Aiming at high energy consumption of data center in container cloud, a virtual resource deployment strategy based on host selection algorithm with Power Full (PF) was proposed. Firstly, the allocation and migration scheme of virtual resource in container cloud was proposed and the significant impact of host selection strategy on energy consumption of data center was found. Secondly, by studying the mathematical relationship between the utilization of host and the utilization of containers, between the utilization of host and the utilization of virtual machines and between the utilization of host and energy consumption of data center, a mathematical model of the energy consumption of data center in container cloud was constructed and an optimization objective function was defined. Finally, the function of host's energy consumption was simulated using linear interpolation method, and a host selection algorithm with PF was proposed according to the clustering property of the objects. Simulation results show that compared with First Fit (FF), Least Full (LF) and Most Full (MF), the energy consumption of the proposed algorithm is averagely reduced by 45%,53% and 49% respectively in the computing service of regular tasks and different host clusters; is averagely reduced by 56%,46% and 58% respectively in the computing service of regular tasks and same host cluster; is averagely reduced by 32%,24% and 12% respectively in the computing service of irregular tasks and different host clusters. The results indicate that the proposed algorithm realizes reasonable virtual resource deployment in container cloud, and has advantage in data center energy saving.

To solve the low efficiency of scheduling in injection molding workshop, an improved job-shop scheduling method was proposed based on clustering mold. The production time was reduced by merging jobs with the same tool list, and the energy consumption was reduced through small model injection machine preferred scheduling. The theoretical analysis and the experimental results show that the proposed mehtod can improve productivity and reduce power consumption more than 50%, making injection molding shop job scheduling be more efficient.

Concerning the problem in which the fixed locomotion track captured from human movement can not be used in transformation between key postures for humanoid robot, a method of similar key posture transformation based on hierarchical Option for humanoid robot was proposed. The multi-level dendrogram of key postures was constructed and the difference of key postures was illustrated in respects of similar joint difference, moment total similar difference, period total similar difference. The hierarchical reinforcement Option learning was introduced, in which the sets of key postures and Option actions were constructed. SMDP-Q method tended to be the optimal Option function by the accumulative rewards of key posture difference and the transformations were realized. The experiments show the validity of the method.

Concerning the problems of lacking the mind states and task handling capability, low real-time caused by congested bandwidth and slow learning from negotiation history, a task negotiation model for multiple mobile-robots was proposed. Firstly, the basic moving state of robot was shown. Secondly, the states of mind (belief, goal, intention, knowledge update, etc.) and ability (cooperation, capability judgment, task allocation, etc.) based on π calculus for the negotiation of multiple mobile-robots were defined. Thirdly, the negotiation model of multiple mobile-robots was constructed, in which the negotiation period, negotiation task, utility estimation, negotiation allocation protocol, learning mechanism were studied. Finally, the validity of model was proved through experiments of robot soccer.