• Journal of Plant Biotechnology
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• v.33 no.1
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• pp.11-18
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• 2006

Salt stress is one of major environmental factors influencing plant growth and development. To identify salt tolerance determinants in higher plants, a large-scale screen was conducted with a bialaphos marker-based T-DNA insertional collection of Arabidopsis ecotype C24 mutants. One line for salt stress-sensitive mutant (referred to as ssm1) exhibited increased sensitivity to both ionic (NaCl) and nonionic (mannitol) osmotic stress in a root growth assay. This result suggests that ssm1 mutant is involved in ion homeostasis and osmotic compensation in plant. Molecular cloning of the genomic DNA flanking T-DNA insert of ssm1 mutant was achieved by mutant genomic DNA library screening. T-DNA insertion appeared in the first exon of an open reading frame on F3M18.7, which is the same as AtSYP61. SSM1 is SYP61/OSM1 that is a member of the SNARE superfamily of proteins required for vesicular/target membrane fusions and factor related to abiotic stress.

• Microbiology and Biotechnology Letters
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• v.26 no.2
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• pp.122-129
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• 1998

It were reported that antifungal mechanism of Enterobacter cloacae is a volatile ammonia that produced by the strain in soil, and the production of ammonia is related to the bacterial urease activity. A powerful bacterium SH14 against soil-borne pathogen Fusarium solani, which cause root rot of many important crops, was selected from a ginseng pathogen suppressive soil. The strain SH14 was identified as Bacillus subtilis by cultural, biochemical, morphological method, and $API^{circledR}$ test. From several in vitro tests, the antifungal substance that is produced from B. subtilis SH14 was revealed as heat-stable and low-molecular weight antibiotic substance. In order to construct the multifunctional biocontrol agent, the urease gene of Bacillus pasteurii which can produce pathogenes-suppressive ammonia transferred into antifungal bacterium. First, a partial BamH I digestion fragment of plasmid pBU11 containing the alkalophilic B. pasteurii l1859 urease gene was inserted into the BamH I site of pEB203 and expressed in Escherichia coli JM109. The recombinant plasmid was designated as pGU366. The plasmid pGU366 containing urease gene was introduced into the B. subtilis SH14 with PEG-induced protoplast transformation (PIP) method. The urease gene was very stably expressed in the transformant of B. subtilis SH14. Also, the optimal conditions for transformation were established and the highest transformation frequency was obtained by treatment of lysozyme for 90 min, and then addition of 1.5 ${mu}g$/ml DNA and 40% PEG4000. From the in vitro antifungal test against F. solani, antifungal activity of B. subtilis SH14(pGu366) containing urease gene was much higher than that of the host strain. Genetical development of B. subtilis SH14 by transfer of urease gene can be responsible for enhanced biocontrol efficacy with its antibiotic action.

• 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.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.198-209
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• 2021

This study evaluated the effects of the dimensional characteristics of containers on the nitrogen status of Quercus serrata, Fraxinus rhynchophylla, and Zelkova serrata in the container nursery stage. Seedlings were grown using 16 container types [four growing densities (100, 144, 196, and 256 seedlings/m²) × four cavity volumes (220, 300, 380, and 460 cm³/cavity)]. Two-way ANOVA was performed to test the differences in nitrogen concentration and seedling content among container types. Additionally, we performed multiple regression analyses to correlate container dimensions and nitrogen content. Container types had a strong influence on nitrogen concentration and the content of the seedling species, with a significant interaction effect between growing density and cavity volume. Cavity volumes were positively correlated with the nitrogen content of the three seedling species, whereas growing density negatively affected those of F. rhynchophylla. Further, nutrient vector analysis revealed that the seedling nutrient loading capacities of the three species, such as efficiency and accumulation, were altered because of the different fertilization effects by container types. The optimal ranges of container dimension by each tree species, obtained multiple regression analysis with nitrogen content, were found to be approximately 180-210 seedlings/m² and 410-460 cm³/cavity for Q. serrata, 100-120 seedlings/m² and 350-420 cm³/cavity for F. rhynchophylla, and 190-220 seedlings/m² and 380-430 cm³/cavity for Z. serrata. This study suggests that an adequate type of container will improve seedling quality with higher nutrient loading capacity production in nursery stages and increase seedling growth in plantation stages.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• 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.

