Study on Cognitive Optical Network Structure and Self-optimization with the Application of Artificial Intelligence Technology

Cognitive optical network is the intermediate to combine artificial intelligence technology with network,and also the important network technology to promote network intelligence level constantly.In the paper,it analyzes the cognitive optical network structure with the application of artificial intelligence technology by starting from the basic conditions of cognitive network and cognitive optional network on the basis of fully understanding the connotation of cognitive network and cognitive optical network,and explores its self-governance functions,so as to better realize the self-optimization and self-configuration of network.

Research of Digital Manufacturing Technology Application on Ultra-precision Optical Workpiece Machining

Digital manufacturing technology can be used in optical field to solve many problems caused by traditional machining. According to the characters of digital manufacturing and the practical applications of ultra-precision machining,the process of digital ultra-precision machining and its technical contents were presented in this paper. In the conclusions,it was stated that the digitalization of ultra-precision machining will be an economical and efficient way for the production of new sorts of optical workpieces.

Application of the Spectrum Peak Positioning Technology Based on BP Neural Network in Demodulation of Cavity Length of EFPI Fiber Optical Sensor

An Extrinsic Fabry-Perot Interferometric (EFPI) fiber optical sensor system is an online testing system for the gas density. The system achieves the measurement of gas density information mainly by demodulating the cavity length of EF- PI fiber optical sensor. There are many ways to achieve the demodulation of the cavity length. For shortcomings of the big intensity demodulation error and complex structure of phase demodulation, this paper proposes that BP neural net-work is used to locate the special peak points in normalized interference spectrum and combining the advantages of the unimodal and bimodal measurement achieves the demodulation of the cavity length. Through online simulation and actual measurement, the results show that the peak positioning technology based on BP neural network can not only achieve high-precision demodulation of the cavity length, but also achieve an absolute measurement of cavity length in large dynamic range.

Optical studies of semiconductor perovskite nanocrystals for classical optoelectronic applications and quantum information technologies: a review

Semiconductor perovskite films are now being widely investigated as light harvesters in solar cells with ever-increasing power conversion efficiencies,which have motivated the fabrication of other optoelectronic devices,such as light-emitting diodes,lasers,and photodetectors.Their superior material and optical properties are shared by the counterpart colloidal nanocrystals(NCs),with the additional advantage of quantum confinement that can yield size-dependent optical emission ranging from the near-UV to near-infrared wavelengths.So far,intensive research efforts have been devoted to the optical characterization of perovskite NC ensembles,revealing not only fundamental exciton relaxation and recombination dynamics but also lowthreshold amplified spontaneous emission and novel superfluorescence effects.Meanwhile,the application of single-particle spectroscopy techniques to perovskite NCs has helped to resolve a variety of optical properties for which there are few equivalents in traditional colloidal NCs,mainly including nonblinking photoluminescence,suppressed spectral diffusion,stable exciton fine structures,and coherent singlephoton emission.While the main purpose of ensemble optical studies is to guide the smooth development of perovskite NCs in classical optoelectronic applications,the rich observations from single-particle optical studies mark the emergence of a potential platform that can be exploited for quantum information technologies.

Technologies for Compensation or Mitigation of Transmission Impairments in Future High-Speed Optical Networks

The presentation will give an overview over different classes of signal impairments in ultra-long-haul and high-speed optical WDM transmission systems and adequate approaches for suppression, mitigation or compensation are discussed.

Multiparameter performance monitoring of pulse amplitude modulation channels using convolutional neural networks

A designed visual geometry group(VGG)-based convolutional neural network(CNN)model with small computational cost and high accuracy is utilized to monitor pulse amplitude modulation-based intensity modulation and direct detection channel performance using eye diagram measurements.Experimental results show that the proposed technique can achieve a high accuracy in jointly monitoring modulation format,probabilistic shaping,roll-off factor,baud rate,optical signal-to-noise ratio,and chromatic dispersion.The designed VGG-based CNN model outperforms the other four traditional machine-learning methods in different scenarios.Furthermore,the multitask learning model combined with MobileNet CNN is designed to improve the flexibility of the network.Compared with the designed VGG-based CNN,the MobileNet-based MTL does not need to train all the classes,and it can simultaneously monitor single parameter or multiple parameters without sacrificing accuracy,indicating great potential in various monitoring scenarios.

