• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.175-201
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• 2023

In the midst of going through a non-face-to-face society, most of human activities narrowed down to the platform, restrictions on external activities are bringing the internal scalability of digital technology. Metaverse is virtually shifting reality and increasing the possibility of utilization in various areas. However, researches linked to the educational effects of metaverse, especially students with disabilities, are still an unknown area that lacks exploration. This paper focuses on the fact that metaverse-education is widening educational fields that meets the various needs of disabled students to realize social good and inclusive education, and communication effects such as resolving barriers to interaction are prominent. As a research method, examining literature research papers linked to AR/VR, metaverse with communication skills, interviews, articles, and columns by experts, and policy suggestions and implications for the special education was conducted. Although the limitations of research are confirmed, significant results are found on inclusive education, which provides educational maximizing effects and realizing human rights through direct immersive experience reflecting the Cone of Experience Theory. Hopefully follow-up studies on meta-edu for disabled students will be carried out in the future, and various interdisciplinary discussions are needed to carefully observe inclusive policies and benefits so that the socially vulnerable are not excluded from technologies in ICT society.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.166-172
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• 2022

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.1-12
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• 2023

Research on the causes of landslides and prediction of vulnerable areas is being conducted globally. This study aims to predict the effective soil depth, a critical element in analyzing and forecasting landslide disasters, using topographic information. Topographic data from various institutions were collected and assigned as attribute information to a 100 m × 100 m grid, which was then reduced through data grading. The study predicted effective soil depth for two cases: three depths (shallow, normal, deep) and five depths (very shallow, shallow, normal, deep, very deep). Three classification models, including K-Nearest Neighbor, Random Forest, and Deep Artificial Neural Network, were used, and their performance was evaluated by calculating accuracy, precision, recall, and F1-score. Results showed that the performance was in the high 50% to early 70% range, with the accuracy of the three classification criteria being about 5% higher than the five criteria. Although the grading criteria and classification model's performance presented in this study are still insufficient, the application of the classification model is possible in predicting the effective soil depth. This study suggests the possibility of predicting more reliable values than the current effective soil depth, which assumes a large area uniformly.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.57-67
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• 2023

Slope failures during rainfall have been observed in mountainous areas of South Korea as a result of the presence of solar power facilities. The seepage behavior and pore pressure distribution differ from typical slopes due to the presence of impermeable solar panels, and the load imposed by the solar power structures also affects the slope behavior. This study aims to develop a method for evaluating the stability of slopes with solar power facilities and to analyze vulnerable points by considering the maximum slope displacement. To assess the slope stability and predict behavior while considering rainfall seepage, a combined seepage analysis and finite difference method numerical analysis were employed. For the selected site, various variables were assumed, including parameters related to the Soil Water Characteristic Curve, strength parameters that satisfy the Mohr-Coulomb failure criterion, soil properties, and topographic factors such as slope angle and bedrock depth. The factors with the most significant influence on the factor of safety (FOS) were identified. The presence of solar power facilities was found to affect the seepage distribution and FOS, resulting in a decreasing trend due to rainfall seepage. The maximum displacement points were concentrated near the upper (crest) and lower (toe) sections of the slope.

• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.53-60
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• 2023

Recently, along with the improvement of high-speed rail and road design speed, the proportion of tunnel construction work is increasing proportionally. In particular, the construction of long tunnels is rapidly increasing due to the mountainous terrain of our country. In this way, due to the trend of tunnels becoming longer, it is difficult to design and construct tunnels by avoiding fault zones. In the case of tunnel construction in mountainous areas, ground investigation is often difficult even during design due to the topographical conditions, making precise ground investigation difficult, and as a result, the upper part of the tunnel is damaged during tunnel construction. When fault zones, which are vulnerable to weathering, exist, the stability of the tunnel during excavation is directly affected by the fault zone distribution, strength characteristics, and groundwater distribution range. In particular, when a fault zone is distributed in the upper part of a tunnel, damage such as tunnel collapse and excessive displacement may occur, and in order to prevent this in advance, countermeasures must be established through analysis of similar cases. Therefore, in this study, when a large-scale fault zone exists in the upper part of a tunnel, the relationship and characteristics of damage to the tunnel structure were analyzed.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• Korean Journal of Culture and Social Issue
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• v.16 no.2
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• pp.125-146
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• 2010

