ID 原文 译文
21655 通过对MNIST数据库中的0~9的手写数字体的识别和CMU-PIE数据库的人脸识别实验，提出的算法减少了学习时间，提高了超参数寻优的效率，进而构建的深层网络能获得较好的分类效果。 Through the recognition experiments on the MNIST database and CMU-PIE face database, the proposed algorithm reduces the training time, and improves the efficiency of hyper-parameters optimization, and then the deep belief network can achieve better classification performance.
21656 试验结果表明，提出的改进算法在处理高维大量的数据时，计算效率有较大提高，其算法有效。 The result shows that the improved algorithm can improve both accuracy and computation efficiency in dealing with high-dimensional and large amounts of data, the new method is effective.
21657 为了产生方位向初始位置和幅度可控的虚假运动场景，该文提出针对合成孔径雷达动目标显示(SAR-GMTI)的双干扰机幅度调制的虚假场景欺骗算法。 To control the initial azimuth position and the magnitude of the false moving scene, a false moving scene jamming method against Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) is proposed based on double jammers and magnitude modulation.
21658 双干扰机分别通过移频和延迟在指定位置产生相同的虚假运动场景。 The identical false scenes can be generated in the desired position by the two jammers using delay and shift-frequency modulation.
21659 设置幅度比控制两幅虚假场景叠加后产生的各点的相位，达到初始方位向位置可控的目的。 To control the initial azimuth position of the false moving scene, the magnitude ratio of the double scenes are set by controlling the phase of each false target which is generated after two false scenes are superimposed.
21660 理论分析表明算法能够在指定位置产生初始方位向位置和幅度可控的虚假运动场景。 Theoretical analysis shows that the false moving scene can be generated with the initial azimuth position and magnitude controllable.
21661 然后分析了干扰效果和影响因素，建立了干扰算法的应用模型，给出了虚假运动场景的成像位置、方位向初始位置和幅度补偿系数的设置方法，并讨论了双站沿方位向间距对算法的影响。 Next, the jamming effects and influencing factors are analyzed, and the application model of interference algorithm is established. Subsequently, the setting methods of the imaging position, the initial azimuth position and the magnitude compensation coefficient are presented. The jamming effect of the distance between the two jammers in azimuth is also analyzed.
21662 最后，仿真实验验证了算法的有效性。 Finally, the validity of the proposed method is verified by simulation experiences.
21663 针对命名数据网络(NDN)存储空间的有效利用和应答内容的高效缓存问题，该文建立了模型并基于拓扑信息采用贪心算法求解，执行过程中考虑兴趣热度对其优化，从而有效缩短网络整体的缓存命中距离。 In order to utilize storage space and fetch content effectively in Named Data Networking (NDN), this paper constructs a model for caching problem and proposes a greedy algorithm based on topology information. To optimize the algorithm, content popularity is introduced into execution. Furthermore, content hit distance is shortened effectively.
21664 该文基于ndnsim及一些真实拓扑数据完成了仿真实验，并对提出的算法与传统的prob算法，默认的沿途全部缓存(CEE)算法及基于度的差异缓存算法(HSS)做出了对比及分析，验证了算法的有效性。 This paper simulates a NDN network based on some real topology data with ndnSIM, and compares the proposed algorithm with traditional prob algorithm, default Cache Everything Everywhere (CEE) algorithm and degree-based Heterogeneous Storage Size (HSS) algorithm through simulation. The results show that the algorithm proposed in this paper has better performance.