• Proceedings of the Plant Resources Society of Korea Conference
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• 2019.10a
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• pp.64-64
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• 2019

Screening and identification of genetic resources based on their phytoconstituents and morphological characters potentially provide baseline data for researchers, breeders, and nutraceutical companies who wish to formulate a nutrient-dense diet and health beneficial supplement. Thus, we evaluated the amount of total phenolic content and major phenolic compounds; examined if phenolic compounds could be used as distinguishing factors for perilla genetic resources; and investigated the relation between some quantitative and qualitative morphological characters with the contents of phenolic compounds in 360 accessions obtained from National Agrobiodiversity Center gene bank, Jeonju, Korea. Total phenolic content (TPC) was estimated using Folin-Ciocalteu colorimetric assay. Individual phenolic compounds were determined using an Ultra-High Performance Liquid Chromatography system equipped with Photodiode Array detector. Considerable variations were observed in TPC (7.99 to 117.47 mg GAE/g DE), rosmarinic acid (RA) (ND to 19.19 mg/g DE), caffeic acid (CA) (ND to 0.72 mg/g DE), apigenin-7-O-diglucuronide (ADG) (ND to 1.24 mg luteolin equivalent (LUE)/g DE), scutellarein-7-O-glucuronide (SG) (ND to 4.32 mg LUE/g DE), and apigenin-7-O-glucuronide (AG) (ND to 1.60 mg LUE/g DE). RA was the most dominant phenolic compound in most accessions (95.3%) followed by SG. The adaxial leaf color was light green, green and dark green in 13.8%, 65.0%, and 21.1 % of the accessions, respectively. 78.8% of the accessions had light green color at the abaxial side with the remaining being described as green. Most of the accessions (96.9%) were cordate shape, the remaining being eclipse. Intensities of green pigment at abaxial and adaxial leaf surfaces were correlated with contents of individual phenolic compounds and TPC whereas leaf length and width had no correlation with TPC, CA and RA, and negatively correlated with ADG, AG, and SG. Leaf shape was not related with content of phenolic compounds, color of leaves, or the length or width of leaves. Accessions IT57426, IT157434, IT267710, and IT267712 which contained relatively high contents of TPC and major phenolic compounds (RA and SG) could be used for further research in breeding and bioassay test. Our study result showed the contents of total phenolics and individual phenolic compounds along with the morphological characters could be useful distinguishing factors for perilla genetic resources.

• Journal of Convergence for Information Technology
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• v.11 no.3
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• pp.250-260
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• 2021

The purpose of this study is to investigate muscle function and symmetry index during whole body vibration exercise using lower extremity training posture for throwing athletes. For throwing athletes in their 20s(6 males, 4 females, age: 24.60±0.92years, height: 177.90±7.40cm, weight: 92.90±22.97kg), lower extremity training postures with squat, carphrase, and lunge movements. Whole body vibration exercise training was performed using. Tensiomyography(TMG) variables Time Delay(Td), Time Contraction(Tc), Time Sustain(Ts) Time Relaxation(Tr), and Displacement Maximumal(Dm) in the lower extremity joint muscles(biceps femoris(BF), gastrocnemius lateral(GL), gastrocnemius medial(GM), rectus femoris(RF), tibialis anterior(TA), lateral vastus(LV), medial latissimus(ML)), were measured to compare and analyze muscle activity, muscle fatigue, and left-right symmetry. The results of the study are left RF, VL, right VM (p<.05) in Td, VM (p<.05) in Tc, GM in Ts (p<.05), left RF in Tr, and right TA (p<. 05) showed a change. Therefore, it has been proven that various whole-body vibration training is an effective exercise with changes in muscle contraction, and stability of the core is secured by symmetry of the left and right muscles. For this reason, the whole body vibration exercise will have a positive effect on rehabilitation training, and it is believed that it will be able to improve performance.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.191-209
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• 2022

