• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.184-192
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• 2022

Valve internal leakage is caused by damage to the internal parts of the valve, resulting in accidents and shutdowns of the piping system. This study investigated the possibility of a real-time leak detection method using the acoustic emission (AE) signal generated from the piping system during the internal leakage of a butterfly valve. Datasets of raw time-domain AE signals were collected and postprocessed for each operation mode of the valve in a systematic manner to develop a data-driven model for the detection and classification of internal leakage, by applying machine learning algorithms. The aim of this study was to determine whether it is possible to treat leak detection as a classification problem by applying two classification algorithms: support vector machine (SVM) and convolutional neural network (CNN). The results showed different performances for the algorithms and datasets used. The SVM-based binary classification models, based on feature extraction of data, achieved an overall accuracy of 83% to 90%, while in the case of a multiple classification model, the accuracy was reduced to 66%. By contrast, the CNN-based classification model achieved an accuracy of 99.85%, which is superior to those of any other models based on the SVM algorithm. The results revealed that the SVM classification model requires effective feature extraction of the AE signals to improve the accuracy of multi-class classification. Moreover, the CNN-based classification can be a promising approach to detect both leakage and valve opening as long as the performance of the processor does not degrade.

• Journal of Microbiology and Biotechnology
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• v.10 no.3
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• pp.415-418
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• 2000

Productivity of a rice plant is greatly influenced by salt stress. One of the ways to achieve tolerance to salinity is to transfer genes encoding protective enzymes from other organisms, such as microorganisms. The bacterial gene, mtlD, which encodes mannitol-1-phosphate dehydrogenase (Mtl-DH), was introduced to the cytosol of a rice plant by an imbibition technique to overproduce mannitol. The germination and survival rate of the imbibed rice seeds were markedly increased by transferring the mtlD gene when it was delivered in either a pBIN19 or pBmin binary vector. When a polymerase chain reaction was performed with the genomic DNAs of the imbibed rice leaves as a template and with mtlD-specific primers, several lines were shown to contain an exogenous mtlD DNA. However, a reverse transcription (RT)-PCR analysis revealed that not all of them showed an expression of this foreign gene. This paper demonstrates that the growth and germination of rice plants transiently transformed with the bacterial gene, mtlD, are enhanced and these enhancements may have resulted from the experssion of the mtlD gene. The imbibition method empolyed in this study fulfills the requirements for testing the function of such a putative gene in vivo prior to the production of a stable transgenic plant.

• Plant Biotechnology Reports
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• v.5 no.2
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• pp.147-156
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• 2011

Efficient Agrobacterium-mediated genetic transformation of Scoparia dulcis L. was developed using Agrobacterium tumefaciens strain LBA4404 harboring the binary vector pCAMBIA1301 with ${\beta}$-glucuronidase (GUS) (uidA) and hygromycin phosphotransferase (hpt) genes. Two-day precultured leaf segments of in vitro shoot culture were found to be suitable for cocultivation with the Agrobacterium strain, and acetosyringone was able to promote the transformation process. After selection on shoot organogenesis medium with appropriate concentrations of hygromycin and carbenicillin, adventitious shoots were developed on elongation medium by twice subculturing under the same selection scheme. The elongated hygromycin-resistant shoots were subsequently rooted on the MS medium supplemented with $1mg\;l^{-1}$ indole-3-butyric acid and $15mg\;l^{-1}$ hygromycin. Successful transformation was confirmed by PCR analysis using uidA- and hpt-specific primers and monitored by histochemical assay for ${\beta}$-GUS activity during shoot organogenesis. Integration of hpt gene into the genome of transgenic plants was also verified by Southern blot analysis. High transformation efficiency at a rate of 54.6% with an average of $3.9{\pm}0.39$ transgenic plantlets per explant was achieved in the present transformation system. It took only 2-3 months from seed germination to positive transformants transplanted to soil. Therefore, an efficient and fast genetic transformation system was developed for S. dulcis using an Agrobacterium-mediated approach and plant regeneration via shoot organogenesis, which provides a useful platform for future genetic engineering studies in this medicinally important plant.

• Asian-Australasian Journal of Animal Sciences
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• v.17 no.10
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• pp.1390-1394
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• 2004

We have achieved efficient transformation system for forage-type tall fescue plants by Agrobacterium tumefaciens. Mature seed-derived embryogenic calli were infected and co-cultivated with each of three A. tumefaciens strains, all of which harbored a standard binary vector pIG121Hm encoding the neomycin phosphotransferase II (NPTII), hygromycin phosphotransferase (HPT) and intron-containing $\beta$-glucuronidase (intron-GUS) genes in the T-DNA region. Transformation efficiency was influenced by the A. tumefaciens strain, addition of the phenolic compound acetosyringone and duration of vacuum treatment. Of the three A. tumefaciens strains tested, EHA101/pIG121Hm was found to be most effective followed by GV3101/pIG121Hm and LBA4404/pIG121Hm for transient GUS expression after 3 days co-cultivation. Inclusion of 100 $\mu$M acetosyringone in both the inoculation and co-cultivation media lead to an improvement in transient GUS expression observed in targeted calli. Vacuum treatment during infection of calli with A. tumefaciens strains increased transformation efficiency. The highest stable transformation efficiency of transgenic plants was obtained when mature seed-derived calli infected with A. tumefaciens EHA101/pIG121Hm in the presence of 100 $\mu$M acetosyringone and vacuum treatment for 30 min. Southern blot analysis indicated integration of the transgene into the genome of tall fescue. The transformation system developed in this study would be useful for Agrobacterium-mediated genetic transformation of tall fescue plants with genes of agronomic importance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.509-517
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• 2001

