ID 原文 译文
47336 在参数求解过程中，分别采用可逆跳变马尔可夫链蒙特卡罗（RJMCMC, reversible jump Markov chain Monte Carlo）方法和最大似然（ML，maximum likelihood）方法估计类属数和模型参数； For estimating the number of classes and the parameters of model, the reversible jump Markov chain Monte Carlo(RJMCMC) and maximum likelihood (ML) estimation were employed, respectively.
47337 最后以最小化噪声平滑因子为准则获取最终分割结果。 Finally, by minimizing thesmoothing factor the final segmentation was obtained.
47338 为了验证提出的分割方法，分别对模拟图像和全色遥感图像进行了可变类分割实验。 In order to verify the proposed segmentation method, the syntheticand real panchromatic images were tested.
47339 实验结果表明提出方法的可行性和有效性。 The experimental results show that the proposed method is feasible andeffective.
47340 提出了一种利用模块度最大化与社区结构属性相结合的社区发现方法。 A kind of community detection method based on the combination of modularity and community structureattributes was proposed.
47341 首先，针对基于模块度最大化的标签传播算法中存在的时间复杂度高的问题，引入传播距离参数，依据“先传播，后合并”的原则，降低了社区合并导致整个网络需要更新带来的较高时间复杂度； Firstly, updating the whole network after communities merging every time could result in thehigh time complexity, therefore, introducing propagation distance parameter and “merger going after label propagation”was utilized to reduce time complexity.
47342 其次，结合社区结构的概念提出了基于模块度最大化的标签传播算法（CDMM-LPA）； Secondly, CDMM-LPA algorithm was proposed by combing label propagationwith community structure.
47343 最后，基于网络数据集，验证并分析了 CDMM-LPA 算法的可行性。 Finally, empirical analysis on data networks verified the validity of the approaches.
47344 实验结果表明，CDMM-LPA 算法在降低了时间复杂度的同时，获得了较高的模块度值和更加稳定的强社区结构。 Theexperimental results show that the CDMM-LPA algorithm has a high modularity value and a more stable communitystructure while reducing the time complexity.
47345 由于目前的代理重签名方案几乎都是基于大数分解、离散对数和椭圆曲线等问题设计的， Most of the existing proxy re-signature schemes were based on the hardness of big integer factoring, discretelogarithm, elliptic curve.