• 한국콘텐츠학회논문지
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• 제21권12호
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• pp.477-489
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• 2021

본 연구는 프로티언 경력지향성이 지속학습활동을 통해 주관적 경력성공에 영향을 미치는 매개과정에 대해서 조직문화 불균형성이 조절변인으로서의 역할을 하는지를 확인하기 위해 실시되었다. 이를 위해 대기업 경력 5년 이상의 사무직 근로자 276명을 대상으로 설문조사를 실시하였으며, SPSS 25와 Process Macro v3.5를 활용하여 자료를 분석하였다. 분석 결과 프로티언 경력지향성이 주관적 경력성공에 영향을 미치는 관계를 지속학습활동이 매개하는 것은 확인되었지만, 조직문화 불균형성의 조절효과 및 조절된 매개효과는 통계적으로 유의하지 않았다. 그러나 프로티언 경력지향성의 하위변인인 '자기주도성'이 지속학습활동을 통해 주관적 경력성공 및 그 하위변인 '고용가능성', '경력만족'에 영향을 미치는 매개과정에 대한 조직문화 불균형성의 조절 효과는 통계적으로 유의하게 나타났다. 마지막으로 본 연구의 시사점에 제한점, 후속연구 제언을 논의하였다.

• The Journal of the Acoustical Society of Korea
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• 제15권2E호
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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.

• 산업융합연구
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• 제20권10호
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• pp.157-167
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• 2022

본 연구는 어린이 교육용 모바일 앱의 인터랙티브 내러티브 디자인이 학습몰입도 증진과 지속사용의도에 미치는 영향을 살펴봄으로써, 인터랙티브 내러티브 디자인의 교육적 효과성을 검증하여 어린이 교육용 앱에 기초한 인터랙티브 내러티브 디자인 방안을 제시하는 것을 목적으로 한다. 연구방법으로는 문헌연구방법 및 설문조사를 활용하여 진행하였다. 구체적으로 문헌연구방법을 통해 인터랙티브 디자인의 개념, 이해, 디자인 구성요소, 학습몰입도, 지속사용의도에 대한 개념과 선행연구를 살펴보았다. 다음으로 한국과 중국의 초등학생 각 100명씩 총 200명을 대상으로 설문조사를 실시하였으며, 응답이 불성실한 설문지 한국 3부, 중국 5부를 제외하고 최종적으로 한국 97명, 중국 95명의 학습자를 대상으로 인터랙티브 내러티브 디자인, 학습몰입도, 지속사용의도를 파악하고 변수 간의 영향 관계를 분석하였다. 본 연구의 연구결과로는 어린이 교육용 앱 사용자인 초등학생을 대상으로 인터랙티브 내러티브 디자인 요소가 학습몰입도와 지속사용의도에 미치는 영향을 살펴본 결과 인터랙티브 내러티브 디자인이 학습몰입도 향상 및 지속사용의도에 긍정적인 영향을 미치는 것으로 나타났다. 이는 수학/과학 교육 시 이론적 개념이나 해석 등을 쉽게 이해하도록 구성하고 있으며, 단계별로 이야기와 이미지가 이어지기 때문에 지루함을 느끼지 않고 학습할 수 있도록 돕기 때문인 것으로 볼 수 있다. 결론적으로 본 연구에서는 인터랙티브 내러티브 디자인은 학습자가 학습에 몰입하고, 지속해서 이를 이용하도록 만든다는 긍정적인 효과를 가지고 있음을 확인할 수 있었다.

• 한국산업정보학회논문지
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• 제28권2호
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• pp.77-94
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• 2023

본 연구의 목적은 메타버스 기반 비대면 수업에서 학습만족도와 지속이용의도에 영향을 미치는 요인을 찾아내는데 있다. 이를 위해 기술수용모형과 정보시스템 성공모형을 토대로 가설을 세우고, 메타버스 플랫폼 중 게더타운을 이용한 수업 경험이 있는 학생을 대상으로 2021년 11월 22일에서 2022년 1월 3일까지 온라인과 오프라인을 기반으로 설문조사를 실시하여 불성실한 응답을 한 설문지를 제외하고 122부를 대상으로 PLS 구조방정식을 이용하여 분석 하였다. 분석결과 플랫폼 품질요인 모두 용이성에 영향을 미치지만, 유용성에서는 콘텐츠 품질만 영향을 가지고 있었다. 용이성은 유용성에 영향을 미치고, 유용성과 용이성이 학습만족도에, 유용성과 학습만족도가 지속이 용의도에 정(+)의 영향을 가지고 있었다. 본 연구의 의의는 메타버스 기반 비대면 수업에서 학습만족도와 지속이용의도에 영향을 미치는 변수를 실증적으로 분석하였다는 점에 있다. 후속 연구에서는 게더타운을 비롯한 다양한 메타버스 기반 플랫폼을 대상으로 학습만족도와 지속이용의도에 영향을 미치는 변수들에 대한 추가 연구가 필요하다.

• 정보처리학회논문지B
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• 제10B권6호
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• pp.587-592
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• 2003

강화학습을 적용하기에 적합한 많은 실세계의 제어 문제들은 연속적인 상태 또는 행동(continuous states or actions)을 갖는다. 연속 값을 갖는 문제인 경우, 상태공간의 크기가 거대해져서 모든 상태-행동 쌍을 학습하는데 메모리와 시간상의 문제가 있다. 이를 해결하기 위하여 학습된 유사한 상태로부터 새로운 상태에 대한 추측을 하는 함수 근사 방법이 필요하다. 본 논문에서는 1-step Q-learning의 함수 근사를 위하여 퍼지 클러스터링을 기초로 한 Fuzzy Q-Map을 제안한다. Fuzzy Q-Map은 데이터에 대한 각 클러스터의 소속도(membership degree)를 이용하여 유사한 상태들을 군집하고 행동을 선택하고 Q값을 참조했다. 또한 승자(winner)가 되는 퍼지 클러스터의 중심과 Q값은 소속도와 TD(Temporal Difference) 에러를 이용하여 갱신하였다. 본 논문에서 제안한 방법은 마운틴 카 문제에 적용한 결과, 빠른 수렴 결과를 보였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제12권1호
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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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• 제16권2호
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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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• 제10권6호
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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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• 제24권2호
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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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• 제13권2호
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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.