Zhang Zhisheng

Speedy Stiffness modeling and designing of plane supporting spring

Electronic instruments for aeronautics and astronautics usually endure stronger impact or vibration under hostile environments, which results in the storage of more deformation energy within the isolators. Another necessary requirement comes from the limitation of the assembly space where the larger isolator is unallowable. Compared with other commonly used vibration isolators, a plane supporting spring is characterized by its greater deformation energy, lower stiffness, better static and dynamic characteristics. However; there exist some difficulties in modeling and designing the plane supporting spring. To solve the problems, a speedy modeling program of APDL (ANSYS Parameter Design Language) based on FEA software ANSYS is developed, which can rapidly establish a series of FEM analytical models of plane supporting spring with single or several spiral lines. Furthermore, the differences of the stiffness in each direction of plane supporting spring are analyzed on the basis of rapid modeling. In particular; axial stiffness and radius stiffness, especially the influencing factors of radius stiffness, are studied carefully. And finally, different spring assembly effects on radius stiffness are deeply explored.

Bayesian image blind restoration based on differential evolution optimization

The difficulty of image blind restoration is the lack of sufficient information of the point spread function to cause the ill-posed problem. In order to achieve satisfactory results of image restoration, and at the same time to speed up the restoration, a Bayesian image blind restoration method based on differential evolution optimization is proposed. Firstly, the Gauss model and Laplace model are introduced as the priori model of the original image and the point spread function. Secondly, the unknown parameters are described by Jeffrey prior distribution. Finally, the differential evolution optimization method is used to alternately estimate the original image; the point spread function and the optimal value of the parameter through iteration. Experimental results prove the effectiveness of the proposed method, and it can get better restoration results compared with other similar methods.

Noise-insensitive and edge-preserving resolution upconversion scheme for digital image based on the spatial general autoregressive model

This study proposes an edge-preserving image interpolation algorithm for both clean images and polluted images. First, the structure of an image window is learnt adaptively by using a spatial general autoregressive (SGAR) model that is a uniform expression for both linear and non-linear autoregressive (AR) models. Parameters of the SGAR model are estimated in a moving window in the input low-resolution image by using the robust generalised M-estimator. Next, the interpolation model is established from the learnt model and a new feedback mechanism in accordance with the residual sum of squares minimisation principle. Finally, the gradient simulated annealing algorithm is used to solve the interpolation model, which can rapidly converge to the global optimum in probability with the help of gradient information. Experiments have been performed using worldwide datasets to evaluate the performance of the authors method. The results demonstrate that their method is superior to a recent AR model-based method and is bicubic, especially when images are polluted by noise such as Gaussian noise, Poisson noise, impulse noise, or a combination of these.