The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using su...The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.展开更多
营养液膜栽培技术(Nutrient Film Technique,NFT)模式下,作物对环境变化更加敏感。为保障作物根区环境条件合理,需要精准调控栽培管道内的温度,从而有效提高水培生菜品质,同时降低整体温室环境调控能耗。采用遗传算法(Genetic Algorithm...营养液膜栽培技术(Nutrient Film Technique,NFT)模式下,作物对环境变化更加敏感。为保障作物根区环境条件合理,需要精准调控栽培管道内的温度,从而有效提高水培生菜品质,同时降低整体温室环境调控能耗。采用遗传算法(Genetic Algorithm,GA)优化BP神经网络模型的输入权重和阈值,以单个NFT栽培槽为研究对象,对槽内根区不同的监测区域分别构建温度预测模型,并与标准的BP神经网络和卷积神经网络(Convolutional Neural Network,CNN)模型进行对比。结果表明,GA-BP预测模型与标准BP和CNN神经网络模型相比,均方根误差分别降低0.82和0.42,平均绝对误差分别降低0.54和0.25,绝对系数分别提高0.08和0.03。该方法可提高基于BP神经网路算法对NFT根区温度预测模型精确度,为根区温度的精准控制提供可靠依据。展开更多
文摘The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.
文摘为了探索高频段室内无线体域网通信的可行性,对11 GHz室内无线体域网的传播特性进行了测量与研究。基于大量的测量数据,给出了11 GHz频段室内无线体域网的路径损耗、阴影效应与均方根时延扩展的统计特性。针对体对体通信时人体相对角度变化的场景,提出了一种具有相对角度影响的路径损耗模型,该模型利用了与身体角度相关的路径损耗指数、浮动截距以及身体角度因子修正相对角度变化引入的路径损耗。为了验证模型的适用性,对比分析了在小型空教室和大型会议室两种不同场景下相对角度变化对信道传播特性的影响。研究结果表明:在收发端距离固定的情况下,路径损耗指数、浮动截距和由相对角度引起的路径损耗(Path Loss caused by Relative Angle,PLRA)均与相对角度具有三角函数关系;在收发端相对角度固定时,PLRA与收发端距离无关,仅与相对角度有关。上述研究结果可以为11 GHz频段在未来室内无线体域网的使用提供理论基础与实践依据。
