To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,a...To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,and they are applied to the prediction modeling of refined gasoline blending conservative formula.Firstly,based on historical measured data of refined gasoline blending and according to the characteristics of the data set,we use bootstrapping to divide the training data set and the test data set.Secondly,considering that parameter selection for the network is difficult,particle swarm optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters,greatly improving optimization efficiency.In addition,the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network,so as to construct a more accurate prediction model for conservative formula.Finally,in order to evaluate the effectiveness of this method,the simulation results are compared with those of traditional modeling methods,which show that the DBNs has better prediction performance than error back propagation and support vector machines,and can provide production guidance for refined gasoline blending formula.展开更多
目的探讨呼气末正压(PEEP)对不同呼吸系统顺应性患者中心静脉压(CVP)的影响。方法将2017年11月至2018年2月入住东南大学附属中大医院重症医学科需要监测CVP的55例机械通气患者依据呼吸系统静态顺应性(Crs)分为高顺应性组[Crs≥0.63 ml/(...目的探讨呼气末正压(PEEP)对不同呼吸系统顺应性患者中心静脉压(CVP)的影响。方法将2017年11月至2018年2月入住东南大学附属中大医院重症医学科需要监测CVP的55例机械通气患者依据呼吸系统静态顺应性(Crs)分为高顺应性组[Crs≥0.63 ml/(cm H_2O·kg),1 cm H_2O=0.098 k Pa]和低顺应性组[Crs<0.63 ml/(cm H_2O·kg)],分别观察2组患者PEEP在5、10、15 cm H_2O下的CVP、心率、血压及呼吸力学的变化。结果高顺应性组和低顺应性组患者CVP均随着PEEP增加而升高,差异具有统计学意义(P<0.05)。高顺应性组患者PEEP分别在5、10、15 cm H_2O时,CVP分别为(8.4±2.7)、(10.3±2.5)、(12.2±2.5)cm H_2O,差异具有统计学意义(P<0.05)。低顺应性组PEEP分别在5、10、15 cm H_2O时,CVP分别为(9.6±2.9)、(11.0±2.8)、(12.2±2.7)cm H_2O,差异具有统计学意义(P<0.05)。与低顺应性组患者相比,高顺应性组患者CVP随着PEEP增加而升高更为显著,差异具有统计学意义(P<0.05)。结论对于机械通气患者,PEEP的增加会引起CVP的增加,呼吸系统顺应性高的患者CVP增加更为显著。展开更多
基金support from the National Natural Science Foundation of China (61763027)the Young Ph.D.Program Foundation of Gansu EducationalCommittee (2021QB-044)
文摘To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,and they are applied to the prediction modeling of refined gasoline blending conservative formula.Firstly,based on historical measured data of refined gasoline blending and according to the characteristics of the data set,we use bootstrapping to divide the training data set and the test data set.Secondly,considering that parameter selection for the network is difficult,particle swarm optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters,greatly improving optimization efficiency.In addition,the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network,so as to construct a more accurate prediction model for conservative formula.Finally,in order to evaluate the effectiveness of this method,the simulation results are compared with those of traditional modeling methods,which show that the DBNs has better prediction performance than error back propagation and support vector machines,and can provide production guidance for refined gasoline blending formula.
文摘目的探讨呼气末正压(PEEP)对不同呼吸系统顺应性患者中心静脉压(CVP)的影响。方法将2017年11月至2018年2月入住东南大学附属中大医院重症医学科需要监测CVP的55例机械通气患者依据呼吸系统静态顺应性(Crs)分为高顺应性组[Crs≥0.63 ml/(cm H_2O·kg),1 cm H_2O=0.098 k Pa]和低顺应性组[Crs<0.63 ml/(cm H_2O·kg)],分别观察2组患者PEEP在5、10、15 cm H_2O下的CVP、心率、血压及呼吸力学的变化。结果高顺应性组和低顺应性组患者CVP均随着PEEP增加而升高,差异具有统计学意义(P<0.05)。高顺应性组患者PEEP分别在5、10、15 cm H_2O时,CVP分别为(8.4±2.7)、(10.3±2.5)、(12.2±2.5)cm H_2O,差异具有统计学意义(P<0.05)。低顺应性组PEEP分别在5、10、15 cm H_2O时,CVP分别为(9.6±2.9)、(11.0±2.8)、(12.2±2.7)cm H_2O,差异具有统计学意义(P<0.05)。与低顺应性组患者相比,高顺应性组患者CVP随着PEEP增加而升高更为显著,差异具有统计学意义(P<0.05)。结论对于机械通气患者,PEEP的增加会引起CVP的增加,呼吸系统顺应性高的患者CVP增加更为显著。
