• Journal of the Korean Society for Library and Information Science
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• v.41 no.4
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• pp.217-227
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• 2007

This study describes a system which makes children use a cooperation study for the education of a library use and applies SDG technology to this cooperation study SDG means a system which is able to do a cooperation work as a multi-inputting device in a computer display. Through the use library utilization education system based on SDG, learners study simultaneously and collaborately. On this thesis. We embodied the understanding of the decimal classification of Korea and the arrangement of a bookshelf and the study of the location tracking as a game style based on an existed study that a cooperation study used SDG is more superior that an individual study used a single device. Librarians through this system will be able to easily and interestingly instruct children for the use of a library.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1079-1082
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• 2017

최근에 각종 생체전위를 활용한 연구가 활발히 이루어 지고 있다 학습효과를 위해서 집중력 향상에 대한 연구는 교육분야에서 중요한 과제이다. 따라서 뇌파(eletroencephalogram)를 활용한 집중력 향상에 필요한 소프트웨어적 연구는 가치가 있는 연구로서 판단할 수 있다. 따라서 금번 연구에서는 뇌파와 컴퓨터 간 신호처리에 중점을 두고 효율적으로 데이터 프로세스를 할 수 있는 게임이라는 매체를 활용한 학습효과 증진 프로그램을 개발하고자 한다. 금번 연구에서는 학습에 필요한 인터페이스 프로그램과 뇌파 수집에 필요한 뇌파수집기를 중점으로 개발하였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• Journal of Korea Game Society
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• v.21 no.6
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• pp.75-86
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• 2021

Brand image refers to how customers, stakeholders and the market see and recognize the brand. A positive brand image leads to continuous purchases, but a negative brand image is directly linked to consumers' buying behavior, such as stopping purchases, so from the corporate perspective, it needs to be quickly and accurately identified. Currently, methods of investigating brand images include surveys and SNS surveys, which have limited number of samples and are time-consuming and costly. Therefore, in this study, we are going to conduct an emotional analysis of text data on social media by utilizing the machine learning based KoBERT model, and then suggest how to use it for game corporate brand image analysis and verify its performance. The result has proved some degree of usability showing the same ranking within five brands when compared with the BRI Korea's brand reputation ranking.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.135-138
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• 2012

본 연구는 다중참여역할수행게임(Massive Multiplayer Online Role Playing Game, 이하 MMORPG)이 자기주도 학습능력에 미치는 효과를 살펴보는데 목적이 있으며, 초등학교에 재학중인 4, 5, 6학년 3개 학급을 실험 대상으로 4주간 실험하였다. 실험집단은 방과 후 수업시간을 이용하여 일주일에 2시간 이상 MMORPG를 활용한 한자수업을 학습하도록 지도하였고, 통제집단은 교사의 수업 진행으로 학습하였다. 실험 전 두 집단 간의 자기주도 학습능력에 대한 동질성을 검증하기 위하여 자기주도 학습준비도 검사(Self-Directed Learning Readiness Scale, 이하 SDLRS)를 이용하여 자기주도 학습능력 사전검사를 실시하였고, 4주간 실험이 끝난 후 자기주도 학습능력 검사의 차이를 알아보았다. 더 나아가 하위요소인 효율적인 학습자라는 개념, 학습에 대한 솔선수범 및 독립성, 창의성, 자신의 학습에 대한 책임감, 기본학습 기능과 문제해결 기술, 미래 지향적인 자기이해 학습기회에 대한 개방성, 학습에 대한 애정과 열성을 다변량분산분석(MANOVA)를 실시하여 통계분석 하였다. 이러한 연구결과들을 바탕으로 다음과 같은 결론을 내릴 수 있다. 첫째, MMORPG를 활용한 한자학습과 일반교실 한자학습은 독립표본 t검정 결과 자기주도 학습능력에서 유의미한 차이가 나타났다. 즉, MMORPG를 활용한 한자학습이 일반교실수업에 의한 학습보다 높은 자기주도 학습능력 정도를 보여 주었다. 둘째, MMORPG를 활용한 한자학습이 하위요소 간에 어떠한 여향을 미치는가를 알아보기 위해 실시한 MANOVA 결과 효율적인 학습자라는 개념, 학습에 대한 솔선수범 및 독립성, 창의성, 자신의 학습에 대한 책임감, 기본학습 기능과 문제해결 기술, 미래 지향적인 자기이해에서는 유의미한 것으로 나왔지만 학습기회에 대한 개방성, 학습에 대한 애정과 열성에서는 유의미한 결과를 얻지 못하였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.29-35
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• 2020

