Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information.However,other performance requirements such as life cycle of nodes,stability and security are ...Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information.However,other performance requirements such as life cycle of nodes,stability and security are not set as primary design objectives.This makes data collection ability of vehicular nodes in real application environment inferior.By considering the features of nodes in wireless IoV,such as large scales of deployment,volatility and low time delay,an efficient data collection algorithm is proposed for mobile vehicle network environment.An adaptive sensing model is designed to establish vehicular data collection protocol.The protocol adopts group management in model communication.The vehicular sensing node in group can adjust network sensing chain according to sensing distance threshold with surrounding nodes.It will dynamically choose a combination of network sensing chains on basis of remaining energy and location characteristics of surrounding nodes.In addition,secure data collection between sensing nodes is undertaken as well.The simulation and experiments show that the vehicular node can realize secure and real-time data collection.Moreover,the proposed algorithm is superior in vehicular network life cycle,power consumption and reliability of data collection by comparing to other algorithms.展开更多
To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two diff...To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.展开更多
The existing research of the flatness control for strip cold rolling mainly focuses on the calculation of the optimum adjustment of individual flatness actuator in accordance with the flatness deviation , which is use...The existing research of the flatness control for strip cold rolling mainly focuses on the calculation of the optimum adjustment of individual flatness actuator in accordance with the flatness deviation , which is used for general flatness control.As the basis of flatness control system , the efficiencies of flatness actuators provide a quantitative description to the law of flatness control.Therefore , the determination of actuator efficiency factors is crucial in flatness control.The strategies of closed loop feedback flatness control and rolling force feed-forward control were established respectively based on actuator efficiency factors.For the purpose of obtaining accurate efficiency factors matrixes of flatness actuators , a self-learning model of actuator efficiency factors was established.The precision of actuator efficiency factors can be improved continuously by the input of correlative measured flatness data.Meanwhile , the self-learning model of actuator efficiency factors permits the application of this flatness control for all possible types of actuators and every stand type.The application results show that the self-learning model is capable of obtaining good flatness.展开更多
基金supported by the National Nature Science Foundation of China(Grant61572188)A Project Supported by Scientif ic Research Fund of Hunan Provincial Education Department(14A047)+4 种基金the Natural Science Foundation of Fujian Province(Grant no.2014J05079)the Young and Middle-Aged Teachers Education Scientific Research Project of Fujian province(Grant nos.JA13248JA14254 and JA15368)the special scientific research funding for colleges and universities from Fujian Provincial Education Department(Grant no.JK2013043)the Research Project supported by Xiamen University of Technology(YKJ15019R)
文摘Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information.However,other performance requirements such as life cycle of nodes,stability and security are not set as primary design objectives.This makes data collection ability of vehicular nodes in real application environment inferior.By considering the features of nodes in wireless IoV,such as large scales of deployment,volatility and low time delay,an efficient data collection algorithm is proposed for mobile vehicle network environment.An adaptive sensing model is designed to establish vehicular data collection protocol.The protocol adopts group management in model communication.The vehicular sensing node in group can adjust network sensing chain according to sensing distance threshold with surrounding nodes.It will dynamically choose a combination of network sensing chains on basis of remaining energy and location characteristics of surrounding nodes.In addition,secure data collection between sensing nodes is undertaken as well.The simulation and experiments show that the vehicular node can realize secure and real-time data collection.Moreover,the proposed algorithm is superior in vehicular network life cycle,power consumption and reliability of data collection by comparing to other algorithms.
基金supported by the National Natural Science Foundation of China (60902055)
文摘To improve the reliability and accuracy of the global po- sitioning system （GPS）/micro electromechanical system （MEMS）- inertial navigation system （INS） integrated navigation system, this paper proposes two different methods. Based on wavelet threshold denoising and functional coefficient autoregressive （FAR） model- ing, a combined data processing method is presented for MEMS inertial sensor, and GPS attitude information is also introduced to improve the estimation accuracy of MEMS inertial sensor errors. Then the positioning accuracy during GPS signal short outage is enhanced. To improve the positioning accuracy when a GPS signal is blocked for long time and solve the problem of the tra- ditional adaptive neuro-fuzzy inference system （ANFIS） method with poor dynamic adaptation and large calculation amount, a self-constructive ANFIS （SCANFIS） combined with the extended Kalman filter （EKF） is proposed for MEMS-INS errors modeling and predicting. Experimental road test results validate the effi- ciency of the proposed methods.
基金Item Sponsored by National Science and Technology Support Plan of China ( 2011BAF15B01 , 2011BAF15B03 )Provincial Natural Science Foundation of Hebei of China ( E2011203004 )
文摘The existing research of the flatness control for strip cold rolling mainly focuses on the calculation of the optimum adjustment of individual flatness actuator in accordance with the flatness deviation , which is used for general flatness control.As the basis of flatness control system , the efficiencies of flatness actuators provide a quantitative description to the law of flatness control.Therefore , the determination of actuator efficiency factors is crucial in flatness control.The strategies of closed loop feedback flatness control and rolling force feed-forward control were established respectively based on actuator efficiency factors.For the purpose of obtaining accurate efficiency factors matrixes of flatness actuators , a self-learning model of actuator efficiency factors was established.The precision of actuator efficiency factors can be improved continuously by the input of correlative measured flatness data.Meanwhile , the self-learning model of actuator efficiency factors permits the application of this flatness control for all possible types of actuators and every stand type.The application results show that the self-learning model is capable of obtaining good flatness.
