Mining top-k granular association rules for recommendation

Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose to mine the top-k granular association rules for each user. First we define three measures of granular association rules. These are the source coverage which measures the user granule size, the target coverage which measures the item granule size, and the confidence which measures the strength of the association. With the confidence measure, rules can be ranked according to their strength. Then we propose algorithms for training the recommender and suggesting items to each user. Experimental are undertaken on a publicly available data set MovieLens. Results indicate that the appropriate setting of granule can avoid over-fitting and at the same time, help obtaining high recommending accuracy.Comment: 12 pages, 5 figures, submitted to Advances in Granular Computing and Advances in Rough Sets, 2013. arXiv admin note: substantial text overlap with arXiv:1305.137

Dynamic modelling of wind turbine and power system for fault ride-through analysis

This paper presents a Simulink model of a wind power system for the holistic analysis of wind turbine and power grid during grid faults, aiming to investigate wind turbine Fault Ride-Through performance. The model comprises a highly detailed dynamic model of a 2MW wind turbine and a generic electrical network model. The simulation result shows the behaviour of both wind turbine and power grid when grid faults occurs. The impact that a grid fault has on wind turbine components and grid transients is illustrated and discussed