Current topology recommendation methods for DC-DC converters predominantly rely on manual experience, often involving the analysis of performance metrics (either manually or via a computer) and subsequently selecting ...Current topology recommendation methods for DC-DC converters predominantly rely on manual experience, often involving the analysis of performance metrics (either manually or via a computer) and subsequently selecting the most suitable topology to meet specific engineering requirements. However, as the number of available topologies increases and engineering demands vary, these methods are increasingly unable to provide optimal recommendations. To address this limitation, the present study presents an automatic optimization topology recommendation method (AO-TRM) for DC-DC converters that can accommodate a broad range of engineering requirements. The proposed method begins by identifying precise engineering requirements and then progresses through three key stages: topology generation, analysis, and recommendation. Two engineering applications are used as case studies to validate the effectiveness and capabilities of the proposed AO-TRM. From a pool of 1 186 topologies, the proposed method successfully identified and recommended optimal topologies based on specific requirements. Finally, experimental results are presented, demonstrating the capability, efficiency, and cost-effectiveness of the proposed method.展开更多
Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of ...Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.展开更多
基金Supported by the National Science Fund for Distinguished Young Scholars(52325704)the Key Program of National Natural Science Foundation of China(52237008).
文摘Current topology recommendation methods for DC-DC converters predominantly rely on manual experience, often involving the analysis of performance metrics (either manually or via a computer) and subsequently selecting the most suitable topology to meet specific engineering requirements. However, as the number of available topologies increases and engineering demands vary, these methods are increasingly unable to provide optimal recommendations. To address this limitation, the present study presents an automatic optimization topology recommendation method (AO-TRM) for DC-DC converters that can accommodate a broad range of engineering requirements. The proposed method begins by identifying precise engineering requirements and then progresses through three key stages: topology generation, analysis, and recommendation. Two engineering applications are used as case studies to validate the effectiveness and capabilities of the proposed AO-TRM. From a pool of 1 186 topologies, the proposed method successfully identified and recommended optimal topologies based on specific requirements. Finally, experimental results are presented, demonstrating the capability, efficiency, and cost-effectiveness of the proposed method.
基金supported by National Key Basic Research Program of China(973 Program) under Grant No.2014CB340404National Natural Science Foundation of China under Grant Nos.61272111 and 61273216Youth Chenguang Project of Science and Technology of Wuhan City under Grant No. 2014070404010232
文摘Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.