• Korean Journal of Animal Reproduction
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• v.25 no.4
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• pp.339-347
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• 2001

Sperm-mediated DNA transfer has a potential to markedly simplify techniques for the generation of transgenic animals. The exogenous DNA transfer by intracytoplasmic sperm injection (ICSI) procedure has been recently introduced in the production of transgenic animals. In this study, the developmental competence and tile expression rates of transgene were investigated after injection of spermatozoon or sperm head with enhanced green fluorescent protein (EGFP) gene into the mature porcine oocytes. The porcine oocytes were injected with intact sperm, membrane-disrupted sperm or sperm head. After injection. embryos were cultured in NCSU23 medium up to the blastocyst stage, and the developmental competence and expression rates were studied. The developmental rate (67.0%) of sperm injection group was higher than that (59.7%) of sperm head injection group, and the rates of EGFP expression were also significantly different between sperm injection and sperm head injection groups (42.1 vs 20.0%) (F＜0.05). In the porcine oocytes injected with sperm treated with different methods of membrane disruption, the removal of sperm membrane did not alter the developmental competence of embryos. The rate of blastocysts at 7 days after injection with intact and membrane disrupted sperm were 15.0 and 14.2%, respectively. The EGFP expression rates, 38.4% in embryos injected with frozen-thawed sperm was higher than that, 22.4% of embryos injected with the Triton X-100 treated sperm. Prior to injection, sperm were cultured in different EGFP gene concentrations from 0.Ol to 1ng/u${mu}ell$. However, no significant difference in developmental rates of embryos among different concentrations of EGFP gene were observed. The highest expression rate of EGFP gene, 37.4% was obtained from the embryos injected with spermatozoa treated with 0.1 ng/${mu}ell$ EGFP gene. These results suggested that exogenous DNA could be attached to the membrane disrupted sperm, and that these sperm could be used as a vector carrying foreign DNA into embryos.

• Tuberculosis and Respiratory Diseases
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• v.55 no.5
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• pp.488-498
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• 2003

Background : Activation of the transcription factor NF-${\kappa}B$ has been shown to protect cells from tumor necrosis factor-alpha, chemotherapy, and radiation-induced apoptosis. NF-${\kappa}B$-dependent cIAP expression is a major antiapoptotic mechanism for that. NF-${\kappa}B$ activation and cIAP expression in A549 lung cancer cells which is relatively resistant to radiation-induced cell death were investigated for the mechanism of radioresistance. Materials and methods : We used A549 lung cancer cells and Clinac 1800C linear accelerator for radiation. Cell viability test was done by MTT assay. NF-${\kappa}B$ activation was tested by luciferase reporter gene assay, Western blot for $I{\kappa}B{\alpha}$ degradation, and electromobility shift assay. For blocking ${\kappa}B$, MG132 and transfection of $I{\kappa}B{\alpha}$-superrepressor plasmid construct were used. cIAP expression was analyzed by RT-PCR and cIAP2 promoter activity was performed using luciferase assay system. Results : MTT assay showed that cytotoxicity even 48 hr after radiation in A549 cells were less than 20%. Luciferas assay demonstrated weak NF-${\kappa}B$ activation of $1.6{\pm}0.2$ fold compared to PMA-induced $3.4{\pm}0.9$ fold. Radiation-induced $I{\kappa}B{\alpha}$ degradation was observed in Western blot and NF-${\kappa}B$ DNA binding was confirmed by EMSA. However, blocking NF-${\kappa}B$ using MG132 and $I{\kappa}B{\alpha}$-superrepressor transfection did not show any sensitizing effect for radiation-induced cell death. The result of RT-PCR for cIAP1 & 2 expression was negative induction while TNF-${\alpha}$ showed strong expression for cIAP1 & 2. The cIAP2 promoter activity also did not show any change compared to positive control with TNF-${\alpha}$. Conclusion : We conclude that activation of NF-${\kappa}B$ does not determine the intrinsic radiosensitivity of cancer cells, at least for the cell lines tested in this study.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.743-752
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• 2019

The flow in the meandering channel is characterized by the spiral motion of secondary currents that typically cause the erosion along the outer bank. Hydraulic structures, such as spur dike and groyne, are commonly installed on the channel bottom near the outer bank to mitigate the strength of secondary currents. This study is to investigate the effects of submerged vanes installed in a $90^{\circ}$ meandering channel on the development of secondary currents through three-dimensional numerical modeling using the hybrid RANS/LES method for turbulence and the volume of fluid method, based on OpenFOAM open source toolbox, for capturing the free surface at the Froude number of 0.43. We employ the second-order-accurate finite volume methods in the space and time for the numerical modeling and compare numerical results with experimental measurements for evaluating the numerical predictions. Numerical results show that the present simulations well reproduce the experimental measurements, in terms of the time-averaged streamwise velocity and secondary velocity vector fields in the bend with submerged vanes. The computed flow fields reveal that the streamwise velocity near the bed along the outer bank at the end section of bend dramatically decrease by one third of mean velocity after the installation of vanes, which support that submerged vanes mitigate the strength of primary secondary flow and are helpful for the channel stability along the outer bank. The flow between the top of vanes and the free surface accelerates and the maximum velocity of free surface flow near the flow impingement along the outer bank increases about 20% due to the installation of submerged vanes. Numerical solutions show the formations of the horseshoe vortices at the front of vanes and the lee wakes behind the vanes, which are responsible for strong local scour around vanes. Additional study on the shapes and arrangement of vanes is required for mitigate the local scour.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.