• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.25 no.4
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• pp.106-116
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• 2020

To examine distribution of the Zostera species growing naturally in Ulleungdo, scuba diving surveys using ships were conducted along the coast and inside the harbors of the island at the end of September 2019. In areas of seagrass occurrence, environmental factors such as nutrient concentrations in water column and sediment pore water, salinity, and sediment organic content were also analyzed. Zostera caulescens meadows appeared in the relatively deep waters (14-24 m MSL) of Cheonbu-ri, Jeodong-ri, Sadong-ri, and Namyang-ri in Ulleungdo, and the total seagrass coverage was approximately 4.9 ha. Approximately 0.9 ha of Zostera marina meadow was found at the depths of 3-5 m MSL within Hyeonpo-hang in Hyeonpo-ri. The average shoot density and biomass of Z. caulescens were 121.9±9.7 shoot m^-2 and 99.0±13.2 gDW m^-2, respectively, with no significant differences by location. The average shoot density and biomass of Z. marina were 193.8±18.8 shoot m^-2 and 102.6±6.8 gDW m^-2, respectively. The nutrient concentrations in the sediment pore water and sediment organic content in the seagrass meadows in Ulleungdo were lower than those in eelgrass meadows on the southern and eastern coasts of Korea. These results will provide useful basic information for the marine protected species, Z. caulescens and Z. marina, and for the conservation of the waters of Ulleungdo, which has been designated as a marine protected area.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.95-103
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• 2023

This paper proposes an artificial intelligence convergence education program for teaching the main concept and principle of Support Vector Machines(SVM) at elementary schools. The developed program, based on Jeju's natural environment theme, explains the decision boundary and margin of SVM by vertical and parallel from 4th grade mathematics curriculum. As a result of applying the developed program to 3rd and 5th graders, most students intuitively inferred the location of the decision boundary. The overall performance accuracy and rate of reasonable inference of 5th graders were higher. However, in the self-evaluation of understanding, the average value was higher in the 3rd grade, contrary to the actual understanding. This was due to the fact that junior learners had a greater tendency to feel satisfaction and achievement. On the other hand, senior learners presented more meaningful post-class questions based on their motivation for further exploration. We would like to find effective ways for artificial intelligence convergence education for elementary school students.

• Journal of the Korean Geotechnical Society
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• v.39 no.11
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• pp.85-95
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• 2023

This study implemented intelligent compaction technology at the construction site of the AY Highway in Gyeonggi Province, with a focus on obtaining the representative intelligent compaction value, CMV. The target CMV for quality control was established through trial construction, and the validation of the compaction quality control process based on intelligent compaction was conducted. The optimal approach for determining the target CMV was confirmed to be through linear regression of the average CMV measured within a 5-m radius from the plate load testing location. Upon assessing compaction quality against the target CMV, it was observed that the quality criteria outlined in the domestic intelligent compaction standard were met. However, the criteria outlined in Austria and the United States were not satisfied. Notably, indicators related to the variability of compaction quality did not meet the specified criteria, suggesting a stringent standard compared to the observed variability of CMV, ranging from 17% to 55%. Consequently, it is recommended to conduct additional field tests to further validate the compaction quality control process based on intelligent compaction. This will aid in confirming and enhancing the appropriateness of the regulations stipulated in each standard.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.713-723
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• 2023

Climate change is significantly affecting coastal areas, and its impacts are expected to intensify. Recent studies on climate change adaptation and risk assessment in coastal regions increasingly integrate the concepts of recovery resilience and vulnerability. The aim of this study is to develop a measurement model for coastal hazard recovery resilience in the context of climate change adaptation. Before constructing the measurement model, a comprehensive literature review was conducted on coastal hazard recovery resilience, establishing a conceptual framework that included operational definitions for vulnerability and recovery resilience, along with several feedback mechanisms. The measurement model for coastal hazard recovery resilience comprised four metrics (MRV, LRV, RTSPV, and ND) and a Coastal Resilience Index (CRI). The developed indices were applied to domestic coastal erosion cases, and regional analyses were performed based on the index grades. The results revealed that the four recovery resilience metrics provided insights into the diverse characteristics of coastal erosion recovery resilience at each location. Mapping the composite indices of coastal resilience indicated that the areas along the East Sea exhibited relatively lower coastal erosion recovery resilience than the West and South Sea regions. The developed recovery resilience measurement model can serve as a tool for discussions on post-adaptation strategies and is applicable for determining policy priorities among different vulnerable regional groups.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.5
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• pp.19-32
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• 2023

