期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
人工智能在电信网络的发展趋势与应用挑战 被引量:4
1
作者 程强 刘姿杉 《信息通信技术与政策》 2019年第7期29-33,共5页
现网面临的网络设备和流量强度迅速增长、运维复杂性增加、技术多元性和鲁棒性需求提高等挑战是不争的事实,人工智能将是下一代电信网络的重要使能技术。目前,学术界和工业界相继对人工智能在电信网络更深入的应用方案进行研究,推动人... 现网面临的网络设备和流量强度迅速增长、运维复杂性增加、技术多元性和鲁棒性需求提高等挑战是不争的事实,人工智能将是下一代电信网络的重要使能技术。目前,学术界和工业界相继对人工智能在电信网络更深入的应用方案进行研究,推动人工智能与电信网络技术的深度结合。本文从电信网络智能化的角度,梳理了人工智能在电信网中的发展现状和标准化进展,分析了其主要应用场景。通过总结人工智能电信网的关键挑战,建议电信运营商和服务商等从商业运营、生态架构、技术发展和安全保护4个层面开展电信网络的智能化演进。 展开更多
关键词 人工智能 电信网智能化 关键挑战
下载PDF
Genetic Algorithm-Based Redundancy Optimization Method for Smart Grid Communication Network 被引量:4
2
作者 SHI Yue QIU Xuesong GUO Shaoyong 《China Communications》 SCIE CSCD 2015年第8期73-84,共12页
This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analy... This paper proposes a redundancy optimization method for smart grid Advanced Metering Infrastructure(AMI) to realize economy and reliability targets.AMI is a crucial part of the smart grid to measure,collect,and analyze data about energy usage and power quality from customer premises.From the communication perspective,the AMI consists of smart meters,Home Area Network(HAN) gateways and data concentrators;in particular,the redundancy optimization problem focus on deciding which data concentrator needs redundancy.In order to solve the problem,we first develop a quantitative analysis model for the network economic loss caused by the data concentrator failures.Then,we establish a complete redundancy optimization model,which comprehensively consider the factors of reliability and economy.Finally,an advanced redundancy deployment method based on genetic algorithm(GA) is developed to solve the proposed problem.The simulation results testify that the proposed redundancy optimization method is capable to build a reliable and economic smart grid communication network. 展开更多
关键词 smart grid advanced metering infrastructure redundancy optimization dataconcentrator genetic algorithm
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部