弦定位模型的准确率达95.3%以上；霍夫变换可排除无效区域对吊弦状态识别的干扰，提高分类网络的训练速度，吊弦状态识别模型准确率达97.5%以上。	Experiments show that the accuracy of dropper detection model is over 95.3%, and the dropper state analysis model can eliminate the impact of meaningless area pixels while accelerating training process, the final state analysis model achieves a high accuracy over 97.5%.
16397	高效视频编码(HEVC)标准相对于H.264/AVC标准提升了压缩效率，但由于引入的编码单元四叉树划分结构也使得编码复杂度大幅度提升。	Compared to H.264/AVC coding standard, High Efficiency Video Coding (HEVC) improves the compression efficiency, but the consequent disadvantage is the significant increase in encoding complexity by using the quad-tree partition.
16398	对此，该文提出一种针对HEVC帧内编码模式下编码单元(CU)划分表征矢量预测的多层特征传递卷积神经网络(MLFT-CNN)，大幅度降低了视频编码复杂度。	A Multi-Layer Feature Transfer Convolutional Neural Network (MLFT-CNN) forCoding Unit (CU) division and characterization vector prediction in HEVC intra coding mode is proposed, which greatly reduces the complexity of video coding.
16399	首先，提出融合CU划分结构信息的降分辨率特征提取模块；	Firstly, a reduced-resolution feature extraction moduleincorporating CU partition structure information is proposed.
16400	其次，改进通道注意力机制以提升特征的纹理表达性能；	Then, the channel attention mechanism isimproved for a better texture expression performance of the feature.
16401	再次，设计特征传递机制，用高深度编码单元划分特征指导低深度编码单元的划分；	After that, the feature transfer mechanismis designed to use the feature division of high-depth coding unit to guide the division of low-depth coding unit.
16402	最后建立分段特征表示的目标损失函数，训练端到端的CU划分表征矢量预测网络。	Finally, the target loss function represented by the segmented feature is established, and the end-to-end CUdivision represents the vector prediction network.
16403	实验结果表明，在不影响视频编码质量的前提下，该文所提算法有效地降低了HEVC的编码复杂度，与标准方法相比，编码复杂度平均下降了70.96%。	The experimental results show that the proposed algorithmeffectively reduces the encoding complexity of HEVC without affecting the video coding quality. Specifically,compared to the standard method, the encoding complexity on the standard test sequence is reduced by 70.96%on average.
16404	基于脑电(EEG)信号的气味识别研究在嗅觉功能客观评价及嗅觉障碍疾病诊断等方面具有重要的应用价值。	The study of odor recognition based on ElectroEncephaloGram (EEG) signals has importantapplication value to objectively evaluating olfactory function and diagnosing olfactory disorders.