Artificial intelligence for optical transport networks:architecture,application and challenges

Optical network plays an important role in telecommunication networks, which supports high-capacity and long-distance transmission of Internet traffic. However, as the scaling and evolving of optical networks, it faces great challenges in terms of network operation, optimization and maintenance. Artificial intelligence(AI) has been proved to have superiority on addressing complex problems, by mimicking cognitive skills similar with human mind. In this paper, we provide a comprehensive investigation of AI applications in optical transport network. First, we give a general AI-based control architecture for optical transport networks. Then, we discuss several typical applications of AI model and algorithms in optical networks. Different use cases are considered, including network planning, quality of transmission(QoT) estimation, network reconfiguration, traffic prediction, failure management and so on. In addition, we also present some potential technical challenges for AI application in optical network for the next years.

基于OBS的Grid网络研究与设计

介绍了Grid网络的概念,给出了基于OBS的Grid网络模型,以及用户网络接口模型及潜在的基于OBS的Grid网络应用模型。最后对基于OBS的Grid网络应用前景做了初步展望。

浅谈配网自动化光纤通信系统施工技术

为了保证配网自动化光纤通信系统的稳定运行和信号的可靠传输,论文简要介绍了配网自动化光纤通信系统,对配网自动化光纤通信技术在施工过程中的施工要求、关键要点和注意事项进行了探究。

全光网在高校数据中心网络中的应用与性能评估

随着高校数据中心网络规模的不断扩大和应用需求的增加,全光网作为一种高带宽、低延迟的网络技术,逐渐在高校数据中心网络中得到应用。文章针对全光网在高校数据中心网络中的应用与性能评估进行了研究。首先介绍了全光网的概念和特点,以及高校数据中心网络的需求和挑战。然后重点探讨了全光网在高校数据中心网络中的应用,包括拓扑结构设计、传输技术应用以及设备选型和部署。接着介绍了全光网在高校数据中心网络中的性能评估,包括评估指标、评估方法和实验设计,并给出了实验结果和分析。最后对研究工作进行了总结,并展望了未来的研究方向。

云计算智能嵌入式技术下光通信网络路由的研究

由于光通信传输带宽需求以及网络规模和复杂度不断增加,数据传输路径具有较多选择性,易导致路由能耗大、效率低。云计算平台可以提供海量的计算资源和存储空间,能够处理大规模的数据集,为此,基于云计算智能嵌入式技术研究一种光通信网络路由。利用嵌入式技术设计光通信网络的信号转换、编码、解码等功能,根据数据传输与网络节点的能耗特征建立网络路由能耗模型;引入蚁群算法,基于蚂蚁觅食原理找出所有可以实现数据传递的路由;采用遗传算法,通过选择、交叉、变异计算找出这些路由中数据传输最佳的路由,即能耗损失最小的路由。实验结果表明,所提技术能够提升数据传输速度,使丢包率在3.0%以下,且通信时延仅为23 ms,降低数据传送网络节点能量消耗,证实了所提方法的路由通信性能良好。

基于GPON的校园全光网建设与实践

目前,广州大学(以下简称“我校”)的新建楼宇正式投入使用。为构造智慧校园建设所需的网络环境,文章以全光网络技术在该大楼率先落地试点,讨论了现今高校传统三层校园网架构存在的弊端及全光校园网的实际落地过程,旨在以千兆无源光网络(Gigabit-capable Passive Optical Networks,GPON)技术为基础实现校园全光网建设。

基于SOTN技术和5G网络切片技术的传输组网技术

为提升网络的传输效率和传输质量,研究基于软件定义光传送网(Software Optical Transport Network,SoTN)技术和第五代移动通信技术(5th Generation Mobile Communication Technology,5G)的传输组网技术。首先分析了5G传输网端到端架构存在的优势及不足,其次采用SOTN技术和5G网络切片技术设计了一种传输组网方案,最后进行实验分析。测试结果表明,该传输组网技术具有较好的应用效果,数据包的传输成功率均在90.00%以上,并且端到端的最大时延为16.11ms,能够保证网络的传输效果。

相衬光学相干弹性成像的超分辨应变测量

相衬光学相干弹性成像的相位分辨率受限于系统的光源带宽,导致层析应变成像质量低。这严重制约了相衬光学相干弹性成像的实际应用与发展。为此,本文提出一种基于数据驱动的超分辨应变测量方法,以解决相位分辨率受限下的应变重构困难问题。首先,根据相衬光学相干弹性成像原理,搭建了层析应变测量模型,用于获取网络训练所需的数据集,解决实际测量过程中超分辨应变标签难以获取的问题。其次,构建深度卷积神经网络,通过带有层析分辨率受限特点的数据驱动方式,让网络学习低分辨相位与高分辨应变之间的映射关系,从而实现相衬光学相干弹性成像的超分辨应变测量。最后,采用数值验证和压缩变形加载实验对本文方法的性能进行验证。实验结果表明本文方法在窄带宽输出下能重构出宽带宽的应变测量效果,并且其信噪比相比于矢量方法和传统应变计算深度神经网络,分别提高了18.4 dB和1.45 dB。本文方法可以突破系统光源的带宽限制,在低分辨相位输入条件下实现超分辨应变测量,从而加快相衬光学相干弹性成像在力学性能表征、内部早期损伤探测等方面的应用前景。

