The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredi...The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.展开更多
Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. ...Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.展开更多
Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water l...Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.展开更多
Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their dail...Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.展开更多
Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of meth...Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.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
The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on expe...The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.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
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
Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power i...Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.展开更多
Farmers have to finish their harvesting with high efficiency,because of time and cost.However,farmers are lacking knowledge and information required for selecting suitable combine harvesters and giving the conditions ...Farmers have to finish their harvesting with high efficiency,because of time and cost.However,farmers are lacking knowledge and information required for selecting suitable combine harvesters and giving the conditions of their rice fields,because both information factors(combine harvester and field condition)impact the field capacity.The field capacity model was generated from combine harvesters with the Thai Hom Mali rice variety(KDML-105).Therefore,this study aimed to determine the prediction model for effective field capacity to combine harvesters when harvesting the Thai Hom Mali rice variety(KDML-105).The methods began by collecting data of 15 combine harvesters,such as field,crop,and machine conditions and operating times;to generate the prediction model for the KDML-105 variety.The prediction model was then validated using 12 combine harvesters that were collected similarly to the model creation.The results showed a root mean square error(RMSE)of 0.24 m^(2)/s for the model.The prediction model can be applied for farmers to select the proper combine harvesters and give their field conditions.展开更多
基金supported by the Youth Program of National Natural Science Foundation of China(No.52404343)the General Program of National Natural Science Foundation of China(No.52274326)+2 种基金the Fundamental Research Funds for the Central Universities,China(No.N2425031)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553)the Liaoning Province Science and Technology Plan Joint Program,China(Key Research and Development Program Project)(No.2023JH2/101800058).
文摘The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.
基金National Natural Science Foundations of China(Nos.41172236,41402243,and 40911120044)Basic Research Project of Jilin University,China(No.450060491448)
文摘Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.
文摘Water level prediction of river runoff is an important part of hydrological forecasting.The change of water level not only has the trend and seasonal characteristics,but also contains the noise factors.And the water level prediction ability of a single model is limited.Since the traditional ARIMA(Autoregressive Integrated Moving Average)model is not accurate enough to predict nonlinear time series,and the WNN(Wavelet Neural Network)model requires a large training set,we proposed a new combined neural network prediction model which combines the WNN model with the ARIMA model on the basis of wavelet decomposition.The combined model fit the wavelet transform sequences whose frequency are high with the WNN,and the scale transform sequence which has low frequency is fitted by the ARIMA model,and then the prediction results of the above are reconstructed by wavelet transform.The daily average water level data of the Liuhe hydrological station in the Chu River Basin of Nanjing are used to forecast the average water level of one day ahead.The combined model is compared with other single models with MATLAB,and the experimental results show that the accuracy of the combined model is improved by 7%compared with the traditional wavelet network under the appropriate wavelet decomposition function and the combined model parameters.
基金National Natural Science Foundation of China(Grant No.51775478)Hebei Provincial Natural Science Foundation of China(Grant Nos.E2016203173,E2020203078).
文摘Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.
文摘Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.
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This work was supported by the Scientific Research Projects of Tianjin Educational Committee(No.2020KJ029)。
文摘The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.
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.
基金supported by the National Natural Science Foundation of China (No. 51507141)the National Key Research and Development Program of China (No. 2016YFC0401409)the Shaanxi provincial education office fund (No. 17JK0547)
文摘Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.
文摘Farmers have to finish their harvesting with high efficiency,because of time and cost.However,farmers are lacking knowledge and information required for selecting suitable combine harvesters and giving the conditions of their rice fields,because both information factors(combine harvester and field condition)impact the field capacity.The field capacity model was generated from combine harvesters with the Thai Hom Mali rice variety(KDML-105).Therefore,this study aimed to determine the prediction model for effective field capacity to combine harvesters when harvesting the Thai Hom Mali rice variety(KDML-105).The methods began by collecting data of 15 combine harvesters,such as field,crop,and machine conditions and operating times;to generate the prediction model for the KDML-105 variety.The prediction model was then validated using 12 combine harvesters that were collected similarly to the model creation.The results showed a root mean square error(RMSE)of 0.24 m^(2)/s for the model.The prediction model can be applied for farmers to select the proper combine harvesters and give their field conditions.
