• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.270-275
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• 2016

We present an approach for acceleration actor-critic algorithm for reinforcement learning with continuous action space. Actor-critic algorithm has already proved its robustness to the infinitely large action spaces in various high dimensional environments. Despite that success, the main problem of the actor-critic algorithm remains the same-speed of convergence to the optimal policy. In high dimensional state and action space, a searching for the correct action in each state takes enormously long time. Therefore, in this paper we suggest a search accelerating function that allows to leverage speed of algorithm convergence and reach optimal policy faster. In our method, we assume that actions may have their own distribution of preference, that independent on the state. Since in the beginning of learning agent act randomly in the environment, it would be more efficient if actions were taken according to the some heuristic function. We demonstrate that heuristically-accelerated actor-critic algorithm learns optimal policy faster, using Educational Process Mining dataset with records of students' course learning process and their grades.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.4
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• pp.352-359
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• 1988

A learning control system which should have memory elements can be designed by utilizing the concept of parameter adaptation for unknown control object system parameters and regard it as a hybrid adaptive control system. A parameter adaptive learning control system applicable to a continuous time system has been already reported. Since there have been rapid developments in digital technology, it is possible to extend a continuous time parameter adaptive learning control system concept to a discrete time case. This problem is treated in this paper. Its justfication is proved and a simulation shows that this algorithms is effective.

• Journal of Engineering Education Research
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• v.23 no.4
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• pp.76-83
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• 2020

The purpose of this study is to conduct Project Based Learning (PBL) in collaboration with industry experts to operate practical subjects in an industry-university-linked teaching method. PBL is a teaching method in which students can learn through actively engaging in real-world and personally meaningful projects. For a long period of time, PBL methodologies have been found to be especially effective in engineering education. This case study deals with the operational results of a practice subject which has been conducted over three years from 2017 to 2019 in Korea University of Technology and Education. The course is for the 4^th grade students in the school of mechatronics engineering. The results of the surveyed learning outcomes (for example, Program Outcomes and Course Learning Outcomes) have been analyzed and reflected in the next years for the Continuous Quality Improvement. By working on practical projects linked to industry, students have been able to develop so-called 4C's capabilities which are Critical Thinking, Creativity, Communication and Collaboration.

• 한국체육학회지인문사회과학편
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• v.55 no.5
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• pp.593-604
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• 2016

The purpose of this study was to examine the effect of teaching behaviors perceived by college students' participating in liberal dance classes on learning motivation and continuous participation intention. The main target audience of this research is college students who participate in liberal dance classes in five universities in Seoul and Gyeonggi. By using purposive sampling among non probability sampling, totally 330 papers had been distributed, and 314 questionnaires had been used for practical analysis. The data process was performed by frequency analysis, exploratory factor analysis, confirmatory factor analysis, reliability analysis, structural equation modeling. The results were as follows: First, teaching behaviors perceived by college students' participating in liberal dance classes had a significant effect on learning motivation. Secondly, teaching behaviors perceived by college students' participating in liberal dance classes had a significant effect on continuous participation intention. Lastly, learning motivation perceived by college students' participating in liberal dance classes had a significant effect on continuous participation Intention. Lastly, there was the mediating effect of learning motivation was inspected in the relationship between teaching behaviors perceived by college students' participating in liberal dance classes and continuous participation intention.

• Korean Journal of Applied Biomechanics
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• v.17 no.1
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• pp.81-90
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• 2007

The effort identifying positioning information of the moving object in real time has been a issue not only in sport biomechanics but also other academic areas. In order to solve this issue, this study tried to track the movement of a pitched ball that might provide an easier prediction because of a clear focus and simple movement of the object. Machine learning has been leading the research of extracting information from continuous images such as object tracking. Though the rule-based methods in artificial intelligence prevailed for decades, it has evolved into the methods of statistical approach that finds the maximum a posterior location in the image. The development of machine learning, accompanied by the development of recording technology and computational power of computer, made it possible to extract the trajectory of pitched baseball from recorded images. We present a method of baseball tracking, based on object tracking methods in machine learning. We introduce three state-of-the-art researches regarding the object tracking and show how we can combine these researches to yield a novel engine that finds trajectory from continuous pitching images. The first research is about mean shift method which finds the mode of a supposed continuous distribution from a set of data. The second research is about the research that explains how we can find the mode and object region effectively when we are given the previous image's location of object and the region. The third is about the research of representing data into features that we can deal with. From those features, we can establish a distribution to generate a set of data for mean shift. In this paper, we combine three works to track baseball's location in the continuous image frames. From the information of locations from two sets of images, we can reconstruct the real 3-D trajectory of pitched ball. We show how this works in real pitching images.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• Proceedings of the KSPS conference
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• 2005.11a
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• pp.103-106
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• 2005

In this paper, we propose a language modeling approach to improve the performance of a large vocabulary continuous speech recognition system. The proposed approach is based on the active learning framework that helps to select a text corpus from a plenty amount of text data required for language modeling. The perplexity is used as a measure for the corpus selection in the active learning. From the recognition experiments on the task of continuous Korean speech, the speech recognition system employing the language model by the proposed language modeling approach reduces the word error rate by about 6.6 % with less computational complexity than that using a language model constructed with randomly selected texts.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.837-845
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• 2022

In this study, tomato maturity was estimated based on deep learning for a harvesting robot. Tomato images were obtained using a RGB camera installed on a monitoring robot, which was developed previously, and the samples were cropped to 128 × 128 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the mean-variance loss was used to learn implicitly the distribution of the data features by class. In the test stage, the tomato maturity was estimated as a continuous index, which has a range of 0 to 1, by calculating the expected class value. The results show that the F1-score of the classification was approximately 0.94, and the performance was similar to that of a deep learning-based classification task in the agriculture field. In addition, it was possible to estimate the distribution in each maturity stage. From the results, it was found that our approach can not only classify the discrete maturation stages of the tomatoes but also can estimate the continuous maturity.

• Knowledge Management Research
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• v.20 no.4
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• pp.149-168
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• 2019

In the era of the Fourth Industrial Revolution, digital transformation is emerging in the education and learning fields. As the use of the mobile Internet and mobile devices has become a daily life, mobile learning that supports a variety of learning in a mobile environment is drawing attention. Mobile learning applications (apps) are expected to expand their use by providing a convenient learning environment anytime, anywhere. This study investigates the use of mobile learning apps in English education, which is one of the most popular learning areas, and empirically examines the factors that influence the continuous use of mobile learning apps. In particular, it analyzes the differences between Chinese and Korean users. The results of this study provide theoretical and practical implications to promote the development of mobile apps suitable for mobile learning environments and the sustainable user growth in mobile learning.