《日经生物技术》编辑部宣称,根据最近整理的与生物技术有关的新仪器和新试剂调查,表明到1988年春季为止使生物技术研究开发自动化的仪器和试剂有了飞速的增加。选择适当的仪器和试剂是加速研究开发不可缺少的一步。所以人们对于各种产...《日经生物技术》编辑部宣称,根据最近整理的与生物技术有关的新仪器和新试剂调查,表明到1988年春季为止使生物技术研究开发自动化的仪器和试剂有了飞速的增加。选择适当的仪器和试剂是加速研究开发不可缺少的一步。所以人们对于各种产品进行比较的情报的需要,已日益迫切。披露这次调查的详细结果的日经生物技术增刊《生物技术仪器和试剂的最新情报,88》已于4月25日由日经 McGrowhill 公司发行。关于测定 DNA 碱基序列的仪器。展开更多
This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characte...This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.展开更多
Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time se...Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.展开更多
文摘《日经生物技术》编辑部宣称,根据最近整理的与生物技术有关的新仪器和新试剂调查,表明到1988年春季为止使生物技术研究开发自动化的仪器和试剂有了飞速的增加。选择适当的仪器和试剂是加速研究开发不可缺少的一步。所以人们对于各种产品进行比较的情报的需要,已日益迫切。披露这次调查的详细结果的日经生物技术增刊《生物技术仪器和试剂的最新情报,88》已于4月25日由日经 McGrowhill 公司发行。关于测定 DNA 碱基序列的仪器。
基金The National Natural Science Foundation of China(No.61273236)the Natural Science Foundation of Jiangsu Province(No.BK2010239)the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061)
文摘This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.
基金Projects(61271321,61573253,61401303)supported by the National Natural Science Foundation of ChinaProject(14ZCZDSF00025)supported by Tianjin Key Technology Research and Development Program,China+1 种基金Project(13JCYBJC17500)supported by Tianjin Natural Science Foundation,ChinaProject(20120032110068)supported by Doctoral Fund of Ministry of Education of China
文摘Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.
