Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data....Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.展开更多
In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural...In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation (PID) control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.展开更多
目的·探讨基于EMS[环境管理(environment management,E)、用药指导(medicine direction,M)与自我监测(self monitoring,S)]管理模式的延续性护理在学龄前喘息性疾病儿童中的应用效果。方法·选取2019年12月至2020年11月,在上...目的·探讨基于EMS[环境管理(environment management,E)、用药指导(medicine direction,M)与自我监测(self monitoring,S)]管理模式的延续性护理在学龄前喘息性疾病儿童中的应用效果。方法·选取2019年12月至2020年11月,在上海交通大学医学院附属儿童医院呼吸科收治的67例0~6岁喘息性疾病患儿,按照随机数字表分为观察组33例和对照组34例,其中失访3例,最终每组32例。观察组采用基于EMS管理模式的延续性护理,对照组给予常规护理和出院电话随访。2组患儿出院后1、3、6个月随访评估儿童呼吸和哮喘测试(Test for Respiratory and Asthma Control in Kids,TRACK)结果、喘息复发情况;出院后6个月随访采用支气管哮喘用药依从性评分表(Medication Adherence Report Scale for Asthma,MARS-A)和护理工作满意度调查表评估用药依从性及护理工作满意度。结果·2组患儿人口学特征及临床基线特征差异无统计学意义。重复测量方差分析结果显示,时间、组别、组别×时间的交互作用对TRACK总分的影响均有统计学意义;出院后1、3、6个月,观察组TRACK总分均显著高于对照组(均P=0.000);2组患儿TRACK总分均随时间推移逐渐上升(P=0.000)。观察组1、3、6个月随访发现喘息复发率分别为25.0%、18.7%、9.4%,均显著低于对照组(分别为50.0%、43.7%、31.3%,均P<0.05);广义估计方程分析显示组间比较差异有统计学意义(P=0.013),观察组干预效果优于对照组(OR=0.292)。出院后6个月观察组MARS-A得分为(4.519±0.395)分,显著高于对照组[(3.994±0.739)分,P=0.001]。护理工作满意度调查结果显示,观察组显著高于对照组(P=0.000)。患儿MARS-A得分与护理工作满意度呈中度正相关(r=0.389,P=0.001)。结论·基于EMS管理模式的延续性护理可显著提高学龄前喘息性疾病儿童的用药依从性和喘息控制水平,明显降低喘息复发率,以及提高护理工作满意度。展开更多
基金This work is supported by the NationalNatural Science Foundation of China(No.52075350)the Major Science and Technology Projects of Sichuan Province(No.2022ZDZX0001)the Special City-University Strategic Cooperation Project of Sichuan University and Zigong Municipality(No.2021CDZG-3).
文摘Regarding the spatial profile extraction method of a multi-field co-simulation dataset,different extraction directions,locations,and numbers of profileswill greatly affect the representativeness and integrity of data.In this study,a multi-field co-simulation data extractionmethod based on adaptive infinitesimal elements is proposed.Themultifield co-simulation dataset based on related infinitesimal elements is constructed,and the candidate directions of data profile extraction undergo dimension reduction by principal component analysis to determine the direction of data extraction.Based on the fireworks algorithm,the data profile with optimal representativeness is searched adaptively in different data extraction intervals to realize the adaptive calculation of data extraction micro-step length.The multi-field co-simulation data extraction process based on adaptive microelement is established and applied to the data extraction process of the multi-field co-simulation dataset of the sintering furnace.Compared with traditional data extraction methods for multi-field co-simulation,the approximate model constructed by the data extracted from the proposed method has higher construction efficiency.Meanwhile,the relative maximum absolute error,root mean square error,and coefficient of determination of the approximationmodel are better than those of the approximation model constructed by the data extracted from traditional methods,indicating higher accuracy,it is verified that the proposed method demonstrates sound adaptability and extraction efficiency.
文摘In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation (PID) control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.
文摘目的·探讨基于EMS[环境管理(environment management,E)、用药指导(medicine direction,M)与自我监测(self monitoring,S)]管理模式的延续性护理在学龄前喘息性疾病儿童中的应用效果。方法·选取2019年12月至2020年11月,在上海交通大学医学院附属儿童医院呼吸科收治的67例0~6岁喘息性疾病患儿,按照随机数字表分为观察组33例和对照组34例,其中失访3例,最终每组32例。观察组采用基于EMS管理模式的延续性护理,对照组给予常规护理和出院电话随访。2组患儿出院后1、3、6个月随访评估儿童呼吸和哮喘测试(Test for Respiratory and Asthma Control in Kids,TRACK)结果、喘息复发情况;出院后6个月随访采用支气管哮喘用药依从性评分表(Medication Adherence Report Scale for Asthma,MARS-A)和护理工作满意度调查表评估用药依从性及护理工作满意度。结果·2组患儿人口学特征及临床基线特征差异无统计学意义。重复测量方差分析结果显示,时间、组别、组别×时间的交互作用对TRACK总分的影响均有统计学意义;出院后1、3、6个月,观察组TRACK总分均显著高于对照组(均P=0.000);2组患儿TRACK总分均随时间推移逐渐上升(P=0.000)。观察组1、3、6个月随访发现喘息复发率分别为25.0%、18.7%、9.4%,均显著低于对照组(分别为50.0%、43.7%、31.3%,均P<0.05);广义估计方程分析显示组间比较差异有统计学意义(P=0.013),观察组干预效果优于对照组(OR=0.292)。出院后6个月观察组MARS-A得分为(4.519±0.395)分,显著高于对照组[(3.994±0.739)分,P=0.001]。护理工作满意度调查结果显示,观察组显著高于对照组(P=0.000)。患儿MARS-A得分与护理工作满意度呈中度正相关(r=0.389,P=0.001)。结论·基于EMS管理模式的延续性护理可显著提高学龄前喘息性疾病儿童的用药依从性和喘息控制水平,明显降低喘息复发率,以及提高护理工作满意度。