At present, there are some problems when video is played on virtual desktop protocol with partitioning mechanism, such as the video is not smooth and the bandwidth is highly occupied. In this paper, a Video Area Detection Algorithm (VRDA) was proposed based on virtual desktop protocol, called SPICE (Simple Protocol for Independent Computing Environment). Video regions were detected in the process of playing video on virtual desktop protocol, each of which was intercepted as a complete video frame, and decompressed by MPEG4 (Moving Pictures Experts Group-4) video compression algorithm instead of the original compression algorithm MJPEG (Motion JPEG) with lower efficiency. A evaluation metric named DAETD (Difference between Actual and Expected Display Time) was proposed to test the fluency of the improved SPICE, meanwhile, the bandwidth consumption of SPICE was also tested. The experimental results show that the proposed algorithm can improve the video fluency and reduce the network bandwidth consumption.

From the perspective of revenue management, a new approach for order acceptance under uncertainty in Make-to-Oder (MTO) manufacturing using average-reward reinforcement learning was proposed. In order to maximize the average expected revenue, the proposed approach took order types and different combinations of price and leadtime as criteria for the classification of the system states based on multi-level pricing mechanism. The simulation results show that the proposed algorithm has learning and selective ability to accept the order. Comparisons made with other order acceptance policies show the effectiveness of the proposed algorithm in average revenue, accepted order types, and adaptability.