294.3855246 - Số nội bộ: 232
lrc@tvu.edu.vn
Liên hệ:
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Tác giả: Nguyễn Bảo Ân. Prof. Don-Lin Yang (Người hướng dẫn Khoa học). |
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Tóm tắt: Ontology is an effective formal representation of knowledge used commonly in artificial intelligent, semantic web, software engineering, and information retrieval. Typically, ontologies are constructed by domain experts using domain knowledge and relevant documents. However, manual acquisition of ontologies from domain documents consumes large costs. In this research we present a support system for Vietnamese ontology construction using pattern-based mechanisms to discover Vietnamese concepts and conceptual relations from Vietnamese text documents. As there are very few existing taxonomies constructed in Vietnamese, we use non-taxonomy based approach. The combination of association rule mining and lexical pattern based learning was used as our main method of concept extraction and conceptual relation detection. We employ GATE (General Architecture for Text Engineering) as an efficient tool for lexical pattern based extraction by taking advantages of its transducer and JAPE grammar. A Vietnamese smartphone ontology is built to demonstrate the result of our proposed system. |
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Tác giả: Nguyễn Thái Sơn; Prof. Chin-Chen Chang (người hướng dẫn khoa học). |
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Feng Chia University. Năm: 2011. |
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Mô tả: 66Tr, kích thước: 30cm. Số định danh: 004.S463. Vị trí: Phòng Tự nghiên cứu.
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Abstract: Data hiding is designed to solve the problem of secure information exchange through networks such as Internet. In that, reversible data hiding is the good technique for recovering original images without any distortion after secret data are extracted from the stego image. This technique continues to attract attention from many researchers. In this thesis, three reversible data hiding schemesare proposed to embed secret data into the VQ and SMVQ compressed image. The first scheme is a reversible data hiding scheme for VQ indices by depending on locally adaptive coding. This scheme is proposed to improve Chang et al.’s scheme [30] in term of embedding capacity and compression rate. Experimental results confirm that the hiding capacity of the first proposed scheme is around 1.36 bpi in most digital images, which is typically higher than that of Chang et al.’s [30]. Moreover, the average compression rate that can be achieved with this proposed scheme is0.49bpp,which outperforms bothLin and Chang’ s scheme (0.50bpp), Tsai (0.50bpp), Chang et al.’ s scheme (0.53bpp), and Yang and Lin’s scheme (0.53bpp). The second scheme is a reversible data hidingscheme for VQ indices by using absolute difference tree. The second scheme exploits the differences between two adjacent indices to embed secret data. Experimental results show that thisscheme can achieve a higher compression rate than an earlier scheme by Yang and Lin[27]. Our scheme’s average compression rate, 0.44 bpp, outperforms that of Yang and Lin’s scheme, which averages 0.53 bpp. Moreover, the embedding capacity of the second scheme can rise to 1.45 bpi, which also is superior to that of Yang and Lin’s scheme (0.91 bpi) as well as Chang et al.’s scheme [26] (0.74 bpi). The third scheme presents a reversible data hiding in SMVQ compression domain. Based on the combination of SMVQ and SOC, secret bits are embedded to achieve embedding rate while maintaining the high embedding capacity. Experimental results show that when a state codebook sized 64 is used, the average compression rate with our scheme is 0.39bpp, which is much better than can be achieved with the schemes proposed by Chang et al. [51] (0.48bpp), Chang and Wu [52](0.49bpp), or Jo and Kim [53] (0.5bpp). Our scheme offers a slightly higher embedding rate than either Jo and Kim’s scheme or Chang and Wu’s scheme. |