Full-search algorithm requires large amount of computation which causes time delay or very complex hardware architecture for real time implementation. In this paper, we propose a fast motion estimator based on bit-plane matching, which reduce the computational complexity and the hardware cost. In the proposed motion estimator, the conventional motion estimation algorithms are applied to the binary images directly extracted from the video sequence. Furthermore, in the proposed VLSI motion estimator, we employ a Pair of processing cores that calculate the motion vector continuously By controlling the data flow in a systolic fashion using the internal shift registers in the processing cores, we avoid using SRAM (local memory) so that we remove the time overhead for accessing the local memory and adopt lower-cost fabrication technology. We modeled and tested the proposed motion estimator in VHDL, and then synthesized the whole system which has been integrated in a 0.6-$\mu$m triple-metal CMOS chip of size 8.15 X 10.84$\textrm{mm}^2$.

• 한국생물공학회:학술대회논문집
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• 2003.04a
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• pp.568-570
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• 2003

The Hyoscyamus niger hyoscyamine $6{\beta}-hydroxylase$ (H6H, EC 1.14.11.11) gene was introduced into the genome of a Scopolia parviflora by the binary vector system using the disarmed Agrobacterium rhizogenes strain KCTC 2703. Expression of H6H enzyme which are involved in alkaloids pathway by western blot analysis using proteins extracted from leaf, stem flower, branch root and main root were examined The enzyme expression was found only in the roots, with no expression in leaf, stem and flower. The alkaloids contents were the most higher in root and then leaf and stem has very small amount of alkaloid contents were analyzed by HPLC. The expression level of H6H in transgenic plants were two or more times than wild type plants. In transgenic plant which constitutively expresses H6H enzyme, high concentration of scopolamine was accumulated.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.4
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• pp.427-432
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• 2009

Seashore Paspalum (Paspalum vaginatum Swartz) is a warm season grass and indigenous to tropical and subtropical regions of coastal areas worldwide. The species is used as feed for cattle and horses and has been very successful for golf courses worldwide. One of the most outstanding characteristics of seashore paspalum is its tolerance to saline soils compared to other warm season turfgrasses. The development of new seashore paspalum cultivars with improved traits could be facilitated through the application of biotechnological strategies. The purpose of this study was to product for herbicide resistant seashore paspalum using Arobacterium-mediated transformation and this study is the first report on transformation and herbicideresistant transgenic plants in seashore paspalum. Embryogenic calli were induced from the seeded variety of pseashore paspalum. Embryogenic calli were transformed with Agrobacterium tumefaciens strain EHA105 carrying the binary vector pCAMBIA3301 with two genes encoding gusA and bar. Transformed calli and plants were selected on medium containing 3 mg/l PPT. PCR detected the presence of the gusA and bar gene, indicating both genes are integrated into the genome of seashore paspalum. A chlorophenol red assay was used to confirm that the bar gene was expressed. By application of herbicide BASTA, the herbicide resistance in the transgenic seashore paspalum plants was confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.25-44
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• 2010

With the increasing popularity of mobile devices, it has become necessary to protect private information and content in these devices. Face recognition has been favored over conventional passwords or security keys, because it can be easily implemented using a built-in camera, while providing user convenience. However, because mobile devices can be used both indoors and outdoors, there can be many illumination changes, which can reduce the accuracy of face recognition. Therefore, we propose a new face recognition method on a mobile device robust to illumination variations. This research makes the following four original contributions. First, we compared the performance of face recognition with illumination variations on mobile devices for several illumination normalization procedures suitable for mobile devices with low processing power. These include the Retinex filter, histogram equalization and histogram stretching. Second, we compared the performance for global and local methods of face recognition such as PCA (Principal Component Analysis), LNMF (Local Non-negative Matrix Factorization) and LBP (Local Binary Pattern) using an integer-based kernel suitable for mobile devices having low processing power. Third, the characteristics of each method according to the illumination va iations are analyzed. Fourth, we use two matching scores for several methods of illumination normalization, Retinex and histogram stretching, which show the best and $2^{nd}$ best performances, respectively. These are used as the inputs of an SVM (Support Vector Machine) classifier, which can increase the accuracy of face recognition. Experimental results with two databases (data collected by a mobile device and the AR database) showed that the accuracy of face recognition achieved by the proposed method was superior to that of other methods.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.