The destruction of concrete building materials in severely cold regions of the north is more severely affected by freeze-thaw cycles,and the relationship between the mechanical properties and pore structure of concret...The destruction of concrete building materials in severely cold regions of the north is more severely affected by freeze-thaw cycles,and the relationship between the mechanical properties and pore structure of concrete with fine aggregate from municipal solid waste(MSW)incineration bottom ash after freeze-thaw cycles is analyzed under the degree of freeze-thaw hazard variation.In this paper,the gray correlation method is used to calculate the correlation between the relative dynamic elastic modulus,compressive strength,and microscopic porosity parameters to speculate on the most important factors affecting their changes.The GM(1,1)model was established based on the compressive strength of the waste incineration ash aggregate concrete,the relative error between the simulated and actual values in the model was less than 5%,and the accuracy of the model was level 1,indicating that the GM(1,1)model can well reflect the change in the compressive strength of the MSW incineration bottom ash aggregate concrete during freeze-thaw cycles.Using the gray correlation method,the correlation between the relative dynamic elastic modulus,compressive strength,air content,specific surface area,pore spacing coefficient,and pore average chord length was calculated,and the pore spacing coefficient and pore average chord length were determined to be highly correlated with each other.This determination can help analyze and infer the deterioration mechanism of concrete subject to freeze-thaw cycles.These results can provide a theoretical basis for guiding the engineering practice of concrete with fine aggregates of household bottom ash in the northern cold region.展开更多
The gray renewal GM (1,1) landslide prediction model was established by improving the gray model. Based on the established model, the author has made prediction of landslide deformation to the Xiangjiapo landslide and...The gray renewal GM (1,1) landslide prediction model was established by improving the gray model. Based on the established model, the author has made prediction of landslide deformation to the Xiangjiapo landslide and the Lianziya dangerous rock body. The results show that the gray renewal GM (1,1) model can supplement the new information in time and remove the old information which reduces the meaning of the information because of time lapse. Therefore, the model is closer to reality.展开更多
The gray relation among the design parameters of the cumulative charge rod penetrator (CCRP) is analyzed adopting the gray systematic theory and method, and the gray related matrix among the parameters is obtained. Th...The gray relation among the design parameters of the cumulative charge rod penetrator (CCRP) is analyzed adopting the gray systematic theory and method, and the gray related matrix among the parameters is obtained. The result parameters of CCRP and the main parameters of influencing the CCRP molding are obtained utilizing numerical simulation. This lays the foundation of CCRP design and provides a kind of effective research method.展开更多
This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h ...This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.展开更多
In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in whi...In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.展开更多
It is adequate to use the gray theory for modeling and forecasting short-term calamity series. The forecast of calamity gray series is equivalent to predicting an extraordinary event in nature. In order to look for th...It is adequate to use the gray theory for modeling and forecasting short-term calamity series. The forecast of calamity gray series is equivalent to predicting an extraordinary event in nature. In order to look for the regularity, the calamity date series, created from the threshold for a fixed time-interval series, are studied. In this paper, the Hurst exponent is applied to defining the long-term memory effect of the simulated calamity series, and is tested for the feasibility of using it as pre-requisite information before the gray modeling and forecasting. Based on the fractional Brownian motion (fBm) model, the time series with a definite length or quantity of data are derived assuming that various kinds of memory effect exist. Different threshold values are defined to yield or to analogize the calamity date series that are required in the prediction of the gray calamity events. After case study, both of the simulated and real seismic data show that the Hurst exponents are greater than 0.5 and, therefore, indicate that the long-term memory ef-fect exists. The correlation between the Hurst exponent and the gray modeling parameter, a, provides criteria for the classification of the forecast.展开更多
Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from...Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks.展开更多
基金supported by the National Natural Science Foundation of China Project 51868058,52068058Inner Mongolia Natural Science Foundation 2018MS05011Inner Mongolia“Grassland Talent”CYYC5039.
