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哥德堡地区基于无线通讯网络的水汽密度监测分析 被引量:1
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作者 苏桂炀 韩瑽琤 +2 位作者 毕永恒 刘昆 Lei BAO 《红外与毫米波学报》 SCIE EI CAS CSCD 北大核心 2020年第1期47-55,共9页
利用无线通讯网络中的微波链路来监测降雨和水汽等是大气环境监测的新技术之一。这个技术可以测量近地面的降雨强度和水汽密度等气象参数,具有时空分辨率高、成本低等优势。利用瑞典爱立信公司(Ericsson)提供的位于哥德堡地区E频段的微... 利用无线通讯网络中的微波链路来监测降雨和水汽等是大气环境监测的新技术之一。这个技术可以测量近地面的降雨强度和水汽密度等气象参数,具有时空分辨率高、成本低等优势。利用瑞典爱立信公司(Ericsson)提供的位于哥德堡地区E频段的微波通讯链路资料、位于链路一端的气象站1资料和由瑞典气象水文研究所(SMHI)气象网站提供的气象站2资料,对2017年06月13日至2017年07月13日近1个月的水汽密度进行反演计算和分析。结果表明:同一区域的不同地点处的气象要素有一定的差异性,同一区域的温度会有一定的浮动(0~4℃),两者之间的相关性为0. 87;微波通讯链路反演的水汽密度结果与研究区域的地面气象站1和气象站2测量结果有很好的一致性,两者之间的相关性分别为0. 89和0. 97,均方根误分别差为0. 75 g m3和0. 79 g m3;利用微波链路,与现有的湿度监测方法相比,可以为现有的天气监测网络提供额外的丰富的数据源。 展开更多
关键词 微波通讯链路 水汽密度反演和监测技术 哥德堡地区 E频段
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InAsP/InGaAsP Strained Microstructures Grown by Gas Source Molecular Beam Epitaxy
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作者 CHEN Yi-Qiao CHEN Jian-Xin +3 位作者 ZHANG Yong-Gang LI Ai-Zhen K.Frbjdh B.Stotz 《Chinese Physics Letters》 SCIE CAS CSCD 2000年第6期435-437,共3页
Device quality InAsP/InGaAsP strained multiquantum-well(MQW)structures are successfully grown by using gas source molecular beam epitaxy method.The grown MQW and InGaAsP quanternary alloy are characterized by using x-... Device quality InAsP/InGaAsP strained multiquantum-well(MQW)structures are successfully grown by using gas source molecular beam epitaxy method.The grown MQW and InGaAsP quanternary alloy are characterized by using x-ray diffraction,room temperature photoluminescence measurements,confirming that optimum growth condition and high quality material have been obtained for device application.The grown laser structures are processed into ridge waveguide lasers.A threshold current as low as 16mA at 250C for 300μm long device has been obtained.Temperature-dependent light-current measurement shows a characteristic temperature of75K. 展开更多
关键词 INGAASP WAVEGUIDE EPITAXY
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A new method to solve the Reynolds equation including mass-conserving cavitation by physics informed neural networks(PINNs)with both soft and hard constraints
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作者 Yinhu XI Jinhui DENG Yiling LI 《Friction》 SCIE EI CAS CSCD 2024年第6期1165-1175,共11页
In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and t... In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is proposed.The complementarity relationship between the pressure and the void fraction is used.There are several difficulties in problem solving,and the solutions are provided.Firstly,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing error.While the void fraction inequality constraint is considered by using the hard constraint with the max-min function.Secondly,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are provided.Lastly,for avoiding the trivial solution the limitation for the mean value of the void fraction is applied.The results are validated against existing data,and both the incompressible and compressible lubricant are considered.Good agreement can be found for both the domain and domain boundaries. 展开更多
关键词 Reynolds equation mass-conserving cavitation physics informed neural networks hard constraints trivial solution
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