• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.79-91
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• 2022

In this study, the effects of self-efficacy, perceived usefulness, perceived ease of use, and depression on college students' academic persistence in the COVID-19 epidemic and the resulting non-face-to-face education situation were identified as mediating effects on learning satisfaction. In the second semester of 2020, a survey was conducted on students enrolled in a four-year university in Daegu and the data were statistically analyzed. The path coefficient was estimated by the Smart PLS bootstrap method and the significance of the path coefficient was verified. The Sobel Test was conducted to verify the mediating effect of academic continuity intention as a parameter. The research results can be summarized as follows. First, it was found that self-efficacy and perceived usefulness had a significant influence in the relationship with learning satisfaction. Second, the relationship between learning satisfaction and academic continuity intention was found to have a significant influence. Third, depression and ease of use did not show any significant influence in the relationship between learning satisfaction. Finally, a Sobel Test was conducted to verify the mediating effect of academic continuity intention with self-efficacy, usefulness, ease of use, and depression as independent variables and learning satisfaction as parameters. As a result of both regression analyses, it was found that β values decreased, and learning satisfaction had a mediating effect. As a result of this study, it is suggested that research to increase learner satisfaction and develop various contents to increase the effectiveness of education that can increase self-efficacy and perceived usefulness should be conducted in parallel. I think this study can be used as basic data in establishing measures to continue studying for college students in natural disaster situations or psychological crisis situations called COVID-19.

• Design Convergence Study
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• v.15 no.2
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• pp.237-250
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• 2016

Along with rapid development, progress of the network technology and digital information technology, human are stepping into the intelligent society of internet. Thereby the quality of living environment and working environment are keep improving. Under the big background of internet era, the timeliness and convenience of smart home system has been improved greatly. While lots of smart products have gradually penetrated into people's daily life. The household appliances are among most popular ones. This paper is intended to compare smart features of household living products among most representative brands in China, Japan and South Korea. The smart features include self-learning, self-adapting, self-coordinating, self-diagnosing, self-inferring, self-organizing, and self adjusting. As result, most smart features of these products showed great similarity. While some features were dominated according to countries such as remote control feature in Korea, energy saving feature in Japan, and one button operation feature in China.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.46-46
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• 2022

유기물의 복잡한 혼합물인 CDOM(Colored or Chromophoric Dissolved Organic Matter)은 하천 내 BOD(Biological Oxygen Demand), COD(Chemical Oxygen Demand) 및 유기 오염물질과 상당한 관련이 있다. CDOM은 가시광선 영역에서 빛을 흡수하는 성질을 가지고 있으며, 최근 원격감지 기술로 CDOM을 모니터링하기 위한 연구가 진행되고 있다. 본 연구에서는 백제보 상류 23km 구간에서 3년(2016~2018) 중 13일의 초분광영상을 활용하여 머신러닝 기반 CDOM을 추정 알고리즘을 개발하고자 한다. 초분광영상은 400~970 nm의 범위의 4 nm 간격 127개 대역의 분광해상도와 2 m의 공간해상도를 가진 항공기 탑재 AsiaFENIX 초분광 센서를 통해 수집하였으며 CDOM은 Millipore polycarbonate filter (𝚽47, 0.2 ㎛)에서 여과된 CDOM 샘플 자료를 200~800 nm의 흡수계수 스펙트럼으로 추출하여 사용하였다. CDOM 값은 전체기간 동안 2.0~11.0 m^-1의 값 분포를 보였으며 5 m^-1이상의 고농도 구간 자료개수가 전체 153개 샘플자료 중 21개로 불균형하다. 따라서 ADASYN(Adaptive Synthesis Sampling Approach)의 oversampling 방법으로 생성된 합성 데이터를 사용하여 원본 데이터의 소수계층 데이터 불균형을 해결하고 모델 예측 성능을 개선하고자 하였다. 생성된 합성 데이터를 입력변수로 하여 ANN(Artificial Neural Netowk)을 활용한 CDOM 예측 알고리즘을 구축하였다. ADASYN 기법을 통한 합성 데이터는 관측된 데이터의 불균형을 해결하여 기계학습 모델의 CDOM 탐지 성능을 향상시킬 수 있으며, 저수지 내 유기 오염물질 관리를 위한 설계를 지원하는데 사용할 수 있을 것으로 판단된다.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.33-39
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• 2023

Although COVID-19 has transitioned to a level 4 infectious disease in 2023 and has entered a stable trend, in special education settings, the importance of supporting the academic and social development gaps of students with disabilities caused by non-face-to-face learning situations such as remote classes during the COVID-19 period is emerging. there is. Accordingly, in this study, in order to identify and support the educational status and academic deficits of students with disabilities after COVID-19, we conducted a survey targeting 2,214 special education teachers in 17 cities and analyzed the results. As a result of the study, due to COVID-19, the developmental delay and educational gap in students with disabilities in terms of academics, emotions, and behavior deepened, and there was a high demand for manpower support, psychological counseling, and medical support for emotional behavior as a way to support this. Based on the results of this study, follow-up results were proposed.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.55-63
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• 2024

This paper discusses the concept and continuously evolving applications of metaverse technology in the field of safety education, and proposes methodologies for employing metaverse technology in safety education. Additionally, it analyzes educational cases using the metaverse to explore specific directions for the advancement of safety education. Therefore, this study aims to propose methodologies for safety education utilizing metaverse technology. Recently, the metaverse has emerged as a new platform in various fields, including education. In particular, safety education using metaverse technology is carried out because it can provide an engaging learning experience by fostering understanding and immersion through interaction, moving away from one-way didactic teaching. This paper proposes a three-stage educational process for safety education using metaverse technology and presents various implementable projects and activity examples for each stage. This approach can contribute to developing practical response skills for various situations, going beyond traditional safety education methods. Future research is expected to deeply explore the long-term effectiveness of this educational methodology and its practical applicability in educational settings.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1101-1118
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• 2017

Temperatures in urban areas are steadily rising due to rapid urbanization and on-going climate change. Since the spatial distribution of heat in a city varies by region, it is crucial to investigate detailed thermal characteristics of urban areas. Recently, many studies have been conducted to identify thermal characteristics of urban areas using satellite data. However,satellite data are not sufficient for precise analysis due to the trade-off of temporal and spatial resolutions.In this study, in order to examine the thermal characteristics of Daegu Metropolitan City during the summers between 2012 and 2016, Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data at 1 km spatial resolution were downscaled to a spatial resolution of 250 m using a machine learning method called random forest. Compared to the original 1 km LST, the downscaled 250 m LST showed a higher correlation between the proportion of impervious areas and mean land surface temperatures in Daegu by the administrative neighborhood unit. Hot spot analysis was then conducted using downscaled daytime and nighttime 250 m LST. The clustered hot spot areas for daytime and nighttime were compared and examined based on the land cover data provided by the Ministry of Environment. The high-value hot spots were relatively more clustered in industrial and commercial areas during the daytime and in residential areas at night. The thermal characterization of urban areas using the method proposed in this study is expected to contribute to the establishment of city and national security policies.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.959-974
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• 2021

Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 gradesranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In thisstudy, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R²)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch's OC-CCI FUI algorithm (R²=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findingssuggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.747-763
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• 2022

Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.