• Economic and Environmental Geology
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• v.55 no.2
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• pp.197-207
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• 2022

This study investigated spectroscopic exploration of Myeonsan formation, the titanium(Ti) ore hostrock, in Joseon supergroup based on machine learning technique. The mineral composition, Ti concentration, spectral characteristics of Myeonsan and non-Myeonsan formation of Joseon supergroup were analyzed. The Myeonsan formation contains relatively larger quantity of opaque minerals along with quartz and clay minerals. The PXRF analysis revealed that the Ti concentration of Myeosan formation is at least 10 times larger than the other formations with bi-modal distribution. The bi-modal concentration is caused by high Ti concentrated sandy layer and relatively lower Ti concentrated muddy layer. The spectral characteristics of Myeonsan formation is manifested by Fe oxides at near infrared and clay minerals at shortwave infrared bands. The Ti exploration is expected to be more effective on detection of hostrock rather than Ti ore because ilmenite does not have characteristic spectral features. The random-forest machine learning classification detected the Myeonsan fomation at 85% accuracy with overall accuracy of 97%, where spectral features of iron oxides and clay minerals played an important role. It indicates that spectral analysis can detect the Ti host rock effectively, and can contribute for UAV based remote sensing for Ti exploration.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1301-1314
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• 2022

Normalized Difference Vegetation Index (NDVI) is utilized as an indicator to represent the vegetation condition on the land surface in various applications such as land cover, crop yield, agricultural drought, soil moisture, and forest disaster. However, satellite optical sensors for visible and infrared rays cannot see through the clouds, so the NDVI of the cloud pixel is not a valid value for the land surface. This study proposed a real-time correction of the underestimation noise for GEO-KOMPSAT-2A (GK2A) daily NDVI and made sure its feasibility through the quantitative comparisons with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and the qualitative interpretation of time-series changes. The underestimation noise was effectively corrected by the procedures such as the time-series correction considering vegetation phenology, the outlier removal using long-term climatology, and the gap filling using rigorous statistical methods. The correlation with MODIS NDVI was higher, and the difference was lower, showing a 32.7% improvement compared to the original NDVI product. The proposed method has an extensibility for use in other satellite products with some modification.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.334-345
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• 2021

The present study investigated the effect of global warming on germination and initial growth across six deciduous oak species (Quercus mongolica, Q. variabilis, Q. serrata, Q. dentata, Q. aliena, and Q. acutissima), which are the dominant tree species in Korea forest ecosystems. Seeds were sown in climate change treatments, with temperatures higher than those of the control (approximately 3.0℃ higher), and CO₂ concentrations higher than those of the control (approximately 2-fold higher). Initial growth in each species was measured every two weeks. Initial growth was more rapid in all oak species at the time of root and shoot emergence under high temperature and CO₂ treatments than in the control group. Leaf emergence in Q. mongolica, Q. variabilis, and Q. serrata occurred earlier under the climate change treatments than under the control. Root length increased significantly in Q. mongolica, Q. variabilis, and Q. dentata under the climate change treatments when compared to under the control. However, Q. aliena and Q. serrata exhibited a contrasting trends, and no significant difference was observed between the species and Q. acutissima. Shoot length increased significantly in Q. aliena under climate change treatments when compared to under the control but decreased in Q. aliena. In addition, no significant difference was observed in shoot length among Q. mongolica, Q. dentata, and Q. acutissima. The results showed that climate change treatments facilitated early growth, rapid emergence from the ground, leaf development, and enhanced belowground growth in Q. mongolica. Conversely, Q. aliena exhibited the lowest aboveground and belowground growth under climate change treatments when compared to other oak species. Climate change treatments had the least impact on Q. acutissima considering the insignificant differences observed in initial growth rates under climate change treatment.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.264-274
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• 2021

This study aims to analyze the impact of well-being tourism motivation on quality of life through well-being tourism satisfaction, focusing on wellness mediated effects. Recently, despite the growing number of well-being tourism pursuing wellness, the lack of research has led to a close analysis of the impact of well-being tourism motivation on quality of life through well-being tourism satisfaction. For this study, a total of 236 people who have experienced well-being tours in the metropolitan area were surveyed from May 10 to May 21, 2021. First, well-being tourism motivation has a positive effect on well-being tourism satisfaction. Second, well-being tourism satisfaction has a positive impact on the quality of life. Third, Wellness has a positive indirect effect as a partial mediator between well-being tourism satisfaction and quality of life The implications of this study are to demonstrate the impact of well-being tourism on quality of life using Wellness' mediators This study suggests that wellness tourism plays an important role in improving the quality of life. It was analyzed that well-being tourism had a positive effect on improving the quality of life at a time when the fatigue of daily life was increased due to prolonged COVID-19. In addition, Wellis analyzed that it is an important factor in enhancing the quality of life for well-being tourists. This contributes not only to the academic contribution to the revitalization of well-being tourism, but also to the development of stress improvement routes to improve people's lives nationally.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.785-799
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• 2022

