Concerning the fact that there are few researches on continuous action recognition in the field of action recognition and single algorithms have poor effect on continuous action recognition, a segmentation and recognition method of continuous actions was proposed based on single motion modeling by combining sliding window method and dynamic programming method. Firstly, the single action model was constructed based on the Deep Belief Network and Hidden Markov Model (DBN-HMM). Secondly, the logarithmic likelihood value of the trained action model and the sliding window method were used to estimate the score of the continous action, detecting the initial segmentation points. Thirdly, the dynamic programming method was used to optimize the location of the segmentation points and identify the single action. Finally, the testing experiments of continuous action segmentation and recognition were conducted with an open action database MSR Action3D. The experimental results show that the dynamic programming based on sliding window can optimize the selection of segmentation points to improve the recognition accuracy, which can be used to recognize continuous action.

When a link weight changes in network with Internet Protocol (IP), routing loops may occur. Such loops increase the network latency and cause packet losses, which cannot meet the needs of high-level real-time service. A fast routing micro-loop avoidance algorithm using a weight sequence was proposed. The link weights were reallocated according to the weight sequence so that no loops would occur during convergence phase. In order to calculate the weight sequence, a safety weight interval was defined to describe the condition for avoiding loops, then the safety interval was used to search a set of safety weight ranges. During calculation, the prunning technology was used to reduce search range and improve efficiency. At last, the final weight sequence was obtained from these ranges. The simulation test results using typical network topology algorithm show that in average five times of link weight reallocation can successfully avoid loops in 87% of topologies. In addition, compared with other existing algorithms using iterative adjustment link weights to solve the routing micro-loop, the computational complexity of the proposed algorithm was greatly reduced by an order of magnitude and the computational efficiency was improved by 30%-80%. The proposed algorithm can greatly shorten the calculation time and more efficiently solve the problem of routing micro-loop, which will avoid network latency and packet loss to provide a high level of service quality.