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Bin Yang Wu,Qiang Zhang,Shun Kai Zhang,Xiao Kun Nie,Yu Han Li,Wanhua Su 한국자동차공학회 2018 International journal of automotive technology Vol.19 No.4
Experiments and simulations were used to investigate the effect of a range of engine operating parameters and fuel characteristics on the particle size and particle number (PN) concentration at low speed and idle speed condition. The occurrence, size, and concentration of particles were tested against a range of parameters including start of injection (SOI), common rail pressure, exhaust gas recirculation (EGR) ratio and load. The results showed that the homogeneity of the mixture had the greatest impact on particle size and number concentration. The performance of particle is different at different levels of load. The particle were of nucleation mode at idle condition, and the cold idle particles had a slightly larger diameter than those produced at hot idle. By using the diesel and under high load, at EGR ratios of less than 20 %, most particles were of nucleation mode. At EGR ratios exceeding 20 %, nucleation-mode particles were gradually replaced by accumulation-mode particles. At EGR ratios above 30 %, most particles were of the accumulation mode. Under the same load, gasoline compression ignition produced particles of smaller size and reduced particulate mass (PM). The use of gasoline extended ignition delay, as the high volatility and octane number of the fuel improved the homogeneity of the mixture. Finally, a linear relationship was found between PM and PN. The relative contribution of the different factors to the formation of nucleationor accumulation-mode particles was investigated.
Pang-Chen Liu,Shun-Kai Yang,Lung-Chin Huang,Huai-Eu Tseng,Fei-Hua Kuo,Tai-Chueh Shih 한국통신학회 2020 한국통신학회 APNOMS Vol.2020 No.09
In recent years, due to customers have higher requirements for 4K/8K video and high-speed internet, telecom operators have begun to deploy FTTH network , but found that it is generally difficult to deploy fiber to the home, so G.fast technology has been favored by most telecom operators around the world and have begun to actively deploy. For the most widely deploy VDSL2 line with maximum rate that can only provide 100M internet service, a intelligent and accurate G.fast 300M high speed service prequalification technology, is a major research topic for telecom operators to promote 300M high-speed internet service. This paper proposes to use AI machine learning to estimate the G.fast line rate by using VDSL2 line attenuation to meet the real-site provision needs of telecommunications operators.