• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.16 no.2
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• pp.106-115
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• 2011

The aim of this study is to assess the importance of ballast water discharge as a vector for the introduction of exotic species into Pusan and Daesan Ports, Korea. We also examined to understand the impacts of environmental factors on the survival success of introduced species by ship's ballast water in laboratory experiments. Seven ship's ballast water originated from the coastal water of China (Taicang, Ningbo and Jinshan), Japan (Tokuyama, Moji and Akita), and Singapore. According to PCA (principal components analysis) analysis, environmental factor in the each ballast and shipside waters were different by bioregion. Based on cluster analysis, the phytoplankton community structures were distinguished for ballast water origin. Most of the major taxonomic groups were diatoms and, the others were dinoflagellate, silcoflagellate and several fresh-waters species. In particular, species number and standing crops of phytoplankton in the ballast tanks decreased with the increasing age ofballast water(r = -0.35 for standing crop; r = -0.63 for species number). In the laboratory study, although phytoplankton in ballast water treatment did not survive even in optimal temperature, the in vivo fluorescence of phytoplankton viability increased under the nutrient typical of shipside water and F/2 medium at $15^{\circ}C$ and $20^{\circ}C$. The diatoms species such as Skeletonema costatum and Thalassiosira pseudonana in ballast water were successfully regrown. On the salinity gradient experiments for Shui Shan (2) vessel, several freshwater species, brackish and marine species were successfully adapted. Of these, S.costatum was able to tolerate a wide range of salinities (10 to 30 psu) and its species-specific viability was suitable for colonization.

• 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.