ID 原文 译文
1413 整个过程无附加信息、无数据溢出、且均可保证可逆性，密钥 1 拥有者和密钥 2 拥有者两种权限互不干涉，是完全可逆可分离算法，平均嵌入率可达到 3 bit/pixel， There is no additional information, no data overflow and reversibility is guaranteed in the whole process, the two permissions do not interfere with each other, and the algorithm is completely re-versible and separable, and the average embedding rate can reach 3 bit/pixel.
1414 通过与 8 种优秀算法的实验比较，表明算法在嵌入容量、可逆性、可分离性率等方面相比于对比算法均有一定优势。 The experimental results show that the algo-rithm is better than the other eight excellent algorithms in terms of embedding capacity, reversibility and separability.
1415 在非均匀采样系统中，存在着刷新时间间隔不确定和时变的情况，这给系统控制器的设计造成了很大困难。 In the non-uniformly sampled systems, there are uncertainties and time-varying of the refresh time interval, which have caused great difficulties to design the system controller.
1416 针对此问题，本文将刷新时间间隔看作时延变量，将不同时延的系统动态变化过程转化为不同子系统的动态变化过程，从而将时延的变化转化为不同子模型之间的切换， To aim at these problems, the refresh time interval is re-garded as the time delay variable, and the system dynamic changing processes in different time delays are characterized by the changing processes of different subsystems. The variety of different time delays is transformed into the switching betweendifferent sub-models.
1417 最终将非均匀采样系统描述为一类具有有限个子系统的离散时间切换系统。 The non-uniformly sampled system is described as a class of discrete-time switched systems with finitesubsystems.
1418 利用输出反馈控制方法，基于平均驻留时间条件，使输出反馈闭环非均匀采样系统指数稳定。 Under the condition of satisfying the average dwell time, the output feedback control method is used to guaranteethe exponential stability of the closed loop non-uniformly sampled system.
1419 最后，通过一个非均匀采样系统仿真实例证明了提出方法的有效性。 Finally, the effectiveness of the proposed methodis demonstrated by the example of the non-uniformly sampled system.
1420 为实现在只有少量标记数据情况下的高质量的图像分类，本文提出了一种基于深度卷积神经网络的图上半监督极化 SAR 图像分类算法。 To realize high quality image classification with few labeled data, a graph-based semi-supervised PolSARimage classification method using deep neural networks is proposed in this paper.
1421 该算法将极化 SAR 图像建模为无向图，并基于该无向图，定义了包含半监督项，卷积神经网络项和类标光滑项的能量函数。 The PolSAR image is modeled as a graph, based on which we design an energy function which incorporates a semi-supervision term, a convolutional neural network(CNN)term and a pairwise smoothness term.
1422 算法所采用的卷积神经网络提取抽象的数据驱动的极化特征。 CNN is responsible for extracting discriminative polarimetric features.