ID 原文 译文
25555 本文研究方法可以进一步用于其它微带电路无源互调规律计算研究。 The model deduced in this paper can also be extended to study the PIM phenomenon in other microstrip circuits．
25556 复杂场景中的运动目标检测是计算机视觉领域的重要问题，其检测准确度仍然是一大挑战。 Moving object detection in complex scenes is an important problem in computer vision domain, and the detection accuracy is still a great challenge．
25557 本文提出并设计了一种用于复杂场景中运动目标检测的深度帧差卷积神经网络(Deep Difference Convolutional Neural Network，DFD-CNN)。 In this paper, we propose and design a deep frame difference convolution neural network (DFDCNN) for moving object detection in complex scenes．
25558 DFDCNN 由 DifferenceNet 和 AppearanceNet 组成，不需要后处理就可以预测分割前景像素。 DFDCNN consists of DifferenceNet and Appearan-ceNet, which can predict and segment the foreground pixels simultaneously without post-processing．
25559 DifferenceNet 具有孪生Encoder-Decoder 结构，用于学习两个连续帧之间的变化，从输入(t 帧和 t + 1 帧)中获取时序信息;AppearanceNet 用于从输入(t 帧)中提取空间信息，并与时序信息融合; DifferenceNet has Sia-mese Encoder-Decoder structure, which is used to learn changes between two consecutive frames and to obtain temporal information from inputs, while AppearanceNet is used to extract spatial information from the input frame, and fuse the temporal information and spatial information by fusion of feature maps．
25560 同时，通过多尺度特征图融合和逐步上采样来保留多尺度空间信息，以提高网络对小目标的敏感性。 Finally, multi-scale spatial information is retained through multi-scale feature map fusion and stepwise up-sampling to improve the sensitivity to small objects．
25561 在公开标准数据集 CDnet2014 和 I2R 上的实验结果表明:DFDCNN 不仅在动态背景、光照变化和阴影存在的复杂场景中具有更好的检测性能，而且在小目标存在的场景中也具有较好的检测效果。 Experiments on two public standard datasets: CDnet2014 and I2R demonstrate that the proposed DFDCNN outperforms the classic algorithms significantly from both qualitative and quantitative aspects. The experimental results illustrate that the proposed DFDCNN shows much better detection performance in complex scenes where dynamic background, illumination variation and shadow exist, and there is improvement for scenes, in which small objects exist．
25562 复杂网络中节点重要性辨识对分析网络结构和功能具有重要作用。 In complex networks, node importance identification plays an important role in analyzing the structure and function．
25563 为了辨识节点重要性，分析节点自身和关联节点的作用，本文构建了一种基于重要度传输矩阵的节点重要性辨识模型。 In order to identify the node's importance and analyze the role of nodes themselves and associated nodes, we construct a node importance identification model based on importance transfer matrix．
25564 首先，基于关联节点与节点之间的最优路径长度、最优路径数目和信息传播率定义了节点间的传输能力。 Firstly, the transmission capability between nodes is defined based on the optimal path length, the number of optimal paths and the information propagation rate between the associated nodes and the nodes．