• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.67-74
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• 2023

Recently, Urban Aircraft Mobility (UAM) has been attracting attention as a transportation system of the future, and small drones also play a role in various industries. The failure of various types of aviation systems can lead to crashes, which can result in significant property damage or loss of life. In the defense industry, where aviation systems are widely used, the failure of aviation systems can lead to mission failure. Therefore, this study proposes an anomaly detection model using deep learning technology to detect anomalies in aviation systems to improve the reliability of development and production, and prevent accidents during operation. As training and evaluating data sets, current data from aviation systems in an extremely low-temperature environment was utilized, and a deep learning network was implemented using the convolutional neural network, which is a deep learning technique that is commonly used for image recognition. In an extremely low-temperature environment, various types of failure occurred in the system's internal sensors and components, and singular points in current data were observed. As a result of training and evaluating the model using current data in the case of system failure and normal, it was confirmed that the abnormality was detected with a recall of 98 % or more.

• Journal of the Korean GEO-environmental Society
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• v.24 no.4
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• pp.5-11
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• 2023

Impulse waves generated by landslides near water bodies can lead to fatal damage to human life and surrounding infrastructure. These impulse waves are generally called landslide-impulsed waves and occur without being limited to a specific area. Recently, localized torrential rains have frequently occurred due to the influence of abnormal weather, both the frequency and scale of landslides occurring in Korea are increasing. Therefore, in this study, the experiments were conducted according to the mass ratio of the landslide models, and among the characteristics of the generated landslide-impulse waves. And the wave amplitude was observed and analyzed. In this study, a total of 75 experiments were conducted by repeating the experiment 5 times for 15 cases with mass ratios of 5 landslide models and 3 types of slope angles. As a result of experiments with different mass ratios of landslide models, if the landslides have the same initial energy, the size of the landslide-impulse waves generated by mixing granular and block forms is higher than the size of the landslide-impulse waves generated by pure granular and block landslides. It is analyzed that the size may be larger.

• Journal of the Korean GEO-environmental Society
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• v.24 no.4
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• pp.23-29
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• 2023

Ground subsidence shows a mechanism in which the upper ground collapses due to the formation of a cavity due to the movement of soil particles in the ground due to the formation of a waterway because of damage to the water supply/sewer pipes. As a result, cavity is created in the ground and the upper ground is collapsing. Therefore, ground subsidence frequently occurs mainly in downtown areas where a large amount of underground facilities are buried. Accordingly, research to predict the risk of ground subsidence is continuously being conducted. This study tried to present a ground subsidence risk prediction model for two districts of ○○ city. After constructing a data set and performing preprocessing, using the property data of underground facilities in the target area (year of service, pipe diameter), density of underground facilities, and ground subsidence history data. By applying the dataset to the machine learning model, it is evaluated the reliability of the selected model and the importance of the influencing factors used in predicting the ground subsidence risk derived from the model is presented.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.189-200
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• 2022

Ground subsidence on urban roads is a social issue that can lead to human and property damages. Therefore, it is crucial to detect underground cavities in advance and repair them. Underground cavity detection is mainly performed using ground penetrating radar (GPR) surveys. This process is time-consuming, as a massive amount of GPR data needs to be interpreted, and the results vary depending on the skills and subjectivity of experts. To address these problems, researchers have studied automation and quantification techniques for GPR data interpretation, and recent studies have focused on deep learning-based interpretation techniques. In this study, we described a hyperbolic event detection process based on deep learning for GPR data interpretation. To demonstrate this process, we implemented a series of algorithms introduced in the preexisting research step by step. First, a deep learning-based YOLOv3 object detection model was applied to automatically detect hyperbolic signals. Subsequently, only hyperbolic signals were extracted using the column-connection clustering (C3) algorithm. Finally, the horizontal locations of the underground cavities were determined using regression analysis. The hyperbolic event detection using the YOLOv3 object detection technique achieved 84% precision and a recall score of 92% based on AP50. The predicted horizontal locations of the four underground cavities were approximately 0.12 ~ 0.36 m away from their actual locations. Thus, we confirmed that the existing deep learning-based interpretation technique is reliable with regard to detecting the hyperbolic patterns indicating underground cavities.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.80-90
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• 2022

