Using the trade statistical method based on asset ownership,this paper recalculated Sino-US bilateral trade volume and the result indicates that against traditional trade statistics,Chinese exports to the US reduced b...Using the trade statistical method based on asset ownership,this paper recalculated Sino-US bilateral trade volume and the result indicates that against traditional trade statistics,Chinese exports to the US reduced by an average of 51%while Chinese imports from the US increased by an average of 41%between 2004 and 2010.Balance of trade is in the range between US$2,189 billion of deficit and US$12.77 billion of surplus on the part of China,which are far smaller than the balance of Sino-US trade calculated by traditional statistical method.In order to reflect the real scale of China's foreign trade and effectively respond to Sino-US trade frictions,it is necessary for China to establish its trade statistics system based on asset ownership.展开更多
Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting for...Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.展开更多
Structural collapse under blast loads is a very complex process. For several decades, the engineering profession has considered some approaches to analyze the essential physics of collapse phenomena. Recently, the int...Structural collapse under blast loads is a very complex process. For several decades, the engineering profession has considered some approaches to analyze the essential physics of collapse phenomena. Recently, the interest in this topic has risen to an apex since the collapse of the World Trade Center towers. A two-step analysis approach to capture the characteristics of structural collapse during explosions is proposed. A numerical example is presented to illustrate the performance of the presented approach.展开更多
基金General Program of National Social Sciences Fund "Development and Application Research for theModel of Estimating the Structure of Sino-US Trade Interests(Approval No.13BJL055)"
文摘Using the trade statistical method based on asset ownership,this paper recalculated Sino-US bilateral trade volume and the result indicates that against traditional trade statistics,Chinese exports to the US reduced by an average of 51%while Chinese imports from the US increased by an average of 41%between 2004 and 2010.Balance of trade is in the range between US$2,189 billion of deficit and US$12.77 billion of surplus on the part of China,which are far smaller than the balance of Sino-US trade calculated by traditional statistical method.In order to reflect the real scale of China's foreign trade and effectively respond to Sino-US trade frictions,it is necessary for China to establish its trade statistics system based on asset ownership.
基金the National Natural Science Foundation of China under Grant Nos.70601029 and 70221001the Knowledge Innovation Program of the Chinese Academy of Sciences under Grant Nos.3547600,3046540,and 3047540the Strategy Research Grant of City University of Hong Kong under Grant No.7001806
文摘Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.
基金the National Basic Research Program(973) of China (No. 2002CB412709)the National Natural Science Foundation of China (No. 50378054)
文摘Structural collapse under blast loads is a very complex process. For several decades, the engineering profession has considered some approaches to analyze the essential physics of collapse phenomena. Recently, the interest in this topic has risen to an apex since the collapse of the World Trade Center towers. A two-step analysis approach to capture the characteristics of structural collapse during explosions is proposed. A numerical example is presented to illustrate the performance of the presented approach.
