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Dai Liang,Derudder Ben,Cao Zhan,Ji Yufan 서울시립대학교 도시과학연구원 2023 도시과학국제저널 Vol.27 No.3
Drawing on data on scientific co-publications derived from the Web of Science for the periods 2002–2006 and 2012–2016, we construct and analyse a key element of China's intercity knowledge networks (CIKNs): scientific collaboration networks. Employing network-analytical and exponential random graph modelling techniques, we examine the evolving structures and driving mechanisms underlying these CIKNs. Our results show that the density of the CIKNs has significantly increased over time. CIKN flows are dense in the Southeastern but sparse in the Northwestern part of China, with the Hu Line acting as a clearly visible border. As the dominant knowledge centre, Beijing is involved in scientific collaboration networks throughout the country, with the diamond-shaped structure anchored by Beijing-Shanghai-Guangzhou-Chengdu becoming evident. We find that preferential attachment and transitivity are significant endogenous processes driving scientific collaboration, while a city's administrative level and R&D investment are the strongest exogenous factors. The impact of GDP and geographical proximity is limited, with institutional proximity being the most sizable of the well-known suite of proximity effects.
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Wei Zhao,Zhi-Gang Yuan,Yue-hong Shen,Yufan Cao,Yimin Wei,Pengcheng Xu,Wei Jian 한국전자통신연구원 2015 ETRI Journal Vol.37 No.4
This paper deals with the problem of blind source separation (BSS), where observed signals are a mixture of delayed sources. In reference to a previous work, when the delay time is small such that the first-order Taylor approximation holds, delayed observations are transformed into an instantaneous mixture of original sources and their derivatives, for which an extended second-order blind identification (SOBI) approach is used to recover sources. Inspired by the results of this previous work, we propose to generalize its first-order Taylor approximation to suit higher-order approximations in the case of a large delay time based on a similar version of its extended SOBI. Compared to SOBI and its extended version for a first-order Taylor approximation, our method is more efficient in terms of separation quality when the delay time is large. Simulation results verify the performance of our approach under different time delays and signal-to-noise ratio conditions, respectively.
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