• Korean Journal of Plant Resources
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• v.17 no.2
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• pp.161-168
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• 2004

Optimal regeneration conditions of ginseng transformants were studied. It has been known that Ferritin Light Heavy Chain (FLHC) gene remove the several heavy metal by combination, store and transport. To obtain the ginseng tolerant to heavy metal, binary vector was introduced in Agrobacterium by tri-parental mating and then Agrobacterium tumefaciens MP90/FLHC was selected on the AB media and MS media containing kanamycin. Explants were co-cultured with Agrobacterium tumefaciens MP90/FLHC, which contained NPT II as a selectable marker, tadpole ferritin heavy chain (FLHC) gene and human ferritin light chain gene and then a number of embryos were induced. The induced embryo transferred to shooting media consisting of MS medium supplemented with GA 10 mg/L. As a result of examination that induced the normal growth of transfomants, transformants showed the equivalent growth in both root and shoot on the media containing the 1/3 MS.

• KSBB Journal
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• v.5 no.3
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• pp.247-253
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• 1990

These studies were carried out to obtain the transformant from carrot cells by using binary vector pGA472 with NPT II gene to confer kanamycin resistance in the plant cells. The binary vector pGA472 was mobilized from E. coli MC1000 into A. tumefaciens strains isolated in the Korea, C23-1. K29-1, and disarmed Ti-plasmid PC2760, and A28l using a tri-parental mating method with E. coli HB101/pRK2013. Transconjugants, C23-1/pGA472, K29-1/pGA472, PC2 760/pGA472 and A28l/pGA472 were obtaind on the minimum AB media containing tetracycline and kanamycin, were comfirmed to hold the Ti-plasmid and pGA472 binary vector on the 0.7% agarose gel. Transformed carrot calli were initiated on the MS media supplemented with l00$\mu\textrm{g}$/ml kanamycin and 250$\mu\textrm{g}$/ml carbenicillin after co-cultivation of carrot explant and transconjugant Agrobacteria. Selected callus was grown vigouousley for subculture on the medium containing 100$\mu\textrm{g}$/ml kanamycin, thus indication that the selected callus was transformed with NPT II gene.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.369-372
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• 2003

In this paper, we propose impr oved binary tree vector quantization based on spatial sensitivity which is one of the human visual properties. We combine the weights based on spatial masking effect according to changes of three primary colors in blocks of images with the process of splitting nodes using eigenvector in binary tree vector quantization. The test results show that the proposed method generates the quantized images with fine color and performs better than the conventional method in terms of clustering the similar regions. Also the proposed method can get the better result in subjective qualify test and PSNR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.285-295
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• 2008

Internet routers perform packet forwarding which determines a next hop for each incoming packet using the packet's destination IP address. IP address lookup becomes one of the major challenges because it should be performed in wire-speed for every incoming packet under the circumstance of the advancement in link technologies and the growth of the number of the Internet users. Many binary search algorithms have been proposed for fast IP address lookup. However, tree-based binary search algorithms are usually unbalanced, and they do not provide very good search performance. Even for binary search algorithms providing balanced search, they have drawbacks requiring prefix duplication. In this paper, a new binary search algorithm which provides the balanced binary search and the number of its entries is much less than the number of original prefixes. This is possible because of composing the binary search tree only with disjoint prefixes of the prefix set. Each node has a prefix vector that has the prefix nesting information. The number of memory accesses of the proposed algorithm becomes much less than that of prior binary search algorithms, and hence its performance for IP address lookup is considerably improved.

• Journal of Korean Society of Forest Science
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• v.77 no.2
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• pp.199-207
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• 1988

Three clones of Populus alba ${\times}$ P. grandidentata have been tested for susceptibility to Agrobacterium tumefaciens strains A281 and A348. We determined the optimum concentration of kanamycin sulfate for effective selection of leaf disc-derived, transgenic tissues transformed using Agrobacterium binary vector pGA472 containing a neomycin phosphotransferase gene (NPT-II) which confers kanamycin resistance. Of the wild type Ti plasmids contained by the two Agrobacterium strains, pTiBo542 of strain A281 appears to be best suited to serve as a helper plasmid for binary vector systems. A relatively low concentration (10mg/l) of kanamycin sulfate inhibited adventitious shoot initiation from leaf discs on regeneration medium. Transformed kanamycin-resistant calli were obtained by culturing Agrobacterium inoculated leaf discs on selective regeneration medium. The transformed kanamycin-resistant calli continued to grow on regeneration media supplemented with kanamycin sulfate to levels of 50 and 200mg/l. The growth of non-co-cultivated control calli was severely inhibited on regeneration medium containing 50mg/l kanamycin sulfate.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• International journal of advanced smart convergence
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• v.5 no.3
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• pp.8-15
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• 2016

In recent years, one of deep learning models called Deep Belief Network (DBN) which formed by stacking restricted Boltzman machine in a greedy fashion has beed widely used for classification and recognition. With an ability to extracting features of high-level abstraction and deal with higher dimensional data structure, this model has ouperformed outstanding result on image and speech recognition. In this research, we assess the applicability of deep learning in dna classification level. Since the training phase of DBN is costly expensive, specially if deals with DNA sequence with thousand of variables, we introduce a new encoding method, using decimal-binary vector to represent the sequence as input to the model, thereafter compare with one-hot-vector encoding in two datasets. We evaluated our proposed model with different contrastive algorithms which achieved significant improvement for the training speed with comparable classification result. This result has shown a potential of using decimal-binary vector on DBN for DNA sequence to solve other sequence problem in bioinformatics.

• Journal of Plant Biology
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• v.38 no.4
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• pp.365-371
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• 1995

Binary vectors, pBI-ActR1, pBI-ActF1 and pBSH-ActR1, were constructed using pGA642, the replication origin of pTi12 and the rice actin promoter. The sizes of pBI-ActR1, pBI-ActF1 and pBSH-ActR1 were 12.9 kb, 13.2 kb and 11.95 kb, respectively. These vectors containing a rice actin promoter followed by a GUS structural gene could induce stronly the expression of GUS gene in transformed rice cells. Rice explants from 3-4 day old seedlings after germinatin were cocultured with A. tumefaceins harboring pBI-ActR1, pBI-ActF1 or pBSH-ActR1, and then GUS expression in the explants was assayed. Transformation of rice explants by these binary vectors was tissue-specific, such that the meristematic regions of shoot apex, root and hypocotyl were transformed by these binary vectors.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.100-107
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• 1994

This article describes the hardware design scheme of Hamming MAXNET algorithm which is appropriate for binary pattern classification with minimum HD measurement between stimulus vector and storage vector. Circuit integration is profitable to Hamming MAXNET because the structure of hamming network have a few connection nodes over the similar neuro-algorithms. Designed hardware is the two-layered structure composed of hamming network and MAXNET which enable the characteristics of low power consumption and fast operation with biline volgate sensing scheme. Proposed Hamming MAXNET hardware was designed as quantize-level converter for simulation, resulting in the expected binary pattern convergence property.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.429-435
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• 2003

In this paper, we propose improved binary tree vector quantization with consideration of spatial sensitivity which is one of the human visual properties. We combine weights in consideration with the responsibility of human visual system according to changes of three primary color in blocks of images with the process of splitting nodes using eigenvector in binary tree vector quantization. Also we propose the novel quality measure of the quantization images that applies MTF(modulation transfer function) to luminance value of quantization error of color image. The test results show that the proposed method generates the quantized images with fine color and performs better than the conventional method in terms of clustering the similar regions. Also the proposed method can get less quantized level images and can reduce the resource occupied by the quantized image.