• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.5
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• pp.54-68
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• 2022

This study proposes urban development concept and strategies for Garden City, focused on Solaseado, Yeongam Heanam Tourism-Leisure Type Enterprise City in Korea. Understanding that an essential element of a garden is the endless care performed by gardeners, the Garden City development concept suggests applying this idea to making planned cities by cultivating the potential natural landscape of the site in the long run. The meaning of Garden City can be defined in three aspects; an attitude and process of planning a city, a system for constructing the spatial structure of a city, and city branding. A Garden City is a city structured with the spirit of a garden, a city where open space networks become the urban structure, and a city that builds its identity through the landscape, respectively. From this point of view, the research draws development strategies with spatial design examples to embody the Garden City concept in Solaseado by following three steps; establishing the main urban axes, creating city networks through the conjunction of the axes, and categorizing and systematizing open spaces within the city. Consequently, the study shows an alternative urban planning model that extends the concept of a Garden City while maintaining the intrinsic landscape as an urban resource. In addition, the conceptual master plan of Solaseado will structure the urban landscape and park system according to the Garden City strategies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.459-468
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• 2022

This study searched and analyzed news and information on the Korea-U.S. Summit to explore which political agenda is spreading among Korean newspapers and social media. The result of the analysis revealed that, on the one hand, the conservative-leaning newspaper, Chosunilbo, covered the unresolved issue between two countries. The principal source of news was the opposition party. On the other hand, the progressive-leaning newspaper, Kyunghany Sinmun, highlighted President Moon's visit to the United States and described the visit to the United States as an achievement. In this paper, the principal source of news is the ruling party. Both conservative and the progressive newspapers did not present a negative view of the United States. In the case of Chosunilbo, it mentioned that foreign policy priority of President Biden is human rights in North Korea. If the two countries do not solve this issue, the relationship between Korea and the United States will not develop further. Second, I searched YouTube videos about the Korea-U.S. summit and conducted a network analysis to understand the influence of YouTube videos and explore their relationship the each other. The results of the analysis revealed that the 10 most influential videos portrayed the Moon government positively. These videos held the achievement of the visit to the United States in highly esteem and framed it positively, similarly to the progressive newspaper.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.56-71
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• 2023

The purpose of this study is to explore the role and effectiveness of argumentation structure and the developmental characteristics of science PCK with Earth science preservice teachers who used argumentation structure as a pedagogical reasoning tool. Since teachers demonstrate PCK in a series of pedagogical reasoning processes using argumentation structures, we explored the characteristics of future-oriented family resemblance-PCK shown by preservice science teachers using argumentation structures. At the end of the semester, we conducted in-depth interviews with 15 earth science preservice teachers who had experienced lesson design and teaching practice using the argumentation structure. Qualitative analysis including a semantic network analysis was conducted based on the in-depth interview to analyze the characteristics of preservice teachers' family resemblance-PCK. Results include that preservice teachers organized their classes systematically by applying the argumentation structure, and structured classes by differentiating argumentation elements from facts to conclusions. Regarding the characteristics of each component of the argumentation structure, preservice teachers had difficulty finding warrant, rebuttal, and qualifier. The area of PCK most affected by the argumentation structure is the science teaching practice, and preservice teachers emphasized the selection of a instructional model suitable for lesson content, the use of various teaching methods and inquiry activities to persuade lesson content, and developing of data literacy and digital competency. Discussed in the conclusion are the potential and usability of argument structure as a pedagogical reasoning tool, the possibility of developing science inquiry and reasoning competency of secondary school students who experience science classes using argumentation structure, and the need for developing a teacher education protocol using argumentation structure as a pedagogical reasoning tool.

• The Journal of Korean Philosophical History
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• no.27
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• pp.417-446
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• 2009

This study was to review the features of the preventive dimension in connection with adolescent deviation on the basis of the morality and ethics held by Confucian doctrine. To find solutions to the problems of adolescent deviation is never easy. As adolescent deviation always does occur, it is important to consider the methods that can minimize and prevent it. The traditional society of Korea laid weight on the education and training in the aspect of preventive measure against such adolescent deviation by emphasizing moral edification and realization of spiritual understanding for it. In this paper, the researcher tried to understand the problem situations by examining the image of such deviation and its type as well as the method on response thereto targeting the young generation of Korea. In addition, the researcher analyzed how the adolescent was recognized in the traditional society that was established on the Confucian values, and moral standards that applied to them, and the process of education as well. Through the moral concepts of Confucianism that were revealed in the Doctrine of the Mean (中庸, pronounced 'Jungyong' in Korean) in particular, the researcher sought the possibility of education on morality and ethics that will be able to prevent adolescent deviation. This study suggests that the morality and ethics held by Confucian doctrine can prevent adolescent deviation and open a new horizon of ethics education.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1693-1705
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• 2023

Rural areas, which account for about 90% of the country's land area, are increasing in importance and value as a space that performs various public functions. However, facilities that adversely affect residents' lives, such as livestock facilities, factories, and solar panels, are being built indiscriminately near residential areas, damaging the rural environment and landscape and lowering the quality of residents' lives. In order to prevent disorderly development in rural areas and manage rural space in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. Data can be acquired through satellite imagery, which can be acquired periodically and provide information on the entire region. Effective detection is possible by utilizing image-based deep learning techniques using convolutional neural networks. Therefore, U-Net model, which shows high performance in semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study, KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020 with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories, and solar panels were produced by hand for training and inference. After training with U-Net, pixel accuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results of this study can be used for monitoring hazardous facilities in rural areas and are expected to be used as basis for rural planning.

