Aiming at the disadvantages of high fixing cost and partial observability of system environment in the process of repairing Autonomous Underwater Vehicle (AUV) software faults, a method was proposed based on micro-reboot mechanism and Partially Observable Markov Decision Process (POMDP) model for failure repair of AUV. To facilitate the implementation of the fine-grained self-repair micro-reboot strategy, a hierarchical structure was built based on micro-reboot combined with the characteristics of AUV software. Meanwhile, a self-repair model was put forward according to the theory of POMDP. With the goal of minimizing the fixing cost, the repair strategy was solved by Point Based Value Iteration (PBVI) algorithm to allow the repair action to execute in the partially observable environment at a lower cost.The simulation results show that the proposed repairing method can solve the AUV software failures caused by the software-aging and system calls. Compared with two-tier micro-repair strategy and three-tier micro-repair fixing strategy, this method is obviously superior to the contrast method in cumulative fault repair time and operational stability.

A novel kernel-based non-negative sparse representation (KNSR) method was presented for face recognition. The contributions were mainly three aspects: First, the non-negative constraints on representation coefficients were introduced into the Sparse Representation (SR) and the kernel function was exploited to depict non-linear relationships among different samples, based on which the corresponding objective function was proposed. Second, a multiplicative gradient descent method was proposed to solve the proposed objective function, which could achieve the global optimum value in theory. Finally, local binary feature and the Hamming kernel were used to model the non-linear relationships among face samples and therefore achieved robust face recognition. The experimental results on some challenging face databases demonstrate that the proposed algorithm has higher recognition rates in comparison with algorithms of Nearest Neighbor (NN), Support Vector Machine (SVM), Nearest Subspace (NS), SR and Collaborative Representation (CR), and achieves about 99% recognition rates on both YaleB and AR databases.