Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin n...Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.展开更多
Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall-thicknesses of the car-beam.Finite element(FE)analysis on hydroforming car-beam was carried out,and the re...Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall-thicknesses of the car-beam.Finite element(FE)analysis on hydroforming car-beam was carried out,and the results were optimized according to multiple quality objectives by the grey system theory.With bending angle,bending radius and hight-difference along the axis direction as variables,orthogonal FE analyses were conducted and the minimum and maximum wall-thicknesses of the billets with different sizes were obtained.Taking the minimum and maximum wall-thicknesses as two references,the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi-objectives to a single quality objective,and the average correlation level of every billet facilitated the optimization of size parameters for hydroforming car beam.The trial production showed that the optimization approach satisfied the need of hydroforming car beams.展开更多
Based on grey entropy analysis,the relational grade of operational parameters with aerobic granular sludge's granulation indicators was studied.The former consisted of settling time(ST),aeration time(AT),superfici...Based on grey entropy analysis,the relational grade of operational parameters with aerobic granular sludge's granulation indicators was studied.The former consisted of settling time(ST),aeration time(AT),superficial gas velocity(SGV),height/diameter(H/D) ratio and organic loading rates(OLR),the latter included sludge volume index(SVI) and set-up time.The calculated result showed that for SVI and set-up time,the influence orders and the corresponding grey entropy relational grades(GERG) were:SGV(0.9935) > AT(0.9921) > OLR(0.9894) > ST(0.9876) > H/D(0.9857) and SGV(0.9928) > H/D(0.9914) > AT(0.9909) > OLR(0.9897) > ST(0.9878).The chosen parameters were all key impact factors as each GERG was larger than 0.98.SGV played an important role in improving SVI transformation and facilitating the set-up process.The influence of ST on SVI and set-up time was relatively low due to its dual functions.SVI transformation and rapid set-up demanded different optimal H/D ratio scopes(10-20 and 16-20).Meanwhile,different functions could be obtained through adjusting certain factors' scope.展开更多
Due to the randomness and time dependence of the factors affecting software reliability, most software reliability models are treated as stochastic processes, and the non-homogeneous Poisson process(NHPP) is the most ...Due to the randomness and time dependence of the factors affecting software reliability, most software reliability models are treated as stochastic processes, and the non-homogeneous Poisson process(NHPP) is the most used one.However, the failure behavior of software does not follow the NHPP in a statistically rigorous manner, and the pure random method might be not enough to describe the software failure behavior. To solve these problems, this paper proposes a new integrated approach that combines stochastic process and grey system theory to describe the failure behavior of software. A grey NHPP software reliability model is put forward in a discrete form, and a grey-based approach for estimating software reliability under the NHPP is proposed as a nonlinear multi-objective programming problem. Finally, four grey NHPP software reliability models are applied to four real datasets, the dynamic R-square and predictive relative error are calculated. Comparing with the original single NHPP software reliability model, it is found that the modeling using the integrated approach has a higher prediction accuracy of software reliability. Therefore, there is the characteristics of grey uncertain information in the NHPP software reliability models, and exploiting the latent grey uncertain information might lead to more accurate software reliability estimation.展开更多
For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the backgrou...For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.展开更多
Transformer substations play a major role in power systems.The fault of a transformer substation will jeopardize the safety and effective operation of the power system.The fault signal of a transformer substation incl...Transformer substations play a major role in power systems.The fault of a transformer substation will jeopardize the safety and effective operation of the power system.The fault signal of a transformer substation includes the fault status and fault occurrence time.In this paper,we propose a transformer substation fault prediction method based on big data analysis.Thus,a new transformer substation fault prediction method is developed by combining the advantages of decision tree algorithms and grey system theory to predict the fault status and occurrence time with high accuracy.As a case study,the transformer substation fault signals obtained from a region in the southwest of China are analyzed using the proposed method based on big data.The experimental results confirm that the proposed method achieves high-accuracy fault prediction.展开更多
Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is co...Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and economy.This paper incorporates structure expansion,parameter optimization and rolling mechanism into a system forecasting framework,and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption,improving the accuracy of the traditional grey models.The optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model adaptability.Then,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province.Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026.Compared with other seven benchmark prediction models,the proposed grey system model performs best in terms of both simulation and prediction performance metrics,providing scientific reference for regional energy planning and electricity market operation.展开更多
文摘Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.
基金Supported by the National Key Technology R&D Program of the 11th Five-Year Plan of China(2006BAF04B05)the Natural Science Foundation of Shanxi Province(2010021024-2)
文摘Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall-thicknesses of the car-beam.Finite element(FE)analysis on hydroforming car-beam was carried out,and the results were optimized according to multiple quality objectives by the grey system theory.With bending angle,bending radius and hight-difference along the axis direction as variables,orthogonal FE analyses were conducted and the minimum and maximum wall-thicknesses of the billets with different sizes were obtained.Taking the minimum and maximum wall-thicknesses as two references,the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi-objectives to a single quality objective,and the average correlation level of every billet facilitated the optimization of size parameters for hydroforming car beam.The trial production showed that the optimization approach satisfied the need of hydroforming car beams.
