Parameter Design of Current Double Closed Loop for T-Type Three-Level Grid-Connected Inverter

To reduce current harmonics caused by switching frequency,T-type grid-connected inverter topology with LCL filter is adopted.In view of the disadvantages of the slow response speed of the traditional current control and the failure to eliminate the influence of the LCL filter on the grid-connected current by using current PI control alone,a current double closed loop PI current tracking control is proposed.Through the theoretical analysis of the grid-connected inverter control principle,the grid-connected inverter control model is designed,and the transfer functionmodel of each control link is deduced,and the current loop PI regulator is designed at last.The simulation results show that the control strategy is feasible.

Personalized Real-Time Movie Recommendation System:Practical Prototype and Evaluation

With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users1 profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.