• Korean Journal of Cognitive Science
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• v.27 no.2
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• pp.159-219
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• 2016

Mathematical ability is important for academic achievement and technological renovations in the STEM disciplines. This study concentrated on the relationship between neural basis of mathematical cognition and its mechanisms. These cognitive functions include domain specific abilities such as numerical skills and visuospatial abilities, as well as domain general abilities which include language, long term memory, and working memory capacity. Individuals can perform higher cognitive functions such as abstract thinking and reasoning based on these basic cognitive functions. The next topic covered in this study is about individual differences in mathematical abilities. Neural efficiency theory was incorporated in this study to view mathematical talent. According to the theory, a person with mathematical talent uses his or her brain more efficiently than the effortful endeavour of the average human being. Mathematically gifted students show different brain activities when compared to average students. Interhemispheric and intrahemispheric connectivities are enhanced in those students, particularly in the right brain along fronto-parietal longitudinal fasciculus. The third topic deals with growth and development in mathematical capacity. As individuals mature, practice mathematical skills, and gain knowledge, such changes are reflected in cortical activation, which include changes in the activation level, redistribution, and reorganization in the supporting cortex. Among these, reorganization can be related to neural plasticity. Neural plasticity was observed in professional mathematicians and children with mathematical learning disabilities. Last topic is about mathematical creativity viewed from Neural Darwinism. When the brain is faced with a novel problem, it needs to collect all of the necessary concepts(knowledge) from long term memory, make multitudes of connections, and test which ones have the highest probability in helping solve the unusual problem. Having followed the above brain modifying steps, once the brain finally finds the correct response to the novel problem, the final response comes as a form of inspiration. For a novice, the first step of acquisition of knowledge structure is the most important. However, as expertise increases, the latter two stages of making connections and selection become more important.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.421-430
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• 2004

Clinically, it is almost impossible for a physician to distinguish subtle changes of frequency spectrum by using a stethoscope alone especially in the early stage of thrombus formation. Considering that reliability of mechanical valve is paramount because the failure might end up with patient death, early detection of valve thrombus using noninvasive technique is important. Thus the study was designed to provide a tool for early noninvasive detection of valve thrombus by observing shift of frequency spectrum of acoustic signals with computer aid diagnosis system. A thrombus model was constructed on commercialized mechanical valves using polyurethane or silicon. Polyurethane coating was made on the valve surface, and silicon coating on the sewing ring of the valve. To simulate pannus formation, which is fibrous tissue overgrowth obstructing the valve orifice, the degree of silicone coating on the sewing ring varied from 20％, 40％, 60％ of orifice obstruction. In experiment system, acoustic signals from the valve were measured using microphone and amplifier. The microphone was attached to a coupler to remove environmental noise. Acoustic signals were sampled by an AID converter, frequency spectrum was obtained by the algorithm of spectral analysis. To quantitatively distinguish the frequency peak of the normal valve from that of the thrombosed valves, analysis using a neural network was employed. A return map was applied to evaluate continuous monitoring of valve motion cycle. The in-vivo data also obtained from animals with mechanical valves in circulatory devices as well as patients with mechanical valve replacement for 1 year or longer before. Each spectrum wave showed a primary and secondary peak. The secondary peak showed changes according to the thrombus model. In the mock as well as the animal study, both spectral analysis and 3-layer neural network could differentiate the normal valves from thrombosed valves. In the human study, one of 10 patients showed shift of frequency spectrum, however the presence of valve thrombus was yet to be determined. Conclusively, acoustic signal measurement can be of suggestive as a noninvasive diagnostic tool in early detection of mechanical valve thrombosis.

• Korean Journal of Environmental Biology
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• v.35 no.4
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• pp.549-556
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• 2017

Rivers continuously transport terrestrial organic carbon matter to the estuary and the ocean, and they play a critical role in productivity and biodiversity in the marine ecosystem as well as the global carbon cycle. The amount of terrestrial organic carbon transporting from the rivers to ocean is an essential piece of information, not only for the marine ecosystem management but also the carbon budget within catchment. However, this phenomenon is still not well understood. Most large rivers in Korea have a well-established national monitoring system of the river flow and the TOC (Total Organic Carbon) concentration from the mountain to the river mouth, which are fundamental for estimating the amount of the TOC flux. We estimated the flux of the total terrestrial organic carbon of five large rivers which flow out to the Yellow Sea, using the data of the national monitoring system (the monthly mean TOC concentration and the monthly runoff of river flow). We quantified the annual TOC flux of the five rivers, showing their results in the following order: the Han River ($18.0{\times}10^9gC\;yr^{-1}$)>>Geum River ($5.9{\times}10^9gC\;yr^{-1}$)>Yeongsan River ($2.6{\times}10^9gC\;yr^{-1}$)>Sumjin River ($2.0{\times}10^9gC\;yr^{-1}$)>>Tamjin River ($0.2{\times}10^9gC\;yr^{-1}$). The amount of the Han River, which is the highest in the Korean rivers, corresponds to be 4% of the annual total TOC flux of in the Yellow River, and moreover, to be 0.6% of Yangtze River.