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Xizhen Liang,Jian Wang,Tao Zhou,Hao Duan,Yueming Zhou 한국화학공학회 2015 Korean Journal of Chemical Engineering Vol.32 No.8
A modified model is established according to the analysis of energy balance acting on an agglomerate of binary mixed nanoparticles in a vibrated fluidized bed (VFB). The sizes of agglomerates of binary mixed nanoparticles are calculated with this model. The average agglomerate size estimated by the model of energy balance decreases with increasing superficial gas velocity. The vibration frequency had a comparatively significant impact on agglomerate sizes that seemed to change regularly and decreased with higher frequency. Both of the experimental and theoretical results showed that vibration led to a smaller agglomerate size, and the average agglomerate sizes calculated by this model provided the closest fit to those determined experimentally.
Social-Aware Collaborative Caching Based on User Preferences for D2D Content Sharing
( Can Zhang ),( Dan Wu ),( Liang Ao ),( Meng Wang ),( Yueming Cai ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.3
With rapid growth of content demands, device-to-device (D2D) content sharing is exploited to effectively improve the service quality of users. Considering the limited storage space and various content demands of users, caching schemes are significant. However, most of them ignore the influence of the asynchronous content reuse and the selfishness of users. In this work, the user preferences are defined by exploiting the user-oriented content popularity and the current caching situation, and further, we propose the social-aware rate, which compre-hensively reflects the achievable contents download rate affected by the social ties, the caching indicators, and the user preferences. Guided by this, we model the collaborative caching problem by making a trade-off between the redundancy of caching contents and the cache hit ratio, with the goal of maximizing the sum of social-aware rate over the constraint of limited storage space. Due to its intractability, it is computationally reduced to the maximi-zation of a monotone submodular function, subject to a matroid constraint. Subsequently, two social-aware collaborative caching algorithms are designed by leveraging the standard and continuous greedy algorithms respectively, which are proved to achieve different approxima-tion ratios in unequal polynomial-time. We present the simulation results to illustratethe performance of our schemes.