A new approach to forecast the middle-lower reaches of the Yangtze River Valley summer rainfall in June―August(JJA) is proposed in this paper.The year-to-year increment of the middle-lower reaches of the Yangtze Rive...A new approach to forecast the middle-lower reaches of the Yangtze River Valley summer rainfall in June―August(JJA) is proposed in this paper.The year-to-year increment of the middle-lower reaches of the Yangtze River Valley is forecasted and hence the summer precipitation could be predicted.In this paper,DY is defined as the difference of a variable between the current year and the preceding year(year-to-year increment).YR denotes the seasonal mean precipitation rate of the middle-lower reaches of the Yangtze River Valley summer rainfall.After analyzing the atmospheric circulation anomalies in winter and spring that were associated with the DY of YR,six key predictors for the DY of YR have been identified.Then the forecast model for the DY of YR is established by using the multi-linear regression method.The predictors for the DY of YR are Antarctic Oscillation,the meridional wind shear between 850hPa and 200hPa over the Indo-Australian region,and so on.The prediction model shows a high skill for the hindcast during 1997-2006,with the average relative root mean square error is at 18%.The model can even reproduce the upward and downward trends of YR during 1984―1998 and 1998―2006.Considering that the current operational forecast models of the summer precipitation over the China region have the average forecast scores at 60%―70% and that the prediction skill for the middle-lower reaches of Yangtze River Valley summer precipitation remains quite limited up to now,thus this new approach to predict the year-to-year increment of the summer precipitation over the Yangtze River Valley(and hence the summer precipitation itself) has the potential to significantly increase the operational forecast skill of the summer precipitation.展开更多
With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual oper...With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.展开更多
基金Supported the National Natural Science Foundation of China (Grant Nos. 40631005,40620130113 and 40523001) the "Korea Enhanced Observing Program ofMeiyu Project"
文摘A new approach to forecast the middle-lower reaches of the Yangtze River Valley summer rainfall in June―August(JJA) is proposed in this paper.The year-to-year increment of the middle-lower reaches of the Yangtze River Valley is forecasted and hence the summer precipitation could be predicted.In this paper,DY is defined as the difference of a variable between the current year and the preceding year(year-to-year increment).YR denotes the seasonal mean precipitation rate of the middle-lower reaches of the Yangtze River Valley summer rainfall.After analyzing the atmospheric circulation anomalies in winter and spring that were associated with the DY of YR,six key predictors for the DY of YR have been identified.Then the forecast model for the DY of YR is established by using the multi-linear regression method.The predictors for the DY of YR are Antarctic Oscillation,the meridional wind shear between 850hPa and 200hPa over the Indo-Australian region,and so on.The prediction model shows a high skill for the hindcast during 1997-2006,with the average relative root mean square error is at 18%.The model can even reproduce the upward and downward trends of YR during 1984―1998 and 1998―2006.Considering that the current operational forecast models of the summer precipitation over the China region have the average forecast scores at 60%―70% and that the prediction skill for the middle-lower reaches of Yangtze River Valley summer precipitation remains quite limited up to now,thus this new approach to predict the year-to-year increment of the summer precipitation over the Yangtze River Valley(and hence the summer precipitation itself) has the potential to significantly increase the operational forecast skill of the summer precipitation.
基金supported by the National Natural Science Foundation of China(Grant Nos.52105515&52188102)the Joint Fund of the Hubei Province of China(Grant No.U20A20294)。
文摘With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.
