Underwater camera platform's low image stabilization accuracy and poor waterproofness seriously restrict the quality of photos. In order to better cope with underwater camera work, this paper proposes a cable-driven underwater camera stabilized platform. It is a mobile platform driven in parallel by four flexible cables. To improve image stabilization accuracy and anti-interference performance, the system's dynamic model is established in a non-inertial reference frame. And the random water wave interference is modeled. Moreover, a novel double-loop integral-type global fast terminal sliding mode control strategy is designed. Lyapunov stability theory is used to analyze the stability of the strategy. Finally, by comparing with the existing global fast terminal sliding mode controller and traditional sliding mode controller, the designed controller is simulated and verified. The results show that the proposed control strategy not only has the advantages of fast response and robustness, but also has the characteristics of rapid convergence in a finite time and high accuracy. This method can provide a valuable reference for the development of underwater camera stabilized platforms.INDEX TERMS Cable-driven parallel robot, dynamic model, global fast terminal sliding mode control, underwater stabilized platform.

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In Wireless sensor network, node error, energy depletion and other factors will lead to the appearance of hole which will cause network failure. In order to make the network more efficient, repair method based on the hybrid network model is proposed, namely activating a number of non-active nodes and calling mobile node to patching hole. This paper proposes two strategies: (1) Wake up the non-active nodes to reduce the hole area. It is proposed based on convex hull area reduction algorithm greedy algorithm for patching hole. (2) Call mobile node to fill hole gaps. Each mobile node covers more intersection arc of hole. The paper gives a Hole Repair Algorithm (HSNHRA, Hybrid Sensor Network Hole Repair Algorithm). Finally, the simulation results show the effectiveness of the proposed scheme, and the comparative analysis based on the experimental results shows the performance of the proposed scheme. It enables hole completely repaired, and the coverage and utilization of nodes have been improved.

The use of a numerical differentiation formula (NDF) is an excellent method for solving stiff ordinary differential equations. However, the NDF method cannot fully adapt to all stiff systems. An optimal general method for optimizing NDF coefficients using a back-propagation neural network is proposed in this work that can be used for different systems of stiff equations. The ranges of stability of the first-to fourthorder coefficients are obtained by analyzing the definition of stability. In order to solve the different stiff systems by changing the NDF coefficients, the relationship between the eigenvalues of the Jacobian matrix and NDF coefficients is analyzed. The back-propagation neural network is used to describe the relationship between them and predict the optimal parameters of different stiff systems. Compared with the NDF coefficients, the absolute and mean square errors of the numerical solutions are smaller. The simulation results show that the numerical solution's accuracy is higher. Because optimizing the NDF coefficients improves the accuracy of the simulation results, the new approach is more suitable for solving stiff ordinary differential equations.INDEX TERMS Numerical differentiation formulae, stiff ordinary differential equations, optimized coefficients, back-propagation neural network, accuracy.

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