Guidance problems with flight time constraints are considered in this article. A new virtual leader scheme is used for design of guidance laws with time constraints. The core idea of this scheme is to adopt a virtual ...Guidance problems with flight time constraints are considered in this article. A new virtual leader scheme is used for design of guidance laws with time constraints. The core idea of this scheme is to adopt a virtual leader for real missiles to convert a guidance problem with time constraints to a nonlinear tracking problem,thereby making it possible to settle the problem with a variety of control methods. A novel time-constrained guidance （TCG） law, which can control the flight time of missiles to a prescribed time,is designed by using the virtual leader scheme and stability method. The TCG law is a combination of the well-known proportional navigation guidance（PNG） law and the feedback of flight time error. What＇ s more, this law is free of singularities and hence yields better performances in comparison with optimal guidance laws with time constraints. Nonlinear simulations demonstrate the effectiveness of the proposed law.展开更多
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 mo...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.展开更多
基金National Natural Science Foundation of China(60674103,60975073)National High-tech Research and Develop-ment Program of China (2006AA04Z260)+1 种基金Research Foundation forDoctoral Program of Higher Education of China (20091102110006 )Aeronautical Science Foundation of China(2008ZC13011)
文摘Guidance problems with flight time constraints are considered in this article. A new virtual leader scheme is used for design of guidance laws with time constraints. The core idea of this scheme is to adopt a virtual leader for real missiles to convert a guidance problem with time constraints to a nonlinear tracking problem,thereby making it possible to settle the problem with a variety of control methods. A novel time-constrained guidance （TCG） law, which can control the flight time of missiles to a prescribed time,is designed by using the virtual leader scheme and stability method. The TCG law is a combination of the well-known proportional navigation guidance（PNG） law and the feedback of flight time error. What＇ s more, this law is free of singularities and hence yields better performances in comparison with optimal guidance laws with time constraints. Nonlinear simulations demonstrate the effectiveness of the proposed law.
文摘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.
