Focused on the issue that the traditional multiplierless Infinite Impulse Response (ⅡR) digital filters have fixed structure and poor performance, a random structure based design method for multiplierless ⅡR digital filters was proposed. Stable 2-order subsystems with shifters were directly used to design the multiplierless filter structure. Firstly, a set of encoding structures of the multiplierless digital filter were created randomly. Then, Differential Evolution with a Successful-Parent-Selecting Framework (SPS-DE) was used to optimize the multiplierless filter structure. The proposed method realized diversified structure design, and SPS-DE effectively balanced exploration and exploitation due to adopting a Successful-Parent-Selecting framework, which achieved good results in the optimization of the multiplierless filter structure. Compared with state-of-the-art design methods, the passband ripple of the multiplierless ⅡR filter designed in this paper is reduced by 43% and the stopband maximum attenuation is decreased by 40.4%. Simulation results show that the multiplierless ⅡR filter designed by the proposed method meets structural requirements and has good performance.

Image representation is a fundamental issue in face recognition, it is desired to design a discriminative and robust feature to alleviate the effect of illumination, occlusion and pose, etc. Motivated by the convolutional architecture and the advantages (stable result and fast convergence) of K-means algorithm in building filter bank, a very simple yet effective face recognition approach was presented. It consists of three main parts:convolutional filters leraning, nonlinear processing and spatial mean pooling. Firstly, K-means was employed based on preprocessed image patches to construct the convolution filters quickly. Each filter was convoluted with face image to extract sufficient and discriminative feature information. Secondly, the typical hyperbolic tangent function was applied for nonlinear projection on the convoluted features. Thirdly, spatial mean pooling was used to denoise and reduce the dimensions of the learned features. The classification stage only requires a novel linear regression classifier. The experimental results on two widely utlized databases such as AR and ExtendedYaleB demonstrate that the proposed method is simple and effective, and has strong robustness to illumination and occlusion.

Service parts logistics is a complex and stable system. From the view of system dynamics, service parts logistics system was analyzed, a system dynamics model containing a central warehouse and two local spare parts warehouses was established, considering lateral transshipment and vertical transshipment emergency, and then the reasonableness was examined. Three conclusions were obtained through simulation analysis. Firstly, volatility of service parts inventory increases before or after the produce idling period, and the period is a sensitive period for spare parts inventory management. Secondly, the shorter product life cycle, the longer product life, which leads to inventory management more difficult, and the more obvious service parts inventory fluctuates. Last, the more average every local warehouse service, the stronger the collaboration capability of every warehouse and the fewer inventory of service parts warehouse.