• The Journal of Natural Sciences
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• v.4
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• pp.103-142
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• 1991

These studies were carried out to select somatic hybrid using selectable marker genes of Nicotiana glauca transformed by NPTII gene and Solanum tuberosum transformed by T- DNA, and to study characteristics of transformant. The results are summarized as follows. 1. Crown gall tumors and hairy roots were formed on potato tuber disc infected by A. tumefaciens Ach5 and A. rhizogenes ATCC15834. These tumors and roots could be grown on the phytohormone free media. 2. Callus formation from hairy root was prompted on the medium containing 2, 4 D 2mg/I with casein hydrolysate lg/l. 3. The survival ratio of crown gall tumor callus derived from potato increased on the medium containing the activated charcoal 0. 5-2. 0mg/I because of the preventions on the other hand, hairy roots were necrosis on the same medium. 4. Callus derived from hairy root were excellently grown for a short time by suspension culture on liquid medium containing 2, 4-D 2mg/I and casein hydrolysate lg/l. 5. The binary vector pGA643 was mobilized from E. coli MC1000 into wild type Agrobacteriurn tumefaciens Ach5, A. tumefaciens $A_4T$ and disarmed A. tuniefaciens LBA4404 using a triparental mating method with E. ccli HB1O1/pRK2013. Transconjugants were obtained on the minimal media containing tetracycline and kanamycin. pGA643 vectors were confirmed by electrophoresis on 0.7% agarose gel. 6. Kanamycin resistant calli were selected on the media supplemented with 2, 4-D 0.5mg/1 and kanamycin $100\mug$/ml after co- cultivating with tobacco stem explants and A. tumefaciens LBA4404/pGA643, and selected calli propagated on the same medium. 7. The multiple shoots were regenerated from kanamycin resistant calli on the MS medium containing BA 2mg/l. 8. Leaf segments of transformed shoot were able to grow vigorusly on the medium supplemented with high concentration of kanamycin $1000\mug$/ml. 9. Kanamycin resistant shoots were rooting and elongated on medium containing kanamycin $100\mug$/ml, but normal shoot were not. 10. For the production of protoplast from potato calli transformed by T-DNA and mesophyll tissue transformed by NPTII gene, the former was isolated in the enzyme mixture of 2.0% celluase Onozuka R-10, 1.0% dricelase, 1.0% macerozyme. and 0.5M mannitol, the latter was isolated in the enzyme mixture 1.0% Celluase Onozuka R-10, 0.3% macerozyme, and 0.7M mannitol. 11. The optimal concentrationn of mannitol in the enzyme mixture for high protoplast yield was 0.8M at both transformed tobacco mesophyll and potato callus. The viabilities of protoplast were shown above 90%, respectively. 12. Both tobacco mesophyll and potato callus protoplasts were fused by using PEG solution. Cell walls were regenerated on hormone free media supplemented with kanamycin after 5 days, and colonies were observed after 4 weeks culture.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.30 no.6
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• pp.465-473
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• 2004

Purpose : The essential triad for nerve regeneration is nerve conduit, supporting cell and neurotrophic factor. In order to improve the peripheral nerve regeneration, we used polyglycolic acid(PGA) tube and brain-derived neurotrophic factor(BDNF) gene transfected Schwann cells in sciatic nerve defects of SD rat. Materials and methods : Nerve conduits were made with PGA sheet and outer surface was coated with poly(lactic-co-glycolic acid) for mechanical strength and control the resorption rate. The diameter of conduit was 1.8mm and the length was 17mm Schwann cells were harvested from dorsal root ganglion(DRG) of SD rat aged 1 day. Schwann cells were cultured on the PGA sheet to test the biocompatibility adhesion of Schwann cell. Human BDNF gene was obtained from cDNA library and amplified using PCR. BDNF gene was inserted into E1 deleted region of adenovirus shuttle vector, pAACCMVpARS. BDNF-adenovirus was multiplied in 293 cells and purified. The BDNF-Adenovirus was then infected to the cultured Schwann cells. Left sciatic nerve of SD rat (250g weighing) was exposed and 14mm defects were made. After bridging the defect with PGA conduit, culture medium(MEM), Schwann cells or BDNF-Adenovirus infected Schwann cells were injected into the lumen of conduit, respectively. 12 weeks after operation, gait analysis for sciatic function index, electrophysiology and histomorphometry was performed. Results : Cultured Schwann cells were well adhered to PGA sheet. Sciatic index of BDNF transfected group was $-53.66{\pm}13.43$ which was the best among three groups. The threshold of compound action potential was between 800 to $1000{\mu}A$ in experimental groups which is about 10 times higher than normal sciatic nerve. Conduction velocity and peak voltage of action potential of BDNF group was the highest among experimental groups. The myelin thickness and axonal density of BDNF group was significantly greater than the other groups. Conclusion : BDNF gene transfected Schwann cells could regenerate the sciatic nerve gap(14mm) of rat successfully.

