ID 原文 译文
57388 改进了现有的基于属性组撤销属性的访问控制方案，使其适用于雾 计算，同时提高了密钥更新效率. The proposed scheme improves the existing access control scheme based on attribute group revoca- tion attributes，making it used in fog computing and improving the efficiency of key update.
57389 另外，利用雾节点实现了用户的撤销，同时为了降低用户端的负担，将部分计算和 存储开销从用户端转移到了临近的雾节点. In addition， users can be revoked by fog nodes. In order to reduce the burden on the user side，part of the calculation overhead is transferred from the user side to the adjacent fog node.
57390 基于判定双线性 diffie-hellman( DBDH) 假设的安全性分析结果表明，所 提方案可以抵抗选择明文攻击. The security analysis based on the de- cisional bilinear diffie-hellman ( DBDH) assumption shows that the proposed scheme can resist chosen plaintext attack.
57391 对方案进行了理论和实验，分析结果表明，所提方案具有较高的撤销效率和实用 价值. Finally，the scheme is analyzed in both theory and experiment. The analysis results show that the proposed scheme has high revocation efficiency and practical value.
57392 针对第 5 代移动通信系统赋能行业应用时在通信、控制、计算与安全方面面临的需求和挑战，提出了面向行业应用赋能的移动通信系统向第 6 代移动通信系统演进的形式———智简无线网络. Currently，the mobile networks represented by the fifth generation of mobile communications system ( 5G) are facing various challenges in empowering industrial applications. The requirements and challenges of 5G empowering industrial applications in communication，control，computation，and securi- ty are analyzed. It formulates the sixth generation of mobile communications system( 6G) evolution of the mobile networks for empowering industrial application，in a special form named as entropy reduced mo- bile networks.
57393 基于信息论并以系统论为指 导，强调利用语义信息的原生智能以及内生简约架构，以实现移动网络赋能行业应用的系统优化，智简无线网络为 移动通信系统向第 6 代移动通信系统的发展提供了演进路径. Based on information theory and directed by systems theory，the entropy reduced mobile networks emphasizes the utilization of endogenous intelligence of semantic information and endogenous simplicity architecture to achieve the global optimization for realizing mobile networks empowering indus- trial applications，which formulates a potential evolution path to 6G.
57394 传统粒子群算法的优点较为明显，但是随着环境复杂度的增高，传统算法的聚类中心敏感度升高，空聚类过多，类标号对聚类结果的影响不足等问题日趋严重. Traditional particle swarm optimization has obvious advantages，but with increased complexity of the environment. When the traditional algorithm is used，the sensitivity of the clustering center is in- creased，there are too many empty clusters，and the performance of the class label has insufficient influ- ence on the clustering results.
57395 为此，提出了一种改进算法，以半监督 K 均值聚类为目标，以自适应 K 值的方式，随机地计算初始化聚类中心，并根据均值聚类算法的需要编码成粒子，同时引入软性约束概念重新构造目标函数;最后使用改进后的算法进行寻优. An improved algorithm is proposed，which aims at semi-supervised K- means clustering; first，the clustering center is initialized by random calculation in an adaptive K-value method，and the particles are encoded according to the needs of the mean clustering algorithm. At the same time，the objective function is reconstructed with the concept of soft constraints，and finally the im- proved algorithm is used for optimization.
57396 所提出的粒子群算法改进了自适应参数，引入了免疫扰动和混沌扰动 2 种扰动方式，同时应用了退火策略和动态聚类策略. The adaptive parameters in the improved particle swarm optimi- zation algorithm is improved，two disturbance methods of immune disturbance and chaos disturbance is introduced，and the annealing strategy and dynamic clustering strategy at the same time is applied.
57397 实验结果表明，该算法在很大程度上解决了上述问题. Ex- periments show that the algorithm has solved the above problem.