• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.477-489
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• 2021

This study was conducted to confirm whether organization culture unbalance plays a role as a moderating variable on the mediation process that protean career orientation influences subjective career success through continuous learning activity. To this end, a survey was carried out on 276 office workers with more than 5 years of work experience in large companies, and the data were analyzed using SPSS 25 and Process Macro v3.5. The results showed that continuous learning activity mediates the relationship of protean career orientation affecting subjective career success, but moderating effect of organizational culture unbalance and the moderated mediation effect were not statistically significant. However, statistical significance was found on the moderating effect of organizational culture unbalance on the mediation process, that 'self-direction', protean career orientation's sub-factor, affects subjective career success and its' sub-factor 'employability', and 'career satisfaction' through continuous learning activity. The significance and limitations of our findings are also discussed.

• The Journal of the Acoustical Society of Korea
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• v.15 no.2E
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• pp.14-23
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• 1996

This paper is a on the neural network to recognize the phonemes, the weight initialization to reduce learning speed, and LR parser for continuous speech recognition. The neural network spots the phonemes in continuous speech and LR parser parses the output of neural network. The whole phonemes recognized in neural network are divided into several groups which are grouped by the similarity of phonemes, and then each group consists of neural network. Each group of neural network to recognize the phonemes consisits of that recognize the phonemes of their own group and VGNN(Verify Group Neural Network) which judges whether the inputs are their own group or not. The weights of neural network are not initialized with random values but initialized from learning data to reduce learning speed. The LR parsing method applied to this paper is not a method which traces a unique path, but one which traces several possible paths because the output of neural network is not accurate. The parser processes the continuous speech frame by frame as accumulating the output of neural network through several possible paths. If this accumulated path-value drops below the threshold value, this path is deleted in possible parsing paths. This paper applies the continuous speech recognition system to the threshold value, this path is deleted in possible parsing paths. This paper applies the continuous speech recognition system to the continuous Korea digits recognition. The recognition rate of isolated digits is 97% in speaker dependent, and 75% in speaker dependent. The recognition rate of continuous digits is 74% in spaker dependent.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.157-167
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• 2022

The purpose of this study is to verify the educational effectiveness of interaction design in mobile APP by observing the impact of interaction design for elementary school education on enhancing learning immersion and continuous use intention, and propose an interaction design scheme based on elementary school education APP. The research methods are literature research and questionnaire survey. Specifically, through the literature research method, the concepts and prior studies on the concept, reviews the continuous use intention and previous research of interaction design. Then, conducts a questionnaire survey on elementary school students in South Korea and China to understand the interaction design, learning immersion, and continuous use intention, and analyzes the relationship between variables.The research result of this study is to observe the influence of interaction design elements within interaction on learning immersion and continuous use intention with elementary school students who are users of elementary school education application as the objects. The results show that interaction design within interaction has a positive impact on improving learning immersion and continuous use intention. It can be thought that this is because in mathematics/science education, it is easy to understand theoretical concepts or explanations, and stories and images will be continued at each stage to help students learn without being bored.In conclusion, this study can confirm that interactive inline design has a positive effect of enabling learners to engage in learning and continue to use.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.77-94
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• 2023

This study aims to determine the factors that affect learning satisfaction and continuous use intention in metaverse-based non-face-to-face classes. Therefore, a hypothesis was established based on the technology acceptance model and information system success model, and a survey was conducted from November 22, 2021 to January 03, 2022 for students who had class experience using Gather.town-a metaverse platform. PLS Structural Equation was conducted on 122 copies, excluding the questionnaires with insincere responses. The results reveal that "all platform quality" factors influenced "easiness," "content quality" influenced "usefulness," "easiness" influenced "usefulness," "easiness" and "usefulness" influenced "learning satisfaction," "usefulness" and "learning satisfaction" had a positive effect on "continuous use intention." This study is significant because it empirically analyzes the variables that affect learning satisfaction and continuous use intention in metaverse-based non-face-to-face classes. In follow-up studies, additional research is required on the variables that affect learning satisfaction and continuous use intention targeting various metaverse-based platforms, including Gather.town.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.587-592
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• 2003

