• Journal of Communications and Networks
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• v.18 no.2
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• pp.218-226
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• 2016

Coordination among users is an inevitable but time-consuming operation in wireless networks. It severely limit the system performance when the data rate is high. We present FC-MAC, a novel MAC protocol that can complete a contention within one contention slot over a joint frequency-code domain. When a node takes part in the contention, it generates randomly a contention vector (CV), which is a binary sequence of length equal to the number of available orthogonal frequency division multiplexing (OFDM) subcarriers. In FC-MAC, different user is assigned with a distinct signature (i.e., PN sequence). A node sends the signature at specific subcarriers and uses the sequence of the ON/OFF states of all subcarriers to indicate the chosen CV. Meanwhile, every node uses the redundant antennas to detect the CVs of other nodes. The node with the minimum CV becomes the winner. The experimental results show that, the collision probability of FC-MAC is as low as 0.05% when the network has 100 nodes. In comparison with IEEE 802.11, contention time is reduced by 50-80% and the throughput gain is up to 200%.

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.307-312
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• 2015

ARPA(Automatic Radar Plotting Aid) is a device to calculate CPA(closest point of approach)/TCPA(time of CPA), true course and speed of targets by vector operation of relative courses and speeds. The purpose of this study is to develop target acquisition and tracking technology for ARPA Radar implementation. After examining the previous studies, applicable algorithms and technologies were developed to be combined and basic ARPA functions were developed as a result. As for main research contents, the sequential image processing technology such as combination of grayscale conversion, gaussian smoothing, binary image conversion and labeling was deviced to achieve a proper target acquisition, and the NNS(Nearest Neighbor Search) algorithm was appllied to identify which target came from the previous image and finally Kalman Filter was used to calculate true course and speed of targets as an analysis of target behavior. Also all technologies stated above were implemented as a SW program and installed onboard, and verified the basic ARPA functions to be operable in practical use through onboard test.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.81-97
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• 2003

In this paper, we suggest a method of robust watermarking for protection of multimedia data using the wavelet transform and artificial neural network. for the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except fur the lowest subband LL$_3$, we apply a calculated threshold about chosen cluster as the biggest. We used binary logo watermarks to make sure that it is true or not on behalf of the Gaussian Random Vector. Besides, we tested a method of dual watermark insertion and extraction. For the purpose of implementation, we decompose a original image using wavelet transform at level 3. After we classify transformed coefficients of other subbands using neural network except for the lowest subband LL$_3$, we apply a above mentioned watermark insert method. In the experimental results, we found that it has a good quality and robust about many attacks.

• Korean Journal of Plant Tissue Culture
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• v.25 no.4
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• pp.225-229
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• 1998

Agrobacterium tumefaciens LBA 4404 harboring binary vector pBI 121 was used for genetic transformation of lettuce(Lactuca Sativa L.). Optimal shoot regeneration from cotyledon explants was obtained in MS medium supplemented with 0.1mg/L NAA and 1.0 mg/L 2ip. In this condition, cotyledon explants were cocultivated with A, tumefaciens for 2 days, and then transferred to selection medium supplemented with 50 mg/L kanamycin and 500 mg/L carbenicillin. These explants were subsequently subcultured every 2 weeks on shoot induction medium. PCR analysis indicated that the GUS gene was stably integrated into the nuclear genome of lettuce. Histochemical analysis based on the enzymatic activity of the CUS protein showed that GUS activity was associated with vascular tissue in leaves and roots. Progenies of Ro plants demonstrated a linked monogenic segregation for GUS gene.

• Journal of Plant Biotechnology
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• v.38 no.3
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• pp.209-214
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• 2011

Transgenic lettuce plants were successfully obtained from hypocotyl explants inoculated with Agrobacterium tumefaciens, which harbored a binary vector plasmid with Bcl-2 gene, related to apoptosis. After culture and selection on MS medium a number of kanamycin-resistant plantlets were regenerated. Polymerase chain reaction, Southern blot analysis and Northern blot analysis were used to identify and characterize the transgenic plants with the integrated Bcl-2 gene. Over 100 transgenic plants have been established in soil and flowered in the greenhouse. T1 progeny of 100 transgenic lettuce inbred lines were inoculated with Sclerotinia sclerotiorum. Expression of the Bcl-2 peptide in transgenic lettuce plants provides high levels of field resistance against Sclerotinia sclerotiorum, causal agent of the agronomically important fungal disease of lettuce.

