• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1633-1641
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• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.127-134
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• 2022

Recently, policies and research to prevent increasing construction accidents have been actively conducted in the domestic construction industry. In previous studies, the prediction model developed to prevent construction accidents mainly used only structured data, so various characteristics of construction sites are not sufficiently considered. Therefore, in this study, we developed a machine learning-based construction accident prediction model that enables the characteristics of construction sites to be considered sufficiently by using both structured and text-type unstructured data. In this study, 6,826 cases of construction accident data were collected from the Construction Safety Management Integrated Information (CSI) for machine learning. The Decision forest algorithm and the BERT language model were used to train structured and unstructured data respectively. As a result of analysis using both types of data, it was confirmed that the prediction accuracy was 95.41 %, which is improved by about 20 % compared to the case of using only structured data. Conclusively, the performance of the predictive model was effectively improved by using the unstructured data together, and construction accidents can be expected to be reduced through more accurate prediction.

• Journal of Food Hygiene and Safety
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• v.37 no.5
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• pp.297-305
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• 2022

Antioxidants are food additives that extend the shelf life of food products by preventing lipid rancidity caused by active oxygen. They can either be naturally-derived or manufactured synthetically via chemical synthesis. In this study, method validation of five synthetic antioxidants, namely butylated hydroxyanisole, butylated hydroxytoluene, tertiary butylhydroquinone, propyl gallate, and disodium ethylenediaminetetraacetic acid, was performed using a high performance liquid chromatography-ultraviolet visible detector, and the method applicability was evaluated by analyzing foods containing antioxidants. The coefficient of determination (R²) average was 0.9997, while the limit of detection and limit of quantification were 0.02-0.53 and 0.07-1.61 mg/kg, respectively. The intra and inter-day accuracies and precisions were 83.2±0.7%-98.7±2.1% and 0.1%-5.7% RSD, respectively. Inter-laboratory validation for accuracy and precision was conducted using the Food Analysis Performance Assessment Scheme quality control material. The results satisfied the guidelines presented by the AOAC International. In addition, the expanded uncertainty was less than 16%, as recommended by CODEX. Consequently, to enhance public health safety, the results of this study can be used as basis data for evaluating the intake of synthetic antioxidants and assessing their risks in Korea.

• Korean Association of Arts Management
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• no.53
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• pp.101-125
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• 2020

This paper presents the problems and improvement methods in estimating demand and benefit, which have been controversial in the feasibility study of building cultural facilities. Although there are justifications for supplying cultural facilities by expanding leisure time and increasing income, the economic burden from the insolvent operation after construction is high. Feasibility studies can prevent these problems in advance. In order to estimate the demand for cultural facilities, similar facilities were selected and the gravity model was used to estimate the demand. In the future, it is necessary to prepare the criteria for setting the reference facility to increase the accuracy of the demand estimation. In addition, in the case of cultural facilities constructed through feasibility study, it is necessary to induce and enforce the disclosure of operational data and information, and to establish a database so that it can be used as a reference facility for demand estimation in future feasibility study on cultural facility. Accurate benefit estimation requires multiple CVM surveys. In addition to the current CVM survey, this paper suggest that supplementary online non-face-to-face surveys is considered. Furthermore, this research suggests that the use of video media for explanation of alternative materials for cultural facilities to be constructed because the WTP may be excessive due to lack of alternatives for survey respondents in the current CVM survey.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.383-392
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• 2022

The restoration of the Nakdong River estuary is one of the most important projects of the Ministry of Environment, Republic of Korea. A real-scale experiment of gate operation was executed from 2019 to 2020, and a pilot operation was performed in 2021. The gate of Nakdong River Estuary Barrier (NEB) is supposed to be continuously opened based on the experiment results. Many critical decisions should be made immediately during the experiment based on the real-time measured data and numerical analysis considering the seawater inflows. The decision-making sequence was made systematically with the accurate estimation of seawater inflow. The estimation of seawater inflow is the main research objective and the equations of seawater inflow were developed, reflecting the structural characteristics of NEB. The inflow equations were developed in two forms, overflow and underflow. ADCP (Acoustic Doppler Current Profiler) was used to measure seawater inflow, check the accuracy of the developed equations, and derive the flow coefficient. The comparison error of the developed equations was about 3% compared to the measured data.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.71-86
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• 2022