This paper is a review of studies that focus on the prediction of a won/dollar exchange rate before and after the covid 19 pandemic. The Korea economy has an unprecedent situation starting from 2021 up till 2022 where the won/dollar exchange rate has exceeded 1,400 KRW, a first time since the global financial crisis in 2008. The US Federal Reserve has raised the interest rate up to 2.5% (2022.7) called a 'Big Step' and the Korea central bank has also raised the interested rate up to 2.5% (2022.8) accordingly. In the unpredictable economic situation, the prediction of the won/dollar exchange rate has become more important than ever. The authors separated the period from 2015.Jan to 2022.Aug into three periods and built a best fitted ARIMA/ARDL prediction model using the period 1. Finally using the best the fitted prediction model, we predicted the won/dollar exchange rate for each period. The conclusions of the study were that during Period 3, when the usual relationship between exchange rates and economic factors appears, the ARDL model reflecting the variable relationship is a better predictive model, and in Period 2 of the transitional period, which deviates from the typical pattern of exchange rate and economic factors, the SARIMA model, which reflects only historical exchange rate trends, was validated as a model with a better predictive performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.3
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• pp.203-212
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• 2023

Since the coronavirus disease-2019 (COVID-19) outbreak, it has been generally believed that a medical technologists (MTs) are supposed to perform polymerase chain reaction tests and next-generation sequencing (NGS) in the hospitals. However, many do not recognize that the duty of MT for clinical genetic testing has not been stated in the Medical Laws (72.5% for MT, N=200; 62.8% for students, N=123). In this regard, to evaluate the feasibility of MT's role for NGS genetic testing, we requested our subjects to fill out an online survey and analyzed the data. Among them, it shows that the scope of MT's role, including NGS performance should include clinical genetic testing (99.5% for MT, N=200; 86.8% for students, N=123). Also, questions on clinical genetics, which is associated with both cellular genetics and molecular genetic questions should be included in the National MT License Problem Bank (97.5% for MT; 71.4% for students). Based on these results, the Korean Association of Medical Technologists needs to cooperate synergically with the Academic Association of Biomedical Laboratory Science with respect to genetic education and legislation for the future benefit of both MTs and students.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.113-123
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• 2023

This paper introduces the Cherry system, a user-independent blockchain donation system. This is a procedure that is delivered to the beneficiary's bank account through a virtual account when a donor makes a donation, so there is no difference from the existing donation delivery method from the user's point of view However, within the blockchain, Cherry Points, a virtual currency based on the user ID, are issued and delivered to the beneficiary, while all transactions and the beneficiary's usage history are managed on the blockchain. By adopting this method, there was an improvement in blockchain performance, with transaction processing exceeding 1,000 TPS in typical transaction condition and service completion within 21.3 seconds. By applying the automatic influence control algorithm to this system, the influence according to stake, which is an individual donation, is greatly reduced to 0.3 after 2 months, thereby concentrating influence could be controlled automatically. In addition, it was designed to enable micro tracking by adding a tracking function by timestamp to the donation ledger for each individual ID, which greatly improved the transparency in the use of donations. From a service perspective, existing blockchain donation systems were handled as limited donation delivery methods. Since it is a direct service in a user-independent method, convenience has been greatly improved by delivering donations in various forms.

• Food Science and Preservation
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• v.30 no.3
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• pp.483-491
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• 2023

This study aimed to compare the bioactive compounds in Centella asiatica (C. asiatica) cultivated in a smart farm and a field and their effects on human keratinocyte cells. C. asiatica was collected in Jeju-do, Korea, and cultured in a smart farm and a field. The main bioactive compounds in the two differentially cultured C. asiatica were identified, and their activation in keratinocytes were assessed. Amplification and sequencing of the internal transcribed spacer (ITS) DNA in the nucleus and psbA-H DNA in the chloroplast were performed for species analysis. A comparison of DNA of plants reported in the NCBI GenBank was performed. The ITS DNA and psbA-H DNA sequences of C. asiatica cultivated in a smart farm and a field were consistent with No. MH768338.1 and No. JQ425422.1, respectively. Analysis of the triterpenes was performed using high performance liquid chromatography (HPLC) and as a result, C. asiatica cultured in a smart farm had more triterpenes than those cultured in a field. The effects of C. asiatica grown in a smart farm on cell proliferation and scratch recovery in HaCaT cells were greater than those grown in a field. These results suggest that C. asiatica cultivated in a smart farm can be effectively utilized as a health functional food.

