• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.28 no.2
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• pp.230-239
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• 2018

Objective: The purpose of this study is to identify the kinds and exposure levels of health hazards in the metalworking process in relation to acute poisoning accidents caused by methanol in 2016. Methods: The number of industries, workplaces, exposed workers, regional distribution, and exposure level of health hazards in metalworking process were investigated based on employee exposure assessment database provided by KOSHA (the Korea Occupational Safety and Health Agency), which was collected from workplace hazard evaluation programs in Korea. Exposure metrics for methanol were assessed by RCR (risk characterization ratio). Results: The numbers of processes, workplaces, and exposed workers of metalworking, which include CNC (computer numerical control) were 25, 14,405, and 169,102 respectively. The numbers of samples of chemical hazards including methanol were 91,325, and it was found that workers in metalworking were exposed to 249 kinds of chemical hazards. There were 16 kinds of special controlled substances including beryllium. It is estimated that the number of workplaces involving CNC process was 2,537, and the number of exposed workers was 27,976. In CNC process, the total number of workplaces handling methanol was 36, and 298 workers were estimated to be exposed. There was no exceeded that surpassed the OEL and 49% of samples were below the limit of detection. Methanol exposure concentrations in Gyeonggido Province were statistically significantly higher than in other areas (p <0.0001). Conclusions: In the metalworking process including CNC, there is exposure to a wide variety of health hazards. There was no sample exceeding the OEL for methanol. Therefore, it is necessary to recognize the limits of the employee exposure assessment system and urgently improve measures to prevent the occurrence of events like methanol poisoning.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.303-311
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• 2017

As new information technology emerges, companies are introducing an Enterprise Security Management system to cope with new security threats, reducing redundant investments and waste of resources and counteracting security threats. Therefore, it is necessary to construct a security evaluation metric based on related standards to demonstrate that the Enterprise Security Management(ESM) System meets security. Therefore, in order to construct a metric for evaluating the security of the ESM, this study analyzed the security quality related requirements of the ESM and constructed a metric for measuring the degree of satisfaction. This metric provides synergies through the unification of security assessments that comply with ISO/IEC 15408 and ISO/IEC 25000 standards. It is expected that the evaluation model of the security quality level of ESM will be established and the evaluation method of ESM will be standardized in the future.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Korean Journal of Exercise Nutrition
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• v.25 no.4
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• pp.24-37
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• 2021

[Purpose] To determine whether physical activity (PA), primarily the recommended 60 minutes of moderate-to-vigorous PA, is associated with gut bacterial microbiota in 10-year-old children. [Methods] The Block Physical Activity Screener, which provides minutes/day PA variables, was used to determine whether the child met the PA recommendations. 16S rRNA sequencing was performed on stool samples from the children to profile the composition of their gut bacterial microbiota. Differences in alpha diversity metrics (richness, Pielou's evenness, and Faith's phylogenetic diversity) by PA were determined using linear regression, whereas beta diversity (unweighted and weighted UniFrac) relationships were assessed using PERMANOVA. Taxon relative abundance differentials were determined using DESeq2. [Results] The analytic sample included 321 children with both PA and 16S rRNA sequencing data (mean age [SD] =10.2 [0.8] years; 54.2% male; 62.9% African American), where 189 (58.9%) met the PA recommendations. After adjusting for covariates, meeting the PA recommendations as well as minutes/day PA variables were not significantly associated with gut richness, evenness, or diversity (p ≥ 0.19). However, meeting the PA recommendations (weighted UniFrac R² = 0.014, p = 0.001) was significantly associated with distinct gut bacterial composition. These compositional differences were partly characterized by increased abundance of Megamonas and Anaerovorax as well as specific Christensenellaceae_R-7_group taxa in children with higher PA. [Conclusion] Children who met the recommendations of PA had altered gut microbiota compositions. Whether this translates to a reduced risk of obesity or associated metabolic diseases is still unclear.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1161-1171
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• 2018

