• The Journal of Bigdata
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• v.7 no.1
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• pp.1-14
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• 2022

The proof-of-work consensus algorithm used by most blockchains is causing a massive waste of computing resources in the form of mining. A useful proof-of-work consensus algorithm has been studied to reduce the waste of computing resources in proof-of-work, but there are still resource waste and mining centralization problems when creating blocks. In this paper, the problem of resource waste in block generation was solved by replacing the relatively inefficient computation process for block generation with distributed artificial intelligence model learning. In addition, by providing fair rewards to nodes participating in the learning process, nodes with weak computing power were motivated to participate, and performance similar to the existing centralized AI learning method was maintained. To show the validity of the proposed methodology, we implemented a blockchain network capable of distributed AI learning and experimented with reward distribution through resource verification, and compared the results of the existing centralized learning method and the blockchain distributed AI learning method. In addition, as a future study, the thesis was concluded by suggesting problems and development directions that may occur when expanding the blockchain main network and artificial intelligence model.

• Journal of Science Education
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• v.37 no.1
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• pp.1-10
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• 2013

The purpose of this study was to examine the effect of multiple intelligences-specific observation strategy on observation skills, achievement and scientific attitude in elementary science class For this study, the observation learning for the multiple intelligences-specific observation strategy was applied to elementary science class. The results of this study were as follows: To examine this learning materials were applied to elementary science curriculum, and an experimental group and a control group were selected from 4th graders at elementary school J located in Incheon. The results of this study were as follows.: 1. this study was found statistically significant difference in the pupil's enhancement of the observation ability(p<.05). Specifically, analysis of elements of the observation abilities, it was effective to increase abilities of classification of statements, qualitative observation and quantitative observation. 2. science academic achievement of the group using this developed program was significantly higher compared with a control group. 3. there weren't differences between the comparison and experimental groups in term of the scientific attitude. But the experimental group showed greater increases in the openness, spontaneity and creativity that are the elements of scientific attitude. In conclusion, the Multiple Intelligences Observation Training was useful to develop the elementary school student's the observation ability, science academic achievement, scientific attitude(openness, spontaneity and creativity) and better be widely applied to science education.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2019.05a
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• pp.3-4
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• 2019

본 논문에서는 복잡한 해상교통 환경 하에서도 해양 안전을 도모할 수 있는 강화학습 기반 지능형 선박 항해 에이전트 개발의 사전단계로서 기존의 강화학습 환경을 분석하였다. 강화학습 기반 접근법은 선박 항해 에이전트 스스로가 복잡하고 동적인 해상교통 환경을 이해하고 주어진 목표를 달성할 수 있도록 도와주는데, 이를 위해서는 에이전트 자신을 제외한 모든 사항들이 정의되는 환경을 보다 정확하고 효과적으로 개발하는 것이 매우 중요하다. 실제 해상교통 환경은 학습 환경으로의 모델링 및 에이전트 학습의 난이도가 매우 높은 환경으로 학습환경이 가질 수 있는 여러 속성들을 적절히 설정하여 선박 항해 에이전트의 활용 목적에 맞는 가성비 높은 환경을 구축하는 것이 바람직하다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.780-783
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• 2024

본 논문에서는 지속적으로 최적화된 인공지능 모델을 적용하기 위한 방안으로 연합 학습(Federated Learning)을 활용한 접근법을 제시한다. 최근 다양한 산업 분야에서 인공지능 활용에 대한 필요성이 증가하고 있다. 금융과 같은 일부 산업은 강력한 보안, 높은 정확도, 규제 준수, 실시간 대응이 요구됨과 동시에 정적 시스템 환경 특성으로 적용된 인공지능 모델의 최적화가 어렵다. 이러한 환경적 한계 해결을 위하여, 연합 학습을 통한 모델의 최적화 방안을 제안한다. 연합 학습은 데이터 프라이버시를 유지하면서 모델의 지속적 최적화를 제공이 가능한 강력한 아키텍처이다. 그러나 연합 학습은 클라이언트와 중앙 서버의 반복적인 통신과 학습으로, 불필요한 자원에 대한 소요가 요구된다. 이러한 연합 학습의 단점 극복을 위하여, 주요도 높은 클라이언트의 선정 및 클라이언트와 중앙 서버의 조기 중단(early stopping) 전략을 통한 지속적, 효율적 최적화가 가능한 연합 학습 모델의 운영 전략을 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.259-265
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• 2006

