• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.885-890
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• 2023

As satellite technology progresses, a growing number of satellites-like CubeSat and radar satellites-are available with a higher spectral and spatial resolutions than previous. National initiatives used to be the main force behind satellite development, but current trendsindicate that private enterprises are also actively exploring and developing new satellite technologies. This special issue examines the recent research results and advanced technology in remote sensing approaches for Earth environment analysis. These results provide important information for the development of satellite sensors in the future and are of great interest to researchers working with artificial intelligence in thisfield. The special issue introduces the latest advances in remote sensing technology and highlights studies that make use of data to monitor and forecast Earth's environment. The objective is to provide direction for the future of remote sensing research.

• Journal of Internet of Things and Convergence
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• v.9 no.4
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• pp.71-75
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• 2023

Changes in modern society are resulting in the emergence of various service models that apply software and artificial intelligence, and all fields are rapidly changing based on software and artificial intelligence. Education on software and artificial intelligence is emerging as a major influencing factor that determines national competitiveness. Following these social changes, interest in the use of software and artificial intelligence is quite high. Starting in 2025, software and artificial intelligence-related curricula are scheduled to be introduced into public education in elementary, middle, and high schools, so many educational activities are becoming active. In this study, based on the content of operating the software and artificial intelligence experience activity program, we would like to propose the efficiency of future learning programs and operating methods for software and artificial intelligence.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.73-80
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• 2024

In this paper, we proposes a method to improve the accuracy of predicting the Korea Composite Stock Price Index (KOSPI) by combining topic modeling and Long Short-Term Memory (LSTM) neural networks. In this paper, we use the Latent Dirichlet Allocation (LDA) technique to extract ten major topics related to interest rate increases and decreases from financial news data. The extracted topics, along with historical KOSPI index data, are input into an LSTM model to predict the KOSPI index. The proposed model has the characteristic of predicting the KOSPI index by combining the time series prediction method by inputting the historical KOSPI index into the LSTM model and the topic modeling method by inputting news data. To verify the performance of the proposed model, this paper designs four models (LSTM_K model, LSTM_KNS model, LDA_K model, LDA_KNS model) based on the types of input data for the LSTM and presents the predictive performance of each model. The comparison of prediction performance results shows that the LSTM model (LDA_K model), which uses financial news topic data and historical KOSPI index data as inputs, recorded the lowest RMSE (Root Mean Square Error), demonstrating the best predictive performance.

• Journal of Korean society of Dental Hygiene
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• v.24 no.3
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• pp.197-208
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• 2024

Objectives: Extended reality technology offers an innovative opportunity to enhance self-directed learning through interactive processes in healthcare education. This study aims to analyze research trends in the application of extended reality technology-based educational content in the field of healthcare. Methods: Through literature search, selection, and exclusion processes based on predefined criteria, we conducted an analysis of domestic healthcare studies employing extended reality technology, ultimately selecting and examining 39 relevant publications. Results: The analysis reveals diverse applications of extended reality across various fields such as medicine, dentistry, and nursing. Positive effects, including increased academic satisfaction, immersion, and interest, are observed, alongside challenges like media usage difficulties and cybersickness. Conclusions: Future research should focus on the development and application of extended reality-based educational content in diverse healthcare curricula, emphasizing both educational approaches and continuous technological advancements.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.67-78
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• 2024

Amidst the onset of the Fourth Industrial Revolution and the prominence of artificial intelligence, societal structures are undergoing significant changes. There is a heightened global interest in AI education for nurturing future talents. Consequently, this research aims to develop an AI-integrated Korean language curriculum for first-year high school students, utilizing the ADDIE model for instructional program development. To assess the program's effectiveness, pre-post assessments were conducted on future core competencies (Collaboration, Communication, Critical Thinking, Creativity) and knowledge information processing skills. The curriculum, spanning nine sessions and incorporating four small projects, sought to provide students with a new experience of AI-integrated Korean language education. As a result, students who participated in the program demonstrated improvement in future core competencies across all areas, and positive outcomes were observed in satisfaction levels and qualitative analysis. Through these findings, it is suggested that this program successfully integrates artificial intelligence into high school Korean language education, potentially contributing to the cultivation of future talents among students.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.121-128
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• 2024

With the recent rise in the use of AI-based online translation tools, interest in their methods and effects on education has grown. This study involved 30 prospective elementary school teachers who completed an English writing task using an AI-based online translation tool. The study focused on assessing the impact of these tools on English writing skills and their practical applications. It examined the usability, educational value, and the advantages and disadvantages of the AI translation tool. Through data collected via writing tests, surveys, and interviews, the study revealed that the use of translation tools positively affects English writing skills. From the learners' perspective, these tools were perceived to provide support and convenience for learning. However, there was also recognition of the need for educational strategies to effectively use these tools, alongside concerns about methods to enhance the completeness or accuracy of translations and the potential for over-reliance on the tools. The study concluded that for effective utilization of translation tools, the implementation of educational strategies and the role of the teacher are crucial.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.848-859
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• 2008

The purposes of this study were to investigate the relationship between science achievement and student-related variables in the 2006 National Assessment of Educational Achievement (NAEA), the subjects of which included 3% of students within the entire population of the grades 6, 9 and 10. The results showed that the more they talk with parents, study by themselves, and read the books, the higher the students achieved in science. The science achievement was also significantly and positively related to self-regulated learning, adaptation to school life and attitude toward science. It is implied that the approach of stimulating internal motive such as interest, attitude toward science and human relations is more effective in resulting in the students' higher science achievement than focusing on external attitudes such as forcing good study habits.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.41-47
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• 2024

Recently, as the practical application and commercialization of unmanned aerial vehicles (UAVs) is pursued, interest in ensuring the safety of the UAV is increasing. Because UAV accidents can result in property damage and loss of life, it is important to develop technology to prevent accidents. For this reason, a technique to detect the abnormal flight state of UAVs has been developed based on the AutoEncoder model. However, the existing detection technique is limited in terms of performance and real-time processing. In this paper, we propose a U-Net based abnormal flight detection technique. In the proposed technique, abnormal flight is detected based on the increasing rate of Mahalanobis distance for the reconstruction error obtained from the U-Net model. Through simulation experiments, it can be shown that the proposed detection technique has superior detection performance compared to the existing detection technique, and can operate in real-time in an on-board environment.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.