• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of Industrial Convergence
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• v.22 no.1
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• pp.105-115
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• 2024

This study aimed to analyze the impact of mental strength on the competitive performance of student Taekwondo sparring athletes. A total of 343 middle school, high school, and university students registered as Taekwondo sparring athletes with the Korea Taekwondo Association were conveniently sampled. The Mental Toughness Test developed by Loehr was utilized after expert consultations. Data analysis involved t-tests and one-way ANOVA to assess the levels of mental strength sub-factors based on general characteristics, followed by post hoc tests using the Schéffe method for intergroup comparisons. Correlation analysis and multiple regression were conducted to examine the relationship between sub-factors of mental strength and competitive ability. The results indicated significant differences in mental strength sub-factors-confidence, level of awakening regulation, visualization and mental imagery regulation, motivation level, positive energy, and attitude control-based on gender and age among Taekwondo sparring student athletes. In terms of perceived competitive ability, significant differences were found based on age and sports experience. Consequently, beyond psychological training, the study revealed that age and diverse experiences positively influence specific aspects of mental strength among Taekwondo sparring student athletes. Therefore, coaching and training for these athletes, particularly during middle and high school years, should incorporate psychological aspects alongside diverse competition experiences and training to help overcome performance evaluation anxieties during matches.

• Journal of Educational Research in Mathematics
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• v.25 no.4
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• pp.571-598
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• 2015

This study aimed to assess the spatial cognition strategies and roles taken by students in the process of solving spatial tasks. For the analysis, this study developed two spatial tests based on the mental rotation test, which were taken by 63 students in their final year in elementary schools. The results of this study showed that in terms of the method of approaching the tasks, students took the comprehensive approach and the partial approach. When solving the tasks, the students were shown to use the imagery thinking or analytic thinking method. In terms of perspective, the students rotated the object or change their perspectives. A comparison of the methods used by the students revealed that when approaching the tasks, the group of students who chose the partial approach had higher scores. In terms of solving the tasks the analytic thinking method, and in terms of perspective, changing perspectives were shown to be more effective. Such effective methods were used more frequently in discrimination tasks than in recognition tasks, and in more complicated items, than in less complicated items. In conclusion, the results of this study suggested that the partial, analytic approach and the change of perspectives are useful strategies in solving tasks which require high cognitive effort.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.444-454
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• 2017

Stop-motion animation is highly valued for realistic imagery and its ability to captivate the five senses of the audience. Despite this, practical research in this area is limited to date. The long hours required to produce stop-motion animation and turn these into profits can be attributed for the lack of knowledge in this area. In production research, the matter is made worse as it is difficult to protect the intellectual property made through the collaborated efforts of students and teaching staff through each laborious stage of planning, broadcasting and making 2D models. Meanwhile as the animation industry finds the investigation of new production processes taxing, it has maintained its focus on pre-existing processes such as the use of pipeline. This paper aims to shed light on new production methods that can be used to improve the effectiveness of existing stop-motion production. In addition, by working with Taktoon Enterprises on its T.V series Galaxy Kids the paper will revise real world production methods including the traditional use of hand made models. Thus by investigating the use of graphic technology such as 3D printing the paper will be able to extend current business models. Research conducted in this paper is a necessary part of overcoming various contents production obstacles. Furthermore, it may help publicize issues faced in production leading to the discussion and sharing of new innovations.

• Journal of the Korean Applied Science and Technology
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• v.38 no.4
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• pp.1056-1069
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• 2021

This study is a similar experimental study before and after the inequality control group to investigate the effect of PETTLEP model-based image training on fundamental nursing practice education on the confidence and competence ability of core basic nursing skills, and participation in autonomous practice. Data were collected by randomly assigning 74 students who understood the purpose of the study and voluntarily agreed to participate in the study among second-year students of the Department of Nursing at U University located in K Province, randomly assigned to an experimental group and a control group. For the collected data, frequency and percentage were used for general characteristics of subjects using SPSS Statistics 23.0 program, skewness and kurtosis were used for normality test, and the dependent variable test for measuring the effect of experimental treatment was analyzed by paired t-test. As a result of the study, PETTLEP model-based image training showed confidence in core basic nursing skills(t=4.18, p<.001) and competence (knowledge(t=2.241, p=.032), nursing skills(t=8.402, p<.001)), there were statistically significant differences in self-practice participation(t=6.822, p<.001). Based on the results of this study, the PETTLEP model-based image training provided Based on the results of this study, it was confirmed that PETTLEP model-based image training can be a teaching and learning method applicable to basic nursing education. In addition, PETTLEP model-based image training is expected to be utilized as a learning method to improve the competence of core basic nursing skills, which are recognized as difficult due to their high level of difficulty.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.132-142
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• 2022

The purpose of this study is to interpret the MCU's universal worldview from the perspective of geometry and to storytell narrative elements with mathematical imagination. For storytelling, data from the Phase 3 series aired from 2016 to 2019 was used. The Phase 3 series stimulates the imagination of the public with the sense of reality shown in the narrative and images based on geometrical theory and various predictions about future technology. Imagination is the driving force for diverse and original thinking about the unexperienced, and the ability to find order in chaos and create new perceptions of matter. The power of imagination is very necessary not only in artistic activities, but also in the scientific field where logic and rationality are important. Bachelard's imagination aims for art, the primitive realm of human beings, and contains sincerity and passion for the wonders of nature and all things. By exploring the MCU's worldview and superhero narrative through geometrical logic and imagination-driven imagery, you can understand the cosmic messages and laws in the film. From a convergence point of view of art and science, various and original techniques based on mathematics and scientific imagination used in MCU video production will help to improve the quality of video analysis.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.33-43
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• 2024

This research assessed the applicability of the You Only Look Once (YOLO)v8 and DeepLabv3+ models for the effective detection of compost heaps, identified as a significant source of non-point source pollution. Utilizing high-resolution imagery acquired through Unmanned Aerial Vehicles(UAVs), the study conducted a comprehensive comparison and analysis of the quantitative and qualitative performances. In the quantitative evaluation, the YOLOv8 model demonstrated superior performance across various metrics, particularly in its ability to accurately distinguish the presence or absence of covers on compost heaps. These outcomes imply that the YOLOv8 model is highly effective in the precise detection and classification of compost heaps, thereby providing a novel approach for assessing the management grades of compost heaps and contributing to non-point source pollution management. This study suggests that utilizing UAVs and deep learning technologies for detecting and managing compost heaps can address the constraints linked to traditional field survey methods, thereby facilitating the establishment of accurate and effective non-point source pollution management strategies, and contributing to the safeguarding of aquatic environments.