• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Journal of The Korean Association of Information Education
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• v.16 no.2
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• pp.151-164
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• 2012

The purpose of this study was to examine moderating effect of academic self-efficacy and learning flow between social presence and academic achievement of cyber university students. For this purpose, the 371 students of W cyber university were participated in the web-survey system for two weeks at the end of second semester in 2011. The results of this study through hierarchical multiple regression analysis indicated that social presence significantly predicted on academic achievement. Academic self-efficacy was not significant moderating variable between social presence and academic achievement. And Learning flow was used as a significant moderated variable in the relationships among social presence and academic achievement. Based on these study results, effective management strategies for improving cyber university students' academic achievement were proposed.

• The Journal of Korean Association of Computer Education
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• v.23 no.2
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• pp.65-72
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• 2020

The purpose of this study was to examine effects of maker education program using the virtual reality content creation platform on the creative problem solving ability and learning flow of elementary school students. To achieve this purpose, we selected a virtual reality content creation platform that elementary school students can handle and share easily, and analyzed its effectiveness by applying the educational program in which the step-by-step activities of the TMSI model were reconstructed in relation to virtual reality content production education among existing maker education teaching and learning models. Through this study confirmed that the maker education program using the virtual reality content creation platform has a positive effect on the improvement of creative problem solving ability and learning flow of elementary school students.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.170-175
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• 2019

This study aimed to analyse the effects of academic self-efficacy of beauty specialized high school students on learning flow and provide basic data needed for their learning instruction. For the purpose, this study surveyed 327 students of beauty specialized high schools located in B metropolitan city and N city. The responses were analysed with the use of the SPSS WIN 21.0. The results are presented as follows: Academic self-efficacy had a significant effect on learning flow and in particular, self-control efficacy and task difficulty preference were important factors. Based on the results above, it is suggested that teachers should present data that is properly converged by techniques and academic knowledge according to levels and steps so that students can have experiences of academic achievements and be encouraged to have higher self-efficiency.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.51-60
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• 2020

Optical character recognition (OCR) has been studied for decades because it is very useful in a variety of places. Nowadays, OCR's performance has improved significantly due to outstanding deep learning technology. Thus, there is an increasing demand for commercial-grade but affordable OCR systems. We have developed a low-cost, high-performance OCR system for the industry with the cheapest embedded developer kit that supports GPU acceleration. To achieve high accuracy for industrial use on limited computing resources, we chose a state-of-the-art text recognition algorithm that uses an end-to-end deep learning network as a baseline model. The model was then improved by replacing the feature extraction network with the best one suited to our conditions. Among the various candidate networks, EfficientNet-B3 has shown the best performance: excellent recognition accuracy with relatively low memory consumption. Besides, we have optimized the model written in TensorFlow's Python API using TensorFlow-TensorRT integration and TensorFlow's C++ API, respectively.

• The Journal of Korean Association of Computer Education
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• v.12 no.6
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• pp.11-19
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• 2009

In the knowledge-information-oriented society, it is difficult for students to solve lots of problems or adapt themselves to society just by using simple knowledge. Students have to develop individual problem solving ability and creative, logical thinking ability. They can develope these abilities by learning computer programming. This thesis studies the influences of a flow-chart learning on the logical thinking ability in Scratch using programming learning. The findings identify that the making algorithm by using flow-chart is more effective in developing logical thinking ability then the making algorithm by using pseudo-code.

• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.1-10
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• 2011

The programming education in K-12 field is processing with conceptual approaches to obtain basic grammar not including higher knowledge processing. This is main reason that can't able to obtain the educational effects. This study aims to research the innovated methodology of programming education which can have educational effect by participating of learners with positive interest, and recognize the effect of the Scratch programming education on elementary school student's learning-flow and programming ability. As a result Scratch programming education has effect on elementary school student's improving the level of learning-flow and the programming ability.

• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.478-487
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• 2012

The purpose of this paper is to investigate which the nature of teacher behavior affects the learning flow in the context of military education. Empirical results show that the leadership of the nature of teacher behavior has a significant effect on three factors of learning flow (flow experience, transformation of time, concentration on task). In addition, the pace of class of the nature of teacher behavior is significantly related to the transformation of time and the concentration on task, and the sense of humor of the nature of teacher behavior significantly affects the concentration on task. The findings provide practical implications on how a military education institution strategically employes the leadership, the sense of humor, and the pace of class in an attempt to select and train instructors.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.41-55
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• 2017

The physical computing education, as the emerging field, is a form of education that helps learners to develop the attitude of scientific inquiry by developing meaningful and creative output through the integration of hardware and software elements. Based on the literature, the authors of the study used science experience, school support and learning flow as the variables that predict the outcome variable which is the attitude of scientific inquiry. The authors collected data from 64 fourth and sixth graders who studied physical computing at an institution for the gifted and talented in Korea and then analyzed them using descriptive statistics, correlation, multiple regression and simple mediation analysis methods. As a result, science experience and learning flow significantly predicted the attitude of scientific inquiry. In addition, learning flow mediated the relationship between science experience and the attitude of scientific inquiry, and the relationship between school support and the attitude of scientific inquiry. Based on these results, the authors propose that to promote the attitude of scientific inquiry in physical computing education, strategies must be implemented for improving science experience, school support and learning flow in instructional design.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.73-81
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• 2010

The main purpose of this study was to examine the effects of meta-cognition, learning flow and problem solving ability of the college engineering students on academic achievement. For this purpose, a total of 396 college engineering freshmen of the six different departments was chosen to conduct a survey. A hypothetical model was proposed, which was composed of meta-cognition, problem solving ability and learning flow as the prediction variables, and academic achievement as the outcome variables. The results of this study through multiple regression analysis showed that meta-cognition, learning flow and problem solving ability significantly influenced on the college engineering studnets' academic achievement. In addition, learning flow was used as a significant mediated variable in the relationships among meta-cognition, problem solving ability and academic achievement. Based on these study results, the above variables investigated in this study should be considered in the design and development of the college engineering courses that enable students to facilitate their problem-solving attitude and improve academic achievement.