474 research outputs found
Prediction of traffic flow based on deep learning
Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. Although existing DNN models can provide better performance than shallow models, it is still an open question to make full use of the spatio-temporal characteristics of traffic flows to improve performance. We propose a novel deep architecture combining CNN and LSTM for traffic flow (RCF) predictio. The model uses CNN to explore temporal correlation and LSTM to explore spatial correlation . Factors such as weather and historical period data are also added to the feature. Its advantage lies in making full use of the spatial-temporal correlation of traffic data and more comprehensively considered the impact of multiple related factors. Aiming at the difficult problem of obtaining spatial features, a feature selection method based on Random Forests is proposed. We use the gini score to represent the spatial connection between intersections to form a network graph constructed based on data. The experimental results show that based on the random forest feature selection and RCF model, the accuracy of traffic prediction reaches 90%
Superconducting properties of novel BiSe-based layered LaOFBiSe single crystals
F-doped LaOBiSe superconducting single crystals with typical size of
240.2 mm are successfully grown by flux method and the
superconducting properties are studied. Both the superconducting transition
temperature and the shielding volume fraction are effectively improved with
fluorine doping. The LaOFBiSe sample exhibits
zero-resistivity at 3.7 K, which is higher than that of the
LaOFBiSe polycrystalline sample (2.4K). Bulk
superconductivity is confirmed by a clear specific-heat jump at the associated
temperature. The samples exhibit strong anisotropy and the anisotropy parameter
is about 30, as estimated by the upper critical field and effective mass modelComment: 5 pages, 5 figures, 2 tables, accepted for publication in Europhysics
Lette
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