• Journal of the Korean Geosynthetics Society
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• v.23 no.2
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• pp.43-52
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• 2024

Recent abnormal climate conditions have increased the risk of slope collapses, which frequently result in significant loss of life and property due to the absence of early prediction and warning dissemination. In this paper, we develop a slope condition analysis system using IoT sensors and AI-based camera to assess the condition of slopes. To develop the system, we conducted hardware and firmware design for measurement sensors considering the ground conditions of slopes, designed AI-based image analysis algorithms, and developed prediction and warning solutions and systems. We aimed to minimize errors in sensor data through the integration of IoT sensor data and AI camera image analysis, ultimately enhancing the reliability of the data. Additionally, we evaluated the accuracy (reliability) by applying it to actual slopes. As a result, sensor measurement errors were maintained within 0.1°, and the data transmission rate exceeded 95%. Moreover, the AI-based image analysis system demonstrated nighttime partial recognition rates of over 99%, indicating excellent performance even in low-light conditions. Through this research, it is anticipated that the analysis of slope conditions and smart maintenance management in various fields of Social Overhead Capital (SOC) facilities can be applied.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.760-767
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• 2020

Sustainable growth of hydroelectric power plants is expected in consideration of climate change and energy security. However, hydroelectric power plants always have a risk of water hammer damage, and safety assurance is very important. The water hammer phenomenon commonly occurs during operations such as rapid opening and closing of the valves and pump/turbine shutdown in pipe systems, which is more common in cases of emergency shutdown. In this study, a computational numerical model was developed using the MOC-FDM scheme to reflect the mechanism of water hammer occurrence. The proposed model was implemented in boundary conditions such as reservoir, pipeline, valve, and pump/turbine conditions and then applied to simulate hypothetical case studies. The analysis results of the model were verified using the analysis results at the main points of the pipe systems. The model produced reasonably good performance and was validated by comparison with the results of the SIMSEN package model. The model could be used as an efficient tool for the safety assessment of hydroelectric power plants based on accurate prediction of transient behavior in the operation of hydropower facilities.

• Ecology and Resilient Infrastructure
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• v.2 no.2
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• pp.185-190
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• 2015

Greenhouse gas (GHG) emission from agricultural lands is recognized as one of important factors of global warming. The objective of this short communication was to evaluate the applicability of different soil GHG emission prediction models on agricultural systems in Korea. Four models, namely, DNDC, DAYCENT, EXPERT-N and COUP, were selected and the basic structure (e.g., components and sub-model), input variables, and output variables were compared. In particular, the availability and compilation of essential input variables were assessed. Major input variables needed for operating these predictive models were found to be available through database systems established by national organizations such as the Korea Meteorological Administration, the Korean Soil Information System, and the Rural Development Administration. However, in order to apply these models in Korea, it was necessary to calibrate and validate each of the models for the domestic landscape settings and climate conditions. In addition, field data of long-term monitoring of GHG emission from agricultural lands are limited and therefore should be measured.

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.801-806
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• 2015

The incidence of flood damage by global climate change has increased recently. Because of the increased frequency of flooding in Korea, the technology of flood prediction and prevalence has developed mainly for large river watersheds. On the other hand, there is a limit on predicting flooding through the most present flood forecasting systems because local floods in small watersheds rise quite quickly with little or no advance warning. Therefore, this study estimated the flood warning rainfall using a flood forecasting model at the two alarm trigger points in the Suamcheon basin, which is an urban basin with backwater effects. The flood warning rainfall was estimated to be 25.4mm/120min ~ 78.8mm/120min for the low water alarm, and 68.5mm/120min ~ 140.7mm/120min for the high water alarm. The frequency of the flood warning rainfall is 3-years for the low water alarm, and 80-years for the high water alarm. The results of this analysis are expected to provide a basic database in forecasting local floods in urban watersheds. Nevertheless, more tests and implementations using a large number of watersheds will be needed for a practical flood warning or alert system in the future.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.291-298
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• 2015

There are many landslides occurred by typhoons and intense rainfall during the summer seasons in Korea. To predict a landslide triggering it is important to understand mechanisms and potential areas of landslides by the geological approaches. However, recent climate changes make difficult to predict landslide based on only conventional prediction methods. Therefore, the importance of a real-time monitoring of landslide using various sensors is emphasized in recent. Many researchers have studied monitoring techniques of landslides and suggested several monitoring systems which can be applicable to the natural terrain. Most sensors of landslide monitoring measure slope displacement, hydrogeologic properties of soils and rocks, changes of stress in soil and rock fractures, and rainfall amount and intensity. The measured values of each sensor are transmitted to a monitoring server in real-time. The ultimate goal of landslide monitoring is to warn landslide occurrence in advance and to reduce damages induced by landslides. This study introduces the current situation of landslide monitoring techniques in each country.

• Journal of Bio-Environment Control
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• v.14 no.2
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• pp.95-105
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• 2005

This study was carried out to file up using effect and requirement of energy for environmental design data of Pleurotus eryngii growing houses. Heating and cooling Degree-Hour (D-H) were calculated and compared for. some Pleurotus eryngii growing houses of sandwich-panel (permanent) o. arch-roofed(simple) type structures modified and suggested through field survey and analysis. Also thermal resistance (R-value) was calculated for the heat insulating and covering materials of the permanent and simple-type, which were made of polyurethane or polystyrene panel and $7\~8$ layers heat conservation cover wall. The variations of heating and cooling D-H simulated for Jinju area was nearly linearly proportional to the setting inside temperatures. The variations of cooling D-H was much more sensitive than those of heating D-H. Therefore, it was expected that the variations of required energy in accordance with setting temperature or actual temperature maintained inside of the cultivation house could be estimated and also the estimated results of heating and cooling D-H could be effectively used far the verification of environmental simulation as well as for the calculation of required energy amounts. When the cultivation floor areas are all equal, panel type houses to be constructed by various combinations of materials were found to by far more effective than simple type pipe house in the aspect of energy conservation maintenance except some additional cost invested initially. And also the energy effectiveness of multi-span house compared to single span together with the prediction of energy requirement depending on the level insulated for the wall and roof area could be estimated. Additionally, structural as well as environmental optimizations are expected to be possible by calculating periodical and/or seasonal energy requirements for those various combinations of insulation level and different climate conditions, etc.

• Atmosphere
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• v.23 no.1
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• pp.85-92
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• 2013

We estimate atmospheric radiative heating effect of aerosols, based on AErosol RObotic NETwork (AERONET) and lidar observations and radiative transfer calculations. The column radiation model (CRM) is modified to ingest the AERONET measured variables (aerosol optical depth, single scattering albedo, and asymmetric parameter) and subsequently calculate the optical parameters at the 19 bands from the data obtained at four wavelengths. The aerosol radiative forcing at the surface and the top of the atmosphere, and atmospheric absorption on pollution (April 15, 2001) and dust (April 17~18, 2001) days are 3~4 times greater than those on clear-sky days (April 14 and 16, 2001). The atmospheric radiative heating rate (${\Delta}H$) and heating rate by aerosols (${\Delta}H_{aerosol}$) are estimated to be about $3\;K\;day^{-1}$ and $1{\sim}3\;K\;day^{-1}$ for pollution and dust aerosol layers. The sensitivity test showed that a 10% uncertainty in the single scattering albedo results in 30% uncertainties in aerosol radiative forcing at the surface and at the top of the atmosphere and 60% uncertainties in atmospheric forcing, thereby translated to about 35% uncertainties in ${\Delta}H$. This result suggests that atmospheric radiative heating is largely determined by the amount of light-absorbing aerosols.