智慧供热系统低碳运行的设计与研究

先进的控制系统是保证区域供暖系统精准管控和节能运行的关键。因此针对能源管控中心和各换热站及其二次侧输配管网建立智慧供热系统,实现区域供暖系统的智慧和低碳运行。该系统首先在软件层基于人工智能技术对各换热站及其二次侧输配管网的主要调节设备进行基于负荷预测的分布式优化提前联动控制策略设计研究;然后在硬件层对现场设备的监控和分布式控制架构进行设计;最后在网络层基于无源光网络技术完成数据传输方案的设计,将软件层控制策略与硬件层监控设备进行有机结合。通过在实际工程中对该智慧供热系统的运行进行调试,能够满足运维人员对系统运行的智慧化需求和末端用户的热舒适需求,实现区域供暖系统的节能低碳运行。

5G和数字孪生下的工业光纤通信网络路由优化

为了提高工业光纤通信网络的路径优化效果和负载均衡能力,提出5G和数字孪生下的工业光纤通信网络路由优化方法,首先结合5G技术和数字孪生技术对工业光纤通信网络展开建模分析,获得网络状态数据;其次,将最小化最大链路利用率和路径时延作为目标,建立工业光纤通信网络路由优化目标函数;最后采用改进的麻雀算法对目标函数求解,实现工业光纤通信网络路由的优化。实验结果表明,所提方法的最大链路利用率在20%以内、路径时延最大值为0.27 s、死亡节点数量为全部节点的1/8,验证了所提方法具有良好的路径优化效果和负载均衡能力。

基于改进迁移学习的光通信网络流量数据连续插值研究

光通信网络流量数据具有大规模和高维度的特点,而数据量纲不一致,数据之间的差异会被放大,使得插值效果不理想,所以提出基于改进迁移学习的光通信网络流量数据连续插值方法。通过Box-Cox变换法对流量数据展开标准化处理,统一数据量级与量纲。通过深度学习理论与VNet技术改进卷积神经网络,通过更新网络参数使连续插值结果与理想数据进行匹配,得到流量数据连续插值结果。实验表明,所提方法的信噪比始终高于27.83 dB,频率-波形分布图与理想数据的频率-波形分布图相似度最高,决定系数在0.8以上,能够获得高质量插值结果。

基于OTN技术的电力光通信网络延时误码修正技术

为了保证电力大数据的完整和安全传输,并保证该数据的传输时效性,研究基于OTN技术的电力光通信网络延时误码修正技术。分析电力光通信网络中的延时和误码特性,结合分析结果和电力企业对光通信网络的传输需求,采用OTN技术优化电力光通信网络,保证电力大数据的传输可靠性。测试结果显示:混频效应指标结果均高于0.922,误码率指标均低于0.0085%;主、从时钟的最大偏差和平均偏差结果最大值分别为0.0031 ms和0.0007 ms;接收端风险均衡度和接收业务风险度的最高值分别为3.4%和1.05%,保证电力大数据传输时效性和完整性。

近红外光谱用于骨组织微波消融热损伤评估研究

为探索术中组织实时热损伤评估模型的效果,本研究使用近红外光谱法研究了微波消融过程中骨组织的热损伤程度。首先,设计骨组织加热实验,研究热损伤程度与骨组织近红外光谱的相关性;其次,设计微波消融实验,采集微波消融中骨组织近红外参数,研究约化散射系数、斜率因子、面积因子等与骨组织状态变化间的关系;最后,设计评估因子,基于BP网络构建骨组织热损伤评估模型。实验结果发现,近红外参数包括面积因子(area(500~550))和斜率因子(slope(524~532)、slope(565~570))在组织消融过程中发生了显著变化,可作为骨组织热损伤评估因子,构建的评估模型可用于评估骨组织热损伤程度。本研究结果可为构建微波消融骨组织损伤评估系统提供重要价值。

电网光通信系统双向运行风险关联分析技术

面向电网通信业务的高可靠传输与处理需求,针对多源风险隐患数据融合处理问题,文章提出一种电网光通信系统双向运行风险关联分析技术。首先,构建基于数字孪生的电网光通信系统风险隐患数据处理分析架构,实现TMS2.0与数字孪生的统一集成。进一步提出风险隐患数据预处理及关联分析技术,实现异常数据的剔除与多系统来源风险隐患数据的融合处理。仿真结果表明,所提技术可有效提升风险隐患数据分析速度与精度,为电网安全稳定运行提供支撑。