Objective: The aim of this study was to investigate diffusion tensor (DT) imaging-derived properties of benign oligemia, true "at risk" penumbra (TP), and the infarct core (IC) during the first 3 hours of stroke onset. Materials and Methods: The study was approved by the local animal care and use committee. DT imaging data were obtained from 14 rats after permanent middle cerebral artery occlusion (pMCAO) using a 7T magnetic resonance scanner (Bruker) in room air. Relative cerebral blood flow and apparent diffusion coefficient (ADC) maps were generated to define oligemia, TP, IC, and normal tissue (NT) every 30 minutes up to 3 hours. Relative fractional anisotropy (rFA), pure anisotropy (rq), diffusion magnitude (rL), ADC (rADC), axial diffusivity (rAD), and radial diffusivity (rRD) values were derived by comparison with the contralateral normal brain. Results: The mean volume of oligemia was $24.7{\pm}14.1mm^3$, that of TP was $81.3{\pm}62.6mm^3$, and that of IC was $123.0{\pm}85.2mm^3$ at 30 minutes after pMCAO. rFA showed an initial paradoxical 10% increase in IC and TP, and declined afterward. The rq, rL, rADC, rAD, and rRD showed an initial discrepant decrease in IC (from -24% to -36%) as compared with TP (from -7% to -13%). Significant differences (p < 0.05) in metrics, except rFA, were found between tissue subtypes in the first 2.5 hours. The rq demonstrated the best overall performance in discriminating TP from IC (accuracy = 92.6%, area under curve = 0.93) and the optimal cutoff value was -33.90%. The metric values for oligemia and NT remained similar at all time points. Conclusion: Benign oligemia is small and remains microstructurally normal under pMCAO. TP and IC show a distinct evolution of DT-derived properties within the first 3 hours of stroke onset, and are thus potentially useful in predicting the fate of ischemic brain.

• The Journal of Society for e-Business Studies
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• v.24 no.3
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• pp.1-18
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• 2019

Due to the great advances in e-Marketplace and changes in type of items purchased from the online market, it has been dramatically increased the demand of the storage and transportation under the special conditions such as restricted temperature. Especially, the cold chain needs the way to transparently measure and monitor the entire network in realtime because it has a very complicated structure and requires totally different criteria at the every different steps and items. In this research, it has been presented the performance evaluation metrics to make contract using service level agreement (SLA), the way to apply the smart contract based on blockchain, the structure of blocks, service platform and application in order to build cold chain which can prevent the risk factors by measuring and sharing information in realtime using block chain technology. In addition, we have proposed the way to store the measured performance and reputation of each player in the block using smart contract based on SLA. With the presented framework, all players including service providers as well as users can secure the information for making the rational decisions. When the service platform is actually built and operated, it seems possible to secure the information in transparently and realtime. Also, it is possible to prevent the risk factors or prepare the preemptive plans to react on them.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of Applied Biological Chemistry
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• v.57 no.3
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• pp.235-242
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• 2014

The purpose of this study is the establishment of scientific processes for making food safety policies. Thus, we investigated pesticide residue level of the agricultural commodities from market, and performed risk assessment. Fifteen agricultural items are chosen based on the frequency of Korean consumption. The samples were collected from 9 cities where populations are more than one million. Total 283 active ingredients were monitoring ( total sample number =232). Single-analysis of target pesticides was for three kinds of possible growth regulators and the multicomponent analysis was for 280 kinds of pesticides, a total of 283 species were selected to perform the pesticide residues. Before monitoring the analytes, the improvements of the analytical methods were done by method validations under the CODEX analytical method development guidelines and can produce metrics that represent the international standards applied in accordance with the guidelines. In addition to residual pesticides detected during monitoring we compare the ADI to EDI values using detected result and dietary consumption data which is extracted from annual market basket survey. The 163 samples were non-detected in the total 232 samples so it means that every agricultural commodity will residual pesticides-free in 70.3%. The detected residual pesticides showed for a total of 69 cases (29.7%). Two of samples violate Korean MRL (0.9%). The ratio of EDI compared to ADI resulted in only from 0.00087 to 0.902%. In result, we can assume that all detected residual pesticides are very safe level and current policies of Korean pesticides control may be working.

• 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.