• Journal of The Korean Association of Information Education
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• v.26 no.2
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• pp.105-120
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• 2022

In addition to the rapid changes in the times caused by the pandemic, the revision of the new curriculum coincides with the change in the proportion of the three elements of learners, society, and subjects that make up the curriculum. In particular, along with the proportion of 'social' in the curriculum, the scope of the word 'educational community' has increased, and the allowable range of curriculum restructuring centered on it has expanded. In order for the intended direction of education to be properly established in the new curriculum, various educational method studies are needed to cultivate newly emerged competencies and literacy. In this study, after selecting the contents and goals of the convergence curriculum based on various criteria for subject selection, the data science program was designed by reconstructing Living Lab's PDIE methodology. As an evaluation factor for this, we tried to analyze the effectiveness of 'creativity', 'problem-solving ability', 'communication ability', 'collaboration ability' among future competencies emphasized in the curriculum. As a result of the study, it was effective in improving creative and communication skills, and this study focuses on verifying the effectiveness of School Living Lab, suggesting the necessity of post-research that expands the application space of research and diversifies the role of educational community subjects.

• Korean Journal of Construction Engineering and Management
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• v.23 no.4
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• pp.3-14
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• 2022

Autonomous equipment requires a large amount of data from various environments. However, it takes a lot of time and cost for an experiment in a real construction sites, which are difficulties in data collection and processing. Therefore, this study aims to develop a virtual environment for autonomous tower cranes technology development and validation. The authors defined automation functions and operation conditions of tower cranes with three performance criteria: operational design domain, object and event detection and response, and minimum functional conditions. Afterward, this study developed a virtual environment for learning and validation for autonomous functions such as recognition, decision making, and control using the Unity game engine. Validation was conducted by construction industry experts with a fidelity which is the representative matrix for virtual environment assessment. Through the virtual environment platform developed in this study, it will be possible to reduce the cost and time for data collection and technology development. Also, it is also expected to contribute to autonomous driving for not only tower cranes but also other construction equipment.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.349-357
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• 2021

Recently, drones are rapidly becoming common and expanding. There is a great need for diversity in whether drone flight data can be used as entertainment technology analysis data. In particular, it is necessary to check whether it is possible to analyze and utilize the flight and operation process of entertainment drones, which are developing through autonomous and intelligent methods, through data analysis and machine learning. In this paper, it was confirmed whether it can be used as a machine learning technology by using FC data in the evaluation of drones for entertainment. As a result, FC data from DJI and Parrot such as Mavic2 and Anafi were unable to analyze machine learning for entertainment. It is because data is collected at intervals of 0.1 second or more, so that it is impossible to find correlation with other data with GCS. On the other hand, it was found that machine learning technologies can be applied in the case of Fixhawk, which used an ARM processor and operates with the Nuttx OS. In the future, it is necessary to develop technologies capable of analyzing the characteristics of entertainment by dividing fixed-wing and rotary-wing flight information. For this, a model shoud be developed, and systematic big data collection and research should be conducted.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.383-390
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• 2021

Recently, the drone industry is rapidly expanding as it is suggested that it will be used in various fields. As the size of the drone market grows, interest in drone-related certificates is also increasing. However, the current drone-related qualification system and education system are insufficient. Thus this study, analyzed the necessity of drone training, the features of functional games, and the effectiveness of educational training using AR through related technical studies to solve the practical difficulties of drone educational training. Later, drone educational training game contents using AR were divided into practice mode and test mode based on the drone national qualification course practical test, and the result screen was displayed at the end of the curriculum so that players could learn by level and evaluate the results on their own. In addition, constructed a hybrid processing system and network and AR operation system for response rate and response speed, implemented drone training game contents utilizing AR based on the design contents. It is expected that the use of game content using AR presented in this paper for drone training will further alleviate environmental difficulties and improve the sense of immersion in play, which will lead to a more effective drone educational training experience.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1743-1747
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• 2022

Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.

• Journal of the Korean Applied Science and Technology
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• v.39 no.4
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• pp.568-579
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• 2022

The purpose of this study was to investigate the relationship between physical activity participation habits, physical fitness level, and academic performance among the Reserve Military Training Corps (ROTC). Participants in the current study were 71 ROTCs from J University. Physical activity participation was investigated using the Global Physical Activity Questionnaire (GPAQ), and the level of physical fitness was measured by health-related physical fitness evaluation. For academic performance, university grade data (GPA) was approved by the head of the relevant school district, and the data were collected with the consent of the participants, and each data was analyzed. The correlation between physical activity participation habits, physical fitness level, and GPA was analyzed, and GPA by physical fitness level were compared. This study found that there was no statistically significant correlation between physical activity participation and academic performance. Otherwise, there was a statistically significant correlation between major subjects and total physical fitness score, cardiorespiratory fitness, muscular endurance, flexibility, and body composition. By the level of physical fitness, the ROTCs in the high-physical fitness group showed statistically significantly higher academic performance than the ROTCs in the low-physical fitness group. In addition, low level of physical fitness ROTC group was lower than the other. In conclusion, ROTCs with a high physical fitness level had a positive effect on improving learning ability compared to those who did not. By recommending an autonomous but systematic exercise training program, it is suggested to improve the physical strength of ROTCs and to cultivate excellent future military officers.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.

• Trans-
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• v.12
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• pp.141-171
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• 2022

This study was conducted to understand media and cultivate personality by using media as data for personality education. To achieve this purpose, the Personality Education Promotion Act and the Korea Educational Development Institute's personality virtues were selected as educational elements, and a personality education program using media was developed in combination with the middle school curriculum. For this study, first, in order to extract personality virtues, 13 personality virtues were finally selected as educational elements by comparing and synthesizing the personality virtues of the Personality Education Promotion Act and the Korea Education Development Institute. The final personality virtues selected are self-esteem, courage, sincerity, self-regulation, wisdom, consideration, communication, courtesy, social responsibility, cooperation, citizenship, justice, and respect for human rights. Second, in order to select media and set the direction of development of personality education programs, the process of collecting media data was confirmed, and the direction and goal of the program were set by analyzing the middle school curriculum. Third, in order to propose a method of applying a personality education program using media, the personality grafting unit was selected by referring to the commentary on all subjects of the 2015 revised curriculum.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.913-924
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• 2022

In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.