The aim of this study was to characterize the emission pathways and the footprint of greenhouse gases (GHG) in Daecheong Reservoir using the G-res Tool, and to evaluate the GHG emission intensity (EI) compared to other energy sources. In addition, the change in GHG emissions was assessed in response to the total phosphorus (TP) concentration. The GHG flux in post-impoundment was found to be 262 gCO₂eq/m²/yr, of which CO₂ and CH₄ were 45.7% and 54.2%, respectively. Diffusion of CO₂ contributed the most, followed by diffusion, degassing, and bubbling of CH₄. The net GHG flux increased to 510 gCO₂eq/m²/yr because the forest (as CO₂ sink) was lost after dam construction. The EI of Daecheong Reservoir was 86.8 gCO₂eq/kWh, which is 3.7 times higher than the global EI of hydroelectric power, due to its low power density. However, it was remarkable to highlight the value to be 9.5 times less than that of coal, a fossil fuel. We also found that a decrease in TP concentration in the reservoir leads to a decrease in GHG emissions. The results can be used to improve understanding of the GHG emission characteristics and to reduce uncertainty of the national GHG inventory of dam reservoirs.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.571-585
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• 2022

As the threat of natural disasters such as droughts, floods, forest fires, and landslides increases due to climate change, social demand for high-resolution soil moisture retrieval, such as Synthetic Aperture Radar (SAR), is also increasing. However, the domestic environment has a high proportion of mountainous topography, making it challenging to retrieve soil moisture from SAR data. This study evaluated the usability of Sentinel-1 SAR, which is applied with the Artificial Neural Network (ANN) technique, to retrieve soil moisture. It was confirmed that the backscattering coefficient obtained from Sentinel-1 significantly correlated with soil moisture behavior, and the possibility of stand-alone use to correct vegetation effects without using auxiliary data observed from other satellites or observatories. However, there was a large difference in the characteristics of each site and topographic group. In particular, when the model learned on the mountain and at flat land cross-applied, the soil moisture could not be properly simulated. In addition, when the number of learning points was increased to solve this problem, the soil moisture retrieval model was smoothed. As a result, the overall correlation coefficient of all sites improved, but errors at individual sites gradually increased. Therefore, systematic research must be conducted in order to widely apply high-resolution SAR soil moisture data. It is expected that it can be effectively used in various fields if the scope of learning sites and application targets are specifically limited.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.181-192
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• 2021

This study surveyed visitors' perceptions of Mudeungsan National Park's Seoseokdae Trail Section (STS) on the visitor reservation system (VRS). Data were collected from 248 visitors to STS selected through convenient sampling in May 2019. The majority (86.9%) of the respondents rated their overall experience in STS as either "no problem at all" or "little problem". Moreover, 43.0% of the respondents were aware of the VRS. The most popular information source for VRS was the Internet (49.7%), followed by other people (18.4%) and broadcasting media, e.g., TV (17.7%). While 69.9% of the respondents thought that implementation of VRS would be effective in improving managerial conditions of the STS, respondents perceived that "cumbersome reservation procedures" (79.3%) of the VRS operation was the most important problem, followed by "unfairness associated with Internet familiarity gap" (78.7%) and "deprivation of the opportunities to visit freely" (76.3%). The support for VRS implementation was higher among higher-aged, married, higher-educated, more frequent STS visitors, Gwangju residents, and solo visitors than the other groups. The "knowledge level about VRS" and "the awareness level about potential problems associated with VRS operation" negatively influenced the support for the implementation of VRS, while "the perceived managerial effectiveness of VRS" positively influenced it.

• Journal of Korea Entertainment Industry Association
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• v.14 no.8
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• pp.513-523
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• 2020

This study has derived the patents of the technology that have been filed and registered so far to investigate the trends of virtual reality(VR) experience contents technology, and analyzed them focusing on core patent technologies. The patents of Korea, USA, Japan, Europe and PCT, which were released until June 2020, were targeted, and patent search was conducted using WISDOMAIN search DB. The keywords for patent search were related to experience technology using VR, and a total of 1,013 data were obtained after creating a search formula by combining the derived keywords. Among them, a total of 65 data were extracted from the result of selecting valid patents, and a political analysis was conducted on them. Looking at the overall application trend, most of Korean patent applications accounted for, and noise patents are system-related devices to implement VR technology. The United States and Europe are focused on developing augmented reality(AR) technology, the study found. The technology of VR experience has increased rapidly since 2017, and the technology growth stage is the period from the beginning to the growth stage. As a result of examining the valid patents related to VR experience, technology was searched in various fields such as rural tour, exhibition, education, and performance, and patents for contents writing and general virtual experience related technology were also searched. If we predict the possibility of development of VR industry in the future, it is necessary to respond to preemption of intellectual property rights by proceeding technology development and patent application for more diverse fields.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.