The water quality index (WQI) has been widely used to evaluate marine water quality. The WQI in Korea is categorized into five classes by marine environmental standards. But, the WQI calculation on huge datasets is a very complex and time-consuming process. In this regard, the current study proposed machine learning (ML) based models to predict WQI class by using water quality datasets. Sihwa Lake, one of specially-managed coastal zone, was selected as a modeling site. In this study, adaptive boosting (AdaBoost) and tree-based pipeline optimization (TPOT) algorithms were used to train models and each model performance was evaluated by metrics (accuracy, precision, F1, and Log loss) on classification. Before training, the feature importance and sensitivity analysis were conducted to find out the best input combination for each algorithm. The results proved that the bottom dissolved oxygen (DO_Bot) was the most important variable affecting model performance. Conversely, surface dissolved inorganic nitrogen (DIN_Sur) and dissolved inorganic phosphorus (DIP_Sur) had weaker effects on the prediction of WQI class. In addition, the performance varied over features including stations, seasons, and WQI classes by comparing spatio-temporal and class sensitivities of each best model. In conclusion, the modeling results showed that the TPOT algorithm has better performance rather than the AdaBoost algorithm without considering feature selection. Moreover, the WQI class for unknown water quality datasets could be surely predicted using the TPOT model trained with satisfactory training datasets.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.67-75
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• 2022

When acquiring a product having an OS, it is very important to identify the exact kernel version of the OS. This is because the product's administrator needs to keep checking whether a new vulnerability is found in the kernel version. Also, if there is an acquisition requirement for exclusion or inclusion of a specific kernel version, the kernel identification becomes critical to the acquisition decision. In the case of the Linux kernel used in various equipment, sometimes it becomes difficult to pinpoint the device's exact version. The reason is that many manufacturers often modify the kernel to produce their own firmware optimized for their device. Furthermore, if a kernel patch is applied to the modified kernel, it will be very different from its base kernel. Therefore, it is hard to identify the Linux kernel accurately by simple methods such as a specific file existence test. In this paper, we propose a static method to classify a specific kernel version by analyzing function names stored in the symbol table. In an experiment with 100 Linux devices, we correctly identified the Linux kernel version with 99% accuracy.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.4
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• pp.321-330
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• 2022

This study attempted to establish an optimal multi-compound simultaneous analysis method that can secure reliable results for 15 - preservatives, 2 - sun screens and 1 - antioxidants of cosmetics using HPLC-PDA. Since the potential of hydrogen (pH) in the mobile phase affects the acid dissociation constant (pKa) of the preservatives, and the peak retention time shift and area change were observed. The peak separation condition was established by adjusting the pH to 0.1% H₃PO₄ addition (mL) when preparing the mobile phase. As a results of method validation, the linearity correlation coefficient (R²) of above 0.999 were obtained, and accuracy 87.9 ~ 101.1%, 0.1 ~ 7.6% precision for two types of cosmetics (cream and shampoo). It was found that the limit of detection (LOD) was 0.1 ~ 0.2 mg/kg and the limit of quantitation (LOQ) was 2.0 ~ 4.0 mg/kg. In addition, it was possible to simultaneously separate p-anisic acid, a natural compound that was difficult to separate in HPLC due to the small difference from methylparaben, a synthetic preservatives. Through this study, it will be effectively used to secure quality control and safety for compound that need restrictions on use cosmetics.

• Journal of the Korean Institute of Gas
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• v.26 no.6
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• pp.30-36
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• 2022

In commercial transactions of energy sources using hydrogen charging stations, high-accuracy flow meters are needed to prevent accidents such as overcharging due to inaccurate measurements and to ensure transparency in hydrogen commercial transactions through accurate measurements. This research developed a Corioli-type flowmeter prototype and conducted a risk assessment to prevent accidents during a process change comparison experiment for existing charging stations to verify the measurement performance. A process change section was defined for the installation of measurement facilities for empirical experiments and HAZOP was conducted. In addition, JSA was also conducted to secure the safety of experimenters, such as preventing valve mis-opening during empirical experiments. Measures were established to improve the risk factors derived through HAZOP, and work procedures were established to minimize human errors and ensure the safety of workers through JSA. The design change and system manufacturing for the installation of the metering system were completed by reflecting the risk assessment results, and safety could be confirmed through the performance comparison test of the developed meter prototype. The developed prototype flow meter showed a total of 30 flow measurements under the operating conditions of 70 MPa, and the average error was -1.58% to 3.96%. Such a metering error was analyzed to have the same performance as a flow meter installed and operated for commercial use.