With the development of technology, the performance of vessel equipment is improved, the structure is more complicated, the automation level is enhanced, the source needed by maintenance is increased and the outlay is...With the development of technology, the performance of vessel equipment is improved, the structure is more complicated, the automation level is enhanced, the source needed by maintenance is increased and the outlay is rising day by day. For these questions, this paper analyzes the factors that affect the outlay of equipment maintenance, and describes the computational principle of the BP (back propagation) artificial neural network and its applications in the maintenance of naval ship and craft. Finally, a dynamic investment prediction model of outlay for the military equipment maintenance is designed. It is important for decreasing the entire ilfe period outlay and drawing up the maintenance plan and programming to analyze the position and action of maintenance outlay in entire life period outlay.展开更多
The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed in...The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14×6×1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator.展开更多
According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the de...According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value.展开更多
Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, ...Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, and it is a significant basis for realizing regional sustainable development. This paper, on the basis of the academician Sun Tiehang's "unification of three" for the eco-city construction, established ecological carrying capacity evaluation indexes for the traditional industrial and mining city—Huainan City; and applied GM–BP neural network coupling model for the dynamic evolution and prediction of ecological carrying capacity of Huainan City in the future decade. The results showed that ecological carrying capacity index of Huainan would be 2.13 by 2025, higher than the loadable state 1, so the ecological carrying capacity would keep in the over-loaded level, but the over-loaded degree would be lower than the current. Carrying capacity of arable land, energy and water resources contribute greatly to the improvement of ecological carrying capacity, thus it is imperative to adjust this unreasonable and unsustainable ecological consumption relationship, enhance environmental protection awareness and high-efficiency utilization of resources, and take an energy-saving and intensive development path.展开更多
Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale a...Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.展开更多
There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good ...There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting.展开更多
In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to pred...In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to predict the continuous B-cell epitopes, and finally the predictive model for the B-cells epitopes was established. Comparing with the other predictive models, the prediction performance of this model is more excellent (AUC = 0.723). For the purpose of verifying the performance of the model, the prediction to the SWISS PROT NUMBER: P08677 was carried on, and the satisfying results were obtained.展开更多
文摘With the development of technology, the performance of vessel equipment is improved, the structure is more complicated, the automation level is enhanced, the source needed by maintenance is increased and the outlay is rising day by day. For these questions, this paper analyzes the factors that affect the outlay of equipment maintenance, and describes the computational principle of the BP (back propagation) artificial neural network and its applications in the maintenance of naval ship and craft. Finally, a dynamic investment prediction model of outlay for the military equipment maintenance is designed. It is important for decreasing the entire ilfe period outlay and drawing up the maintenance plan and programming to analyze the position and action of maintenance outlay in entire life period outlay.
文摘The HCl emission characteristics of typical municipal solid waste(MSW) components and their mixtures have been investigated in a Φ150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent(CaO) is added during the incineration process. Based on these experimental results, a 14×6×1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator.
基金Supported by the National Natural Science Foundation of China(50778107)
文摘According to the neural network theory, combined with the technical characteristicsof the hole-by-hole detonation technology, a BP network model on the forecast forblasting vibration parameters was built.Taking the deep hole stair demolition in a mine asan experimental object and using the raw information and the blasting vibration monitoringdata collected in the process of the hole-by-hole detonation, carried out some training andapplication work on the established BP network model through the Matlab software, andachieved good effect.Also computed the vibration parameter with the empirical formulaand the BP network model separately.After comparing with the actual value, it is discoveredthat the forecasting result by the BP network model is close to the actual value.
基金Sponsored by National Natural Science Foundation of China(41101566)
文摘Evaluation of ecological carrying capacity is an important method of analyzing regional sustainable development, study on ecological carrying capacity is to settle the contradictions between resource and environment, and it is a significant basis for realizing regional sustainable development. This paper, on the basis of the academician Sun Tiehang's "unification of three" for the eco-city construction, established ecological carrying capacity evaluation indexes for the traditional industrial and mining city—Huainan City; and applied GM–BP neural network coupling model for the dynamic evolution and prediction of ecological carrying capacity of Huainan City in the future decade. The results showed that ecological carrying capacity index of Huainan would be 2.13 by 2025, higher than the loadable state 1, so the ecological carrying capacity would keep in the over-loaded level, but the over-loaded degree would be lower than the current. Carrying capacity of arable land, energy and water resources contribute greatly to the improvement of ecological carrying capacity, thus it is imperative to adjust this unreasonable and unsustainable ecological consumption relationship, enhance environmental protection awareness and high-efficiency utilization of resources, and take an energy-saving and intensive development path.
基金National Natural Science Foundation of China(No.41874125,No.41430322).
文摘Accurate shear wave velocity is very important for seismic inversion.However,few researches in the shear wave velocity in organic shale have been carried out so far.In order to analyze the structure of organic shale and predict the shear wave velocity,the authors propose two methods based on petrophysical model and BP neural network respectively,to calculate shear wave velocity.For the method based on petrophysics model,the authors discuss the pore structure and the space taken by kerogen to construct a petrophysical model of the shale,and establish the quantitative relationship between the P-wave and S-wave velocities of shale and physical parameters such as pore aspect ratio,porosity and density.The best estimation of pore aspect ratio can be obtained by minimizing the error between the predictions and the actual measurements of the P-wave velocity.The optimal porosity aspect ratio and the shear wave velocity are predicted.For the BP neural network method that applying BP neural network to the shear wave prediction,the relationship between the physical properties of the shale and the elastic parameters is obtained by training the BP neural network,and the P-wave and S-wave velocities are predicted from the reservoir parameters based on the trained relationship.The above two methods were tested by using actual logging data of the shale reservoirs in the Jiaoshiba area of Sichuan Province.The predicted shear wave velocities of the two methods match well with the actual shear wave velocities,indicating that these two methods are effective in predicting shear wave velocity.
基金This research was funded by the National Natural Science Foundation of China(No.61304208)Scientific Research Fund of Hunan Province Education Department(18C0003)+2 种基金Research project on teaching reform in colleges and universities of Hunan Province Education Department(20190147)Changsha City Science and Technology Plan Program(K1501013-11)Hunan Normal University University-Industry Cooperation.This work is implemented at the 2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open project,grant number 20181901CRP04.
文摘There are many influencing factors of fiscal revenue,and traditional forecasting methods cannot handle the feature dimensions well,which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend.The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso.It can reduce the dimensionality of the original data,make separate predictions for each explanatory variable,and then use neural networks to make multivariate predictions,thereby making up for the shortcomings of traditional methods of insufficient prediction accuracy.In this paper,we took the financial revenue data of China’s Hunan Province from 2005 to 2019 as the object of analysis.Firstly,we used Lasso regression to reduce the dimensionality of the data.Because the grey prediction model has the excellent predictive performance for small data volumes,then we chose the grey prediction model to obtain the predicted values of all explanatory variables in 2020,2021 by using the data of 2005–2019.Finally,considering that fiscal revenue is affected by many factors,we applied the BP neural network,which has a good effect on multiple inputs,to make the final forecast of fiscal revenue.The experimental results show that the combined model has a good effect in financial revenue forecasting.
文摘In order to identify continuous B-cell epitopes effectively and to increase the success rate of experimental identification, the modified Back Propagation artificial neural network (BP neural network) was used to predict the continuous B-cell epitopes, and finally the predictive model for the B-cells epitopes was established. Comparing with the other predictive models, the prediction performance of this model is more excellent (AUC = 0.723). For the purpose of verifying the performance of the model, the prediction to the SWISS PROT NUMBER: P08677 was carried on, and the satisfying results were obtained.