A total of 181 college students(61 males 121 females) with at least 5 years of living abroad (Returnee Group) and another group of 181 students (92 males and 93 females) without extended period of living abroad (Comparison Group) participated in the study by completing a questionnaire consisting of Acculturation Index, Multidimensional Acculturation Scale, Student Adaptation to College Questionnaire, Revised UCLA Loneliness Scale, CES-D, and WHOQOL. The results indicated that the Returnee Group, compared to the Comparison Group, reported as good adjustment toward college life in Korea and positive attitude toward the Korean identity, but a higher level of loneliness. When the Returnee Group were divided into 4 different groups on the basis of acculturation pattern, the Integration and Assimilation Type reported a better adjustment to college life, lower depression and loneliness and better quality of life than the Marginalization Type. The Mariginalization Type appears to be the most vulnerable group, experiencing difficulties in all areas of adjustment, and is clearly in need of interventions. Limitations of the present study and suggestions for future research were discussed.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.2
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• pp.34-44
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• 2024

Recently, due to abnormal climate caused by climate change, natural disasters such as floods, landslides, and soil outflows are rapidly increasing. In Korea, more than 63% of the land is vulnerable to slope disasters due to the geographical characteristics of mountainous areas, and in particular, Quarry mines soil and rocks, so there is a high risk of landslides not only inside the workplace but also outside.Accordingly, this study built a DEM using UAV and aviation LiDAR for monitoring the quarry, conducted a time series change analysis, and proposed an optimal DEM construction method for monitoring the soil collection site. For DEM construction, UAV and LiDAR-based Point Cloud were built, and the ground was extracted using three algorithms: Aggressive Classification (AC), Conservative Classification (CC), and Standard Classification (SC). UAV and LiDAR-based DEM constructed according to the algorithm evaluated accuracy through comparison with digital map-based DEM.

• Journal of Korean Society of Disaster and Security
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• v.17 no.2
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• pp.1-11
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• 2024

Most reservoirs in South Korea are earthen dams, mainly because they are cost-effective and easy to construct. However, earthen dams are highly vulnerable to seepage and overtopping, making them prone to sudden failure during excessive flooding. Such sudden failures can lead to a rapid increase in flood discharge, causing significant damage to downstream rivers and inhabited areas. This study investigates the effect of riprap placement on the slopes of earthen dams in delaying dam failure. Delaying the failure time is crucial as it allows more time for evacuation, significantly reducing potential casualties, which is essential from a disaster response perspective. Hydraulic experiments were conducted in a straight channel, using two different sizes of riprap for protection. Unlike previous studies, these experiments were performed under unsteady flow conditions to reflect the impact of rising water levels inside the dam. The target dam for the study was a cofferdam installed in a diversion tunnel. Experimental results indicated that the presence of riprap protection effectively prevented slope failure under the tested conditions. Without riprap protection, increasing the size of the riprap delayed the failure time. This delay can reduce peak discharge, mitigating damage downstream of the dam. Furthermore, these findings can serve as critical reference material for establishing emergency action plans (EAP) for reservoir failure.

• Journal of Environmental Impact Assessment
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• v.26 no.5
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• pp.365-375
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• 2017

This study investigated in the vascular plants of special protection zones (the native land of Iris koreana in Gaeamsa Temple and Soeppulbawi Rock) of Byeonsanbando National Park and identified the whole flora. The numbers of vascular plants were summarized as 255 taxa including 71 families, 166 genera, 222 species, 2 subspecies, 27 varieties and 4 forms. Woody plants were identified as 114 taxa (44.7%) and herbaceous plants as 141 taxa (55.3%). A total of 178 taxa were identified in the area of Gaeamsa Temple, and 184 taxa were found in the Soeppulbawi Rock. As a legal protected species, the endangered wild plant II grade Iris koreana designated by the Ministry of Environment was confirmed. A total of 6 taxa of rare plants were identified, each of which was divided into 1 taxa of critically endangered (CR; Iris koreana), 1 taxa of vulnerable (VU; Ilex cornuta), and 4 taxa of least concern (LC; Asarum maculatum, Viola albida, Chionanthus retusa and Tricyrtis macropoda). The Korean endemic plants were 11 taxa (Populus tomentiglandulosa, Lonicera subsessilis, Carex okamotoi, etc.). In the specific plants by floristic region were 38 taxa, a degree I were 23 taxa (Euscaphis japonica, Hedera rhombea, Lophatherum gracile, etc.), 7 taxa of a degree II (Viola violacea, Ainsliaea apiculata, Cephalanthera falcata, etc.), 6 taxa of a degree III (Ilex cornuta, Callicarpa mollis, Mitchella undulata, etc.), 1 taxa of a degree IV (Carex remotiuscula), 1 taxa of a degree V (Iris koreana). The Iris koreana special protection area that is more natural and healthier than any other areas in Byeonsanbando National Park. Therefore, it should be possible to continue its role as a special protection area through regular monitoring in the future.