• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.101-127
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• 2021

The purpose of this study is to design and develop a teaching-learning process plan for process-based assessment, focusing on the unit related to life design in middle school home economics(HE: Home Economics part of 「Technology and Home Economics」), to propose a feedback plan after implementing it, and to evaluate the plan through participatory observation and interviews. The student reflection journals, teacher's class journals, participatory observation journals, interviews, and performance tasks, were collected and analyzed to provide foundational date to be utilized for feedback to students, and class improvement. The research results are as follows: First, the developed teaching-learning process plan consists of a total of 8 sessions, i.e. 2 sessions for each of the four learning themes, under the practical question of "What should I do to live the life I want?" The portfolio was composed of five evaluation topics and for evaluation, oral presentation, observational evaluation, self-assessment, and peer evaluation were considered. Second, during the class, feedback from teachers, feedback from fellow students, feedback through results, and a plan to record them were provided. Third, from the analysis of collected data including observation journals and interviews, it was apparent that the students recognized the necessity of process-based assessment after the class, and students acknowledged that through the process-based evaluation in which they are evaluated on the efforts they made and provided with feedbacks, they participated more in class, and it lead them to experience a sense of growth and a feeling that they took a step forward into their future. Teachers suggested that the class through feedback was suitable for the unit and the capacity of the class, but the difficulty they experienced in giving feedback was presented as a disadvantage. For the process-based assessment, follow-up research is needed on various ways to provide feedback on-line and off-line through changes in the perception of assessment.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.51-62
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• 2020

The purpose of this study was to verify whether the quality of service provided by culture and arts education has a bearing on the service value of dance education, and to verify the role of the intention of reuse of culture and arts education participants in this relationship. The survey was conducted from October 1, 2019 to December 30, 2019, and a sample was collected from stratified sampling methods for the general public participating in culture and arts education. In order to verify the relationship between the independent variable and the dependent variable, multiple regression analysis was performed, and the role of the parameter in the relationship between the independent variable and the dependent variable was examined using the three-step intermediary regression analysis. The research results are as follows. First, as a result of analyzing the relationship between the sub-factors of service quality and emotional value, it was found that facilities and environmental services had the greatest impact. Second, the results of analyzing the relationship between the sub-factors of service quality and functional value showed that the most significant impact was on facilities and environmental services. Third, as a result of analyzing the relationship between the sub-factors of service quality and social values, it was found that facilities and environmental services had the greatest impact. Fourth, In the relationship between contents and instructor service and service value, the intention to reuse was found to have a full mediating effect. In addition, in the relationship between facility and environmental services and service value, the intention to reuse was found to have a partial mediating effect. Based on the quality of service of culture and arts education, this study suggested a way for dance education to work with the region.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Life Science
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• v.31 no.11
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• pp.1028-1036
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• 2021

Firefly luciferase (FLuc) can function as an efficient marker in the gene and viral therapies. Nonetheless, its clinical translation has been unaccomplished with the concerns on its exogenous nature and the similarity with human fatty acyl-CoA synthetase. In this study, we aimed to show safety of FLuc by conducting a set of preclinical experiments and a human use. Initially, FLuc permeability across the plasma membrane was investigated by delivering the FLuc-carrying viral vector, OTS-412, or the FLuc recombinant protein. After in vitro infection of OTS-412 into different cancer cell lines, FLuc activity was detected only in the cell lysates, but not in culture media. In addition, recombinant FLuc protein further showed the impermeability against the plasma membrane. Similar result was also observed in the in vivo experiment. After being injected into the VX2 tumor-bearing rabbit, the FLuc exclusively resided within the tumor tissue without being detected in the blood plasma or other organs. Human cancer cell lines originated from various organs were lysed and treated to the FLuc, and none of the human substrates was reactive against the FLuc. As a final step, FLuc recombinant protein was intravenously injected into a human. The luciferase was degraded with the half-life of 20 to 30 minutes in blood, and was untraceable from 1.5 hr after the injection. In addition, the blood plasma was nonresponsive against the fatty acids. Hematological analysis was also comparable between the pre- and post-injection. Altogether, our study collectively demonstrates the safety of the firefly luciferase.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.289-303
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• 2020

