Traditional bipedal robot walking is controlled by trajectory tracking， while human walking is in the passive state in most of the time. Aiming at the problem that the semi-passive bipedal robot with variable length telescopic legs starts to walk from a static condition， a starting and walking human-like control method was proposed. Firstly， a serial elasticity driven Bipedal Spring-Loaded Inverted Pendulum （B-SLIP） model was used. Then， the Lagrange method was used to establish the walking dynamics equation. With the self-stability of the proposed model， in the double support stage， the energy error Proportional-Integral （PI） feedback control and lazy control method were used to control the hind leg extension and contraction. In the single support stage， the swing-leg swing back method was used to control the height and forward speed of the robot. Simulation results show that the proposed control strategy can enable the bipedal robot to realize the starting and walking process on the horizontal plane， and the corresponding control system has anti-interference ability against external period disturbance force.

In order to solve the problem of large correlation coefficients when obtaining the observation matrix from the optimized Gram matrix in Compressive Sensing (CS), based on the optimized Gram matrix obtained in the existing algorithm, the value of the row vector in the observation matrix when the objective function takes the extreme value was obtained based on the extreme value of the equivalent transformation of the objective function, the analytic formula of the row vector when the objective function takes the extreme value was elected from the values mentioned above by Singular Value Decomposition (SVD) of the error matrix, then a new observation matrix optimization algorithm was put forward by using the idea of optimizing the target matrix row by row in the K-SVD algorithm, the observation matrix was optimized iteratively row by row, and the difference between the correlations of the observation matrix generated by adjacent two iterations was taken as a measure of whether or not the iteration is completed. Simulation results show that the relevance between the observation matrix and the sparse base in the improved algorithm is better than that in the original algorithm, thus reducing the reconstruction error.

Considering massive scale of cloud storage solutions, the traditional data distribution strategy confronts challenges to improve scalability and flexibility. This paper proposed an efficient data distribution strategy. Based on consistent hashing algorithm, the strategy introduced the virtualization technology, and employed virtual node to improve load balance. Moreover, the strategy used a new capacity-aware method to improve the performance of the cloud storage system. The evaluation experiments demonstrate that the proposed data distribution strategy improves system performance in both homogeneous and heterogeneous distributed storage architectures.