• Journal of Wetlands Research
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• v.19 no.3
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• pp.366-374
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• 2017

The objective of this study is to explore the applicability of very short-range-forecast rainfall for the early warning of mud-debris flows. An artificial neural network was applied to use the very short-range-forecast rainfall data. The neural network is learned by using the relationship between the radar and the AWS, and forecasted rainfall is estimated by replacing the radar rainfall with the MAPLE data as the very short-range-forecast rainfall data. The applicability of forecasted rainfall by the MAPLE was compared with the AWS rainfall at the test-bed using the rainfall criteria for cumulative rainfall of 6hr, 12hr, and 24hr respectively. As a result, it was confirmed that forecasted rainfall using the MAPLE can be issued prior to the AWS warning.

• Journal of the Korean Geosynthetics Society
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• v.17 no.4
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• pp.11-19
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• 2018

Although many researches related to monitoring and automatic measuring devices for early warning system during slope failure have been carried out in Korea and aboard, most of the researches have installed measuring devices on the slope surface, and there are only few researches about warning systems that can detect and warn before slope failure and disaster occurs. In this study, slope failure simulation experiment was performed by attaching sensors to rock bolts, and initial slope behavior characteristics during slope failure were analyzed. Also, the experiment results were compared and reviewed with varied slope conditions, i.e. shotcrete slope and natural slope, and varied material conditions, i.e. GFRP and steel rock bolt. This study can be used as a basic data in development of warning and alarm system for early evacuation through early detection and warning before slope failure occurs in steep slopes and slope failure vulnerable areas.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.2
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.632-644
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• 2019

The remote monitoring and warning system for dangerous chemicals is designed with the concept of the Cyber-Physical System (CPS) in this paper. The real-time perception, dynamic control, and information service of major hazards chemicals are realized in this CPS system. The CPS system architecture, the physical layer and the applacation layer, are designed in this paper. The terminal node is mainly composed of the field collectors which complete the data acquisition of sensors and video in the physical layers, and the use of application layer makes CPS system safer and more reliable to monitor the hazardous chemicals. The cloud application layer completes the risk identification and the prediction of the major hazard sources. The early intelligent warning of the major dangerous chemicals is realized and the security risk images are given in the cloud application layer. With the CPS technology, the remote network of hazardous chemicals has been completed, and a major hazard monitoring and accident warning online system is formed. Through the experiment of the terminal node, it can be proved that the terminal node can complete the mass data collection and classify. With this experiment it can be obtained the CPS system is safe and effective. In order to verify feasible, the multi-risk warning based on CPS is simulated, and results show that the system solves the problem of hazardous chemicals enterprises safety management.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.156-165
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• 2000

This paper describes modeling and simulation of automotive forward collision warning and avoidance system using CASE(Computer-Aided Systems Engineering) tool. The system is composed or many sensors, a controller, warning devices, brakes and etc. The system was modeled by one activity chart, fourteen state charts and one module chart. Rear-end collision scenarios was generated by Simulink and used to support Stalemate model. The resulting model was used to evaluate the correctness of function and behavior of the system. A simulator for the system has been designed and used to validate the model under realistic operating conditions in the laboratory. To model and simulate the system's functionality and behavior brings clarity to system design early in the system development.

• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.849-855
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• 2006

Taste and odor (T&O) problems in drinking water supplies caused by eutrophication have become increasingly important because aesthetic qualities are the primary measures by which consumers estimate the quality of their drinking water. In order to overcome T&O problem, it is necessary to early detection method for T&O compounds before these compounds enter to water treatment plant. In this background, a early waming device for T&O compounds was developed and its performance tested under different operating condition. According to the experimental results on the adsorption efficiency of T&O compounds, when the raw water flowrate was 5 mL/min, the optimum stripping time and air flowrate were 5 hrs and 0.5 L/min, respectively. Comparison of activated carbon showed that foreign activated carbon was better than domestic activated carbon in terms of adsorption efficiency.

• The Plant Pathology Journal
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• v.17 no.6
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• pp.389.1-389
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• 2001

• Proceedings of the Korean Environmental Health Society Conference
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• 2006.06a
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• pp.162-169
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• 2006

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.10a
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• pp.169-177
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• 1994

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.21-28
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• 2016

Numeric weather prediction is important to prevent meteorological disasters such as heavy rain, heat wave, and cold wave. The Korea meteorological administration provides a realtime special weather report and the rural development administration demonstrates information about 2-day warning of agricultural disasters for farms in a few regions. To improve the early warning systems for meteorological hazards, a nation-wide high-resolution dataset for weather prediction should be combined with web-based GIS. This study aims to develop a web service prototype for early warning of meteorological hazards, which integrates web GIS technologies with a weather prediction database in a temporal resolution of 1 hour and a spatial resolution of 1 km. The spatially and temporally high-resolution dataset for meteorological hazards produced by downscaling of GME was serviced via a web GIS. In addition to the information about current status of meteorological hazards, the proposed system provides the hourly dong-level forecasting of meteorologic hazards for upcoming seven days, such as heavy rain, heat wave, and cold wave. This system can be utilized as an operational information service for municipal governments in Korea by achieving the future work to improve the accuracy of numeric weather predictions and the preprocessing time for raster and vector dataset.