ID 原文 译文
19815 最后，实验结果验证了该文提出的检测器能够有效地改善雷达对目标的检测性能。 Finally, the experiment results show that the detector presented by the paper can improve the detection performance effectively.
19816 近年来，通过引入移动设备(ME)为无线传感器网络(WSNs)进行无线充电和数据收集成为一个研究热点。 Recently, the mobile charging and data collecting by using Mobile Equipment (ME) in WirelessSensor Networks (WSNs) is a hot topic.
19817 传统方法一般先根据节点的充电需求优先级确定移动路径，再根据该路径依次对节点进行数据收集。 Existing studies determine usually the traveling path of ME accordingto the charging requirements of sensor nodes firstly, and then handle the data collecting.
19818 该文同时考虑充电需求和数据收集两个维度，以最大化ME的总能量利用率和最小化数据收集平均时延为目标，建立多目标一对多充电及数据收集模型。 In this paper, charging requirement and data collecting are taken into consideration simultaneously. A one-to-many charging and datacollecting model for ME is established with two optimization objectives, maximizing the total energy utilizationand minimizing the average delay of data collecting.
19819 在ME携带的行驶能量和充电能量不足的前提下，设计路径规划策略和均衡化充电策略，并改进多目标蚁群算法对该文问题进行求解。 Due to the limited energy of the ME, the path planning strategy and the equalization charging strategy are designed. An improved multi-objective ant colony algorithmis proposed to solve the problem.
19820 实验结果表明，该文算法在多种场景下的目标值、Pareto解的数量、Pareto解集的均匀性、分布范围等性能指标均优于NSGA-II算法。 Experiments show that the objective values, the number of Pareto solutions, the homogeneity of Pareto solutions and the distribution of Pareto solutions obtained by the proposed algorithm are all superior over NSGA-II algorithm.
19821 为准确有效地实现自然图像的压缩感知(CS)重构，该文提出一种基于图像非局部低秩(NLR)和加权全变分(WTV)的CS重构算法。 In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed.
19822 该算法考虑图像的非局部自相似性(NSS)和局部光滑特性，对传统的全变分(TV)模型进行改进，只对图像的高频分量设置权重，并用一种差分曲率的边缘检测算子来构造权重系数。 The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator.
19823 此外，算法以改进的TV模型与NLR模型为约束构建优化模型，并分别采用光滑非凸函数和软阈值函数来求解低秩和全变分优化问题，很好地利用了图像的自身性质，保护了图像的细节信息，并提高了算法的抗噪性和适应性。 Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable.
19824 仿真结果表明，与基于NLR的CS算法相比，相同采样率下，该文所提算法的峰值信噪比最高可提高2.49 dB，且抗噪性更强，验证了算法的有效性。 Experimental results show that, compared with the CS reconstruction algorithm via non-local lowrank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases by 2.49 dBat most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.