OPAX(Operational-X Transfer Path Analysis)法是一种新型的传递路径分析方法,利用已有的数据拟合出耦合点的激励力,计算出各路径对目标点的贡献量,从而可进行传递路径分析.它具有高效率、高精度的特点.以某中型客车为研究对象,利用OPA...OPAX(Operational-X Transfer Path Analysis)法是一种新型的传递路径分析方法,利用已有的数据拟合出耦合点的激励力,计算出各路径对目标点的贡献量,从而可进行传递路径分析.它具有高效率、高精度的特点.以某中型客车为研究对象,利用OPAX法对其进行了结构传递路径分析,识别出主要贡献路径,然后采取相应措施对悬置进行改进,且取得了良好效果.展开更多
After announcing the goal of building a moderately prosperous society in all respects by 2020, the Chinese leadership also called for a new path of industrialization, putting a premium on quality and new development c...After announcing the goal of building a moderately prosperous society in all respects by 2020, the Chinese leadership also called for a new path of industrialization, putting a premium on quality and new development concepts. Unlike traditional industrialization in the broad or narrow sense, new-type industrialization features synergy between primary, secondary, and tertiary industries, integration between traditional economy and the new economy, environmental protection, technology progress, and innovation. It represents an inclusive approach to industrial development. At the fundamental level, the success of China’s new-type industrialization can be attributed to China’s inclusive learning and innovations.展开更多
Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is propose...Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.展开更多
文摘OPAX(Operational-X Transfer Path Analysis)法是一种新型的传递路径分析方法,利用已有的数据拟合出耦合点的激励力,计算出各路径对目标点的贡献量,从而可进行传递路径分析.它具有高效率、高精度的特点.以某中型客车为研究对象,利用OPAX法对其进行了结构传递路径分析,识别出主要贡献路径,然后采取相应措施对悬置进行改进,且取得了良好效果.
文摘After announcing the goal of building a moderately prosperous society in all respects by 2020, the Chinese leadership also called for a new path of industrialization, putting a premium on quality and new development concepts. Unlike traditional industrialization in the broad or narrow sense, new-type industrialization features synergy between primary, secondary, and tertiary industries, integration between traditional economy and the new economy, environmental protection, technology progress, and innovation. It represents an inclusive approach to industrial development. At the fundamental level, the success of China’s new-type industrialization can be attributed to China’s inclusive learning and innovations.
文摘Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.
