• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.42.3-42.3
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• 2019

We translate Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) ultraviolet (UV) multi-channel images into another UV single-channel image using a deep learning algorithm based on conditional generative adversarial networks (cGANs). The base input channel, which has the highest correlation coefficient (CC) between UV channels of AIA, is 193 Å. To complement this channel, we choose two channels, 1600 and 304 Å, which represent upper photosphere and chromosphere, respectively. Input channels for three models are single (193 Å), dual (193+1600 Å), and triple (193+1600+304 Å), respectively. Quantitative comparisons are made for test data sets. Main results from this study are as follows. First, the single model successfully produce other coronal channel images but less successful for chromospheric channel (304 Å) and much less successful for two photospheric channels (1600 and 1700 Å). Second, the dual model shows a noticeable improvement of the CC between the model outputs and Ground truths for 1700 Å. Third, the triple model can generate all other channel images with relatively high CCs larger than 0.89. Our results show a possibility that if three channels from photosphere, chromosphere, and corona are selected, other multi-channel images could be generated by deep learning. We expect that this investigation will be a complementary tool to choose a few UV channels for future solar small and/or deep space missions.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.142-142
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• 2022

Adjusting weather radar data is a prerequisite for its use in various hydrological studies. Effect of spatial variables are considered to adjust weather radar data in many of these researches. The existence of diverse topography in South Korea has increased the importance of analyzing these variables. In this study, some spatial variable like slope, elevation, aspect, distance from the sea, plan and profile curvature was considered. To investigate different topographic conditions, tried to use three radar station of Gwanaksan, Gwangdeoksan and Gudeoksan which are located in northwest, north and southeast of South Korea, respectively. To form the suitable fuzzy model and create the best membership functions of variables, ANFIS-PSO model was applied. After optimizing the model, the correlation coefficient and sensitivity of adjusted Quantitative Precipitation Estimation (QPE) based on spatial variables was calculated to find how variables work in adjusted QPE process. The results showed that the variable of elevation causes the most change in rainfall and consequently in the adjustment of radar data in model. Accordingly, the sensitivity ratio calculated for variables shows that with increasing rainfall duration, the effects of these variables on rainfall adjustment increase. The approach of this study, due to the simplicity and accuracy of this method, can be used to adjust the weather radar data and other required models.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.99-100
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• 2022

As the danger of exposure to the asbestos has been revealed, the importance of demolition asbestos in existing buildings has been raised. Extensive body of study has been conducted to evaluate the risk of demolition asbestos, but there were confined types of variables caused by not reflecting categorical information and limitations in collecting quantitative information. Thus, this study aims to derive a model that predicts the risk in workplace of demolition asbestos by collecting categorical and continuous variables. For this purpose, categorical and continuous variables were collected from asbestos demolition reports, and the risk assessment score was set as the dependent variable. In this study, the influence of each variable was identified using logistic regression, and the risk prediction model methodologies were compared through decision tree regression and artificial neural network. As a result, a conditional risk prediction model was derived to evaluate the risk of demolition asbestos, and this model is expected to be used to ensure the safety of asbestos demolition workers.

• Journal of The Geomorphological Association of Korea
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• v.26 no.3
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• pp.31-42
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• 2019

The geomorphological changes of an unvegetated part of the Okjukdong coastal dune were analyzed between 2008 and 2018. Its natural landscape has been destroyed after artificial forestation, but there is no quantitative evidence on these changes. In this study, we measured the unvegetated area using a total station and a network RTK-GPS in 2008, 2014, and 2018. Using Krging method for the three point data sets, we constructed digital elevation models (DEMs) and analyzed topographic changes between the three years. The results showed that the sand of the study area decreased in volume from 2008 to 2014, because sand supply from the nearby beach was blocked by coastal forests. The sand volume temporarily increased from 2014 to 2018, because of the dune nourishment conducted in 2017. It seems that the upper part of the sand dune has shrunk, but the sand at the bottom has increased over the last decade.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2314-2333
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• 2023

It is significant to predict the performance degradation of complex electromechanical systems. Among the existing performance degradation prediction models, belief rule base (BRB) is a model that deal with quantitative data and qualitative information with uncertainty. However, when analyzing dynamic systems where observable indicators change frequently over time and working conditions, the traditional belief rule base (BRB) can not adapt to frequent changes in working conditions, such as the prediction of aeroengine performance degradation considering working condition. For the sake of settling this problem, this paper puts forward a new hidden belief rule base (HBRB) prediction method, in which the performance of aeroengines is regarded as hidden behavior, and operating conditions are used as observable indicators of the HBRB model to describe the hidden behavior to solve the problem of performance degradation prediction under different times and operating conditions. The performance degradation prediction case study of turbofan aeroengine simulation experiments proves the advantages of HBRB model, and the results testify the effectiveness and practicability of this method. Furthermore, it is compared with other advanced forecasting methods. The results testify this model can generate better predictions in aspects of accuracy and interpretability.

