• Computers and Concrete
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• v.29 no.2
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• pp.107-115
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• 2022

With the improvements in computer technologies, utilization of image processing techniques has increased in many areas (such as medicine, defence industry, other industries etc.) Many different image processing techniques are used for surface analysis, detection of manufacturing defects, and determination of physical and mechanical characteristics of composite materials. In this study, cementitious composites were obtained by addition of Grounded Granulated Blast-Furnace Slag (GGBFS), Styrene Butadiene polymer (SBR), and Grounded Granulated Blast-Furnace Slag and Styrene Butadiene polymer together (GGBFS+SBR). Expanded Polystyrene (EPS) beads were added to these cementitious composites in different ratios (20%, 40% and 60%). The mechanical and physical characteristics of the composites were determined, and homogeneity indexes of the composites were determined by image processing techniques to determine EPS distribution forms in them. Physical and mechanical characteristics of the produced samples were obtained by applying consistency, density, water absorption, compressive strength (7 and 28 days), flexural strength (7 and 28 days) and tensile splitting strength (7 and 28 days) tests on them. Also, visual examination by using digital microscope, and image analysis by using image processing techniques with open source coded ImageJ program were performed. As a result of the study, it is determined that GGBFS and SBR addition strengthens the adhesion sites formed as it increases the adhesion power of the mixture and helps to get rid of the segregation problem caused by EPS. As a result of the image processing analysis it is demonstrated that GGBFS and SBR addition has positive contribution on homogeneity index.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.172-172
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• 2020

In recent years, soil erosion has come to be regarded as an essential environmental problem in human life. Soil erosion causes various on- and off-site problems such as ecosystem destruction, decreased agricultural productivity, increased riverbed deposition, and deterioration of water quality in streams. To solve these problems caused by soil erosion, it is necessary to quantify where, when, how much soil erosion occurs. Empirical erosion models such as the Universal Soil Loss Equation (USLE) family models have been widely used to make spatially distributed soil erosion vulnerability maps. Even if the models detect vulnerable sites relatively well by utilizing big data related to climate, geography, geology, land use, etc. within study domains, they do not adequately describe the physical process of soil erosion on the ground surface caused by rainfall or overland flow. In other words, such models remain powerful tools to distinguish erosion-prone areas at the macro scale but physics-based models are necessary to better analyze soil erosion and deposition and eroded particle transport. In this study, the physics-based Surface Soil Erosion Model (SSEM) was upgraded based on field survey information to produce sediment yield at the watershed scale. The modified model (hereafter MoSE) adopted new algorithms on rainfall kinematic energy and surface flow transport capacity to simulate soil erosion more reliably. For model validation, we applied the model to the Doam dam watershed in Gangwon-do and compared the simulation results with the USLE outputs. The results showed that the revised physics-based soil erosion model provided more improved and reliable simulation results than the USLE in terms of the spatial distribution of soil erosion and deposition.

• Journal of Environmental Science International
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• v.32 no.6
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• pp.453-464
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• 2023

To this day, conflicts have intensified between managers who want to preserve biosphere reserves and citizen who want to develop them. Based on this problem, this study seeks to investigate the establishment of a forum for communication between various stakeholders and to promote the economic development of local communities while preserving biodiversity. First, in terms of conservation, the results indicated that Gyeonggi Province and Namyangju City highly valued direct conservation activities in biosphere reserves, whereas Pocheon and Uijeongbu City highly valued indirect conservation functions through management or monitoring. Second, in terms of development, it was found that there were differences in the roles, perceptions and responsibilities with respect to biosphere reserves among the different layers of government: the central government agency, the Cultural Heritage Administration, the metropolitan government, Gyeonggi-do, and the local governments, Pocheon, Namyangju, and Uijeongbu. Third, in terms of logistical support, which serves as a function for communication and practical participation among management entities, the results suggested that it was necessary to establish a comprehensive decision-making organization for efficient management and operation and to provide opportunities for active participation. The study can be utilized as a basic reference for developing efficient communication by management entities in protected areas with similar challenges.