基金supported by the National Natural Science Foundation of China (No. 50878034)
文摘Based on grey entropy analysis,the relational grade of operational parameters with aerobic granular sludge's granulation indicators was studied.The former consisted of settling time(ST),aeration time(AT),superficial gas velocity(SGV),height/diameter(H/D) ratio and organic loading rates(OLR),the latter included sludge volume index(SVI) and set-up time.The calculated result showed that for SVI and set-up time,the influence orders and the corresponding grey entropy relational grades(GERG) were:SGV(0.9935) > AT(0.9921) > OLR(0.9894) > ST(0.9876) > H/D(0.9857) and SGV(0.9928) > H/D(0.9914) > AT(0.9909) > OLR(0.9897) > ST(0.9878).The chosen parameters were all key impact factors as each GERG was larger than 0.98.SGV played an important role in improving SVI transformation and facilitating the set-up process.The influence of ST on SVI and set-up time was relatively low due to its dual functions.SVI transformation and rapid set-up demanded different optimal H/D ratio scopes(10-20 and 16-20).Meanwhile,different functions could be obtained through adjusting certain factors' scope.
基金supported by the National Natural Science Foundation of China (71671090)the Fundamental Research Funds for the Central Universities (NP2020022)the Qinglan Project of Excellent Youth or Middle-Aged Academic Leaders in Jiangsu Province。
文摘Due to the randomness and time dependence of the factors affecting software reliability, most software reliability models are treated as stochastic processes, and the non-homogeneous Poisson process(NHPP) is the most used one.However, the failure behavior of software does not follow the NHPP in a statistically rigorous manner, and the pure random method might be not enough to describe the software failure behavior. To solve these problems, this paper proposes a new integrated approach that combines stochastic process and grey system theory to describe the failure behavior of software. A grey NHPP software reliability model is put forward in a discrete form, and a grey-based approach for estimating software reliability under the NHPP is proposed as a nonlinear multi-objective programming problem. Finally, four grey NHPP software reliability models are applied to four real datasets, the dynamic R-square and predictive relative error are calculated. Comparing with the original single NHPP software reliability model, it is found that the modeling using the integrated approach has a higher prediction accuracy of software reliability. Therefore, there is the characteristics of grey uncertain information in the NHPP software reliability models, and exploiting the latent grey uncertain information might lead to more accurate software reliability estimation.
基金supported by the National Natural Science Foundation of China (71871106)the Blue and Green Project in Jiangsu Provincethe Six Talent Peaks Project in Jiangsu Province (2016-JY-011)
文摘For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.
基金supported by the National Key Research and Development Program of China under Grant No.2017YFB0902000the National Natural Science Foundation of China under Grant No.61503063the Scientific and Technical Supporting Programs of Sichuan Province under Grants No.2016GFW0170 and No.2016GZ0143.
文摘Transformer substations play a major role in power systems.The fault of a transformer substation will jeopardize the safety and effective operation of the power system.The fault signal of a transformer substation includes the fault status and fault occurrence time.In this paper,we propose a transformer substation fault prediction method based on big data analysis.Thus,a new transformer substation fault prediction method is developed by combining the advantages of decision tree algorithms and grey system theory to predict the fault status and occurrence time with high accuracy.As a case study,the transformer substation fault signals obtained from a region in the southwest of China are analyzed using the proposed method based on big data.The experimental results confirm that the proposed method achieves high-accuracy fault prediction.
基金supported in part by the National Social Science Fund of China under Grant No.22FGLB035Fujian Provincial Federation of Social Sciences under Grant No.FJ2023B109.
文摘Accurate and reasonable prediction of industrial electricity consumption is of great significance for promoting regional green transformation and optimizing the energy structure.However,the regional power system is complicated and uncertain,affected by multiple factors including climate,population and economy.This paper incorporates structure expansion,parameter optimization and rolling mechanism into a system forecasting framework,and designs a novel rolling and fractional-ordered grey system model to forecast the industrial electricity consumption,improving the accuracy of the traditional grey models.The optimal fractional order is obtained by using the particle swarm optimization algorithm,which enhances the model adaptability.Then,the proposed model is employed to forecast and analyze the changing trend of industrial electricity consumption in Fujian province.Experimental results show that industrial electricity consumption in Fujian will maintain an upward growth and it is expected to 186.312 billion kWh in 2026.Compared with other seven benchmark prediction models,the proposed grey system model performs best in terms of both simulation and prediction performance metrics,providing scientific reference for regional energy planning and electricity market operation.
