The energy frequency distribution of non-stationary signal could not be got correctly with short-time Fourier transform. A new method was proposed to separate the audio sources from a single mixture based on Hilbert-Huang transform. Hilbert transform combined with Intrinsic Mode Functions (IMFs) constituted Hilbert Spectrum (HS) of mixture, which was a time-frequency representation of a non-stationary signal. The HS of mixture was used to derive the independent source subspaces. The time domain source signals were reconstructed by applying the inverse transformation. The simulated results show that the proposed method is efficient and improves the separation performance. It was observed that HS-based TF representation performed better than using STFT.

Traditional Description Logic (DL) does not fit to handle the problems with incomplete information, tracit knowledge or even contradiction premise. Therefore it is not sufficient to be the logical foundation of the Semantic Web. For this reason, the matter-element and divergence rules of Extenics were introduced to extend the traditional DL. Firstly, the semantic explanation of matterelement was given. Then the matter-element and divergence rules were used to extend the Tableau algorithm to be new Tableau-E algorithm and TableauE′algorithm, thus realizing the extension and consistency checking of Abox (assertion of individuals), and making up the deficiency of traditional DL.