移动互联业务的持续增长以及物联网等各类通信应用的广泛普及对移动通信系统的功能和性能提出了新的挑战。第5代(5G)移动通信系统基于对已有通信技术的融合及演进,引入新的无线传输及网络技术,将实现多种应用场景用户业务传输性能保障...移动互联业务的持续增长以及物联网等各类通信应用的广泛普及对移动通信系统的功能和性能提出了新的挑战。第5代(5G)移动通信系统基于对已有通信技术的融合及演进,引入新的无线传输及网络技术,将实现多种应用场景用户业务传输性能保障。软件定义网络(software defined networking,SDN)通过采用集中控制的新型网络架构,将传统数据转发设备的数据转发与逻辑控制功能进行分离,实现了数据层与控制层的解耦,从而可有效解决传统网络结构封闭僵化、数据传输转发性能高度受限、资源利用率低等问题,满足业务差异化需求、提升业务部署效率。近年来,5G网络架构采用SDN已成为业界及学术界共识,已有较多文献提出基于SDN的5G移动网络架构。在对5G应用场景、关键技术以及SDN技术进行概述的基础上,对基于SDN的5G网络架构相关研究进行详细阐述,并对未来研究方向进行了展望。展开更多
A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i...A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.展开更多
The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control meth...The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.展开更多
文摘移动互联业务的持续增长以及物联网等各类通信应用的广泛普及对移动通信系统的功能和性能提出了新的挑战。第5代(5G)移动通信系统基于对已有通信技术的融合及演进,引入新的无线传输及网络技术,将实现多种应用场景用户业务传输性能保障。软件定义网络(software defined networking,SDN)通过采用集中控制的新型网络架构,将传统数据转发设备的数据转发与逻辑控制功能进行分离,实现了数据层与控制层的解耦,从而可有效解决传统网络结构封闭僵化、数据传输转发性能高度受限、资源利用率低等问题,满足业务差异化需求、提升业务部署效率。近年来,5G网络架构采用SDN已成为业界及学术界共识,已有较多文献提出基于SDN的5G移动网络架构。在对5G应用场景、关键技术以及SDN技术进行概述的基础上,对基于SDN的5G网络架构相关研究进行详细阐述,并对未来研究方向进行了展望。
文摘A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.
文摘The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.