Many real world control problems have continuous states and actions. When the state space is continuous, the reinforcement learning problems involve very large state space and suffer from memory and time for learning all individual state-action values. These problems need function approximators that reason action about new state from previously experienced states. We introduce Fuzzy Q-Map that is a function approximators for 1 - step Q-learning and is based on fuzzy clustering. Fuzzy Q-Map groups similar states and chooses an action and refers Q value according to membership degree. The centroid and Q value of winner cluster is updated using membership degree and TD(Temporal Difference) error. We applied Fuzzy Q-Map to the mountain car problem and acquired accelerated learning speed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.910-917
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• 2020

In fire suppression, continuous delivery of water or foam to the fire source is essential. The present study concerns fire suppression in a ship under sea condition, by introducing reinforcement learning technique to aiming of fire extinguishing nozzle, which works in a ship compartment with six degrees of freedom movement by irregular waves. The physical modeling of the water jet and compartment motion was provided using Unity 3D engine. In the reinforcement learning, the change of the nozzle angle during the scenario was set as the action, while the reward is proportional to the ratio of the water particle delivered to the fire source area. The optimal control of nozzle aiming for continuous delivery of water jet could be derived. Various algorithms of reinforcement learning were tested to select the optimal one, the proximal policy optimization.

• International Journal of Contents
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• v.16 no.2
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• pp.78-91
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• 2020

In this study, an investigation was conducted into the influencing factors for the learning motivation of players in the game, including experience, vicarious experience, the need of achievement, the need of power, and mastery motivation. Then, a discussion was conducted regarding the role played by learning motivation, learning performance, and satisfaction with continuous use. A survey was conducted with 519 players, most at the intermediate gaming level in . As demonstrated by the results of this study, experience, vicarious experience, the need of power, and the mastery of motivation have significant positive association with the players' motivation of learning the game. Learning performance and satisfaction have a positive impact on the continuity of use. Additionally, the correlation between the need of achievement and learning motivation is insignificant. Overall, the research results confirm the significance of the social-cognitive theory relative to the learning motivation. Players began to transform, satisfied with their achievements in the game, as well as gradually evolving toward self-improvement to achieve satisfaction. It offers a new explanation and crucial reference for mastering the gaming trend among the contemporary players.

• Journal of Power Electronics
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• v.10 no.6
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• pp.595-603
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• 2010

In this paper, teaching in a "switching converter (SC) design" course using problem-based learning (PBL) with dynamicbehavior- model simulation, given at Lunghwa University of Science and Technology (LHU), Taiwan, is proposed. The devised methodology encourages students to design and implement the SCs and regulate the controller's parameters in frequency domain by using 'sisitool' ('bode') in the MATLAB toolbox. The environment of PBL with converter characterization modeling and simulation reforms the learning outcome greatly and speeds up the teaching-learning process. To qualify and evaluate the learning achievements, a hands-on project cooperated with the continuous assessment approach is performed to modulate the teaching pace and learning direction in good time. Results from surveys conducted in the end of the course provided valuable opinions and suggestions for assessing and improving the learning effect of the proposed course successively. Positive feedbacks from the examinations, homework, questionnaires, and the answers to the lecturer's quizzes during class indicated that the presented pedagogy supplied more helpfulness to students in comparisons with conventional teaching paradigm, their learning accomplishments were better than expected as well.

• Educational Technology International
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• v.24 no.2
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• pp.203-236
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• 2023

The purpose of this study is to explore the structural relationship between COVID-19-induced sub-dimensions of Online Self-Regulated Learning (OSRL) and satisfaction in online courses conducted in the 'post-COVID-19 era,' as well as to investigate the moderating effects of situational variables such as 'course planning,' 'device type,' and 'course repetition.' To achieve this, the study constructs a measurement model with sub-dimensions of Environment Structuring, Learning Strategy, Help Seeking, and Self-Evaluation as components of OSRL. Participants in this study were selected from university students who enrolled in online courses offered by the Department of Education at University A in the metropolitan area. The research findings reveal several key insights. First, among the sub-dimensions of Online Self-Regulated Learning, Environment Structuring, Learning Strategy, and Self-Evaluation significantly influence satisfaction with online courses. Second, students' satisfaction with online courses significantly influences their intention to continue participating in such courses. Third, 'course planning' during online course hours and 'course repetition' play a moderating role in the relationship between sub-dimensions of Online Self-Regulated Learning and satisfaction. Based on the discussion of these research results, this study concludes by suggesting some future implications and challenges of online courses.

• 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.