Learning and emotions are very important parts to implement intelligent robots. In this paper, we implement intelligent virtual character based on reinforcement learning which interacts with user and have internal emotion model. Virtual character acts autonomously in 3D virtual environment by internal state. And user can learn virtual character specific behaviors by repeated directions. Mouse gesture is used to perceive such directions based on artificial neural network. Emotion-Mood-Personality model is proposed to express emotions. And we examine the change of emotion and learning behaviors when virtual character interact with user.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.27-40
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• 2022

Medical AI, which has lately made significant advances, is playing a vital role, such as assisting clinicians with diagnosis and decision-making. The field of chest X-rays, in particular, is attracting a lot of attention since it is important for accessibility and identification of chest diseases, as well as the current COVID-19 pandemic. However, despite the vast amount of data, there remains a limit to developing an effective AI model due to a lack of labeled data. A research that used federated learning on chest X-ray data to lessen this difficulty has emerged, although it still has the following limitations. 1) It does not consider the problems that may occur in the Non-IID environment. 2) Even in the federated learning environment, there is still a shortage of labeled data of clients. We propose a method to solve the above problems by using the self-supervised learning model as a global model of federated learning. To that aim, we investigate a self-supervised learning methods suited for federated learning using chest X-ray data and demonstrate the benefits of adopting the self-supervised learning model for federated learning.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.100-107
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• 2019

Recently, reinforcement learning (RL) has expanded from the research phase of the virtual simulation environment to a wide range of applications, such as autonomous driving, natural language processing, recommendation systems, and disease diagnosis. However, RL is less likely to be used in these complex real-world environments. In contrast, inverse reinforcement learning (IRL) can obtain optimal policies in various situations; furthermore, it can use expert demonstration data to achieve its target task. In particular, IRL is expected to be a key technology for artificial general intelligence research that can successfully perform human intellectual tasks. In this report, we briefly summarize various IRL techniques and research directions.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.47-56
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• 2020

The purpose of this study is to comprehensively analyze elementary school teachers' perceptions of the use of artificial intelligence in education. Recently, interest in the use of artificial intelligence has increased in the field of education. However, there is a lack of research on the perceptions of elementary school teachers using AI in education. Using descriptive statistics, multiple linear regression analysis, and semantic differential meaning scale, 69 elementary school teachers' perceptions of using AI in education were analyzed. As a results, artificial intelligence technology was perceived as most suitable method for assisting activities in class and for problem-based learning. Factors which influence the use of AI in education were learning contents, learning materials, and AI tools. AI in education had the features of personalized learning, promoting students' participation, and provoking students' interest. Further, instructional strategies or models that enable optimized educational operation should be developed.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.199-204
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• 2021

Along with the development of technology in the fourth industrial revolution, the fields that can apply artificial intelligence technology are rapidly increasing. In order to improve computational thinking, overseas countries such as the U.S. and the U.K. are already using various AI education platforms to provide artificial intelligence education. Therefore, there is an increasing need for elementary school pre-service teachers in Korea to strengthen their AI education capabilities along with the existing software education. However, it may be difficult for learners with low levels of programming experience and AI education experience to choose an AI education platform that can sustain their learning motivation. Therefore, in this study, the factors related to learning motivation in the AI education platform were explored using the ARCS model. Through this, we present the factors required by the AI education platform for motivation and sustain of learning.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1181-1187
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• 2010

This paper proposes a mobile AI (Artificial Intelligence) conducting decision-making in the game through education for intelligent character on the basis of Neural Network. Neural Network is learned through the input/output value of the algorithm which defines the game rule and the problem solving method. The learned character is able to perceive the circumstances and make proper action. In this paper, the mobile AI using Neural Network has been step-by-step designed, and a simple game has been materialized for its functional experiment. In this game, the goal, the character, and obstacles exist on regular 2D space, and the character, evading obstacles, has to move where the goal is. The mobile AI can achieve its goals in changing environment by learning the solution to several problems through the algorithm defined in each experiment. The defined algorithm and Neural Network are designed to make the input/output system the same. As the experimental results, the suggested mobile AI showed that it could perceive the circumstances to conduct action and to complete its mission. If mobile AI learns the defined algorithm even in the game of complex structure, its Neural Network will be able to show proper results even in the changing environment.