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Regional Evolution features and Coordinated Development Strategies for Northeast China

Chinese Geographical Science 2006年第4期16卷 378-382页

作者： MEI Lin XU Xiaopo CHEN Mingxiu College of Urban and Environmental Sciences Northeast Normal University Changchun 130024 China College of Humanities and Sciences Northeast Normal University Changchun 130117 China

Northeast China, as the most important production base of agriculture, forestry, and livestock-breeding as well as the old industrial base in the whole country, has been playin a key role in the construction and development of China＇s economy. However, after the policy of reform and open-up was taken in China. the economic development speed and efficiency ofthis area have turned to be evidently lower than those of coastal area and the national average level as well, which is so-called ＇Northeast Phenomenon＇ and ＇Neo-Northeast Phenomenon＇. In terms of those phenomena, this paper firstly reviews the spatial and temporal features of the regional evolution of this area so as to unveil the profound forming causes of ＇Northeast Phenomena＇ and ＇Neo-Northeast Phenomena＇. And then the paper makes a further exploration into the status quo of this region and its forming causes by analyzing its economy gross, industrial structure, product structure, regional eco-categories, etc. At the end of the paper, the authors put forward the basic coordinated development strategies for Northeast China. namely we can revitalize this area by means of adjustment of economic structure, regional coordination, planning urban and rural areas as a whole, institutional innovation, etc.

关键词： regional evolution spatial-temporal feature coordinated development strategy Northeast China

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A Bayes CNN-LSTM approach for aero-engine remaining life predictions

A Bayes CNN-LSTM approach for aero-engine remaining life pre...

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第43届中国控制会议

作者： Jiangyan Zhu Jun Ma Jiande Wu Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology

Remaining life prediction(RUL) plays an important role in prognostic health *** recent years,deep learning-based methods have been widely used for RUL ***,deep learning-based methods all focus on point prediction of RUL,ignoring the uncertainty associated with inaccuracies in the data and *** the prominence of Bayesian methods in uncertainty modelling,this paper uses a Monte Carlo dropout(MC-Dropout)-based Bayesian approximation to capture the epistemic and contingent uncertainty in the prediction process.A Bayes CNN-LSTM-based spatial-temporal feature extraction model is proposed to implement RUL prediction and quantify *** proposed method is also validated on the CMAPSS dataset and compared with CNN,LSTM and CNN-LSTM to verify the effectiveness of Bayesian neural networks.

关键词： Remaining life prediction Bayesian neural networks Epistemic uncertainty spatial-temporal feature

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