• Economic and Environmental Geology
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• v.34 no.1
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• pp.1-12
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• 2001

The crystal structure of biotite-1M from Bancroft, Ontario, was determined by Rietveld refinement method using high-resolution neutron powder diffraction data at -26.3$^{\circ}C$, 2$0^{\circ}C$, 30$0^{\circ}C$, $600^{\circ}C$, 90$0^{\circ}C$. The crystal structure has been refined to a R sub(B) of 5.06%-11.9% and S (Goodness of fitness) of 2.97-3.94. The expansion rate of a, b, c unit cell dimensions with elevated temperature linearly increase to $600^{\circ}C$. The expansivity of the c dimension is $1.61{\times}10^{40}C^{-1}$, while $2.73{\times}10^{50}C^{-1}$ and $5.71{\times}10^{-50}C^{-1}$ for the a and b dimensions, respectively. Thus, the volume increase of the unit cell is dominated by expansion of the c axis as increasing temperature. In contrast to the trend, the expansivity of the dimensions is decreased at 90$0^{\circ}C$. It may be attributed to a change in cation size caused by dehydroxylation-oxidation of $Fe^{2+}$ to $Fe^{3+}$ in vacuum condition at such high temperature. The position of H-proton was determined by the refinement of diffraction pattern at low temperature (-2.63$^{\circ}C$). The position is 0.9103${\AA}$ from the O sub(4) location and located at atomic coordinates (x/a=0.138, y/b=0.5, z/c=0.305) with the OH vector almost normal to plane (001). According to the increase of the temperature, $\alpha$* (tetrahedral rotation angle), $t_{oct}$ (octahedral sheet thickness), mean distance increase except 90$0^{\circ}C$ data. But the trend is less clearly relative to unit cell dimension expansion because the expansion is dominant to the interlayer. Also, ${\Psi}$ (octahedral flattening angle) shows no trends as increasing temperature and it may be because the octahedron (M1, M2) is substituted by Mg and Fe.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.686-697
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• 2018

Due to the advance in remote sensing technology, it has become easier to more frequently obtain high resolution imagery to detect delicate changes in an extensive area, particularly including forest which is not readily sub-classified. Time-series analysis on high resolution images requires to collect extensive amount of ground truth data. In this study, the potential of land coverage mapas ground truth data was tested in classifying high-resolution imagery. The study site was Wonju-si at Gangwon-do, South Korea, having a mix of urban and natural areas. KOMPSAT-3A imagery taken on March 2015 and land coverage map published in 2017 were used as source data. Two pixel-based classification algorithms, Support Vector Machine (SVM) and Random Forest (RF), were selected for the analysis. Forest only classification was compared with that of the whole study area except wetland. Confusion matrixes from the classification presented that overall accuracies for both the targets were higher in RF algorithm than in SVM. While the overall accuracy in the forest only analysis by RF algorithm was higher by 18.3% than SVM, in the case of the whole region analysis, the difference was relatively smaller by 5.5%. For the SVM algorithm, adding the Majority analysis process indicated a marginal improvement of about 1% than the normal SVM analysis. It was found that the RF algorithm was more effective to identify the broad-leaved forest within the forest, but for the other classes the SVM algorithm was more effective. As the two pixel-based classification algorithms were tested here, it is expected that future classification will improve the overall accuracy and the reliability by introducing a time-series analysis and an object-based algorithm. It is considered that this approach will contribute to improving a large-scale land planning by providing an effective land classification method on higher spatial and temporal scales.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.