• Journal of Information Processing Systems
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• 제16권5호
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• pp.1223-1230
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• 2020

Recently, extensive studies have been conducted to apply deep learning to reinforcement learning to solve the state-space problem. If the state-space problem was solved, reinforcement learning would become applicable in various fields. For example, users can utilize dance-tutorial systems to learn how to dance by watching and imitating a virtual instructor. The instructor can perform the optimal dance to the music, to which reinforcement learning is applied. In this study, we propose a method of reinforcement learning in which the action space is dynamically adjusted. Because actions that are not performed or are unlikely to be optimal are not learned, and the state space is not allocated, the learning time can be shortened, and the state space can be reduced. In an experiment, the proposed method shows results similar to those of traditional Q-learning even when the state space of the proposed method is reduced to approximately 0.33% of that of Q-learning. Consequently, the proposed method reduces the cost and time required for learning. Traditional Q-learning requires 6 million state spaces for learning 100,000 times. In contrast, the proposed method requires only 20,000 state spaces. A higher winning rate can be achieved in a shorter period of time by retrieving 20,000 state spaces instead of 6 million.

• 한국산학기술학회논문지
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• 제8권6호
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• pp.1673-1681
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• 2007

본 연구에서는 차세대 고부가가치 지식산업이 될 u-러닝에 대한 개념, 그리고 국내 외 u-러닝 시장 동향 및 u-러닝 산업추진상의 문제점을 살펴보았다. 이와 더불어 국내 u-러닝 산업의 SWOT 분석을 통해 국내 산업 경쟁력 확보하기 위한 정책과제 및 추진방향을 도출하였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2024년도 춘계학술발표대회
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• 한국게임학회 논문지
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• 제10권6호
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• pp.37-45
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• 2010

본 논문의 목적은 G러닝의 교육적 효과에 대한 분석이다. 교육에 있어서 화두인 구성주의 학습의 효과적인 학습 도구로써 온라인 게임 기반의 G러닝이 사용되기 시작하고 있다. 이에 본 연구는 온라인 게임 기반의 G러닝 'SKY 수학'을 개발하여 학업 성취도에 긍정적 영향을 미치고 있는 G러닝의 정의적 C영역에 대하여 알아보았다. G러닝을 통한 교육을 통하여 학생들의 태도, 자신감이 상승하였다. 이러한 발견을 바탕으로 G러닝이 효과적인 학습 도구로 활용 될 수 있음을 논의하였다.

• 품질경영학회지
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• 제35권1호
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• pp.73-80
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• 2007

The learning, which is a view extended from and replaces the concept of continuous improvement, has become the very core concept in Malcolm Baldrige National Quality Award. The most potent one among 11 core values in MBNQA Model is 'organizational and personal learning'. Learning also plays a critical role to get the good scores for 6 categories except results category among 7 categories of MBNQA Model. But the concept of learning often has been overlooked by the people who are interested in the MB award or TQM. This paper reviews and analyzes the concept of learning in the MB criteria since it first appears in 1993 to enlighten the importance of learning.

• 지식경영연구
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• 제9권4호
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• pp.17-33
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• 2008

This study proposes University 2.0 as a model case of open, flexible, and distributed learning environment for higher education based on theoretical foundations and perspectives. As web 2.0 technologies emerge into the field of education, ways of generating and disseminating information and knowledge have been drastically changed. Professors are no longer the only source of knowledge. Students using internet often become prosumers of knowledge who search and access information through the web as well as publish their own knowledge using the web. A concept and framework of University 2.0 is introduced for implementing the new interactive learning paradigm with an open, flexible and distributed learning environment for higher education. University 2.0 incorporates online and offline learning environments with various educational media. Furthermore, it employs various learning strategies and integrates formal and informal learning through learning communities. Both instructors and students in University 2.0 environment are expected to be active knowledge generators as well as creative designers of their own learning and teaching.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.285-292
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• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• 여성건강간호학회지
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• 제26권1호
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• pp.5-9
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• 2020

Artificial intelligence (AI), which includes machine learning and deep learning has been introduced to nursing care in recent years. The present study reviews the following topics: the concepts of AI, machine learning, and deep learning; examples of AI-based nursing research; the necessity of education on AI in nursing schools; and the areas of nursing care where AI is useful. AI refers to an intelligent system consisting not of a human, but a machine. Machine learning refers to computers' ability to learn without being explicitly programmed. Deep learning is a subset of machine learning that uses artificial neural networks consisting of multiple hidden layers. It is suggested that the educational curriculum should include big data, the concept of AI, algorithms and models of machine learning, the model of deep learning, and coding practice. The standard curriculum should be organized by the nursing society. An example of an area of nursing care where AI is useful is prenatal nursing interventions based on pregnant women's nursing records and AI-based prediction of the risk of delivery according to pregnant women's age. Nurses should be able to cope with the rapidly developing environment of nursing care influenced by AI and should understand how to apply AI in their field. It is time for Korean nurses to take steps to become familiar with AI in their research, education, and practice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.397-419
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• 2024

Online learning has become a widespread practice for students and teachers in acquiring and delivering knowledge. Education platforms have become prominent in the 21st century with the evolution of technology and the accessibility to online learning. As a result, various learning management systems (LMSs) have been introduced to facilitate online interaction between users. For instance, communication between students and teachers at school. However, there is a need to emphasise user engagement in LMS to enhance the online learning experience amongst students since the design of LMS affects user engagement. This study utilised a systematic literature review (SLR) that examined 74 articles published between 2014 and 2023, focusing on cross-cultural design (CCD), user-centred design (UCD), and usability in LMS design. This study aimed to review CCD and its association with UCD, user interfaces (UI), and user experience (UX) in the context of LMS. CCD has been introduced as an approach to design that embraces different cultures, languages, and social contexts, while UCD plays a significant role in defining user engagement for LMS. All elements in CCD and UCD help create a better user experience for LMS. Besides, this study reviewed the usability of selected LMS to give insights to developers in creating a positive user engagement. An insight into cultural factors that influence the usability of LMS has revealed their value for LMS design, such as the UI/UX elements. Initially, this study may guide future researchers in improving education quality by emphasising CCD and LMS usability, which can enhance user engagement.

• 지식경영연구
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• 제15권4호
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• pp.57-78
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• 2014

Since team performance has become one of the core factors for companies' success, companies are putting every effort to raise team productivity. In this vein, the purpose of this study was to examine the influence of team learning behavior upon team dynamic capabilities, team effectiveness, and to verify the mediating effect of team dynamic capabilities in corporations. 312 employees were randomly selected to participate in an questionnaire survey. The result has shown that the static correlation exists between team learning behavior, team dynamic capabilities, and team effectiveness. Team dynamic capabilities mediated the relationship between team learning behavior and team effectiveness. Based on the findings, the study implies that learning behaviors among team members should be supported in order to improve its outcome, and HR representatives must help to develop dynamic capabilities.