• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.282-289
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• 2022

Analyzing the impact of climate change on the Korean Peninsula on the forest ecosystem is important for the management of subtropical forest bioresources. In this study, we collected location data and bioclimatic variables of the warm-temperate woody plant species, Myrica rubra and Cyzygium buxifolium, and applied the MaxEnt model based on the collected data to estimate the potential distribution area. Precipitation and temperature seasonality in the warmest quarter were the main environmental factors that determined the distribution of M. rubra, and the main environmental factors for S. buxifolium were precipitation in the warmest quarter and precipitation in the wettest quarter. The results of the MaxEnt model by administrative district, the M. rubra showed an area increase rate of 4.6 - 17.7% in the SSP2-4.5 climate change scenario and 13.8 - 30.5% in the SSP5-8.5 climate change scenario. S. buxifolium showed area increase rates of 4.8 - 32.2% in the SSP2-4.5 climate change scenario and 12.9 - 48.6% in the SSP5-8.5 climate change scenario. This study is meaningful in establishing a database and identifying future potential distribution areas of warm and subtropical plants by applying climate change scenarios.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.31-38
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• 2022

The article highlights the educational potential of information and communication technologies in the study of philological disciplines in higher education institutions. The study aims to analyze the didactic potential of ICT in the study of philological disciplines, as well as to check the scientific hypothesis that the use of ICT in HEIs in the study of philological disciplines will intensify and enhance the effectiveness of the learning process. To confirm the validity of the hypothesis, experimental testing was carried out and the results are illustrated in the article. The above-mentioned goal of the study determined the use of theoretical and empirical methods: analysis, synthesis, generalization, and systematization of pedagogical and scientific-methodological literature to clarify the state of research problem development and to identify pedagogical foundations on which the process of ICT use is based, comparison and prediction; questioning and testing of educational process participants to understand the effectiveness of ICT use in their training in HEIs. The research results showed positive changes in all analyzed criteria in the experimental group, which is due to the introduction of additional ICT tools into the educational process of the mentioned group. The scientific novelty of the study consists in highlighting the main characteristics and didactic functions of ICT in the learning process of philological students; in covering the classification of ICT, ICT tools, and typology of training sessions using ICT in the study of philological disciplines. In the conclusion it is summarized that the introduction of modern ICT in the educational process allows intensifying the learning process, implementation of a variety of ideas, increases the pace of classes and material assimilation, influencing the motivation for learning, increases the amount of independent work of students.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.535-549
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• 2022

Damage to forests, such as broken or falling trees, has increased due to the increased intensity and frequency of abnormal climate events, such as strong winds and heavy rains. However, it is difficult to respond to them in advance based on prediction since structural defects such as cavities and bumps inside trees are difficult to identify with a visual inspection. Non-invasive sonic tomography (SoT) is a method of estimating internal defects while minimizing physical damage to trees. Although SoT is effective in diagnosing internal defects, its accuracy varies depending on the species. Therefore, it is necessary to analyze the reliability of its measurement results before applying it in the field. In this study, we measured internal defects in wood by cross-applying destructive resistance micro drilling on old Pinus densifloraSiebold & Zucc. and Ginkgo bilobaL., which are representative tree species in Korea, to verify the reliability of SoT and compared the evaluation results. The t-test for the mean values of the defect measurement between the two groups showed no statistically significant difference in pine trees and some difference in ginkgo trees. Linear regression analysis results showed a positive correlation with an increase in defects in SoT images when the defects in the drill resistance graph increased in both species.

• Journal of Chest Surgery
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• v.55 no.2
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• pp.143-150
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• 2022

Background: The effectiveness of extracorporeal membrane oxygenation (ECMO) for patients with refractory cardiogenic shock or cardiac arrest is being established, and serum lactate is well known as a biomarker of end-organ perfusion. We evaluated the efficacy of pre-ECMO lactate for predicting 6-month survival in patients with acute coronary syndrome (ACS) undergoing ECMO. Methods: We reviewed the medical records of 148 patients who underwent veno-arterial (VA) ECMO for ACS between January 2015 and June 2020. These patients were divided into survivors and non-survivors based on 6-month survival. All clinical data before and during ECMO were compared between the 2 groups. Results: Patients' mean age was 66.0±10.5 years, and 116 (78.4%) were men. The total survival rate was 45.9% (n=68). Cox regression analysis showed that the pre-ECMO lactate level was an independent predictor of 6-month mortality (hazard ratio, 1.210; 95% confidence interval [CI], 1.064-1.376; p=0.004). The area under the receiver operating characteristic curve of pre-ECMO lactate was 0.64 (95% CI, 0.56-0.72; p=0.002; cut-off value=9.8 mmol/L). Kaplan-Meier survival analysis showed that the cumulative survival rate at 6 months was significantly higher among patients with a pre-ECMO lactate level of 9.8 mmol/L or less than among those with a level exceeding 9.8 mmol/L (57.3% vs. 31.8%, p=0.0008). Conclusion: A pre-ECMO lactate of 9.8 mmol/L or less may predict a favorable outcome at 6 months in ACS patients undergoing VA-ECMO. Further research aiming to improve the accuracy of predictions of reversibility in patients with high pre-ECMO lactate levels is essential.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.605-612
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• 2021

Amid growing global damage due to abnormal weather caused by global warming, the introduction of eco-friendly cars is accelerating to reduce greenhouse gas emissions from internal combustion engines. Accordingly, many studies are being conducted in each country to prepare for the explosion of hydrogen fuel in semi-closed spaces such as tunnels and underground parking lots to ensure the safety of hydrogen-electric vehicles. As a result of predicting the explosion pressure of the hydrogen tank using the equivalent TNT model, it was found to be about 1.12 times and 2.30 times higher at a height of 1.5 meters, respectively, based on the case of 52 liters of hydrogen capacity. A review of the impact on the human body and buildings by converting the predicted maximum explosive pressure into the amount of impact predicted that all predicted values would result in lung damage or severe partial destruction. The predicted degree of damage was applied only by converting the amount of impact caused by the explosion, and considering the additional damage caused by the explosion, it is believed that the actual damage will increase further and safety and disaster prevention measures should be taken.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Korean Journal of Plant Taxonomy
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• v.52 no.3
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• pp.184-195
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• 2022

The invasive alien species Brassica tournefortii Gouan (Brassicaceae) is herein reported for the first time in Korea, from Gunsan-si, Gochang-gun, and Jeju-si. Brassica tournefortii can easily be distinguished from B. juncea and B. napus by its dense stiff hairs at the base of the stem and leaves, basally and distally branched stems, partially dehiscent fruits, and seeds that become mucilaginous in the presence of moisture. Although some taxonomists have classified this species as belonging to Coincya Rouy based on its fruit and seed characteristics, the existence of one vein on the fruit valves and our maximum likelihood analysis using internal transcribed spacer sequences placed it in Brassica. Distribution data, photographs, and a description of B. tournefortii are presented herein. Moreover, potential changes in the distribution of B. tournefortii were predicted under different climate scenarios, but our analysis showed that the probability of the spreading of this species is low. Nevertheless, continuous monitoring is necessary for an accurate assessment. The results of the present study can be used to conduct an invasion risk assessment and can assist with the effective management of this invasive alien species.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.241-251
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• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.