1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred milli...1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology (IT) and communication technology (CT). Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT&T, have been contributing to the performance improvement of IT service and CT equipment.展开更多
In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by exis...In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by existence scalar sector with new five Higgs bosons;two of them are electrically charged and the other three Higgs bosons are neutral charged. Our analysis based on the Monte Carlo data produced from the simulation of 2HDM with proton antiproton collisions at the Tevatron = 1.96 TeV (Fermi Lab) and proton proton collisions at the LHC = 14 TeV (CERN) with final state includes electron, muon, multiple jets and missing transverse energy via the production and decay of the new Higgs in the hard process where the dominant background (electrons and muons) for this process comes from the Standard Model processes via the production and decay of top quark pair. We assumed that the branching ratio of charged Higgs boson to tau lepton and neutrino is 100%. We used the Artificial Neural Networks (ANNs) which are an efficient technique to discriminate the signal of charged Higgs boson from the SM background for charged Higgs boson masses between 80 GeV and 160 GeV. Also we calculated the production cross section at different energies, decay width, branching ration and different kinematics distribution for charged Higgs boson and for the final state particles.展开更多
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
基金supported in part by Ministry of Education/China Mobile joint research grant under Project No.5-10Nanjing University of Posts and Telecommunications under Grants No.NY214135 and NY215045
文摘1 IntroductionNowadays in China, there are more than six hundred million netizens [1]. On April 11, 2015, the nmnbet of simultaneous online users of the Chinese instant message application QQ reached two hundred million [2]. The fast growth ol the lnternet pusnes me rapid development of information technology (IT) and communication technology (CT). Many traditional IT service and CT equipment providers are facing the fusion of IT and CT in the age of digital transformation, and heading toward ICT enterprises. Large global ICT enterprises, such as Apple, Google, Microsoft, Amazon, Verizon, and AT&T, have been contributing to the performance improvement of IT service and CT equipment.
文摘In this work we present an analysis of a search for charged Higgs boson in the context of Two Doublet Higgs Model (2HDM) which is an extension of the Standard Model of particles physics where the 2HDM predicts by existence scalar sector with new five Higgs bosons;two of them are electrically charged and the other three Higgs bosons are neutral charged. Our analysis based on the Monte Carlo data produced from the simulation of 2HDM with proton antiproton collisions at the Tevatron = 1.96 TeV (Fermi Lab) and proton proton collisions at the LHC = 14 TeV (CERN) with final state includes electron, muon, multiple jets and missing transverse energy via the production and decay of the new Higgs in the hard process where the dominant background (electrons and muons) for this process comes from the Standard Model processes via the production and decay of top quark pair. We assumed that the branching ratio of charged Higgs boson to tau lepton and neutrino is 100%. We used the Artificial Neural Networks (ANNs) which are an efficient technique to discriminate the signal of charged Higgs boson from the SM background for charged Higgs boson masses between 80 GeV and 160 GeV. Also we calculated the production cross section at different energies, decay width, branching ration and different kinematics distribution for charged Higgs boson and for the final state particles.
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.