• Journal of Korean Tunnelling and Underground Space Association
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• v.11 no.4
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• pp.361-371
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• 2009

Resistivity tomography is very effective geophysical method to find out the resistivity distribution and its change in time around a tunnel. Thus, the resistivity tomogram can provide helpful information which is necessary for the effective maintenance of the tunnel. However, an air filled tunnel severely distorts tomography data, especially when the current or potential electrode is placed near the tunnel. Moreover, the distortion can often lead to misinterpretation of tomography monitoring data. To solve these problem, we developed a resistivity modeling and time-lapse inversion program which include a tunnel. In this study, using the developed program we assured that the inversion including a tunnel gives much more accurate image around a tunnel, compared with the conventional tomogram where the tunnel is not included. We also confirmed that the time-lapse inversion of resistivity monitoring data defines well resistivity changed areas around a tunnel in time.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.

• Smart Media Journal
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• v.11 no.7
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• pp.61-69
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• 2022

In the learned machine learning, the performance of machine learning degrades at the same time as drift occurs in terms of learning models and learning data over time. As a solution to this problem, I would like to propose the concept and evaluation method of ML drift to determine the re-learning period of machine learning. An XAI test and an XAI test of an apple image were performed according to strawberry and clarity. In the case of strawberries, the change in the XAI analysis of ML models according to the clarity value was insignificant, and in the case of XAI of apple image, apples normally classified objects and heat map areas, but in the case of apple flowers and buds, the results were insignificant compared to strawberries and apples. This is expected to be caused by the lack of learning images of apple flowers and buds, and more apple flowers and buds will be studied and tested in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.941-946
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• 2023

To improve the lift, cruise speed, and range of eVTOL aircraft, which are being considered as future transportation vehicles, this paper introduces the concepts of Tilt Rotor and Tandem Wing to the aircraft. We developed an aircraft and conducted flight experiments to obtain flight videos and flight logs. The results of the analysis of the flight videos and flight logs showed that the aircraft's moment was excessively forward and the attitude was not recovered. To address this problem, we modified the wing incidence angles and surface areas in XFLR5 to obtain the optimal pitching moment coefficients to ensure vertical stability. We then analyzed the redesigned aircraft, developed using CATIA, through XFLR5. The results of this study provide valuable insights, suggesting that the incorporation of Tilt Rotor and Tandem Wing designs can contribute to achieving stable pitching moment coefficients. This innovative approach offers a promising avenue to significantly enhance vertical stability in UAM vehicles, paving the way for future advancements in the field.

• Journal of the Economic Geographical Society of Korea
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• v.26 no.3
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• pp.310-323
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• 2023

Urbanization and income distribution issues are global interest, and the results of studies on the impact of urbanization on income inequality are different for each country and period. This study analyzes the impact of urbanization on income inequality using regional data from 2000-2021. In particular, serial correlation, spatial dependence, and common factor effects of the Gini coefficient are confirmed and analyzed through a dynamic spatial panel regression model. As a result, urbanization has a positive effect on reducing income inequality. Therefore, it is necessary to continuously promote regional urbanization to improve the income distribution problem. Areas with already high urbanization rates should reduce income inequality by narrowing the wage gap by expanding training opportunities for low-skilled workers, and need to come up with measures to prevent counter-urbanization.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.19-33
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• 2024

CCTV for crime prevention is expanding; however, due to the absence of guidelines for determining installation locations, CCTV is being installed in locations unrelated to areas with frequent crime occurrences. In this study, we developed a CCTV Priority Installation Index and applied it in a case study area. The index consists of crime vulnerability and surveillance vulnerability indexes, calculated using machine learning algorithms to predict crime incident counts per grid and the proportion of unmonitored area per grid. We tested the index in a pilot area and found that utilizing the Viewshed function in CCTV visibility analysis resolved the problem of overestimating surveillance area. Furthermore, applying the index to determine CCTV installation locations effectively improved surveillance coverage. Therefore, the CCTV Priority Installation Index can be utilized as an effective decision-making tool for establishing smart and safe cities.