The purpose of this study is to provide adequate datas on number of people who are in golf related field and also further expend that number for golf industry. The main stream of this study is to map out present golf related jobs and how this study can help golf industry in general. To make a greater improvements on golf industry with more job opportunity following ideas were presented. First, improving education on "hand on experiences on the field of golf industry" Better educating potential employees for golf industry for the right positions can enhance overall work environment. To do so, the society and the schools must come to agreement to provide adequate curriculum for people. Second, implementing "a golf club division program." The support from Ministry of Culture and Sports and Tourism Department, which are govern by Republic of Korea, are aggressively working to expend the golf business and also recruit elite personnel like former tour players to work on the field to better operate the whole system. Third, performing a thorough research on current golf related jobs and numbers and diversity on the field. Fourth, developing a specific and a differentiated golf job fields for better future for people and students who majored in golf industry. So that they cam feel secured and have a sense of dignity. Finally, strengthening the golf industry's competitiveness. The golf related companies have to step up to higher gear. With working in harmony between golf industry and government can assure of brighter future for our next generation.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.21 no.1
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• pp.15-21
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• 2021

Purpose: Urea cycle disorder (UCD) is an inherited inborn error of metabolism, acting on each step of urea cycle that cause various phenotypes. The purpose of the study was to investigate the long-term clinical consequences in different groups of UCD to characterize it. Methods: Twenty-two patients with UCD genetically confirmed were enrolled at Pusan National University Children's hospital and reviewed clinical features, biochemical and genetic features retrospectively. Results: UCD diagnosed in the present study included ornithine transcarbamylase deficiency (OTCD) (n=10, 45.5%), argininosuccinate synthase 1 deficiency (ASSD) (n=6, 27.3%), carbamoyl-phosphate synthetase 1 deficiency (CPS1D) (n=3, 13.6%), hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (HHHS) (n=2, 9.1%), and arginase-1 deficiency (ARG1D) (n=1, 4.5%). The age at the diagnosis was 32.7±66.2 months old (range 0.1 to 228.0 months). Eight (36.4%) patients with UCD displayed short stature. Neurologic sequelae were observed in eleven (50%) patients with UCD. Molecular analysis identified 37 different mutation types (14 missense, 6 nonsense, 6 deletion, 6 splicing, 3 delins, 1 insertion, and 1 duplication) including 14 novel variants. Progressive growth impairment and poor neurological outcomes were associated with plasma isoleucine and leucine concentrations, respectively. Conclusion: Although combinations of treatments such as nutritional restriction of proteins and use of alternative pathways for discarding excessive nitrogen are extensively employed, the prognosis of UCD remains unsatisfactory. Prospective clinical trials are necessary to evaluate whether supplementation with BCAAs might improve growth or neurological outcomes and decrease metabolic crisis episodes in patients with UCD.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Journal of Space Technology and Applications
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• v.1 no.3
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• pp.283-301
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• 2021

In order to satisfy the intellectual curiosity of mankind to explore the unknown, National Aeronautics and Space Administration (NASA) in the United States and European Space Agency (ESA) in Europe are embarking on various R&D under the motto of the grand dream of pioneering space into a safe and sustainable environment. In the 2020s and 30s, it is expected that advanced giant observation equipment will be in operation, such as the development of a 10-meter-class telescope in space. In Korea, following the development of the 0.15 m Near-Infrared Imaging Spectrometer (NISS), Korea Astronomy and Space Science Institute (KASI) is also participating a 0.2 m Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) as an international cooperation partner in small exploration telescope. However, domestic experience in the development and operation of the space telescopes is still insufficient, and there is no plan with long-term prospects for constructing telescopes. In order to answer questions about the unknown world that mankind has not experienced using our own equipment, planning and preparation for the construction of a space telescope through close cooperation among industry-university-institute-government is urgently needed. In this paper, the necessity, background, development goals, and expected effects of the development of the Korean Space Telescope are summarized conceptually, and a working group (WG) is also proposed. In the WG activities, Korea shall take the lead in establishing the Korean-style space telescope development plan, and will start a valuable step to establish the national direction in the field of space astronomy and related technologies. We hope that the WG will be another milestone in Korea's space development.