• Research in Plant Disease
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• v.29 no.3
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• pp.316-320
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• 2023

Recently, the domestic plant disease control policy for fire blight has been implemented partial control in addition to burial control. In this study, an on-site management survey was conducted targeting orchards that implemented partial disease control from 2019 to 2020 in order to find efficient implementation methods for partial disease control. As a result of an investigation into 22 pear and apple orchards in Cheonan and Chungju, 7 orchards were buried. The upper part of the cut infected plants was burned at 16 orchards and covered with plastic vinyl after lime treatment at 6 orchards. The lower stumps of cut infected plants were burned at 7 orchards and covered with plastic vinyl after lime treatment at 15 orchards. There were two orchards where suckers appeared on the stumps even though covers were applied. There was no infection by Erwinia amylovora in the suckers. The conservation condition of lime treatment was good, but warning signs were absent at 6 orchards. Most orchards treated the stumps and surrounding areas with glyphosate-isopropylamine herbicide. The effect of partial control was judged to be safe.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.317-321
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• 2022

In response to Incheon tap water accident in 2019, the Ministry of Environment has created the "Comprehensive Measures for Water Safety Management" to improve water operation management, provide systematic technical support, and respond to accidents. Accordingly, K-water is making a smart water supply management system for the entire process of tap water. In order to advance the response to water accidents, it is essential to secure the reliability of real-time water operation data such as flow rate, pressure, and water level, and to develop and apply a warning algorithm in advance using big data analysis techniques. In this paper, various statistical techniques are applied using water supply operation data (flow, pressure, water level, etc) to prepare the foundation for the selection of the optimal operating range and advancement of the monitoring and alarm system. In addition, the arrival time is analyzed through cross-correlation analysis of changes in raw water turbidity between the water intake and water treatment plants. The purpose of this paper is to study the model that predicts the raw water turbidity of a water treatment plant by applying raw water turbidity data considering the time delay according to the flow rate change.

• Korean Journal of Ecology and Environment
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• v.56 no.1
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• pp.1-13
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• 2023

Targeting Microcystin (MC), which is most abundantly detected in the North-Han River water area, we analyzed the relationship between the MC biosynthesis gene (mcyA gene), cyanobacteria cell density, and MC concentration, derived an RNA-MC conversion formula, and derived the cyanobacteria. The concentration of MC present in cells was predicted. In the North-Han River waters, the mcyA gene was found mainly at downstream sites of the North-Han River after Muk-Hyeon Stream junction, and higher copy numbers were found on average than other sites. In the Uiam Lake waters upstream of the North-Han River, the mcyA gene copy number increased at the Kong-Ji Stream point, and after September, the mcyA gene copy number decreased throughout the North-Han River waters. The expression of the mcyA gene was concentrated in the short period of summer due to the spatio-temporal difference between upstream and downstream water bodies. The mcyA gene expression level was not only highly correlated with MC concentration, but also correlated with the cell density of Microcystis aeruginosa and Dolichospermum circinale, which are known to biosynthesize MC. Six conversion formulas derived based on the RNA-MC relationship showed statistical significance (p<0.05) and exhibited high correlation coefficients (r) of 0.9 or higher. The expression level of MC biosynthesis gene present in eRNA determines the synthesis of cyanotoxin substances in water, quickly quantifies gene activity, and can be fully utilized for early warning of MC development.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.703-709
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• 2023

It is most desirable to train with a real crane in an environment similar to that of a port for crane operation training in charge of loading and unloading in a port, but it has time and space limitations and cost problems. In order to overcome these limitations, VR(Virtual Reality) based crane training programs and related devices are receiving a lot of attention. In this paper, we designed and implemented a VR-based harbor crane simulator operating on an HMD. The simulator developed in this paper consists of a crane simulator program that operates on the HMD, an IoT driving terminal that processes trainees' crane operation input, and a training server that stores trainees' training information. The simulator program provides VR-based crane training scenarios implemented with Unity3D, and the IoT driving terminal developed based on Arduino is composed of two controllers and transmits the user's driving operation to the HMD. In particular, the crane simulator in this paper uses a training server to create a database of environment setting values for each educator, progress and training time, and information on driving warning situations. Through the use of such a server, trainees can use the simulator in a more convenient environment and can expect improved educational effects by providing training information.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.1
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• pp.61-67
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• 2022

