Presented here are the Generalized BCS Equations incorporating Fermi Energy for the study of the {Δ, Tc, jc(T)} values of both elemental and composite superconductors (SCs) for all T ≤ Tc, where Δ, Tc and jc(T) den...Presented here are the Generalized BCS Equations incorporating Fermi Energy for the study of the {Δ, Tc, jc(T)} values of both elemental and composite superconductors (SCs) for all T ≤ Tc, where Δ, Tc and jc(T) denote, respectively, one of the gap values, the critical temperature and the T-dependent critical current density. This framework, which extends our earlier study that dealt with the {Δ0, Tc, jc(0)} values of an SC, is also shown to lead to T-dependent values of several other related parameters such as the effective mass of electrons, their number density, critical velocity, Fermi velocity (VF), coherence length and the London penetration depth. The extended framework is applied to the jc(T) data reported by Romijn et al. for superconducting Aluminium strips and is shown not only to provide an alternative to the explanation given by them, but also to some novel features such as the role of the Sommerfeld coefficient γ(T) in the context of jc(T) and the role of VF(T) in the context of a recent finding by Plumb et al. about the superconductivity of Bi-2212.展开更多
针对轧机厚度机理模型逐渐不满足现有的控制精度要求的现象,提出了一种基于数据驱动的热轧带钢厚度预测与控制方法.该方法通过对输入空间数据进行在线聚类划分,在各子空间使用最小二乘支持向量机(least square support vector machine,L...针对轧机厚度机理模型逐渐不满足现有的控制精度要求的现象,提出了一种基于数据驱动的热轧带钢厚度预测与控制方法.该方法通过对输入空间数据进行在线聚类划分,在各子空间使用最小二乘支持向量机(least square support vector machine,LS-SVM)在线算法建立非线性模型,并预测系统的输出值,利用预测控制方法求得控制量,根据控制器加权策略得到全局控制量.仿真结果证明了该方法的有效性.展开更多
文摘Presented here are the Generalized BCS Equations incorporating Fermi Energy for the study of the {Δ, Tc, jc(T)} values of both elemental and composite superconductors (SCs) for all T ≤ Tc, where Δ, Tc and jc(T) denote, respectively, one of the gap values, the critical temperature and the T-dependent critical current density. This framework, which extends our earlier study that dealt with the {Δ0, Tc, jc(0)} values of an SC, is also shown to lead to T-dependent values of several other related parameters such as the effective mass of electrons, their number density, critical velocity, Fermi velocity (VF), coherence length and the London penetration depth. The extended framework is applied to the jc(T) data reported by Romijn et al. for superconducting Aluminium strips and is shown not only to provide an alternative to the explanation given by them, but also to some novel features such as the role of the Sommerfeld coefficient γ(T) in the context of jc(T) and the role of VF(T) in the context of a recent finding by Plumb et al. about the superconductivity of Bi-2212.
文摘针对轧机厚度机理模型逐渐不满足现有的控制精度要求的现象,提出了一种基于数据驱动的热轧带钢厚度预测与控制方法.该方法通过对输入空间数据进行在线聚类划分,在各子空间使用最小二乘支持向量机(least square support vector machine,LS-SVM)在线算法建立非线性模型,并预测系统的输出值,利用预测控制方法求得控制量,根据控制器加权策略得到全局控制量.仿真结果证明了该方法的有效性.
