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During the application of solid expandable tubular (SET) technology, the friction between expansion cone and tubular results in high expansion pressure, high operational risk, and severe abrasion of expansion cone over long distance operation. So far, traditional methods have not been able to effectively achieve the purpose of reducing the friction and improving the wear resistance of expansion cone. The bionic non-smooth surface has been studied and confirmed to have characters of reducing friction and wear, and these characteristics have been successfully applied in many fields. In this paper, the bionic non-smooth theory was applied on the surface of expansion cone, and bionic cone was fabricated by laser texturing and carbon based coating. The ground tests proved that the bionic surface treatment on the expansion cone could reduce the expansion pressure by 15% at least and improve the wear resistance.

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We apply the semi-supervised recursive autoencoders (RAE) model for the sentiment classification task of Tibetan short text, and we obtain a better classification effect. The input of the semi-supervised RAE model is the word vector. We crawled a large amount of Tibetan text from the Internet, got Tibetan word vectors by using Word2vec, and verified its validity through simple experiments. The values of parameter α and word vector dimension are important to the model effect. The experiment results indicate that when α is 0.3 and the word vector dimension is 60, the model works best. Our experiment also shows the effectiveness of the semi-supervised RAE model for Tibetan sentiment classification task and suggests the validity of the Tibetan word vectors we trained.