In this paper, we propose a new structured entity recognition DeNERT model. Recently, the field of natural language processing has been actively researched using pre-trained language representation models with a large amount of corpus. In particular, the named entity recognition, which is one of the fields of natural language processing, uses a supervised learning method, which requires a large amount of training dataset and computation. Reinforcement learning is a method that learns through trial and error experience without initial data and is closer to the process of human learning than other machine learning methodologies and is not much applied to the field of natural language processing yet. It is often used in simulation environments such as Atari games and AlphaGo. BERT is a general-purpose language model developed by Google that is pre-trained on large corpus and computational quantities. Recently, it is a language model that shows high performance in the field of natural language processing research and shows high accuracy in many downstream tasks of natural language processing. In this paper, we propose a new named entity recognition DeNERT model using two deep learning models, DQN and BERT. The proposed model is trained by creating a learning environment of reinforcement learning model based on language expression which is the advantage of the general language model. The DeNERT model trained in this way is a faster inference time and higher performance model with a small amount of training dataset. Also, we validate the performance of our model's named entity recognition performance through experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.46-54
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• 2004

As a consequence of expansion in information technology, various services continually have been provided us through the internet in these days. Since the beginning of the mobile service, contents for its service actively have been developing now; there fore, the existing internet service system is gradually changing into the wireless mobile service system. However, mobile service now in use is mostly for entertainment such as bell sound, game and so on. The existing contents are very insufficient to satisfy the education. In this thesis, we have developed new mobile contents that are combined with Item Pool System. Students can connect to the learning contents by using the mobile device in anywhere and anytime in order to promote the efficiency of study.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1088-1091
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• 2017

최근 그래픽스 기반의 가상현실 기술의 발전과 관심이 증가하면서 3D 객체와의 자연스러운 상호작용을 위한 방법들 중 손 제스처 인식에 대한 연구가 활발히 진행되고 있다. 본 논문은 가상현실 3D 오브젝트와의 상호작용을 위한 MCSVM 기반의 손 제스처 인식을 제안한다. 먼저 다양한 손 제스처들을 립모션을 통해 입력 받아 전처리를 수행한 손 데이터를 전달한다. 그 후 이진 결정 트리로 1차 분류를 한 손 데이터를 리샘플링 한 뒤 체인코드를 생성하고 이에 대한 히스토그램으로 특징 데이터를 구성한다. 이를 기반으로 MCSVM 학습을 통해 2차 분류를 수행하여 제스처를 인식한다. 실험 결과 3D 오브젝트와 상호작용을 위한 16개의 명령 제스처에 대해 평균 99.2%의 인식률을 보였고 마우스 인터페이스와 비교한 정서적 평가 결과에서는 마우스 입력에 비하여 직관적이고 사용자 친화적인 상호작용이 가능하다는 점에서 게임, 학습 시뮬레이션, 설계, 의료분야 등 많은 가상현실 응용 분야에서의 입력 인터페이스로 활용 될 수 있고 가상현실에서 몰입도를 높이는데 도움이 됨을 알 수 있었다.

• Journal of Korea Game Society
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• v.17 no.6
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• pp.113-120
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• 2017

With the development of virtual reality technology, in recent years, user-friendly hand gesture interface has been more studied for natural interaction with a virtual 3D object. Most earlier studies on the hand-gesture interface are using relatively simple hand gestures. In this paper, we suggest an intuitive hand gesture interface for interaction with 3D object in the virtual reality applications. For hand gesture recognition, first of all, we preprocess various hand data and classify the data through the binary decision tree. The classified data is re-sampled and converted to the chain-code, and then constructed to the hand feature data with the histograms of the chain code. Finally, the input gesture is recognized by MCSVM-based machine learning from the feature data. To test our proposed hand gesture interface we implemented a 'Virtual Block' game. Our experiments showed about 99.2% recognition ratio of 16 kinds of command gestures and more intuitive and user friendly than conventional mouse interface.

• The Journal of the Korea Contents Association
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• v.12 no.10
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• pp.541-554
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• 2012

This study suggests the effectiveness of G-learning English afterschool classes implemented to elementary school students at low achievement level in English. These days, the use of games in teaching and learning, known as G-learning, has gradually expanded, so it is necessary to consider how to adapt G-learning generally in English education. A G-learning afterschool English class was implemented to 23 low-level 5th grade students in an elementary school located in Daejon for 12 weeks. This study set two hypotheses aiming to determine the effectiveness in achievement and affectiveness of the participants. Pre and post achievement tests were conducted. Also, survey and FGI (focused group interview) were carried out twice with the participants. The study found that students' spelling awareness, vocabulary recognition and dialogue comprehension ability (hypothesis 1) were all improved with statistical significance. Moreover, after the class, participants' confidence and interest toward English study showed meaningful increases.