Topological solitons in the CPN model

We investigate the solitons in the CPN supercript stop model in terms of the decomposition of gauge potential. Based on the phi-mapping topological current theory, the charge and position of solitons is determined by the properties of the typical component. Furthermore, the motion and the bifurcation of multi-soliton is discussed. And the knotted solitons in high dimension is explored also.

BRST invariant theory of a generalized 1+1 Dimensional nonlinear sigma model with topological term

We give a generalized Lagrangian density of 1 + 1 Dimensional O( 3) nonlinear sigma model with subsidiary constraints, different Lagrange multiplier fields and topological term, find a lost intrinsic constraint condition, convert the subsidiary constraints into inner constraints in the nonlinear sigma model, give the example of not introducing the lost constraint. N = 0, by comparing the example with the case of introducing the lost constraint, we obtain that when not introducing the lost constraint, one has to obtain a lot of various non-intrinsic constraints. We further deduce the gauge generator, give general BRST transformation of the model under the general conditions. It is discovered that there exists a gauge parameter beta originating from the freedom degree of BRST transformation in a general O( 3) nonlinear sigma model, and we gain the general commutation relations of ghost field.

Asymmetric heat conduction in nonlinear lattices

In this Letter, we conduct an extensive study of the two-segment Frenkel-Kontorova model. We show that the rectification effect of the heat flux reported in recent literature is possible only in the weak interfacial coupling limit. The rectification effect will be reversed when the properties of the interface and the system size change. These two types of asymmetric heat conduction are governed by different mechanisms though both are induced by nonlinearity. An intuitive physical picture is proposed to interpret the reversal of the rectification effect. Since asymmetric heat conduction depends critically on the properties of the interface and the system size, it is probably not an easy task to fabricate a thermal rectifier or thermal diode in practice.

基于克隆选择算法的非线性优化迭代学习控制；Clonal selection algorithm based nonlinear optimal iterative learning control

为实现对模型不确定的有约束非线性系统在特定时间域上输出轨迹的有效跟踪,将改进的克隆选择算法用于求解迭代学习控制中的优化问题。提出基于克隆选择算法的非线性优化迭代学习控制。在每次迭代运算后,一个克隆选择算法用于求解下次迭代运算中的最优输入,另一个克隆选择算法用于修正系统参考模型。仿真结果表明,该方法比GA-ILC具有更快的收敛速度,能够有效处理输入上的约束以及模型不确定问题,通过少数几次迭代学习就能取得满意的跟踪效果。