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A Phase-Shift Modulation Strategy for a Bidirectional CLLC Resonant Converter
Wenmin Hua,Hongfei Wu,Zhiyuan Yu,Yan Xing,Kai Sun 전력전자학회 2019 ICPE(ISPE)논문집 Vol.2019 No.5
A novel phase-shift modulation strategy is applied to a bidirectional CLLC resonant converter (RC). Different from the well-known pulse-frequency modulation strategy, the switching frequency of the CLLC RC is constant, which is beneficial for optimal design and control of the converter. Theoretical analysis proves that voltage gain is independent on load and resonant tank. As a result, topology is optimized with ensuring symmetry of converter. Simulation model is built in Simulink and a 1kW prototype is made. Simulation and experimental results verify the voltage transfer ratio. Dynamic response with load stepping up and down is stable and rapid. In addition, soft-switching of all the power switches can be achieved to reduce switching losses and improve efficiency. Detailed operation principles and characteristics of the modulation strategy are analyzed and verified with experimental results.
( Jianli Zhao ),( Zhengbin Fu ),( Qiuxia Sun ),( Sheng Fang ),( Wenmin Wu ),( Yang Zhang ),( Wei Wang ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.5
Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.