The underflow concentration prediction of deep-cone thickener is a difficult problem in paste filling. The existing prediction model only determines the influence of some parameters on the underflow concentration, but...The underflow concentration prediction of deep-cone thickener is a difficult problem in paste filling. The existing prediction model only determines the influence of some parameters on the underflow concentration, but lacks a prediction model that comprehensively considers the thickening process and various factors. This paper proposed a model which analyzed the variation of the underflow concentration from a number of influencing factors in the concentrating process. It can accurately predict the underflow concentration. After preprocessing and feature selection of the history data set of the deep-cone thickener, this model uses the eXtreme gradient boosting(XGBOOST) in machine learning to deal with the relationship between the influencing factors and the underflow concentration, so as to achieve a more comprehensive prediction of the underflow concentration of the deep-cone thickener. The experimental results show that the underflow concentration prediction model based on XGBOOST shows a mean absolute error(MAE) of 0.31% and a running time of 1.6 s on the test set constructed in this paper, which fully meet the demand. By comparing the following three classical algorithms: back propagation(BP) neural network, support vector regression(SVR) and linear regression, we further verified the superiority of XGBOOST under the conditions of this study.展开更多
Baum-Welch algorithm most likely results in underflow in practice. In some literatures, such as 'Scaling' algorithm was introduced to solve the problem. In applications, however, some mistakes were found in th...Baum-Welch algorithm most likely results in underflow in practice. In some literatures, such as 'Scaling' algorithm was introduced to solve the problem. In applications, however, some mistakes were found in the equations presented in these literatures. The practical calculations show that the original algorithm often results in poor or even none convergence and rather higher error rate in speech recognition. The mistakes in these literatures and brings forward the correct equations are analysed. The speech recognition system using the revised algorithm can converge well and has lower error rate.展开更多
[Objection] The aim was to study application of channels constructed wetland in treatment of domestic sewages in new countryside. [Method] Supported by construction of new rural areas project in China, we surveyed on ...[Objection] The aim was to study application of channels constructed wetland in treatment of domestic sewages in new countryside. [Method] Supported by construction of new rural areas project in China, we surveyed on new rural areas in Gannan, Jiangxi Province, summarized source, characteristics and treatment of domestic sewage in countryside, and designed channels constructed wetland for sewage treatment. [Result] The technique is proven effective in sewage treatment. After the technique was conducted for a trial run for four months, removal rates of COD, TP and TN averaged 73.07%, 73.25% and 72.36%, respectively. After contin- uous sampling for six times, effluent COD was analyzed 20-35 mg/L, TP was 0.60- 1.19 mg/L and TN was 6.88-11.21 mg/L, better than that of Standard 1B ruled by Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant. [Conclusion] Thanks for low investment, channels constructed wetland can be built by trans- formation of bottom land, proving a good way for treatment of diffuse pollution source and control of water non-point pollution.展开更多
基金supported by the National Key Research and Development Program of China(2016YFB0700500)the National Science Foundation of China(61572075,61702036)+1 种基金Fundamental Research Funds for the Central Universities(FRF-TP-17-012A1)China Postdoctoral Science Foundation(2017M620619)。
文摘The underflow concentration prediction of deep-cone thickener is a difficult problem in paste filling. The existing prediction model only determines the influence of some parameters on the underflow concentration, but lacks a prediction model that comprehensively considers the thickening process and various factors. This paper proposed a model which analyzed the variation of the underflow concentration from a number of influencing factors in the concentrating process. It can accurately predict the underflow concentration. After preprocessing and feature selection of the history data set of the deep-cone thickener, this model uses the eXtreme gradient boosting(XGBOOST) in machine learning to deal with the relationship between the influencing factors and the underflow concentration, so as to achieve a more comprehensive prediction of the underflow concentration of the deep-cone thickener. The experimental results show that the underflow concentration prediction model based on XGBOOST shows a mean absolute error(MAE) of 0.31% and a running time of 1.6 s on the test set constructed in this paper, which fully meet the demand. By comparing the following three classical algorithms: back propagation(BP) neural network, support vector regression(SVR) and linear regression, we further verified the superiority of XGBOOST under the conditions of this study.
文摘Baum-Welch algorithm most likely results in underflow in practice. In some literatures, such as 'Scaling' algorithm was introduced to solve the problem. In applications, however, some mistakes were found in the equations presented in these literatures. The practical calculations show that the original algorithm often results in poor or even none convergence and rather higher error rate in speech recognition. The mistakes in these literatures and brings forward the correct equations are analysed. The speech recognition system using the revised algorithm can converge well and has lower error rate.
基金Supported by Project of the National Eleventh-Five Year Research Program of China(2008BAD96B04)~~
文摘[Objection] The aim was to study application of channels constructed wetland in treatment of domestic sewages in new countryside. [Method] Supported by construction of new rural areas project in China, we surveyed on new rural areas in Gannan, Jiangxi Province, summarized source, characteristics and treatment of domestic sewage in countryside, and designed channels constructed wetland for sewage treatment. [Result] The technique is proven effective in sewage treatment. After the technique was conducted for a trial run for four months, removal rates of COD, TP and TN averaged 73.07%, 73.25% and 72.36%, respectively. After contin- uous sampling for six times, effluent COD was analyzed 20-35 mg/L, TP was 0.60- 1.19 mg/L and TN was 6.88-11.21 mg/L, better than that of Standard 1B ruled by Discharge Standard of Pollutants for Municipal Wastewater Treatment Plant. [Conclusion] Thanks for low investment, channels constructed wetland can be built by trans- formation of bottom land, proving a good way for treatment of diffuse pollution source and control of water non-point pollution.