文摘The destruction of concrete building materials in severely cold regions of the north is more severely affected by freeze-thaw cycles,and the relationship between the mechanical properties and pore structure of concrete with fine aggregate from municipal solid waste(MSW)incineration bottom ash after freeze-thaw cycles is analyzed under the degree of freeze-thaw hazard variation.In this paper,the gray correlation method is used to calculate the correlation between the relative dynamic elastic modulus,compressive strength,and microscopic porosity parameters to speculate on the most important factors affecting their changes.The GM(1,1)model was established based on the compressive strength of the waste incineration ash aggregate concrete,the relative error between the simulated and actual values in the model was less than 5%,and the accuracy of the model was level 1,indicating that the GM(1,1)model can well reflect the change in the compressive strength of the MSW incineration bottom ash aggregate concrete during freeze-thaw cycles.Using the gray correlation method,the correlation between the relative dynamic elastic modulus,compressive strength,air content,specific surface area,pore spacing coefficient,and pore average chord length was calculated,and the pore spacing coefficient and pore average chord length were determined to be highly correlated with each other.This determination can help analyze and infer the deterioration mechanism of concrete subject to freeze-thaw cycles.These results can provide a theoretical basis for guiding the engineering practice of concrete with fine aggregates of household bottom ash in the northern cold region.
文摘The gray renewal GM (1,1) landslide prediction model was established by improving the gray model. Based on the established model, the author has made prediction of landslide deformation to the Xiangjiapo landslide and the Lianziya dangerous rock body. The results show that the gray renewal GM (1,1) model can supplement the new information in time and remove the old information which reduces the meaning of the information because of time lapse. Therefore, the model is closer to reality.
文摘The gray relation among the design parameters of the cumulative charge rod penetrator (CCRP) is analyzed adopting the gray systematic theory and method, and the gray related matrix among the parameters is obtained. The result parameters of CCRP and the main parameters of influencing the CCRP molding are obtained utilizing numerical simulation. This lays the foundation of CCRP design and provides a kind of effective research method.
基金supported by the National Natural Science Foundation of China(7117111370901041)
文摘This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.
文摘In order to know the ventilating capacity of imperial smelt furnace(ISF), and increase the output of plumbum, an intelligent modeling method based on gray theory and artificial neural networks(ANN) is proposed, in which the weight values in the integrated model can be adjusted automatically. An intelligent predictive model of the ventilating capacity of the ISF is established and analyzed by the method. The simulation results and industrial applications demonstrate that the predictive model is close to the real plant, the relative predictive error is 0.72%, which is 50% less than the single model, leading to a notable increase of the output of plumbum.
文摘It is adequate to use the gray theory for modeling and forecasting short-term calamity series. The forecast of calamity gray series is equivalent to predicting an extraordinary event in nature. In order to look for the regularity, the calamity date series, created from the threshold for a fixed time-interval series, are studied. In this paper, the Hurst exponent is applied to defining the long-term memory effect of the simulated calamity series, and is tested for the feasibility of using it as pre-requisite information before the gray modeling and forecasting. Based on the fractional Brownian motion (fBm) model, the time series with a definite length or quantity of data are derived assuming that various kinds of memory effect exist. Different threshold values are defined to yield or to analogize the calamity date series that are required in the prediction of the gray calamity events. After case study, both of the simulated and real seismic data show that the Hurst exponents are greater than 0.5 and, therefore, indicate that the long-term memory ef-fect exists. The correlation between the Hurst exponent and the gray modeling parameter, a, provides criteria for the classification of the forecast.
基金supported by the Graduate Innovation Project of Beijing Jiaotong University(No.2020YJS098)。
文摘Vehicle information on high-speed trains can not only determine whether the various parts of the train are working normally,but also predict the train’s future operating status.How to obtain valuable information from massive vehicle data is a difficult point.First,we divide the vehicle data of a high-speed train into 13 subsystem datasets,according to the functions of the collection components.Then,according to the gray theory and the Granger causality test,we propose the Gray-Granger Causality(GGC)model,which can construct a vehicle information network on the basis of the correlation between the collection components.By using the complex network theory to mine vehicle information and its subsystem networks,we find that the vehicle information network and its subsystem networks have the characteristics of a scale-free network.In addition,the vehicle information network is weak against attacks,but the subsystem network is closely connected and strong against attacks.
