• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of the Korean Geotechnical Society
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• v.38 no.7
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• pp.5-24
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• 2022

The current performance evaluation of slope anchors qualitatively determines the physical bonding between the anchor head and ground as well as cracks or breakage of the anchor head. However, such performance evaluation does not measure these primary factors quantitatively. Therefore, the time-dependent management of the anchors is almost impossible. This study is an evaluation of the 3D numerical model by SfM which combines UAS images with terrestrial LiDAR to collect numerical data on the damage factors. It also utilizes the data for the quantitative maintenance of the anchor system once it is installed on slopes. The UAS 3D model, which often shows relatively low precision in the z-coordinate for vertical objects such as slopes, is combined with terrestrial LiDAR scan data to improve the accuracy of the z-coordinate measurement. After validating the system, a field test is conducted with ten anchors installed on a slope with arbitrarily damaged heads. The damages (such as cracks, breakages, and rotational displacements) are detected and numerically evaluated through the orthogonal projection of the measurement system. The results show that the introduced system at the resolution of 8K can detect cracks less than 0.3 mm in any aperture with an error range of 0.05 mm. Also, the system can successfully detect the volume of the damaged part, showing that the maximum damage area of the anchor head was within 3% of the original design guideline. Originally, the ground adhesion to the anchor head, where the z-coordinate is highly relevant, was almost impossible to measure with the UAS 3D numerical model alone because of its blind spots. However, by applying the combined system, elevation differences between the anchor bottom and the irregular ground surface was identified so that the average value at 20 various locations was calculated for the ground adhesion. Additionally, rotation angle and displacement of the anchor head less than 1" were detected. From the observations, the validity of the 3D numerical model can obtain quantitative data on anchor damage. Such data collection can potentially create a database that could be used as a fundamental resource for quantitative anchor damage evaluation in the future.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.3 no.3
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• pp.177-186
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• 1970

The spawning of the abalone, Haliotis discus hannai, was induced In October 1969 by air ex-position for about 30 minutes. At temperatures of from 14.0 to $18.8^{\circ}C$, the youngest trochophore stage was reached within 22 hours after the egg was laid. The trochophore was transformed into the veliger stage within 34 hours after fertilization. For $7\~9$ days after oviposition the veliger floated in sea water and then settled to the bottom. The peristomal shell was secreted along the outer lip of the aperture of the larval shell, and the first respiratory pore appears at about 110 days after fertilization. The shell attained a length of 0.40 mm in 15 days, 1.39 mm in 49 days, 2.14 mm in 110 days, 5.20 mm in 170 days and 10.00 mm in 228 days respectively. Monthly growth rate of the shell length is expressed by the following equation :$L=0.9981\;e^{0.18659M}$ where L is shell length and M is time in month. The density of floating larvae in the culture tank was about 10 larvae per 100 co. The number of larvae attached to a polyethylene collector ($30\times20\;cm$) ranged from 10 to 600. Mortality of the settled larvae on the polyethylene collector was about $87.0\%$ during 170 days following settlement. The culture of Nauicula sp. was made with rough polyethylene collectors hung at three different depths, namely 5 cm, 45 cm and 85 cm. At each depth the highest cell concentration appeared after $15\~17$ days, and the numbers of cells are shown as follows: $$5\;cm\;34.3\times10^4\;Cells/cm^2$$ $$45\;cm\;27.2\times10^4\;Cells/cm^2$$ $$85\;cm\;26.3\times10^4\;Cells/cm^2$$ At temperatures of from 13.0 to $14.3^{\circ}C$, the distance travelled by the larvae (3.0 mm In shell length) averaged 11.36 mm for a Period of 30 days. Their locomation was relatively active between 6 p.m. and 9 p.m., and $52.2\%$ of them moved during this period. When the larvae (2.0 mm in shell length) were kept in water at $0\;to\;\~1.8^{\circ}C$, they moved 1.15cm between 4 p.m. and 8 p.m. and 0.10 cm between midnight and 8 a.m. The relationships between shell length and body weight of the abalone sampled from three different localities are shown as follows: Dolsan-do $W=0.2479\;L^{2.5721}$ Huksan-do $W=0.1001\;L^{3.1021}$ Pohang $W=0.9632\;L^{2.0611}$