• Journal of Aerospace System Engineering
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• v.16 no.5
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• pp.70-77
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• 2022

This paper proposes database generation and management system for small-pixelized airborne target recognition. The proposed system has five main features: 1) image extraction from in-flight test video frames, 2) automatic image archiving, 3) image data labeling and Meta data annotation, 4) virtual image data generation based on color channel convert conversion and seamless cloning and 5) HOG/LBP-based tiny-pixelized target augmented image data. The proposed framework is Python-based PyQt5 and has an interface that includes OpenCV. Using video files collected from flight tests, an image dataset for airborne target recognition on generates by using the proposed system and system input.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.193-202
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• 2023

In recent years, the outstanding performance of large language models (LLMs) trained on extensive datasets has become a hot topic. Since studies on LLMs are available on open-source approaches, the ecosystem is expanding rapidly. Models that are task-specific, lightweight, and high-performing are being actively disseminated using additional training techniques using pre-trained LLMs as foundation models. On the other hand, the performance of LLMs for Korean is subpar because English comprises a significant proportion of the training dataset of existing LLMs. Therefore, research is being carried out on Korean-specific LLMs that allow for further learning with Korean language data. This paper identifies trends of open source based LLMs and introduces research on Korean specific large language models; moreover, the applications and limitations of large language models are described.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.50-53
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• 2022

In order to detect an object in yolv5, a process of annotating location information on an existing image is required when learning an image. The most representative method is to draw a bounding box on an image to store location information as meta information. However, if the boundary of the object is ambiguous, it will be difficult to make a bounding box. A representative example would be to classify parts that are not fire and parts that are fire. Therefore, in this paper, images of 100 samples judged to have caught fire were learned by varying the number of boxes. The results showed better fire detection performance in the model where the bounding box was trained by annotating it with three boxes by segmenting it slightly more than annotating it with one box by holding the edge as large as possible during annotating it with one box.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.489-502
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• 2022

In this study, a Gaussian process regression (GPR) model as well as six GPR-based metaheuristic optimization models, including GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, and GPR-SSO, were developed to predict fly-rock distance in the blasting operation of open pit mines. These models included GPR-SCA, GPR-SSO, GPR-MVO, and GPR. In the models that were obtained from the Soungun copper mine in Iran, a total of 300 datasets were used. These datasets included six input parameters and one output parameter (fly-rock). In order to conduct the assessment of the prediction outcomes, many statistical evaluation indices were used. In the end, it was determined that the performance prediction of the ML models to predict the fly-rock from high to low is GPR-PSO, GPR-GWO, GPR-MVO, GPR-MFO, GPR-SCA, GPR-SSO, and GPR with ranking scores of 66, 60, 54, 46, 43, 38, and 30 (for 5-fold method), respectively. These scores correspond in conclusion, the GPR-PSO model generated the most accurate findings, hence it was suggested that this model be used to forecast the fly-rock. In addition, the mutual information test, also known as MIT, was used in order to investigate the influence that each input parameter had on the fly-rock. In the end, it was determined that the stemming (T) parameter was the most effective of all the parameters on the fly-rock.

• Communications of Mathematical Education
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• v.37 no.4
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• pp.591-614
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• 2023

For a long time, mathematics education institutions such as NCTM(National Council of Teachers of Mathematics) have emphasized the essential role of writing, and recent surveys by the Ministry of Education report a decline in foundational academic skills in the post-COVID19 period. The purpose of this study is to redefine the significance of mathematics writing in mathematics education, focusing on competencies highlighted in the field, particularly in the areas of problem-solving, communication, and reasoning. The research findings indicate that writing in problem-solving enhances cognitive organization, fostering the ability to grasp concepts and methods. Writing in communication builds confidence through the meta-cognitive process, and writing in inference allows self-awareness of step-by-step identification of areas lacking understanding. Particularly in the future society where artificial intelligence(AI) is utilized, changes in the learning environment necessitate research for the establishment of authenticity judgment through writing and the cultivation of a proper writing culture.

