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		题名	作者	年代	出处	被引量
	1	Late Quaternary (30.7–9.0 cal ka BP) vegetation history in Central Asia inferred from pollen records of Lake Balikun, northwest China显示文摘	Cheng-Bang An Shi-Chen Tao Jiaju Zhao Fa-Hu Chen Yanbin Lv Weimiao Dong Hu Li Yongtao Zhao Ming Jin Zongli Wang	2013	Journal of Paleolimnology2013，，2：	1
	2	Dust variation recorded by lacustrine sediments from arid Central Asia since ~<ce:hsp sp='0.10'/>15 cal ka BP and its implication for atmospheric circulation显示文摘	Cheng-Bang An Jiaju Zhao Shichen Tao Yanbin Lv Weimiao Dong Hu Li Ming Jin Zongli Wang	2010	Quaternary Research2010，，3：	1
	3	Intelligent model prediction of fluctuant increase of maximum electric field in XLPE insulation using long short-term memory network algorithm显示文摘The electric field distortion due to space charge accumulations plays a significant role in the ageing,degradation and breakdown in failure of HVDC power cables.Currently,limited experimental results of the electric field dominated by space charges are insufficient to diagnose the power cables.This paper proposes an improved long short-term memory network(LSTM)model for predicting the fluctuating maximum electric field(Emax)in cross-linked polyethylene(XLPE)cable insulation.The various Emax data derived from the complex space charge behaviours were measured using the pulsed electroacoustic method.The model uses regularisation and dropout feedback in the LSTM unit,reducing the phenomenon of over-fitting due to the limited data.It enhances the prediction accuracy and ability of long time prediction by improving the prediction of Emax with the non-linear fluctuation.The predicted Emax approaches 190 kV/mm under 150 kV/mm and 60°C after 2 h.The predicted large variation in Emax under 120 kV/mm and 20°C after 4 h ranges from 130 to 160 kV/mm.It indicates high electric stress in the cable insulation during continuous operation.The proposed LSTM model is of great importance to guide the diagnosis of cable degradation in HVDC power cables.	Weiwang Wang Jiaju Lv Yong Feng Xinyuan Li Shengtao Li	2023	High Voltage2023，8，1：	0

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