• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.203-209
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• 2019

In this work, a new land-specific resistance measuring device (GM) and a measuring probe (Grounding Rod) are connected to the WENNER quadrant as power-line communication (PLC). In groups of two (P1,P2) probes, five to ten probes are installed in series on the ground at intervals of 1m, 2m, 4m, 8m, and 16m, respectively. If the PLC signal from the GMD is detected by the receiver of the Probe 1 (P1) for measurement, the minute voltage and current for measurement flow from the PSD (power supply) attached to the probe to the ground, and then, through the soil between P1 and P2, enters the Probe 1 (P2). The resistance value is then measured by the principle of voltage drop due to ground resistance. Measure the earth resistance every T seconds up to 1 trillion and store the measured data on the Arduino Server mounted on the main equipment. Stored measurement data can be derived from formulas by Ohm's Law and from inherent resistance (here,). Data obtained in real time will be linked to CDGES programs installed on Main PC, enabling data analysis and real-time monitoring of the ground environment on land. In addition, a three-dimensional display is possible with 3D graph support by identifying seasonal characteristics such as temperature and humidity of land (soils). The limitations of the study will require specific application measures of Test Bed for commercial access to a model that has been developed and operated experimentally.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.187-193
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• 2022

In the Yeongdong region of Gangwon-do, coastal areas are important resources in terms of cultural, social and economic aspects. However, the coast of Gangwon-do is experiencing severe erosion, and it is concerned that its adverse effects will gradually increase. In this study, coastline changes of Yangyang and Gangneung in Gangwon-do were tracked and analyzed over a long period of time. In order to build time series image data, aerial photos from the 1940s to the present were mainly used, and data from CORONA satellite, which operated from the 1960s to the early 1970s, were collected and used together. Using 51cm resolution ortho image and 2m resolution Digital Elevation Model(DEM) as reference, ground control points were selected to perform geometric correction on the aerial photos and CORONA images. Subsequently, Canny edge detector applied to these images to extract the coastlines. As a result of analyzing the extracted and vectorized coastlines by overlaying them in chronological order, erosion and deposition occurring around the artificial structures and on the nearby beaches were observed. In this study, the effect of seasonal variation, tide, and various coastal management including the beach filling were not considered. Because coastal erosion is greatly affected by geographic factors, each local government must find its own solution. Continuous research and local data accumulation are required.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.10 no.1
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• pp.100-112
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• 2005

Soil temperature was measured from the surface to 40 cm depth at three stations with different heights in tidal flat of Gomso Bay, west coast of Korea, for one month in every season 2004 to examine the thermal structure and the variation. Mean temperature in surface layer was higher in summer and lower in winter than in lower layer, reflecting the seasonal variation of vertically propagating structure of temperature by heating and cooling from the tidal flat surface. Standard deviation of temperature decreased from the surface to lower layer. Periodic variations of solar radiation energy and tide mainly caused short term variation of soil temperature, which was also intermittently influenced by precipitation and wind. Time series analysis showed the power spectral energy peaks at the periods of 24, 12 and 8 hours, and the strongest peak appeared at 24 hour period. These peaks can be interpreted as temperature waves forced by variations of solar radiation, diurnal tide and interaction of both variations, respectively. EOF analysis showed that the first and the second modes resolved 96% of variation of vertical temperature structure. The first mode was interpreted as the heating antl cooling from tidal flat surface and the second mode as the effect of phase lag produced by temperature wave propagation in the soil. The phase of heat transfer by 24 hour period wave, analyzed by cross spectrum, showed that mean phase difference of the temperature wave increased almost linearly with the soil depth. The time lags by the phase difference from surface to 10, 20 and 40cm were 3.2,6.5 and 9.8 hours, respectively. Vertical thermal diffusivity of temperature wave of 24 hour period was estimated using one dimensional thermal diffusion model. Average diffusivity over the soil depths and seasons resulted in $0.70{\times}10^{-6}m^2/s$ at the middle station and $0.57{\times}10^{-6}m^2/s$ at the lowest station. The depth-averaged diffusivity was large in spring and small in summer and the seasonal mean diffusivity vertically increased from 2 cm to 10 cm and decreased from 10 cm to 40 cm. Thermal propagation speeds were estimated by $8.75{\times}10^{-4}cm/s,\;3.8{\times}10{-4}cm/s,\;and\;1.7{\times}10^{-4}cm/s$ from 2 cm to 10 cm, 20 cm and 40 cm, respectively, indicating the speed reduction with depth increasing from the surface.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.10-21
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• 2021

This study analyzed how external variables, such as the supply policy of shared bicycles and the spread of infectious diseases, affect the demand for shared bicycle use in the COVID-19 era. In addition, this paper presents a methodology for more accurate predictions. The Seasonal Auto-Regulatory Integrated Moving Average with Exogenous stressors methodology was applied to capture the effects of exogenous variables on existing time series models. The exogenous variables that affected the future demand for shared bicycles, such as COVID-19 and the supply of public bicycles, were statistically significant. As a result, from the supply volume and COVID-19 outbreak according to the scenario, it was estimated that approximately 46,000 shared bicycles would be supplied by 2022, and the COVID-19 cases would continue to be at the current level. In addition, approximately 32 million and 45 million units per year will be needed in 2021 and 2024, respectively.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.