针对工件内表面开口裂纹单面单侧定量检测困难的问题,采用相控阵超声绝对声时法(Absolute Arrival Time Technique,AATT)从单面单侧对不同厚度试块中的底面人工开口裂纹自身高度测量进行了数值仿真和实验,并对焊缝中的自然裂纹进行了AAT...针对工件内表面开口裂纹单面单侧定量检测困难的问题,采用相控阵超声绝对声时法(Absolute Arrival Time Technique,AATT)从单面单侧对不同厚度试块中的底面人工开口裂纹自身高度测量进行了数值仿真和实验,并对焊缝中的自然裂纹进行了AATT测量。对比AATT测量结果与常规衍射时差法超声检测(Time of Flight Diffraction,TOFD)测量结果并分析,发现AATT从单面单侧对底面开口裂纹进行定量测量可以达到与常规TOFD同样的测量精度。AATT操作简便,可单面单侧定量测量2 mm以上底面开口裂纹的自身高度。展开更多
Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacoki...Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.展开更多
文摘针对工件内表面开口裂纹单面单侧定量检测困难的问题,采用相控阵超声绝对声时法(Absolute Arrival Time Technique,AATT)从单面单侧对不同厚度试块中的底面人工开口裂纹自身高度测量进行了数值仿真和实验,并对焊缝中的自然裂纹进行了AATT测量。对比AATT测量结果与常规衍射时差法超声检测(Time of Flight Diffraction,TOFD)测量结果并分析,发现AATT从单面单侧对底面开口裂纹进行定量测量可以达到与常规TOFD同样的测量精度。AATT操作简便,可单面单侧定量测量2 mm以上底面开口裂纹的自身高度。
基金Project(31200748)supported by the National Natural Science Foundation of China
文摘Due to the nature of ultra-short-acting opioid remifentanil of high time-varying,complex compartment model and low-accuracy of plasma concentration prediction,the traditional estimation method of population pharmacokinetics parameters,nonlinear mixed effects model(NONMEM),has the abuses of tedious work and plenty of man-made jamming factors.The Elman feedback neural network was built.The relationships between the patients’plasma concentration of remifentanil and time,patient’age,gender,lean body mass,height,body surface area,sampling time,total dose,and injection rate through network training were obtained to predict the plasma concentration of remifentanil,and after that,it was compared with the results of NONMEM algorithm.In conclusion,the average error of Elman network is 6.34%,while that of NONMEM is 18.99%.The absolute average error of Elman network is 27.07%,while that of NONMEM is 38.09%.The experimental results indicate that Elman neural network could predict the plasma concentration of remifentanil rapidly and stably,with high accuracy and low error.For the characteristics of simple principle and fast computing speed,this method is suitable to data analysis of short-acting anesthesia drug population pharmacokinetic and pharmacodynamics.
