The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to elimin...The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.展开更多
Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality cata...Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.展开更多
It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adop...It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adopted Holt-Winter Seasonal Model,ARMA and linear regression model to predict the passenger flow of Sanya Airport from 2017 to 2018.In order to reduce the prediction error and improve the prediction accuracy at meanwhile,the combinatorial weighted method is adopted to predict the data in a combined manner.Upon verification,this method has been proved to be an effective approach to predict the airport passenger flow.展开更多
Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat i...Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island(SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression(MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature(LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model(CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.展开更多
目前,配电网运维检修成本结构模糊、管理相对粗放,易造成地区配置失衡,设备资产运维检修的薄弱环节无法得到合理加强。为此,提出了一种基于贝叶斯平均模型(Bayes model averaging,BMA)-改进灰色关联法的配电网设备资产运检成本影响因素...目前,配电网运维检修成本结构模糊、管理相对粗放,易造成地区配置失衡,设备资产运维检修的薄弱环节无法得到合理加强。为此,提出了一种基于贝叶斯平均模型(Bayes model averaging,BMA)-改进灰色关联法的配电网设备资产运检成本影响因素评价分析方法,从经济因素、设备因素、环境因素和网络结构等方面解析影响运检成本的潜在影响因素,基于BMA方法进行关键变量筛选,并采用改进反熵-灰色关联分析法对影响因素的关联度进行量化分析,找到影响配电网运检成本的薄弱环节。以实际供电区域为例,筛选出影响配电网设备资产运检成本的9项关键因素,得到该供电区域的综合评分和建设薄弱项,并结合区域发展的具体情况,验证了该方法的有效性和合理性。展开更多
基金supported by the National Natural Science Foundation of China(71071077)the Ministry of Education Key Project of National Educational Science Planning(DFA090215)+1 种基金China Postdoctoral Science Foundation(20100481137)Funding of Jiangsu Innovation Program for Graduate Education(CXZZ11-0226)
文摘The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.
文摘Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.
文摘It is crucial to correctly predict the passenger flow of an air route for the construction and development of an airport.Based on the passenger flow data of Sanya Airport from 2008 to 2016,this paper respectively adopted Holt-Winter Seasonal Model,ARMA and linear regression model to predict the passenger flow of Sanya Airport from 2017 to 2018.In order to reduce the prediction error and improve the prediction accuracy at meanwhile,the combinatorial weighted method is adopted to predict the data in a combined manner.Upon verification,this method has been proved to be an effective approach to predict the airport passenger flow.
基金Under the auspices of National Natural Science Foundation of China(No.41977411,41771383)Technology Research Project of the Education Department of Jilin Province(No.JJKH20210445KJ)。
文摘Given the rapid urbanization worldwide, Urban Heat Island(UHI) effect has been a severe issue limiting urban sustainability in both large and small cities. In order to study the spatial pattern of Surface urban heat island(SUHI) in China’s Meihekou City, a combination method of Monte Carlo and Random Forest Regression(MC-RFR) is developed to construct the relationship between landscape pattern indices and Land Surface Temperature(LST). In this method, Monte Carlo acceptance-rejection sampling was added to the bootstrap layer of RFR to ensure the sensitivity of RFR to outliners of SUHI effect. The SHUI in 2030 was predicted by using this MC-RFR and the modeled future landscape pattern by Cellular Automata and Markov combination model(CA-Markov). Results reveal that forestland can greatly alleviate the impact of SUHI effect, while reasonable construction of urban land can also slow down the rising trend of SUHI. MC-RFR performs better for characterizing the relationship between landscape pattern and LST than single RFR or Linear Regression model. By 2030, the overall SUHI effect of Meihekou will be greatly enhanced, and the center of urban development will gradually shift to the central and western regions of the city. We suggest that urban designer and managers should concentrate vegetation and disperse built-up land to weaken the SUHI in the construction of new urban areas for its sustainability.
文摘目前,配电网运维检修成本结构模糊、管理相对粗放,易造成地区配置失衡,设备资产运维检修的薄弱环节无法得到合理加强。为此,提出了一种基于贝叶斯平均模型(Bayes model averaging,BMA)-改进灰色关联法的配电网设备资产运检成本影响因素评价分析方法,从经济因素、设备因素、环境因素和网络结构等方面解析影响运检成本的潜在影响因素,基于BMA方法进行关键变量筛选,并采用改进反熵-灰色关联分析法对影响因素的关联度进行量化分析,找到影响配电网运检成本的薄弱环节。以实际供电区域为例,筛选出影响配电网设备资产运检成本的9项关键因素,得到该供电区域的综合评分和建设薄弱项,并结合区域发展的具体情况,验证了该方法的有效性和合理性。