• Journal of Plant Biotechnology
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• v.38 no.3
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• pp.198-202
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• 2011

Agrobacterium tumefaciens-mediated cotyledonary-node explants transformation was used to produce transgenic cucumber. Cotyledonary-node explants of cucumber (Cucumis sativus L. cv., Eunsung) were co-cultivated with Agrobacterium strains (EHA101) containing the binary vector (pPZP211) carrying with CaMV 35S promoter-nptII gene as selectable marker gene and 35S promoter-DQ gene (unpublished data) as target gene. The average of transformation efficiency (4.01%) was obtained from three times experiments and the maximum efficiency was shown at 5.97%. A total of 9 putative transgenic plants resistant to paromomycin were produced from the cultures of cotyledonary-node explants on selection medium. Among them, 6 transgenic plants showed that the nptII gene integrated into each genome of cucumber by Southern blot analysis.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.68-76
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the ring connection neural network in which each neuron is only to its nearest neurons with binary synaptic weights of ±1, has been inconnected vestigated Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of n-neuron network to produce a limit cycle of n- or 2n-period are also given The results show that the estimated number of limit cycle is an exponential function of n. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.266-274
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• 2009

An abdominal aortic aneurysm occurs most commonly in older individuals (between 65 and 75), and more in men and smokers. The most important complication of an abdominal aortic aneurysm is rupture, which is most often a fatal event. An abdominal aortic aneurysm weakens the walls of the blood vessel, leaving it vulnerable to bursting open, or rupturing, and spilling large amounts of blood into the abdominal cavity. surface modeling is very useful to surgery for quantitative analysis of abdominal aortic aneurysm. the 3D representation and surface modeling an abdominal aortic aneurysm structure taken from Multi Detector Computed Tomography. The construction of the 3D model is generally carried out by staking the contours obtained from 2D segmentation of each CT slice, so the quality of the 3D model strongly defends on the precision of segmentation process. In this work we present deformable model algorithm. deformable model is an energy-minimizing spline guided by external constraint force. External force which we call Gradient Vector Flow, is computed as a diffusion of a gradient vectors of gray level or binary edge map derived from the image. Finally, we have used snakes successfully for abdominal aortic aneurysm segmentation the performance of snake was visually and quantitatively validated by experts.

• Mycobiology
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• v.49 no.5
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• pp.491-497
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• 2021

An endolichenic fungus Xylaria grammica EL000614 produces grammicin, a potent nematicidal pyrone derivative that can serve as a new control option for root-knot nematodes. We optimized an Agrobacterium tumefaciens-mediated transformation (ATMT) protocol for X. grammica to support genetic studies. Transformants were successfully generated after co-cultivation of homogenized young mycelia of X. grammica with A. tumefaciens strain AGL-1 carrying a binary vector that contains the bacterial hygromycin B phosphotransferase (hph) gene and the eGFP gene in T-DNA. The resulting transformants were mitotically stable, and PCR analysis showed the integratin of both genes in the genome of transformants. Expression of eGFP was confirmed via fluorescence microscopy. Southern analysis showed that 131 (78.9%) out of 166 transformants contained a single T-DNA insertion. Crucial factors for producing predominantly single T-DNA transformants include 48 h of co-cultivation, pretreatment of A. tumefaciens cells with acetosyringone before co-cultivation, and using freshly prepared mycelia. The established ATMT protocol offers an efficient tool for random insertional mutagenesis and gene transfer in studying the biology and ecology of X. grammica.

• Journal of Biomedical Engineering Research
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• v.40 no.2
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• pp.62-67
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• 2019

The purpose of this study was to evaluate the performance of deep neural network model in order to determine whether there is a risk factor for coronary artery disease based on the cardiac variation parameter. The study used unidentifiable 297 data to evaluate the performance of the model. Input data consists of heart rate parameters, which are SDNN (standard deviation of the N-N intervals), PSI (physical stress index), TP (total power), VLF (very low frequency), LF (low frequency), HF (high frequency), RMSSD (root mean square of successive difference) APEN (approximate entropy) and SRD (successive R-R interval difference), the age group and sex. Output data are divided into normal and patient groups, and the patient group consists of those diagnosed with diabetes, high blood pressure, and hyperlipidemia among the various risk factors that can cause coronary artery disease. Based on this, a binary classification model was applied using Deep Neural Network of deep learning techniques to classify normal and patient groups efficiently. To evaluate the effectiveness of the model used in this study, Kernel SVM (support vector machine), one of the classification models in machine learning, was compared and evaluated using same data. The results showed that the accuracy of the proposed deep neural network was train set 91.79% and test set 85.56% and the specificity was 87.04% and the sensitivity was 83.33% from the point of diagnosis. These results suggest that deep learning is more efficient when classifying these medical data because the train set accuracy in the deep neural network was 7.73% higher than the comparative model Kernel SVM.