• Journal of Korean Society of Forest Science
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• v.107 no.1
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• pp.96-107
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• 2018

This study analyzed the linkage between the Forestry sector Official Development Assistance (ODA) Project in South Korea and the Sustainable Development Goals (SDGs) of United Nations (UN), Suggested direction of ODA project focusing on the implementation of the SDGs. Forestry sector ODA project data in South Korea have collected from Economic Development Cooperation Fund (EDCF) statistical inquiry system developed by The Export-Import Bank of Korea. According to the analysis result, Forestry sector ODA project in South Korea have been actively implemented in the fields of forestry development, forestry policy and administration. In both fields, Korea Forest Service and Korea International Cooperation Agency (KOICA) carried out the most projects. The Forestry sector ODA project data in South Korea are classified technical development, capacity building, construction of infrastructure and afforestation based on their objectives and contents. SDGs emphasizes the importance of national implementation assessment and this study analyze linkage between ODA activity content in each classification item and 2016 Korea Forest Service Performance Management Plan indicator. Analyzed the 2016 Korea Forest Service Performance Management Plan indicator and SDGs target and SDGs indicator were identified. finally, SDGs goals were recognized. In conclusion, Forestry sector ODA project in South Korea are associated with the SDGs Goal 1 (No Poverty), Goal 2 (Zero Hunger), Goal 6 (Clean Water and Sanitation), Goal 13 (Climate Action), Goal 15 (Life on Land) and Goal 17 (Partnership for The Goals). Therefore, With the launch of the SDGs, This study analyzed the linkage among the Forestry sector ODA Project in South Korea, the 2016 Korea Forest Service Performance Management Plan and the SDGs. it presented the limitations of Forestry sector ODA Project in South Korea and made proposals for the implementation of the SDGs.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.19-43
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• 2016

This study examines the causes of the Asian exchange rate crisis and compares it to the European Monetary System crisis. In 1997, emerging countries in Asia experienced financial crises. Previously in 1992, currencies in the European Monetary System had undergone the same experience. This was followed by Mexico in 1994. The objective of this paper lies in the generation of useful insights from these crises. This research presents a comparison of South Korea, United Kingdom and Mexico, and then compares three different models for prediction. Previous studies of economic crisis focused largely on the manual construction of causal models using linear techniques. However, the weakness of such models stems from the prevalence of nonlinear factors in reality. This paper uses a structural equation model to analyze the causes, followed by a neural network model to circumvent the linear model's weaknesses. The models are examined in the context of predicting exchange rates In this paper, data were quarterly ones, and Consumer Price Index, Gross Domestic Product, Interest Rate, Stock Index, Current Account, Foreign Reserves were independent variables for the prediction. However, time periods of each country's data are different. Lisrel is an emerging method and as such requires a fresh approach to financial crisis prediction model design, along with the flexibility to accommodate unexpected change. This paper indicates the neural network model has the greater prediction performance in Korea, Mexico, and United Kingdom. However, in Korea, the multiple regression shows the better performance. In Mexico, the multiple regression is almost indifferent to the Lisrel. Although Lisrel doesn't show the significant performance, the refined model is expected to show the better result. The structural model in this paper should contain the psychological factor and other invisible areas in the future work. The reason of the low hit ratio is that the alternative model in this paper uses only the financial market data. Thus, we cannot consider the other important part. Korea's hit ratio is lower than that of United Kingdom. So, there must be the other construct that affects the financial market. So does Mexico. However, the United Kingdom's financial market is more influenced and explained by the financial factors than Korea and Mexico.