ID 原文 译文
1913 根据目标函数的性质得到了计算简便的近似均值矩阵。 The approximate mean matrix with low computational complexity is obtained ac-cording to the properties of the objective function.
1914 利用不同方法得到的均值矩阵，提出了几种新的基于谱范数的矩阵 CFAR 检测器。 In addition, we propose several matrix CFAR detectors based on different mean matrix estimation methods.
1915 通过检测势分析和仿真实验验证了其检测性能优于现有的其他矩阵 CFAR 检测器，复杂度分析也表明了其计算复杂度低于现有的其他矩阵 CFAR检测器，为海杂波背景下的雷达目标检测提供了新的有效技术手段。 Finally, the detection power analysis and simulation results show that the detection perform-ance of the proposed methods with lower computational complexity are superior to other existing matrix CFAR detectors. It provides a new effective technique for radar target detection under sea clutter background.
1916 协同过滤作为推荐系统核心技术，面临严重的评分数据稀疏性问题。 Collaborative filtering, as the core technology of recommendation systems, is currently facing the sparsityproblem of rating data.
1917 融合物品文本信息可以有效的解决数据稀疏性问题，然而，目前的方法侧重于提取文本的单维特征，忽略了物品语义表示的多维特性。 This can be effectively solved through integrating item text information. However, current methods focus on extracting the one-dimensional features of the text, neglecting its multidimensional semantic features.
1918 深度挖掘物品内容的多维特性可以更加精细化描述物品的语义信息，有助于提升推荐效果。 Digging deep-ly into the multidimensional semantic features of the text can improve the recommendations.
1919 为此，本文提出基于胶囊网络的概率生成模型。 To help achieve this goal, aprobabilistic matrix factorization model based on multidimensional semantic representation learning is proposed in the present study.
1920 模型利用胶囊网络挖掘文本的多维语义特征，并以正则化方式融入概率矩阵分解框架，建立用户与物品之间的内在关系。 The model uses a capsule network to mine the multidimensional semantic features of the text, and then integrates it in-to the probabilistic matrix decomposition framework using the regularization method to reveal hidden features linking usersand items.
1921 实验结果表明本文提出的模型具有更高的评分预测精度。 Experimental results show that the proposed model has higher prediction accuracy.
1922 遵循控制转发分离思想，软件定义无线传感器网络(Wireless Sensor Network，WSN)数据转发采用基于流的实现方式。 Following the idea of separation of control and forwarding, the data forwarding of software-defined WSN(Wireless Sensor Network)is implemented in a flow-based manner.