• Spatial Information Research
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• v.20 no.5
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• pp.1-13
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• 2012

This study intends to evaluate the seasonal flow direction of carbon dioxide in Northeast Asia by using GOSAT, the first Greenhouse Observing SATellite, in an attempt to overcome costly, laborious and time consuming ground observation which has been frequently pointed out in existing studies. For this purpose, missing values were supplemented by applying the Kriging interpolation and the overall flow direction of carbon dioxide was determined through anisotoropy semi-variogram. As a result, it was found that the overall spatial distribution of carbon dioxide in Northeast Asia varies depending on the latitude, and that carbon dioxide mainly flows southeast or east in spring, autumn and winter, but northeast or north in summer. Similar to the flow of monsoons in Northeast Asia, these results show that carbon dioxide flows mainly from the west to the east, which proves that carbon dioxide discharged from China is influencing even the Korean Peninsula and Japan. However, as the flow of carbon dioxide varies depending on a variety of factors such as artificial sources, plant respiration, and the absorption and discharge of the ocean, follow-up studies are requested to evaluate such variables and the correlations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.1-13
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• 2021

In this study, a time series analysis technique was applied to calibrate and predict traffic data for various purposes, such as planning, design, maintenance, and research. Existing algorithms have limitations in application to data such as traffic data because they show strong periodicity and seasonality or irregular data. To overcome and supplement these limitations, we applied the SARIMA model, an analytical technique that combines the autocorrelation model, the Seasonal Auto Regressive(SAR), and the seasonal Moving Average(SMA). According to the analysis, traffic volume prediction using the SARIMA(4,1,3)(4,0,3) 12 model, which is the optimal parameter combination, showed excellent performance of 85% on average. In addition to traffic data, this study is considered to be of great value in that it can contribute significantly to traffic correction and forecast improvement in the event of missing traffic data, and is also applicable to a variety of time series data recently collected.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• Journal of the International Relations & Interdisciplinary Education
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• v.2 no.2
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• pp.81-95
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• 2022

The core of this study is to develop an edutech utilization program to be applied to 5th grade students by utilizing school curriculum time, and the conclusions based on the results of the study are as follows. First, for the development of future educational programs using edutech, a content preference survey was conducted and significant responses were confirmed from 27 teachers and 216 students, excluding missing values. In the future education implementation, UCC (video shooting, editing, etc.) and work activities (3D pen, 3D printer, etc.) were selected based on the need for separate edtech devices. Second, in order to develop a future education program using edutech, the future education class module was set in 4 stages based on previous research. First of all, in Make a foundation, theories by subject are developed, and in Open an activity, future education experience activities using key edutech are developed. In Organize evaluation, individual self-evaluation was conducted, and based on this, customized in-depth supplementary activities were conducted in Act individually. Third, in order for future education programs using edutech to be organized in the regular curriculum, sufficient connectivity with the curriculum must be secured. The basis for systematic and stable research was prepared by analyzing the curriculum of the 5th grade subject of the study and securing hours in connection with creative experiential activities. The data developed through this process were modified and supplemented based on the content validity test. The fact that the program application and verification steps were not performed is a limitation of this study, but it is expected that this program will expand the possibility of future education practice in the school field.