The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve mod...The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve model in consideration of the shortage of current surface subsidence time functions. By analyzing the characteristics of the new time function, we found that it could meet the dynamic process, the velocity change process and the acceleration change process during surface subsidence. Then its rationality had been verified through project cases. The results show that the proposed time function model can give a good reflection of the regularity of surface subsidence in mined-out area and can accurately predict surface subsidence. And the prediction data of the model are a little greater than measured data on condition of proper measured data quantity, which is safety in the engineering. This model provides a new method for the analysis of surface subsidence in mined-out area and reference for future prediction, and it is valuable to engineering application.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,bas...Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,based on which,the time prediction model is constructed for strong earthquakes in the Sichuan-Yunnan region.Prediction analysis shows that there is risk of generating four earthquakes with M ≥ 6.7 in the Sichuan-Yunnan region in the future 16 years,and there are strong signals for M ≥6.7earthquakes for periods 2012-2021 and 2025-2029.The strong earthquakes may occur around 2014-2015,2019 and 2027.展开更多
By generalizing the concept of mean in mathematical statistics to a mean generation function(MGF), the extended matrix of MGF is defined and then a new model of time series is presented.A calculatingseheme for modelli...By generalizing the concept of mean in mathematical statistics to a mean generation function(MGF), the extended matrix of MGF is defined and then a new model of time series is presented.A calculatingseheme for modelling of monovariate time series is deduced cooperating with a normalization procedure of vector and a couple score criterion.An example of climatic prediction for ten-year scale is given in this paper,the tendency of variation for every year can be predicted skillfully with the model.展开更多
When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a ...When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.展开更多
基金supported by the Key Program of the National Natural Science Foundation of China (No. 50334060)
文摘The surface subsidence is a common environmental hazard in mined-out area. Based on careful analysis of the regularity of surface subsidence in mined-out area, we proposed a new time function based on Harris curve model in consideration of the shortage of current surface subsidence time functions. By analyzing the characteristics of the new time function, we found that it could meet the dynamic process, the velocity change process and the acceleration change process during surface subsidence. Then its rationality had been verified through project cases. The results show that the proposed time function model can give a good reflection of the regularity of surface subsidence in mined-out area and can accurately predict surface subsidence. And the prediction data of the model are a little greater than measured data on condition of proper measured data quantity, which is safety in the engineering. This model provides a new method for the analysis of surface subsidence in mined-out area and reference for future prediction, and it is valuable to engineering application.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
文摘Since the 20 thcentury,the time intervals of M ≥6.7 strong earthquakes in the SichuanYunnan region show obvious regularity.Using the years of the strong events,a twodimensional time coordinate system is generated,based on which,the time prediction model is constructed for strong earthquakes in the Sichuan-Yunnan region.Prediction analysis shows that there is risk of generating four earthquakes with M ≥ 6.7 in the Sichuan-Yunnan region in the future 16 years,and there are strong signals for M ≥6.7earthquakes for periods 2012-2021 and 2025-2029.The strong earthquakes may occur around 2014-2015,2019 and 2027.
文摘By generalizing the concept of mean in mathematical statistics to a mean generation function(MGF), the extended matrix of MGF is defined and then a new model of time series is presented.A calculatingseheme for modelling of monovariate time series is deduced cooperating with a normalization procedure of vector and a couple score criterion.An example of climatic prediction for ten-year scale is given in this paper,the tendency of variation for every year can be predicted skillfully with the model.
文摘When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic time series,results are often not sufficiently good because nonlinear variations in the time series.In this paper, a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the time series of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records in the last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR model are much better than that of the AR model.The latter can predict the flood/drought runs with a length only less than two years,while the formal can predict more than three-year length runs.This may be due to the fact that the SETAR model can renew the model according to the run-turning points in the process of predic- tion,though the time series is nonstationary.