Intraoperative neurophysiological monitoring (INM) ensures the stability and safety of specific surgeries in high-risk groups. As part of INM, intensive tests are conducted during the surgical process. When INM tests are applied during surgery, a delay in notifying the operating surgeon in cases of neurological defects can cause serious irreversible sequelae to the patient. Aortic replacement, which is necessitated due to aortic aneurysms and aortic dissection, is a complicated procedure that blocks the blood flow to the heart. When arteries that branch out from the aorta and supply blood to the spinal cord are replaced, blood flow to the spinal cord decreases, resulting in spinal ischemia. In aortic surgery, INM plays an important role in preventing spinal ischemia and serious complications by quickly detecting the early signs of spinal ischemia during cross-clamping and reporting it to the surgeon. Therefore, this paper was prepared to help examiners who conduct INM by detailing the process, method, time, and warning criteria for INM. This paper identifies the need for INM in aortic surgery and the process flow for a smooth test, accurate and rapid examination, and subsequent reporting.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5B
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• pp.429-439
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• 2009

When Catastrophic extreme flood occurs due to dam break, the response time for flood warning is much shorter than for natural floods. Numerical models can be powerful tools to predict behaviors in flood wave propagation and to provide the information about the flooded area, wave front arrival time and water depth and so on. But flood wave propagation due to dam break can be a process of difficult mathematical characterization since the flood wave includes discontinuous flow and dry bed propagation. Nevertheless, a lot of numerical models using finite volume method have been recently developed to simulate flood inundation due to dam break. As Finite volume methods are based on the integral form of the conservation equations, finite volume model can easily capture discontinuous flows and shock wave. In this study the numerical model using Riemann approximate solvers and finite volume method applied to the conservative form for two-dimensional shallow water equation was developed. The MUSCL scheme with surface gradient method for reconstruction of conservation variables in continuity and momentum equations is used in the predictor-corrector procedure and the scheme is second order accurate both in space and time. The developed finite volume model is applied to 2D partial dam break flows and dam break flows with triangular bump and validated by comparing numerical solution with laboratory measurements data and other researcher's data.

• Journal of the Korean Geotechnical Society
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• v.39 no.1
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• pp.15-25
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• 2023

Recent weather changes have led to an increase in heavy rainfall resulting in frequent large-scale slope failures. To minimize damage to life and property, a measurement system is used in slope failure warning systems. However, understanding the slope failure behavior is difficult as the measurement system only measures a specific point. Therefore, numerical analysis must be p erformed with the measurement system. The soil water characteristic curve (SWCC) drying curve and boundary conditions that consider evapotranspiration and precipitation have been applied to numerical analysis, but the hysteresis of SWCC affects the numerical analysis results. To address this, a new evapotranspiration calculation method is proposed and applied to boundary conditions, and the measurement data are compared with the results of the numerical analysis. This method takes into account the different infiltration behaviors on evapotranspiration according to the drying and wetting curves of the SWCC, and allows for a more rational prediction of water movement on unsaturated slopes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.551-560
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• 2009

This paper describes a newly developed pollutant transport model named ARPTM which was designed to simulate the transport and characteristics of pollutant materials after an accidental spill in upstream of river system up to a given position in the downstream. In particular, the ARPTM incorporated ADCP data to compute longitudinal dispersion coefficient and advection velocity which are necessary to apply one-dimensional advection-dispersion equation. ARPTM was built on top of the geographic information system platforms to take advantage of the technology's capabilities to track geo-referenced processes and visualize the simulated results in conjunction with associated geographic layers such as digital maps. The ARPTM computes travel distance, time, and concentration of the pollutant cloud in the given flow path from the river network, after quickly finding path between the spill of the pollutant material and any concerned points in the downstream. ARPTM is closely connected with a recently developed GIS-based Arc River database that stores inputs and outputs of ARPTM. ARPTM thereby assembles measurements, modeling, and cyberinfrastructure components to create a useful cyber-tool for determining and visualizing the dynamics of the clouds of pollutants while dispersing in space and time. ARPTM is expected to be potentially used for building warning system for the transport of pollutant materials in a large basin.

• Convergence Security Journal
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• v.23 no.2
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• pp.27-36
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• 2023

In recent years, ransomware attacks have become more organized and specialized, with the sophistication of attacks targeting specific individuals or organizations using tactics such as social engineering, spear phishing, and even machine learning, some operating as business models. In order to effectively respond to this, various researches and solutions are being developed and operated to detect and prevent attacks before they cause serious damage. In particular, honeypots can be used to minimize the risk of attack on IT systems and networks, as well as act as an early warning and advanced security monitoring tool, but in cases where ransomware does not have priority access to the decoy file, or bypasses it completely. has a disadvantage that effective ransomware response is limited. In this paper, this honeypot is optimized for the user environment to create a reliable real-time dynamic honeypot file, minimizing the possibility of an attacker bypassing the honeypot, and increasing the detection rate by preventing the attacker from recognizing that it is a honeypot file. To this end, four models, including a basic data collection model for dynamic honeypot generation, were designed (basic data collection model / user-defined model / sample statistical model / experience accumulation model), and their validity was verified.