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Magnetic Particle Imaging (MPI) is a new imaging technology that enables the illustration of SPIO tracer positions with gradient and drive magnetic fields. Currently, the gradient magnetic field of the 1D-MPI system is mainly generated by a permanent magnet. In addition, the gradient value is unchangeable and the size of the detected particles is limited in this way, although it can also create a gradient magnetic field that forms a zero magnetic field point (FFP) in MPI. Moreover, the driving magnetic field is mostly generated in the form of a solenoid. Unfortunately, this approach creates a closed space, which is a pain for claustrophobic patients. In this paper, a new structure is proposed to uses a solenoid to replace the permanent magnet in order to detect more sized particles. The gradient value of the magnetic field can be adjusted with controlling the value of magnetic field of the electromagnetic (EM) coil generated by different current intensity. On the other hand, another open structure coil is designed which can drive FFP in an open test environment instead of the solenoid. The feasibility of the two coil structures proposed in this paper are verified by magnetic field simulation studies.

For chaotic economic systems with multi-parameters, we introduce the predictive control into the control of chaotic economic systems and propose a small parameters perturbations control algorithm based on predictive control. By making small control parameters perturbations, which is achieved by solving a constrained nonlinear programming problem, the chaotic motion in an unknown chaotic system is stabilized onto the desired unstable fixed point. The proposed control system has much faster response, so it requires much shorter time to stabilize the chaotic system. The algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulation demonstrate the effectiveness of the approach.