• Architectural research
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• v.25 no.4
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• pp.73-84
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• 2023

Indoor thermal conditions greatly affect the health and comfort of humans who occupy the space in it. The purpose of this research is to analyze the influence of water and vegetation elements as a microclimate modifier in buildings to obtain thermal comfort through the study of thermal environment models. This research covers two objects, namely public buildings and housing in Makassar City, South Sulawesi Prov-ince - Indonesia. Quantitative methods through field surveys and measurements based on thermal and personal variables. Data analysis based on ASHRAE 55 2020 standard. The data was processed with a parametric statistical approach and then simulated with the Computational Fluid Dynamics (CFD) simulation method to find a thermal prediction model. The model was made by increasing the ventilation area by 2.0 m2, adding 10% vegetation with shade plant characteristics, moving water features in the form of fountains and increasing the pool area by 15% to obtain PMV + 0.23, PPD + 8%, TSV-1 - +0, Ta_25.7℃, and relative humidity 63.5 - 66%. The evaluation shows that the operating temperature can analyze the visitor's comfort temperature range of >80% and comply with the ASHRAE 55-2020 standard. It is concluded that water elements and indoor vegetation can be microclimate modifiers in buildings to create desired comfort conditions and adaptive con-trols in buildings such as the arrangement of water elements and vegetation and ventilation systems to provide passive cooling effects in buildings.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.81-91
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• 2023

As abnormal weather conditions escalate, water disasters such as droughts and floods occur more frequently. These natural disasters are fatal to agricultural reservoirs, where the operation techniques vary greatly depending on the season and weather conditions, and response through intake works is limited. In response, governments like the Korea Rural Community Corporation have researched efficient water supply methods through irrigation channels. Therefore, previous studies analyzed the irrigation process using numerical models to determine an efficient irrigation system. However, SWMM and EPANET used in previous studies are limited in quantitative agricultural irrigation process analysis. Therefore, this study developed AIRISS to simulate and analyze agricultural irrigation. Specifically, we simulated the irrigation process in the Ssangbong area of South Korea and simulated the irrigation process to verify the performance of the numerical model. AIRISS, developed in this study, is specialized in simulating the agricultural irrigation process. It can check the supply to each paddy and the condition of each paddy.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2021.04a
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• pp.47-47
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• 2021

This study was carried out to obtain a basic information for the improvement of human health and the development of variety through analysis of organic compounds, contents of three CQA(3-caffeoylquinic acid, 4-caffeoylquinic acid and 5-caffeoylquinic acid) and five anthocyanin (petunidin-3-p-cumaroylrutinoside-5-glucoside, pelargonidin-3-p-cumaroylrutin-oside-5-glucoside, peonidin-3-p-cumaroylrutinoside-5-glucoside, pelargonidin-3-p-feruloyl-rutinoside-5-glucoside and peonidin-3-feruloylrutinoside-5-glucoside)to color potatoes is Hong-young(HY) and Ja-young(JY). The analytical results on organic compounds in color potatoes were shown as follow, The contents of CQA and Anthocyanin of JY variety were shown to be higher than HY, while CQA and Anthocyanin were appeared to be highest in peel of JY. Overall, JY had higher amount of physicochemical properties than HY. The results of this study reveal the quantitative analysis of functional compounds seperated from various kind of potatoes, which will enable the acquisition of new bioactive candidates and the establishment of new profit generation models for farmers.

• Korean Journal of Artificial Intelligence
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• v.12 no.1
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• pp.1-6
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• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• Asia pacific journal of information systems
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• v.31 no.1
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• pp.17-42
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• 2021

This study examines the adoption of subscription-based video on demand (SVOD) streaming services among consumers. Primarily, we explore the moderating effect of the two models of streaming services, standalone streaming services and bundled streaming services, on the users' adoption. We employ the Unified Theory of Acceptance and Use of Technology (UTAUT2) model in this study. We utilize the data collected from 337 Indian respondents and find that all constructs of the UTAUT2 model act as motivators of adoption. Gender, age, and experience of the respondent also play a moderating role in the adoption of streaming services. We also find that providing bundled streaming service positively moderates price-value and hedonic motivation of adoption. The study is perhaps the first of its kind that aims to understand the motivations for adoption of SVOD services, particularly in the Indian context, which has the fastest-growing base of internet users in the world.