ID 原文 译文
21975 最后，利用严格可控理论找出网络中的驱动节点组，并根据信息传播的特征选取驱动节点集，对信息传播进行控制。 Finally, the exact controllability theory is used to find the driver node groups, and then the set of driver nodes are selected according to the characteristics of information propagation to control information propagation.
21976 实验结果表明，该文所提传播控制方法能对信息传播的促进或抑制进行有效控制，为社交网络信息传播控制提供新的方法和思路。 Experimental results show that the proposed method can effectively promote or suppress the information propagation, which provides some ideas for the information propagation control in social networks.
21977 为解决 5G 网络切片间资源分配的问题，该文提出一种基于在线双向拍卖 (ODA)的资源调度机制。 In order to solve the problem of resource allocation between 5G virtual network slice, a resource scheduling mechanism based on Online Double Auction (ODA) is proposed.
21978 该机制首先针对不同的业务需求和业务收益确定网络切片的优先级和单位资源报价； Firstly, the priority of network slices and unit resource quotes are determined according to different traffic needs and traffic benefits.
21979 其次明确最大化网络收益的目标建立线下单向拍卖模型； Then, to maximize the network revenue, an offline single auction model is established.
21980 进一步，考虑资源的动态分配和回收利用，提出价格更新算法实时更新资源价格； Further, based on the resources dynamic allocation and recycling, the price-updating algorithm is proposed to update the resource price in real time.
21981 最后，综合线下单向拍卖机制和价格动态变化机制建立在线双向拍卖模型，为切片动态分配资源。 Finally, the offline single auction mechanism and the price update mechanism are combined to establish ODA model and allocate resources dynamically for the network slices.
21982 仿真结果表明，该机制在提高网络收益的同时可以保证各切片用户的 QoS 需求。 The simulation results show that the proposed mechanism can improve network revenue and guarantee the QoS requirement of each slice user.
21983 针对无源时差(TDOA)定位的非线性方程解算问题，论文使用一种名为樽海鞘群算法(SSA)的新的群体智能优化算法。 To solve the nonlinear equation problems of Time-Difference-Of-Arrival (TDOA) passive location, a new swarm intelligence optimization algorithm called Salp-Swarm-Algorithm (SSA) is used.
21984 首先，该算法采用一种新的群体更新模型，充分平衡迭代过程中的探索行为与开发行为，在保证搜索的全局性与个体的多样性的同时，改善了其他智能优化算法容易陷入局部极值的问题。 Firstly, a new renewal model of salps is proposed to balance exploration and exploitation properly during iteration in SSA. SSA not only ensures the wholeness of searching and the diversity of individuals, but also improves the problem that other intelligent optimization algorithms fall into local optima easily.