• Information Systems Review
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• v.12 no.2
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• pp.89-104
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• 2010

Recently, ubiquitous sensor network (USN) has been applied to many areas including environment monitoring. A few studies applied the USN to disaster prevention and emergency management, in particular, aiming to conserve cultural heritage. USN is an useful technology to do online real-time monitoring for the purpose of early detection of the fire which is a critical cause of damage and destruction of cultural heritages. It is necessary to online monitor the cultural heritages that human has a difficulty to access or their external appearance and beauty are important, by using the USN. However, there exists false warning from USN-based monitoring systems without human intervention. In this paper, we presented an alternative to resolve the problem by applying ontology. Our intelligent fire early detection systems for conserving cultural heritages are based on ontology and inference rules, and tested under laboratory environments.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.444-455
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• 2009

Great concerns on geotechnical risk & hazard assessment have been increased due to human and economic damage by natural disasters with recent global climate changes. In this paper, geotechnical problems in particular, landslides which is interested in European countries and North America, were mainly discussed. For these, 18 key topics on geotechnical risk and hazards which had been discussed at the LARAM 2008 workshop in Italy were analyzed after grouping by subjects. Main topic contents consisted of applications such as field measurement, early warning systems, uncertainty analysis of parameters using radar, optical data and statistical theory and so on. And the problems related to analysis of vulnerability and deformation due to earthquakes, investigation of gas zone using seismic reflection data in a landslide area, risk quantification and hazard assessment of landslide movements and multi-dimensional analysis for stability of complex slopes were attracted. Also, there were studies on risk matters of cultural heritage, the blockglide of clayey ground, simulations of debris flows based on GIS, quantification of the failure processes of rock slopes, a meshless method for 3D crack modelling, and finally risk assessment for cryological processes due to global warming.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.339-342
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• 2008

Recently the life and property damage caused by urban inundation have increased. In order to prevent the damage by inundation the researches for displaying the flooded areas through integrating SWMM and GIS have been progressed. However most of flood analysis systems only have used the GIS to display the flooded areas, and don't provide the integration disaster information to prevent the inundation. In this paper, we design a prototype for the Map Viewer based Spatial DB for the integrated urban floooded area management system. And we implement the spatial DB conversion module.

• Structural Monitoring and Maintenance
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• v.6 no.2
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• pp.161-171
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• 2019

There have been a few checking frameworks intended to ensure and improve the nature of their regular habitat. The greater part of these frameworks are constrained in their capacities. In this paper, the insightful checking framework intended for debacle help and administrations has been exhibited. The ideal administrations, necessities and coming about plan proposition have been indicated. This has prompted a framework that depends fundamentally on ecological examination so as to offer consideration and security administrations to give the self-governance of indigenous habitats. In this sense, ecological acknowledgment is considered, where, in light of past work, novel commitments have been made to help include based and PC vision situations. This epic PC vision procedure utilized as notice framework for avalanche identification depends on changes in the normal landscape. The multi-criteria basic leadership strategy is used to incorporate slope data and the level of variety of the highlights. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• Smart Structures and Systems
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• v.30 no.6
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• pp.557-569
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• 2022

Vibration-based structural health monitoring (SHM) is crucial for the dynamic maintenance of civil building structures to protect property security and the lives of the public. Analyzing these vibrations with modern artificial intelligence and deep learning (DL) methods is a new trend. This paper proposed an unsupervised deep learning method based on a convolutional autoencoder (CAE), which can overcome the limitations of conventional supervised deep learning. With the convolutional core applied to the DL network, the method can extract features self-adaptively and efficiently. The effectiveness of the method in detecting damage is then tested using a benchmark model. Thereafter, this method is used to detect damage and instant disaster events in a rubber bearing-isolated gymnasium structure. The results indicate that the method enables the CAE network to learn the intact vibrations, so as to distinguish between different damage states of the benchmark model, and the outcome meets the high-dimensional data distribution characteristics visualized by the t-SNE method. Besides, the CAE-based network trained with daily vibrations of the isolating layer in the gymnasium can precisely recover newly collected vibration and detect the occurrence of the ground motion. The proposed method is effective at identifying nonlinear variations in the dynamic responses and has the potential to be used for structural condition assessment and safety warning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.896-898
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• 2012

Recently, many countries are making efforts for the development of ocean resources because the necessity and importance of the ocean resources are increased. Underwater sensor networks have emerged as a very powerful technique for many applications, including monitoring, measurement, surveillance and control and envisioned to enable applications for oceanographic data collection, ocean sampling, environmental and pollution monitoring, offshore exploration, disaster prevention, tsunami and seaquake warning, assisted navigation, distributed tactical surveillance, and mine reconnaissance. The idea of applying sensor networks into underwater environments (i.e., forming underwater sensor networks) has received increasing interests in monitoring aquatic environments for scientific, environmental, commercial, safety, and military reasons. The data obtained by observing around the environment are wireless-transmitted by a radio set with various waves. According to the technical development of the medium set, some parameters restricted in observing the ocean have been gradually developed with the solution of power, distance, and corrosion and watertight by the seawater. The actual matters such as variety of required data, real-time observation, and data transmission, however, have not enough been improved just as various telecommunication systems on the land. In this paper, a wireless management system will be studied through a setup of wireless network available at fishery around the coast, real-time environmental observation with RF sensor, and data collection by a sensing device at the coastal areas.

• Journal of Korea Water Resources Association
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• v.30 no.6
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• pp.691-698
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• 1997

The risk assessment model for dam and levee is applied to a river where two adjacent dams are located in the upstream of the watershed. "A" dam is proven to be safe with 200-year precipitation and unsafe with PMP condition, whereas "B" dam to be safe with 200-year precipitation and PMP condition. The computed risk considering the uncertainties of the runoff coefficient. initial water depth and relevant data of the dam and spillway turn out to be equivalent results in Monte-Carlo and AFOSM method. In levee risk model, this study addresses the uncertainty of water surface elevation by Manning's equation. Monte-Carlo simulation with the variations of Manning's roughness coefficient is calculated by assuming that it follows atriangular distribution. The model can be used for preparing flood risk maps, flood warning systems, and establishing nation's flood disaster protection plan.

• Journal of the Korean Society of Hazard Mitigation
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• v.3 no.2 s.9
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• pp.111-118
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• 2003

Korea High-Speed Railway has various safety systems to secure safe and stable transportation and makes assurance doubly sure to minimize casualty and property damage caused by natural disasters. But, there is no regulation that reflects domestic railway line, climate, topographical and geological characteristic in introducing the foreign regulations yet. Therefore, it is necessary for us to modify a regulation which is suitable to domestic high-speed railway. In this study, it is possible to establish more reasonable boundary rainfall by grasping and improving the troubles with existing boundary rainfall that is scheduled to be utilized for high-speed rail-transport operation control under rainfall. Also, it is possible to insure the safety of train by four steps such as 'normal operation', 'warning issue', 'train speed control' and 'train stop' using the established boundary rainfall. It will go far toward minimizing the occurrence of natural disasters.