• Journal of Biomedical Engineering Research
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• v.45 no.4
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• pp.162-172
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• 2024

To analyse the overall research trends in digital therapeutics, this study conducted a quantitative bibliometric analysis of articles published in the last 10 years from 2014 to 2023. We extracted bibliographic information of studies related to digital therapeutics from the Web of Science (WOS) database and performed publication status, citation analysis and keyword analysis using R (version 4.3.1) and VOSviewer (version 1.6.18) software. A total of 1,114 articles were included in the study, and the annual publication growth rate for digital therapeutics was 66.1%, a very rapid increase. "health" is the most used keyword based on Keyword Plus, and "cognitive-behavioral therapy", "depression", "healthcare", "mental-health", "meta-analysis" and "randomized controlled-trial" are the research keywords that have driven the development and impact of digital therapeutic devices over the long term. A total of five clusters were observed in the co-occurrence network analysis, with new research keywords such as "artificial intelligence", "machine learning" and "regulation" being observed in recent years. In our analysis of research trends in digital therapeutics, keywords related to mental health, such as depression, anxiety, and disorder, were the top keywords by occurrences and total link strength. While many studies have shown the positive effects of digital therapeutics, low engagement and high dropout rates remain a concern, and much research is being done to evaluate and improve them. Future studies should expand the search terms to ensure the representativeness of the results.

• Journal of Industrial Convergence
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• v.22 no.10
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• pp.111-123
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• 2024

With an increase in the number of individuals with ostomies globally, online-based programs are being used to educate ostomates. Therefore, this study analyzed the effects, characteristics, outcomes, and implications of online self-care education programs for ostomates. This systematic review searched the MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Central Register of Controlled Trials (Central) electronic databases to conduct a meta-analysis of randomized controlled trials published up to December 2023. Two investigators independently extracted data and assessed study quality based on the risk of bias. Of the 519 identified studies, eight were relevant, which were divided by intervention: three studies used telehealth, three used mobile applications, and two, multimedia learning training. The educational programs in the relevant studies focused on ostomy knowledge, self-care, and prevention of complications; while some included psychological support. Online-based education programs can improve ostomy patients' adjustment and self-efficacy, reduce complications, and improve quality of life. Online-based education programs have easier accessibility by patients and nurses; it can effectively improve ostomy patients' quality of life and prevent future complications.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.15-28
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• 2016

The purpose of this study is to investigate the changes of students' modeling ability in terms of 'meta-modeling knowledge' and 'modeling practice' through co-construction of scientific model. Co-construction of scientific model instructions about astronomy were given to 41 middle-school students. The students were given a before and after instruction modeling ability tests. The results show that students' 'meta-modeling knowledge' has changed into a more scientifically advanced thinking about models and modeling after the instruction. Students were able to be aware that 'they could express their thoughts using models', 'many models could be used to explain a single phenomena' and 'scientific models may change' through co-construction modeling process. The change in the 'modeling practice' of the students was divided into four cases (the level improving, the level lowering, the high-level maintaining, the low-level maintaining) depending on the change of pre-posttest levels. The modeling practice level of most students has improved through the instruction. These changes were influenced by co-construction process that provides opportunities to compete and compare their models to other models. Meanwhile, the modeling practice level of few students has lowered or maintained low level. Science score of these students at school was relatively high and they thought that the goal of learning is to get a higher score in exams by finding the correct answer. This means that students who were kept well under traditional instruction may feel harder to adapt to co-construction of scientific model instruction, which focuses more on the process of constructing knowledge based on evidences.

• Information Systems Review
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• v.18 no.1
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• pp.57-78
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• 2016

Hacking has raised many critical issues in the modern world, particularly because the size and cost of the damages caused by this disruptive activity have steadily increased. Accordingly, many significant studies have been conducted by behavioral scientists to understand hackers and their practices. Nonetheless, only qualitative methods, such as interviews, meta-studies, and media studies, have been employed in such studies because of hacker sampling limitations. Existing studies have determined that intrinsic motivation was the dominant factor influencing hackers, and that their techniques were mainly acquired from online hacking communities. However, such results have yet to be causally proven. This study attempted to identify the causal factors influencing the motivational and environmental factors encouraging hackers to learn hacking skills. To this end, hacker community members using the theory of planned behavior were observed to identify the causal factors of their learning of hacking skills. We selected a group of students who were developing their hacking skills. The survey was conducted over a two-week period in May 2015 with a total of 227 students as respondents. After list-wise deletion, 215 of the responses were deemed usable (94.7 percent). In summary, the hackers were aware that hacking skills are considered socially unethical, and their attitudes toward the learning of hacking skills were affected by both intrinsic and extrinsic motivations. In addition, the characteristics of the online hacking community affected their perceived behavioral control. This study introduced new concepts in the process of conducting a causal relationship analysis on a hacker sample. Moreover, this research expanded the discussion on the causal direction of subjective norms in unethical research, and empirically confirmed that both intrinsic and extrinsic motivations affect the learning of hacking skills. This study also made a practical contribution by raising the educational and policy response issues for ethical hackers and demonstrating the necessity to intensify the punishment for hacking.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.