ID 原文 译文
25235 仿真结果表明较算法 NSGA-II 和 MEOA/D，该算法能在限定的截止期和预算的条件下具有更高的资源利用率。 The simulation results show that compared with the algorithms NSGA-II and MEOA/D, the algorithm can have higher resource utilization under the limited deadline and budget．
25236 为了克服无人机在传输数据过程中能量消耗较大的问题，本文首先根据实际场景建立了无人机的传输能量消耗模型，其次利用离散线性状态空间近似和线性化技术对该模型进行近似处理，最后提出了基于凹凸过程(ConCave-Convex Procedure，CCCP)的迭代算法。 In order to overcome the large consumption problem of unmanned aerial vehicle (UAV) in the process of data transmission, we first establish the model of transmission energy consumption of UAV, and the model is treated approximately by applying the technology of discrete linear state-space approximation and linearization. Finally, we proposed a CCCP (Con-Cave-Convex Procedure) based algorithm.
25237 数值仿真结果表明，提出的算法收敛迅速并能达到较好的效果。 The numerical simulation results show that the proposed algorithm can quickly converge and can achieve excellent results．
25238 基于迭代优化的传统视频压缩感知重构算法运行时间长，参数的自适应性较低，限制了其实用性和泛化能力。 The traditional iterative optimized based video compression sensing algorithms are limited by long running time and low adaptability of parameters, resulting in low practicability and generalization．
25239 利用神经网络强大的计算能力和运行速度快、参数可学习的优点，本文首先提出了帧间组稀疏网络(VGSＲ-Net)，用神经网络将图像块组映射到更高维的稀疏表示域中，并利用可学习的阈值提取帧间相关特征。 Taking advantage of the powerful computing power, fast speed and learnable parameters of neural networks, this paper first proposes a group sparse representation network (VGSＲ-Net), which maps the image block group to a higher-dimensional sparse domain through convolution, and uses a learnable threshold to denoise and extract inter-frame correlation．
25240 在此基础上，提出了两阶段混合递归增强重构网络(2sＲEＲ-VGSＲ-Net)。 On this basis, a two-stage recursive enhance reconstruction network (2sＲEＲ-VGSＲ-Net) is proposed．
25241 首先，利用 VGSＲ-Net 对初始重构结果进行初步增强;然后，引入 STMC-Net 实现运动估计，并利用残差重构网络进一步重构当前帧丢失的信息，得到更高质量的重构结果。 First, we perform VGSＲ-Net to preliminarily enhance the initial reconstruction and then introduce STMC-Net as motion estimation, and the compensated frames are fed into the residual reconstruction network to further extract the missing detail and enhance the current frame．
25242 在第二阶段重构过程中采用混合递归结构，充分利用已有的高质量重构帧信息。 The second stage of reconstruction adopts a hybrid recursive structure with the aim of making full use of the existing better quality reconstructed frames.
25243 仿真结果表明，所提算法与现有最优迭代优化重构算法 SSIM-InterF-GSＲ相比重构性能提升了 1.99dB;和基于深度学习的重构网络 CSVideoNet 相比，性能提升了 4.60dB。 The simulation results show that the proposed algorithm improves the PSNＲ(Peak Signal to Noise Ｒatio) by 1.99dB compared with the existing state-of-art traditional compressed video sensing reconstruction algorithms SSIM-InterF-GSＲ, while improves the PSNＲ by 4.60dB with the comparation of the network-based algorithm CSVideoNet．
25244 不确定性分析是量化不确定性从而获得统计特性的过程。 This paper proposes an improved intrusive generalized polynomial chaos expansion (gPCE) method for uncertainty quantification (UQ) in ground penetrating radar (GPＲ) modeling．