• Asian Pacific Journal of Cancer Prevention
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• v.15 no.23
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• pp.10513-10524
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• 2015

Background: Metastasis is a major reason for poor prognosis in patients with cancer, including hepatocellular carcinoma (HCC). A salient feature is the ability of cancer cells to colonize different organs. Long non-coding RNAs (lncRNAs) play important roles in numerous cellular processes, including metastasis. Materials and Methods: In this study, the lncRNA expression profiles of two HCC cell lines, one with high potential for metastasis to the lung (HCCLM3) and the other to lymph nodes (HCCLYM-H2) were assessed using the Arraystar Human LncRNA Array v2.0, which contains 33,045 lncRNAs and 30,215 mRNAs. Coding-non-coding gene co-expression (CNC) networks were constructed and gene set enrichment analysis (GSEA) was performed to identify lncRNAs with potential functions in organ-specific metastasis. Levels of two representative lncRNAs and one representative mRNA, RP5-1014O16.1, lincRNA-TSPAN8 and TSPAN8, were further detected in HCC cell lines with differing metastasis potential by qRT-PCR. Results: Using microarray data, we identified 1,482 lncRNAs and 1,629 mRNAs that were differentially expressed (${\geq}1.5$ fold-change) between the two HCC cell lines. The most upregulated lncRNAs in H2 were RP11-672F9.1, RP5-1014O16.1, and RP11-501G6.1, while the most downregulated ones were lincRNA-TSPAN8, lincRNA-CALCA, C14orf132, NCRNA00173, and CR613944. The most upregulated mRNAs in H2 were C15orf48, PSG2, and PSG8, while the most downregulated ones were CALCB, CD81, CD24, TSPAN8, and SOST. Among them, lincRNA-TSPAN8 and TSPAN8 were found highly expressed in high lung metastatic potential HCC cells, while lowly expressed in no or low lung metastatic potential HCC cells. RP5-1014O16.1 was highly expressed in high lymphatic metastatic potential HCC cell lines, while lowly expressed in no lymphatic metastatic potential HCC cell lines. Conclusions: We provide the first detailed description of lncRNA expression profiles related to organ-specific metastasis in HCC. We demonstrated that a large number of lncRNAs may play important roles in driving HCC cells to metastasize to different sites; these lncRNAs may provide novel molecular biomarkers and offer a new basis for combating metastasis in HCC cases.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4E
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• pp.156-163
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• MALSORI
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• no.48
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• pp.107-116
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• 2003

This paper deals with a new parameter for voice detection which is used for many areas of speech engineering such as speech synthesis, speech recognition and speech coding. CV (Coefficient of Variation) of speech spectrum as well as other feature parameters is used for the detection of speech. CV is calculated only in the specific range of speech spectrum. Average magnitude and spectral magnitude are also employed to improve the performance of detector. From the experimental results the proposed voice detector outperformed the conventional energy-based detector in the sense of error measurements.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1626-1629
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• 2003

The study is expressed on the web browser using virtual reality of developer manufacturing process and method or manufactured goods conviction for designer and developer with visualized model. This study purpose of basic feature with VRML file and Java and VRML with AWT to get WC code was presented. The study process is equal to the real thing modeling on using Pro/Engineer and exports on the VRML1.0. The condition converts VRML1.0 to VRML2.0 on the CROSS ROADS. And then Cosmo World is coding and manufacturing simulation is expressed on the Cosmo Player.

• The Magazine of the IEIE
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• v.36 no.11
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• pp.66-82
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• 2009

Probabilistic atlases for the human brain structure are more suitable than single brain atlases for representing population anatomy. In this study, we hypothesized the group-specific probabilistic atlas for accurate characteristic feature coding. Our proposed method for a new group comparison study, using a subpopulation specific probabilistic atlas, was based on this hypothesis. A knowledge-based automatic labeling technique using nonlinear registration was applied to encode group-specific regional probabilistic information. Direct atlas-based comparison using volume counting above the probability threshold, distance measurement and correlation analysis were performed based on the probabilistic atlas. Here, we applied this method for comparison between Korean and occidental groups. The results showed that this method could provide simple but intuitive regions of interest-based group analysis for the entire cortex area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.208-223
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• 2015

To solve the problem of face recognition with complex changes and further improve the efficiency, a new near-infrared face recognition algorithm which fuses E-GV-LBP and FKNN algorithm is proposed. Firstly, it transforms near infrared face image by Gabor wavelet. Then, it extracts LBP coding feature that contains space, scale and direction information. Finally, this paper introduces an improved FKNN algorithm which is based on spatial domain. The proposed approach has brought face recognition more quickly and accurately. The experiment results show that the new algorithm has improved the recognition accuracy and computing time under the near-infrared light and other complex changes. In addition, this method can be used for face recognition under visible light as well.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.5
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• pp.1025-1030
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• 2005

With the increasing use of multimedia technologies, image compression requires higher performance as well as new functionality. Specially, in the specific area of still image encoding, a new standard, JPEG2000 was developed. This paper proposed Set Partitioning Sorting Algorithm that uses a method to optimized selection of threshold from feature of wavelet transform coefficients and to removes sign bit in LL area on JPEG2000. Experimental results show the proposed algorithm achieves more improved bit rate.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.69-77
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• 2002

Speaker verification can be classified in two categories, text-dependent speaker verification and text-independent speaker verification. In this paper, we discuss text-dependent speaker verification. Text-dependent speaker verification system determines whether the sound characteristics of the speaker are equal to those of the specific person or not. In this paper we obtain the speaker data using a sound card in various noisy conditions, apply a new Hidden LMS (Least Mean Square) adaptive algorithm to it, and extract LPC (Linear Predictive Coding)-cepstrum coefficients as feature vectors. Finally, we use a competitive learning neural network for speaker verification. The proposed hidden LMS adaptive filter using a neural network reduces noise and enhances features in various noisy conditions. We construct a separate neural network for each speaker, which makes it unnecessary to train the whole network for a new added speaker and makes the system expansion easy. We experimentally prove that the proposed method improves the speaker verification performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2590-2606
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• 2017

Generally, research of classical image classification algorithms assume that training data and testing data are derived from the same domain with the same distribution. Unfortunately, in practical applications, this assumption is rarely met. Aiming at the problem, a domain adaption image classification approach based on multi-sparse representation is proposed in this paper. The existences of intermediate domains are hypothesized between the source and target domains. And each intermediate subspace is modeled through online dictionary learning with target data updating. On the one hand, the reconstruction error of the target data is guaranteed, on the other, the transition from the source domain to the target domain is as smooth as possible. An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition. Experimental results verify the effectiveness of the proposed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.302-308
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• 1988

The paper describes a very simple algorithm for reducing the encoding complexity of vector quantization(VQ), exploiting the feature of a vector currently being encoded. A proposed VQ of 16(=4x4) vector dimension shows a slight performance degradation of about 0.1-1.9dB, however, with only 16-32 among 256 codeword searches, i.e., with just 1/16-1/8 search complexity compared to a full-search VQ. And the proposed VQ scheme is also compared to outperform tree-search VQ with regard to their SNR performance and memory requirement.