• Journal of Life Science
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• v.12 no.1
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• pp.96-105
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• 2002

To investigate expression of the artificial gene encoding a repeated tripeptide lysyl-glutamyl-tryptophan in tobacco plant, the plant binary vector, pART404 has been constructed, which contains the duplicated CaMV 35S promoter, an artificial gene coding for repetitive polymer (Lys-Glu-Trp)$_{64}$, and nopaline synthase (nos) terminator. The recombinant expression vector was introduced in Nicotiana tabacum (var. Xanthi) via Agrobacterium tumefaciens-mediated trans-formation. The transgenic calli selected by kanamycin containing medium were then regenerated to whole plants. Southern blot analysis indicated that five transgenic plants (No. 1, 7, 9, 43, 45) showed the hybridizing signals at 1.1 kb of the expected size on EcoRI digestion and each of the transgenic plants contained 1 or 3 copies of the artificial gene inserted into its genome. By northern blot analysis, the size of the hybridized total RNA was estimated to be approximately 1.2 kb and the RNA appeared generally to have the integrity. Western blot indicated that the protein was detected at the position of 33 kDa and the expression level of the polypeptide in the transgenic plant (No. 45) was measured to approximately 0.1% of the total protein.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.57-62
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• 2005

Microarray gene expression profiling technology is one of the most important research topics in clinical diagnosis of disease. Given thousands of genes, only a small number of them show strong correlation with a certain phenotype. To identify such an optimal subset from thousands of genes is intractable, which plays a crucial role when classify multiple-class genes express models from tumor samples. This paper proposes an efficient classifier design method to simultaneously select the most relevant genes using an intelligent genetic algorithm (IGA) and design an accurate classifier using Support Vector Machine (SVM). IGA with an intelligent crossover operation based on orthogonal experimental design can efficiently solve large-scale parameter optimization problems. Therefore, the parameters of SVM as well as the binary parameters for gene selection are all encoded in a chromosome to achieve simultaneous optimization of gene selection and the associated SVM for accurate tumor classification. The effectiveness of the proposed method IGA/SVM is evaluated using four benchmark datasets. It is shown by computer simulation that IGA/SVM performs better than the existing method in terms of classification accuracy.

• Plant Biotechnology Reports
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• v.4 no.2
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• pp.129-138
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• 2010

An efficient transformation system for high-throughput functional genomic studies of kiwifruit has been developed to overcome the problem of necrosis in Actinidia arguta explants. The system uses Agrobacterium tumefaciens strain EHA105 harbouring the binary vector pART27-10 to inoculate leaf strips. The vector contains neomycin phosphotransferase (nptII) and ${\beta}$-glucuronidase (GUS) (uidA) genes. A range of light intensities and different strengths of Murashige and Skoog (MS) basal salt media was used to overcome the problem of browning and/or necrosis of explants and calli. Callus browning was significantly reduced, resulting in regenerated adventitious shoots when the MS basal salt concentration in the culture medium was reduced to half-strength at low light intensity ($3.4\;{\mu}mol\;m^{-2}\;s^{-1}$) conditions. Inoculated leaf strips produced putative transformed shoots of Actinidia arguta on half-MS basal salt medium supplemented with 3.0 $mg\;l^{-1}$ zeatin, 0.5 $mg\;l^{-1}$ 6-benzyladenine, 0.05 $mg\;l^{-1}$ naphthalene acetic acid, 150 $mg\;l^{-1}$ kanamycin and 300 $mg\;l^{-1}$ $Timentin^{(R)}$. All regenerated plantlets were deemed putativ transgenic by histochemical GUS assay and polymerase chain-reaction analysis.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.13-20
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• 2004

In the real-time automatic face recognition technique, accurate face detection is essential and very important part because it has the effect to face recognition performance. In this paper, we use color information, edge information, and binary information to detect candidate regions of eyes from Input image, and then detect face candidate region using the center point of the detected eyes. We verify both eye candidate region and face candidate region using Support Vector Machines(SVM). It is possible to perform fast and reliable face detection because we can protect false detection through these verification process. From the experimental results, we confirmed the Proposed algorithm in this paper shows excellent face detection rate over 99%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1531-1536
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• 2011

We propose an automatic swearword filter system for online game chatting by using Support Vector Machines(SVM). We collected chatting sentences from online games and tagged them as normal sentences or swearword included sentences. We use n-gram syllables and lexical-part of speech (POS) tags of a word as features and select useful features by chi square statistics. Each selected feature is represented as binary weight and used in training SVM. SVM classifies each chatting sentence as swearword included one or not. In experiment, we acquired overall 90.4% of F1 accuracy.

• ETRI Journal
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• v.32 no.5
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• pp.784-794
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• 2010

Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance-based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the Cohn-Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors.

• Asia pacific journal of information systems
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• v.25 no.3
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• pp.579-596
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• 2015

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

• Korean Journal of Plant Tissue Culture
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• v.21 no.1
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• pp.55-58
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• 1994

Agrobacterium tumefaciens LABA4404 harboring plant binary vector, pBI121, was used for genetic transformation of lettuce (Lactuca sativa t.). Cotyledon segments were infected with A. tumefaciens LBA4404 by cocultivation method and regenerated. Regenerated letture was subject to molecular analyses for integration into plant nuclear genome and expression of ${\beta}$-glucumnidase (GUS) gene. Southern and Northern blot analyses demonstrated that GUS gene was integrated into plant nuclear genome and expressed into its mRNA. The expression of GUS gene into its protein was confirmed by specetrophotometric assay of GUS activity.

• Journal of Korea Multimedia Society
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• v.8 no.8
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• pp.1057-1068
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• 2005

This paper proposes a real-time face detection system which detects multiple faces from low resolution video such as web-camera video. First, It finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next, it generates reduced feature vector for each face region candidate by using principle component analysis. Finally, it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine) based binary classification. According to experiment results, the proposed method achieves real-time face detection from low resolution video. Also, it reduces the false detection rate than existing methods by using PCA and SVM based face classification step.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.93-105
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• 2001

In the lossless step of Lattice Vector Quantization(LVQ), the lattice codewords produced at quantization step are enumerated into radius sequence and index sequence. The radius sequence is run-length coded and then entropy coded, and the index sequence is represented by fixed length binary bits. As bit rate increases, the index bit linearly increases and deteriorates the coding performances. To reduce the index bits across the wide range of bit rates, we developed a novel lattice enumeration algorithm adopting the set partitioning method. The proposed enumeration method shifts down large index values to smaller ones and so reduces the index bits. When the proposed lossless coding scheme is applied to a wavelet based image coding, the proposed scheme achieves more than 10% at bit rates higher than 0.3 bits/pixel over the conventional lossless coding method, and yields more improvement as bit rate becomes higher.