ID 原文 译文
20035 此外，研究还发现，相较于传统稀疏编码方法，卷积稀疏编码更有利于提取PD语音数据的高层特征；同样，迁移学习也有利于提高该算法性能。 Besides, compared with sparsecoding, convolutional sparse coding can be beneficial to extracting high level features from PD data set;moreover, it is proved that transfer learning is effective.
20036 针对利用抽象语义(AMR)图来预测摘要子图存在的语义结构不完整问题，该文提出一种基于整数线性规划(ILP)重构AMR图结构的语义摘要算法。 In order to solve the incomplete semantic structure problem that occurs in the process of using the Abstract Meaning Representation (AMR) graph to predict the summary subgraph, a semantic summarization algorithm is proposed based on Integer Linear Programming (ILP) reconstructed AMR graph structure.
20037 首先将数据预处理生成一个AMR总图；然后基于统计特征从AMR总图中抽取出摘要子图重要节点信息； Firstly,the text data are preprocessed to generate an AMR total graph. Then the important node information of thesummary subgraph is extracted from the AMR total graph based on the statistical features.
20038 最后利用ILP的方法来对摘要子图中节点关系进行重构，利用完整的摘要子图恢复生成语义摘要。 Finally, the ILPmethod is applied to reconstructing the node relationships in the summary subgraph, which is further utilizedto generate a semantic summarization.
20039 实验结果表明，相比其他语义摘要方法，所提方法的ROUGE值和Smatch值都有显著提高，最多分别提高了9%和14%，该方法有利于提高语义摘要的质量。 The experimental results show that compared with other semanticsummarization methods, the ROUGE index and Smatch index of the proposed algorithm are significantlyimproved, up to 9% and 14% respectively. This method improves significantly the quality of semanticsummarization.
20040 鉴于稀疏ISAR成像方法的成像质量受到待成像场景的稀疏表示不准确的限制，该文将字典学习(DL)技术引入到ISAR稀疏成像中，以提升目标成像质量。 In view of the imaging quality of sparse ISAR imaging methods is limited by the inaccurate sparserepresentation of the scene to be imaged, the Dictionary Learning (DL) technique is introduced into ISARsparse imaging to get better sparse representation of the scene.
20041 该文给出基于离线DL和在线DL两种ISAR稀疏成像方法。 An off-line DL based imaging method and anon-line DL based imaging method are proposed.
20042 前者通过已有同类目标ISAR图像进行学习，获得更优稀疏表示，后者在成像过程中从现有数据中通过优化获得稀疏表示。 The off-line DL imaging method can obtain a better sparserepresentation via a dictionary learned from the available ISAR images. The on-line DL imaging method canobtain the sparse representation from the data currently considered by jointly optimizing the imaging and DLprocesses.
20043 仿真和实测ISAR数据成像结果表明，结合离线DL和在线DL的成像方法均可获得比现有方法更优的成像结果，离线DL成像优于在线DL成像，而且前者计算效率优于后者。 The results of both simulated and real ISAR data show that the on-line DL imaging method and the off-line dictionary imaging method are both able to better sparsely represent the target scene leading to better imaging results. The off-line DL based imaging method works better than the on-line DL based imaging methodwith respect to both imaging quality and computational efficiency.
20044 水下线谱目标被动检测中，目标辐射线谱信号的方位、频率、个数等信息通常未知，且线谱检测性能容易受到宽带干扰及背景噪声的影响。 For the passive detection of underwater line-spectrum target, the information such as the azimuth,frequency and the number of the line-spectrum signals is usually unknown, and the line-spectrum detectionperformance is affected by broadband interferences and noise.