The further development of catalytic elements has been plagued by activation and binary problems.The automatic shift model that has emerged in recent years helps components achieve full range.However,the detection dat...The further development of catalytic elements has been plagued by activation and binary problems.The automatic shift model that has emerged in recent years helps components achieve full range.However,the detection data still remains unstable in the shift area(7%∼13%).This paper proposes a Catalytic Combustion and Thermal Conductivity(CCTC)model for the specified range,which can be explained fromtwo aspects based on the existing methods.On the one hand,it uses iterative location search to process heterogeneous data,judges the prediction position of data points,and then givesweight evaluation.On the other hand,it corrects the abnormal points,determines the abnormal points in the horizontal direction,and gives the replacement value through the data of adjacent points.The experimental results show that the CCTC model reduces the sum of variance from 17 of the automatic shift model to 13,and the comparison of experimental variance is reduced by 23%.In the full-scale real-time data,the experimental variance of CCTC model and automatic shift model is reduced by 18%.In conclusion,CCTC is a cross section stability framework for full-scale methane measurement,in which the specified heterogeneous combination and anomaly point correction methods improve the stability.展开更多
In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in popularity.There are numero...In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in popularity.There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices.Using regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing information.It is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price movement.In today’s forecasting studies,ensemble learning is a very prevalent and well-liked study methodology.The regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse models.The Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate models.The Average Model has a strong applicability in the field of regression prediction and significantly increases computational efficiency.The technique is also easier to replicate and very effective in regression investigations.Before using regression processing techniques,this work creates an average of different regression models using the AM(Average Model)algorithm in a novel way.By evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price predictions.The experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accuracy is greatly improved compared with other algorithms,and has a good experimental effect in house price prediction.展开更多
基金supported by Major Science and Technology Project of Sichuan Province[No.2022YFG0315,2022YFG0174]Sichuan Gas Turbine Research Institute stability support project of China Aero Engine Group Co.,Ltd[GJCZ-2019-59]Key project of Chengdu[No.2019-YF09-00044-CG].
文摘The further development of catalytic elements has been plagued by activation and binary problems.The automatic shift model that has emerged in recent years helps components achieve full range.However,the detection data still remains unstable in the shift area(7%∼13%).This paper proposes a Catalytic Combustion and Thermal Conductivity(CCTC)model for the specified range,which can be explained fromtwo aspects based on the existing methods.On the one hand,it uses iterative location search to process heterogeneous data,judges the prediction position of data points,and then givesweight evaluation.On the other hand,it corrects the abnormal points,determines the abnormal points in the horizontal direction,and gives the replacement value through the data of adjacent points.The experimental results show that the CCTC model reduces the sum of variance from 17 of the automatic shift model to 13,and the comparison of experimental variance is reduced by 23%.In the full-scale real-time data,the experimental variance of CCTC model and automatic shift model is reduced by 18%.In conclusion,CCTC is a cross section stability framework for full-scale methane measurement,in which the specified heterogeneous combination and anomaly point correction methods improve the stability.
基金This work was supported in part by Sichuan Science and Technology Program(Grant No.2022YFG0174)in part by the Sichuan Gas Turbine Research Institute stability support project of China Aero Engine Group Co.,Ltd(Grant No.GJCZ-0034-19)。
文摘In the field of computer research,the increase of data in result of societal progress has been remarkable,and the management of this data and the analysis of linked businesses have grown in popularity.There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices.Using regression methods in machine learning to segment the data set,examine the major factors affecting it,and forecast home prices is the most popular method for examining pricing information.It is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price movement.In today’s forecasting studies,ensemble learning is a very prevalent and well-liked study methodology.The regression integration computation of large housing datasets can use a lot of computer resources as well as computation time,and ensemble learning uses more resources and calls for more machine support in integrating diverse models.The Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models,combining the best benefits of separate models.The Average Model has a strong applicability in the field of regression prediction and significantly increases computational efficiency.The technique is also easier to replicate and very effective in regression investigations.Before using regression processing techniques,this work creates an average of different regression models using the AM(Average Model)algorithm in a novel way.By evaluating essential models with 90%accuracy,this technique significantly increases the accuracy of house price predictions.The experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms,and the prediction accuracy is greatly improved compared with other algorithms,and has a good experimental effect in house price prediction.
