ID 原文 译文
24725 通过电压原理识别出部分异常箱表关系样本集，随后构建CNN（卷积神经网络）异常箱表关系识别模型，通过样本三分类赋权值实现类别均衡处理； Through the principle of voltage, abnormal box-table relationship sample sets were identified. And by three-class weighting balance, the CNN (convolutional neural network) abnormal box-table relationship recognition model was constructed.
24726 并在模型推广应用过程中，采用强化学习实现离线模型的在线泛化学习，并以分组模型交互学习和竞争优化的方式筛选出最优泛化识别模型。 In addition, the grouped parallel generalization learning of recognition model was realized by reinforcement learning.
24727 实验证明，通过人工核查和数据反馈，该方法可实现模型对异常样本数据分布规律的自拟合学习，提高模型对不同应用环境的泛化性，进一步降低人工现场核查工作量，保障低压台区用户拓扑网络关系的准确性。 The experiment proves that, through self-learning the distribution of newly identified abnormal sample data, which improve the generalization to different environments. This reduces the workload of manual on-site verification and ensures the accuracy of the topology network relationship in the low-voltage station area.
24728 为提高具有帧间位移平移特性的视频图像的信噪比和去噪时效，本文提出了基于非局部自相似序列集的视频图像盲去噪算法。 A blind video image denoising algorithm based on non-local self-similar series sets is proposed to improve the peak signal-to-noise ratio and denoising efficiency of video images with displacement characteristics.
24729 选取与待去噪视频图像前后相邻的若干图像帧，在每一图像帧中寻找具有典型特征的图像块群，并通过在前一帧图像中查找和该图像块群具有最小差异度的块群来确定帧间的精确位移； Image block-groups with typical features are detected in each frame, and accurate inter-frame displacement between the image and previous frame is computed based on image block-matching.
24730 将待去噪视频图像划分成若干图像块，根据帧间位移快速构建每个图像块的自相似序列集； The noise image is divided into several image blocks, and the self-similar series set of each image blocks is constructed quickly according to the inter-frame displacement.
24731 随后将每个自相似序列集中的二维图像块整合成三维矩阵后进行三维变换，并对变换系数进行自适应阈值处理； A 3D transform is applied to the self-similar series set, followed by an adaptive-threshold of the transform coefficients to attenuate the noise.
24732 再将三维逆变换后的图像块融合生成去噪图像。 The 3D estimate after inverse 3D transformation are aggregated to obtain the finial image.
24733 实验结果表明，在噪声方差未知的情况下，本文算法所得去噪视频图像具有较好的信噪比和视觉效果，并且有较高的运行效率。 Experimental results show that the proposed algorithm has significate advantages in PSNR and visual effect in unknown noise variance, meanwhile it also has higher efficiency.
24734 针对多个虚拟网络请求(Virtual Network Request, VNR)动态到达的网络场景，本文提出一种基于成本及功耗联合优化的软件定义网络(Software-Defined Networking, SDN)虚拟网络映射（Virtual Network Embedding, VNE）算法。 For the network scenario where multiple virtual network requests (VNRs) arrive dynamically, a cost and power consumption joint optimization based virtual network embedding (VNE) algorithm was proposed for software-d⁃fined networking (SDN).