The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.427-433
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• 2022

Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.113-119
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• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.17-24
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• 2024

In this paper, we propose a highly secure technique to hide confidential data in image pixels using a quadruple encryption techniques. In the proposed technique, the boundary surface where the image outline exists and the flat surface with little change in pixel values are investigated. At the boundary of the image, in order to preserve the characteristics of the boundary, one bit of confidential data that has been multiply encrypted is spatially encrypted again in the LSB of the pixel located at the boundary to hide the confidential data. At the boundary of an image, in order to preserve the characteristics of the boundary, one bit of confidential data that is multiplely encrypted is hidden in the LSB of the pixel located at the boundary by spatially encrypting it. In pixels that are not on the border of the image but on a flat surface with little change in pixel value, 2-bit confidential data that is multiply encrypted is hidden in the lower 2 bits of the pixel using location-based encryption and spatial encryption techniques. When applying the proposed technique to hide confidential data, the image quality of the stego-image is up to 49.64dB, and the amount of confidential data hidden increases by up to 92.2% compared to the existing LSB method. Without an encryption key, the encrypted confidential data hidden in the stego-image cannot be extracted, and even if extracted, it cannot be decrypted, so the security of the confidential data hidden in the stego-image is maintained very strongly. The proposed technique can be effectively used to hide copyright information in general commercial images such as webtoons that do not require the use of reversible data hiding techniques.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.27 no.1
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• pp.31-44
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• 2024

The coastal dune ecosystem is one of the ecosystems under the most development pressure in Korea. Therefore, it is necessary to study the ecological location and related ecological phenomena of coastal dune plants, but related studies are lacking. Through this study, we intend to conduct research on the structure and restoration of dune plants, focusing on the coastal dunes in Jeju Island, which are affected by artificial development pressure and the continuous increase in tourists among many coastal dunes in Korea. Ecosystems of coastal sand dunes for vegetation survey in Jeju Island are selected based on naturalness and preservation. In this study, 23 major coastal dunes on Jeju Island including Udo were selected. In the coastal dunes of Jeju Island, a whole species survey and quadrat survey were carried out. The vegetation survey at study sites were conducted on May to September 2022, when the vegetation is clearly visible. At the survey site, the dune area was identified at the beginning and the plant species were recorded until no more new species appeared. Vegetation survey in the field was performed by 103 quadrat establishments and was conducted using Braun-Blanquet method. A total of 277 species appeared, and the most common species were Vitex rotundifolia and Calystegia soldanella. The frequency of both Vitex rotundifolia and Calystegia soldanella was approximately over 90%. The proportion of woody and herbaceous in all emerging species was 7.2% and 92.8%, respectively. The total number of species found in the quadrat survey was 98. As a result of classifying plant communities based on species dominance in the quadrats, it was analyzed into 30 plant communities. The plant communities that appeared with a frequency of 2 or more on the main island of Jeju were Vitex rotundifolia, Imperata cylindrica var. koenigii, Ischaemum antephoroides, Wedelia prostrata, Elymus mollis, Calystegia soldanella, Artemisia scoparia, and Tetragonia tetragonoides. The DCCA(detrended canonical correspondence analysis) based on the vegetation and environment factor matrix showed that the height and covers of the dominant plant species explain significantly the variation and distribution of coastal sand dune species on Jeju island. Thus, we may propose a plan to restore the coastal dunes of Jeju island as helping colonization and establishment of mainly sand dune native perennials and trees, preserving native plant communities that are declining and preserving present tree strips of Pinus thunbergii, Litsea japonica, Pittosporum tobira and Vitex rotundifolia.