• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.1
• /
• pp.150-163
• /
• 2022

Recently, the gated community has been increasing due to various reasons such as demand for differentiated areas and security, but various problems have been raised, including regional conflicts, traffic restrictions and disconnection of surrounding areas. Therefore, this study empirically considered what kind of spatial effect the gated community has on the surrounding area by analyzing the vitality using floating population big data and analyzing pedestrian accessibility using network analysis and social network analysis. As a result, it was found that the overall vitality in the study area was greatly affected by the land use and the building use. However, focusing on apartment complexes, even in the same land use, when the form of the complex is open to the outside, there is a lot of floating population, so the vitality is high. In terms of accessibility, assuming that the gated community is open, it was found that as the physical connectivity improved, there were more roads for pedestrians to choose from, and the accessibility improved as traffic and exchanges occurred in the disconnected space. The value of improving property rights and residential environment is also precious, but it is necessary to review how to reflect the improvement of local permeability in enhancing the publicness of cities and the value and direction of communities that can coexist with the region.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1273-1283
• /
• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_2
• /
• pp.725-735
• /
• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Convergence for Information Technology
• /
• v.11 no.8
• /
• pp.84-91
• /
• 2021

In the 21st century, a number of storm and flood disasters caused by rapidly changing climate change is increasing, and the number of flood accidents at construction sites is also increasing. However, no specific reduction measures have been presented and thereby safety management to prevent flood accident need to be improved. Therefore, in this study, the inundation pattern by downpour at the excavation site was interpreted and the inundation risk quantification method was used to classify the risk magnitude. Finally, using the fish-bone diagram, we derived the major reasons of inundation accident at construction site systematically. The simulation results showed that the inundation depths of small-scale excavation sites and excavation sites exceeded 3 m due to the fluid flowing through the excavation surface. In addition, depending on the excavation site, a high velocity temporarily observed and decreased due to the storage effect, or high velocity surpassing 10 m/s continued. Since this type of flooding can pose a risk to most or all workers, if proper management measures are insufficient, fatal damage to life and property could occur. Consideration of the roots of these disasters is judged to be helpful in understanding the causes of inundation accidents that result in casualties and presenting accident reduction measures.

• Applied Chemistry for Engineering
• /
• v.34 no.1
• /
• pp.15-22
• /
• 2023

Recent studies have actively investigated ways to improve the economic feasibility and efficiency of the Fischer-Tropsch process by increasing the yields of the monocyclic aromatic compounds (BTEX). In this study, ethylene was selected as a model of F-T-derived hydrocarbons, and the ethylene-to-aromatics (ETA) reaction was investigated according to changes in acid characteristics, mesopores, and crystallinity of HZSM-5 (HZ5). Fluorinated HZ5 was prepared by calcination followed by impregnation of an aqueous NH₄F solution having different molar concentrations in HZ5, and the structural and chemical properties of F/HZ5 were investigated through Brunauer-Emmett-Teller (BET), solid-state nuclear magnetic resonance (NMR), X-ray photoelectron spectroscopy (XPS), NH₃-temperature-programmed desorption (TPD), and pyridine-IR spectroscopy. The ETA reactions were performed at 673 K under 0.1 MPa, and fluorinating HZ5 by an aqueous NH₄F solution of 0.17 M improved ethylene conversion, BTEX selectivity, and catalytic stability due to acidity, mesopore fraction, and crystallinity.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.26 no.5
• /
• pp.191-202
• /
• 2022

Markov envelope as a theoretical solution of the parabolic wave equation with Markov approximation for the von Kármán type random medium is studied and approximated with the convolution of two probability density functions (pdf) of normal and gamma distributions considering the previous studies on the applications of Radiative Transfer Theory (RTT) and the analysis results of earthquake records. Through the approximation with gamma pdf, the constant shape parameter of 2 was determined regardless of the source distance r_o. This finding means that the scattering process has the property of an inhomogeneous single-scattering Poisson process, unlike the previous studies, which resulted in a homogeneous multiple-scattering Poisson process. Approximated Markov envelope can be treated as the normalized mean square (MS) envelope for ground acceleration because of the flat source Fourier spectrum. Based on such characteristics, the path duration is estimated from the approximated MS envelope and compared to the empirical formula derived by Boore and Thompson. The results clearly show that the path duration increases proportionately to r_o^1/2-r_o², and the peak value of the RMS envelope is attenuated by exp (-0.0033r_o), excluding the geometrical attenuation. The attenuation slope for r_o≤100 km is quite similar to that of effective attenuation for shallow crustal earthquakes, and it may be difficult to distinguish the contribution of intrinsic attenuation from effective attenuation. Slowly varying dispersive delay, also called the medium effect, represented by regular pdf, governs the path duration for the source distance shorter than 100 km. Moreover, the diffraction term, also called the distance effect because of scattering, fully controls the path duration beyond the source distance of 300 km and has a steep gradient compared to the medium effect. Source distance 100-300 km is a transition range of the path duration governing effect from random medium to distance. This means that the scattering may not be the prime cause of peak attenuation and envelope broadening for the source distance of less than 200 km. Furthermore, it is also shown that normal distribution is appropriate for the probability distribution of phase difference, as asserted in the previous studies.

• Journal of Korea Water Resources Association
• /
• v.55 no.5
• /
• pp.345-356
• /
• 2022

A severe flooding occured at a small urban catchment in Daejeon-si South Korea on July 30, 2020 causing significant loss of property (inundated 78 vehicles and two apartments) and life (one casualty and 56 victims). In this study, a retrospective analysis of the inundation event was implemented using a physically-based urban flood model, H12 with high-resolution data. H12 is an integrated 1-dimensional sewer network and 2-dimensional surface flow model supported by hybrid parallel techniques to efficiently deal with high-resolution data. In addition, we evaluated the impact of the flooding barriers which were installed after the flood disaster. As a result, it was found that the inundation was affected by a combination of multiple components including the shape of the basin, the low terrain of the inundation area located in the downstream part of the basin, and lack of pipe capacity to drain discharge from the upstream during heavy rain. The impact of the flooding barriers was analyzed by modeling with and without barriers on the high-resolution terrain input data. It was evaluated that the flood barriers effectively lower the water depth in the apartment complex. This study demonstrates capability of high-resolution physically-based urban modeling to quantitatively assess the past inundation event and the impact of the reduction measures.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.1
• /
• pp.175-183
• /
• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Tunnel and Underground Space
• /
• v.33 no.1
• /
• pp.29-41
• /
• 2023

The brittleness of rocks plays an important role in determining the fragmentation and failure behavior of rock. However, there is still no standard method to evaluate the brittleness of rock, and previous studies have suggested the several definitions for estimation of brittleness of rock. Even in the process of mechanical rock excavation and drilling, the brittleness of rock is considered as an important property for evaluating the excavation efficiency of mechanical excavators or boreability of rock. The previous studies have been carried out to investigate the correlation between different brittleness of rock and cutting efficiency and boreability of rock. This study introduced a method for calculating the brittleness of rock from punch penetration test, and analyzed the correlation between the brittleness of rock calculated by the uniaxial compressive and Brazilian tensile strengths and that from punch penetration test. From the results of correlation analysis, the relationship between various brittleness was confirmed, and it was found that PSI and BI₃ showed a good correlation with the strength-based brittleness index. In addition, the results indicated that B₃ and B₄ are suitable to represent the brittleness of rock in the field of mechanical rock excavation.

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