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Shanghai, China

The Shanghai University of Finance and Economics , founded in 1917, is a finance- and economics-oriented research university located in Shanghai, the People's Republic of China. The university is under the direct administration of the Ministry of Education of the People's Republic of China and is among the national Project 211. The Shanghai University of Finance and Economics is a top-ranked research university specializing in economics, finance and business studies. As the oldest financial university in China, SUFE has developed its own spirit over the years., SUFE has enjoyed a reputation of being the best finance and economics universities in China for many years. The university had been consistently ranked No. 1 in the “finance and economics” category in 2003, 2004, 2005, 2007 and 2008 by the Chinese university ranking . Since 2005, the university has achieved substantial improvements in terms of research and started to gain international reputation. According to Tilburg University’s Economics Schools Research Ranking in 2012, SUFE is ranked 120th in the world, 9th in Asia and 3rd in mainland China, only after Tsinghua University and Peking University.In 2005, SUFE introduced the academic tenure and became the first university in China to adopt this system. As of December 2012, there were more than 120 tenure-track faculty members at SUFE, of which most are Ph.D. graduates from renowned overseas universities. Wikipedia.

Wang Q.,Shanghai University of Finance and Economics
Nonlinear Analysis: Real World Applications | Year: 2011

In this paper, the optimal homotopy-analysis method is used to find the travelling-wave solution of the Kawahara equation. The method used here contains three auxiliary convergence-control parameters, which provide us with a simple way to adjust and control the convergence region of the solution. By minimizing the averaged residual error, the optimal convergence-control parameters can be obtained, which give much better approximations than those given by usual homotopy-analysis method. © 2010 Elsevier Ltd. All rights reserved. Source

Wang L.,Shanghai University of Finance and Economics
Neurocomputing | Year: 2013

In this paper, we are concerned with a class of high-order neural networks (HONNs). Rigorous analysis shows that the state components exhibit different dynamical behaviors with respect to external inputs lying in different ranges. And by dividing the index set {1, 2, ...., n} into four subsets Nj,j=1,2,3,4, according to different external input ranges, we can conclude that the HONNs have exact 3#N2 equilibrium points, 2#N2 of them are locally stable and others are unstable, here #N2 represents the number of elements in the subset N2. The results obtained improve and extend some related works. A numerical example is presented to illustrate the effectiveness of our criteria. © 2013 Elsevier B.V. Source

Hu Y.,Shanghai University of Finance and Economics
Energy Policy | Year: 2012

Fast economic growth in China has generated energy and environmental problems. Fixed-asset investments have contributed significantly to energy consumption. In China, an energy conservation assessment (ECA), a mechanism similar to the existing environmental impact assessment (EIA), has been applied to improve the energy efficiency of new fixed-asset investment projects. In this paper the origin and development of the ECA system is analyzed and the major features of ECA are discussed. To identify the success and failure of the ECA system, case studies are analyzed and comparison between ECA and EIA, which has been used in China for over 30 years, is made. Based on the analysis, recommendations are provided for the improvement of the ECA system in China. Despite the ECA system only being established for a relatively short time, it has clearly achieved significant success. With further efforts it could play an important role in achieving the goals of improving China's energy efficiency and reducing green house gas emissions. © 2012 Elsevier Ltd. Source

Chang N.,Shanghai University of Finance and Economics
Energy Policy | Year: 2013

Concerns about the equity and efficiency of current allocation principles related to responsibility for carbon dioxide (CO2) emissions have been presented in the recent literature. The objective of this paper is to design a calculation framework for shared responsibility from the perspective of border tax adjustments. The advantage of this framework is that it makes the shared responsibility principle and border carbon taxation complementary to each other; these are important policies for reducing global CO2 emissions, but they are individually supported by developing and developed countries. As an illustration, the proposed framework is applied to data from China in 2007. The empirical results show that for the Chinese economy as a whole, changing from the production-based criterion to the shared responsibility approach would lead to an 11% decrease in its responsibility for CO2 emissions. Moreover, the differences observed between the production-based criterion and the shared responsibility approach are considerable in several sectors; for example, changing from the production-based criterion to the shared principle would lead to a 60% decrease in the responsibility of the textile sector. © 2013 Elsevier Ltd. Source

Zhang L.-H.,Shanghai University of Finance and Economics
Pattern Recognition Letters | Year: 2011

For linear discriminant analysis (LDA), the ratio trace and trace ratio are two basic criteria generalized from the classical Fisher criterion function, while the orthogonal and uncorrelated constraints are two common conditions imposed on the optimal linear transformation. The ratio trace criterion with both the orthogonal and uncorrelated constraints have been extensively studied in the literature, whereas the trace ratio criterion receives less interest mainly due to the lack of a closed-form solution and efficient algorithms. In this paper, we make an extensive study on the uncorrelated trace ratio linear discriminant analysis, with particular emphasis on the application on the undersampled problem. Two regularization uncorrelated trace ratio LDA models are discussed for which the global solutions are characterized and efficient algorithms are established. Experimental comparison on several LDA approaches are conducted on several real world datasets, and the results show that the uncorrelated trace ratio LDA is competitive with the orthogonal trace ratio LDA, but is better than the results based on ratio trace criteria in terms of the classification performance. © 2010 Elsevier B.V. All rights reserved. Source

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