Currently, the domestic early warning system is issued differently for each disaster, and is operated independently by relevant organizations from central government to local governments. Representative domestic disaster warning systems include disaster broadcasting using CBS(Cell Broadcasting Service) and DMB(Digital Multimedia Broadcasting) Automatic Emergency Alert Service, DITS(Disaster Information Transform System) transmitted and displayed on TV screens, automatic response system, automated rainfall warning system, and disaster message board. However, due to the difference in the method of issuing each emergency alert at the site of an emergency disaster, the alerts are issued at different times for each media, and the delivered content is also not integrated. If these systems are integrated, it is expected that damage to people's property and lives will be minimized by sharing and integrated management of disaster information such as voice, video, and data to comprehensively judge and make decisions about disaster situations. Therefore, in this study, we present a plan for the integration of the disaster warning system along with the analysis of the operation status of the domestic early warning system.

• 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.30 no.2B
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• pp.169-177
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• 2010

Recently due to the typhoon and extreme rainfall induced by abnormal weather and climate change, the probability of severe damage to human life and property is rapidly increasing. Thus it is necessary to create adequate and reliable flood risk map in preparation for those natural disasters. The study area is Seo-gu in Daegu which is located near Geumho river, one of the tributaries of Nakdong river. Inundation depth and velocity at each time were calculated by applying FLUMEN model to the target area of interest, Seo-gu in Daegu. And the research of creating flood risk map was conducted according to the Downstream Hazard Classification Guidelines of USBR. The 2-dimensional inundation analysis for channels and protected lowland with FLUMEN model was carried out with the basic assumption that there's no levee failure against 100 year precipatation and inflow comes only through the overflowing to the protected lowland. The occurrence of overflowing was identified at the levee of Bisan-dong located in Geumho watershed. The level of risk was displayed for house/building residents, drivers and pedestrians using information about depth and velocity of each node computed from the inundation analysis. Once inundation depth map and flood risk map for each region is created with this research method, emergency action guidelines for residents can be systemized and it would be very useful in establishing specified emergency evacuation plans in case of levee failure and overflowing resulting from a flood.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.12
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• pp.937-946
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• 2017

Recently, urban structures including buildings are becoming increasingly large and super high-rise in order to make human life more convenient. As the number of super high-rise buildings increases, however, the risk of fire and other disasters is increasing. Especially, it is expected that deaths and injuries will be tremendous than imagined if the evacuation guidance is not provided promptly and precisely for the occupants in case of a fire in super high-rise buildings. Therefore, rapid rescue should be done for those who are in need of residence or rescue in the building. To do this, identification of the size and location of people inside the building should be preceded. To do this, first, we conduct a preliminary study on existing location tracking technologies to identify occupants. Based on this, in this paper, we will study how to improve evacuation time in case of a fire in super high-rise buildings. For this purpose, we utilize the location tracking technology to identify the number of people in real time and improve the density when a disaster such as a fire occurs.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.1-13
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• 2023

As interest in urban safety has increased since COVID-19, various institutions have developed and used indicators that evaluate the safety city model. Yongsan-gu was ranked No. 1 in 2021 by Social Safety Index evaluation and was selected as the safest city in Korea. However, the Itaewon disaster in Yongsan-gu in 2022 caused many casualties. The study of indicators for evaluating cities' safety was necessary. This study aims to examine domestic and foreign safe city models and review the differences between each model and the indicators used to evaluate safe cities. As a result of collecting 11 safe city models and analyzing each evaluation index, safe city models can be classified into program-based safe city models, such as the World Health Organization's International safe community and the UN Office for Disaster Risk Reduction's International Safe city. Considering the diversification of threats to safety, it is reasonable to comprehensively consider digital security, health safety, infrastructure safety, personal safety, environmental safety, traffic safety, fire safety, crime safety, life safety, suicide, and infectious diseases when evaluating safe cities as evaluation parameters.

• 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.