• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.849-858
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• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• Journal of Korean Society of Disaster and Security
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• v.16 no.3
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• pp.1-15
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• 2023

This study focuses on modeling the impact of ammonia leakage from the storage tank in a combined cycle power plant's flue gas denitrification facility. It employs accident impact assessments and diffusion models to determine the optimal scenarios for ammonia storage tank leakage accidents. The study considers the operating conditions of variables as standard conditions for predicting the extent of damage. The Taean combined cycle power plant is chosen as the target area, taking into account seasonal factors such as temperature, humidity, wind speed, atmospheric stability, and wind direction. By utilizing a Gaussian diffusion model, the concentration of ammonia gas at various locations is estimated to assess the potential extent of external damage resulting from a leak. The study reveals that in conditions of high temperature and stable atmosphere within the specified range, lower wind speeds contribute to increased damage to the human body due to ammonia diffusion.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.496-504
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• 2023

Countries worldwide are striving to find new sources of sustainable energy without carbon emission due to the increasing impact of global warming. With the advancement of the fourth industrial revolution on a global scale, there has been a substantial rise in energy demand. Simultaneously, there is a growing emphasis on utilizing energy sources with minimal or zero carbon content to ensure a stable power supply while reducing greenhouse gas emissions. In this comprehensive overview, a comparative analysis of carbon reduction policies of government was conducted. Based on international carbon neutrality scenarios and the presence of remaining thermal power generation, it can be categorized into two types: "Rapid" and "Safety". For the domestic scenario, the projected power demand and current greenhouse gas emissions in alignment with "The 10th Basic Plan for Electricity Supply and Demand" was examined. Considering all these factors, an overview of the current status of carbon neutrality technologies by focusing on the energy sector, encompassing transitions, hydrogen, transportation and carbon capture, utilization, and storage (CCUS) was offered followed by summarization of key technological trends and government-driven policies. Furthermore, the central aspects of the domestic carbon reduction strategy were proposed by taking account of current mega trends in the energy sector which are highlighted in international scenario analyses.

• Journal of Korean Elementary Science Education
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• v.42 no.4
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• pp.481-496
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• 2023

This study aimed to evaluate the effects of utilizing educational game content for science learning on science-related attitudes. The content was applied to 24 students in an after-school science club at an elementary school in Gyeongsangbuk-do Province followed by a pre and posttest analysis using the Attitude About the Relevance of Science Test and the Creative Personality Test. This study used Tino's Journey, which was developed by the Korea Creative Content Agency and is currently distributed for free through the Ministry of Education to develop nine lessons that include scientific scenarios and concepts presented in the game. The results demonstrated that science lessons utilizing educational game content significantly influenced the science-related attitudes of the students. Among the subdomains, enjoyment of science lessons increased the most followed by the attitude toward scientific inquiry, social meaning of science, and hobby of science. However, the commonness of scientists, acceptance of scientific attitudes, and career in science did not reveal significant differences. This study classified the students into two groups (i.e., high and low, n=12 each) using the Creative Personality Test in advance. This study performed covariate analysis with the score for pre-science-related attitude as the covariate. Result revealed that the scores for science-related attitude significantly differed between the high and low groups. Specifically, the increase in the scores of the low group was larger than that of the high group. Lastly, the study presented implications for the utilization of educational game content in science learning.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1077-1086
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• 2023

The searchable encryption model allows to selectively search for encrypted data stored on a remote server. In a real-world scenarios, the model must be able to support multiple search keywords, multiple data owners/users. In this paper, these models are referred to as Multi Ranked Searchable Encryption model. However, at the time this paper was written, the proposed models use fully-trusted trapdoor centers, some of which assume that the connection between the user and the trapdoor center is secure, which is unlikely that such assumptions will be kept in real life. In order to improve the practicality and security of these searchable encryption models, this paper proposes a new Multi Ranked Searchable Encryption model which uses random keywords to protect search words requested by the data downloader from an honest-but-curious trapdoor center with an external attacker without the assumptions. The attacker cannot distinguish whether two different search requests contain the same search keywords. In addition, experiments demonstrate that the proposed model achieves reasonable performance, even considering the overhead caused by adding this protection process.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1451-1466
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• 2023

In the context of maritime emergencies, the utilization of drones has rapidly increased, with a particular focus on their application in search and rescue operations. Deep learning models utilizing drone images for the rapid detection of distressed vessels and other maritime drift objects are gaining attention. However, effective training of such models necessitates a substantial amount of diverse training data that considers various weather conditions and vessel states. The lack of such data can lead to a degradation in the performance of trained models. This study aims to enhance the performance of deep learning models for distress ship detection by developing a maritime environment simulator to augment the dataset. The simulator allows for the configuration of various weather conditions, vessel states such as sinking or capsizing, and specifications and characteristics of drones and sensors. Training the deep learning model with the dataset generated through simulation resulted in improved detection performance, including accuracy and recall, when compared to models trained solely on actual drone image datasets. In particular, the accuracy of distress ship detection in adverse weather conditions, such as rain or fog, increased by approximately 2-5%, with a significant reduction in the rate of undetected instances. These results demonstrate the practical and effective contribution of the developed simulator in simulating diverse scenarios for model training. Furthermore, the distress ship detection deep learning model based on this approach is expected to be efficiently applied in maritime search and rescue operations.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.765-774
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• 2023

Due to climatechange, precipitation variability has increased, leading to more frequentoccurrences of droughts and floods. To establish measures for managing waterresources in response to the increasing uncertainties of climate conditions, itis necessary to understand the variability of natural river discharge and theimpact of reservoir operation modeling considering dam inflow and artificialwater supply. In this study, an integrated rainfall-runoff and reservoiroperation modeling was applied to analyze the water supply reliability andflood risk for a multipurpose dam catchment under climate change conditions. Therainfall-runoff model employed was the modèle du Génie Rural à 4 paramètresJournalier (GR4J) model, and the reservoir operation model used was an R-basedmodel with the structure of HEC-Ressim. Applying the climate change scenariosuntil 2100 to the established integrated model, the changes in water supplyreliability and flood risk of the Happcheon Dam were quantitatively analyzed.The results of the water supply reliability analysis showed that under SSP2-4.5conditions, the water supply reliability was higher than that under SSP5-8.5conditions. Particularly, in the far-future period, the range of flood risk widened,and both SSP2-4.5 and SSP5-8.5 scenarios showed the highest median flood riskvalues. While precipitation and runoff were expected to increase by less than10%, dam-released flood discharge was projected to surge by over 120% comparedto the baseline

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.689-693
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• 2023

Domestic bridges with a service life of more than 30 years are expected to account for approximately 54% of all bridges within the next 10 years. As bridges rapidly deteriorate, it is necessary to establish an appropriate maintenance plan. Recent domestic and international research have focused on the integration of BIM to digitize bridge maintenance information and then enhance accessibility and usability of the information. Accordingly, this study developed a BIM-FEM interoperability algorithm for bridge decks to convert maintenance information into data and efficiently manage the history of maintenance. After creating an initial crack BIM based on an exterior damage map, bridge specification and damage information were linked to a numerical analysis that performs damage analysis considering damage scenarios and design loads. The spread of cracks obtained from the analysis results were updated into the BIM. Based on the damage spread information on the BIM, an automated technology was also developed to assess both the current and future condition ratings of the bridge deck. This approach can enable an efficient maintenance of the deck using the history data from bridge inspection and diagnosis as well as future information on cracks and defects. The expected early detection and prevention would ultimately improve the lifespan and safety of bridges.