• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.575-578
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• 2020

최근 교육부는 초등학교부터 중학교까지 정보통신기술(ICT)와 인공지능(AI)에 관한 소양을 길러주기 위해 AI, VR 등 최첨단 기술을 적용한 '지능형 과학실'을 2024년까지 모든 학교에 구축할 방침이라고 한다. 하지만 국내 VR 교육은 학년별, 교과과정에 맞춘 콘텐츠가 부족하고, VR 교육 전용 LMS(학습관리 시스템)의 부재로 현실로 도입하기에 부족하다. 본 논문에서는 VR 교육 특성에 맞는 LMS 대안과 10분 내외의 VR 체험을 뒷받침할 맞춤 콘텐츠로서 'Web & VR Hybrid Content'를 제안한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.14-21
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• 2021

Green networking has become a issue to reduce energy wastes and CO2 emission by adding energy managing mechanism to wired data networks. Energy consumption of the overall wired data networks is driven by access networks, expect for end devices. However, on a global scale, it is more difficult to manage centrally energy, measure and model the real energy use and energy savings potential of the access networks. This paper presented the multiple linear regression model to predict energy consumption of wired access networks using supervised learning of machine learning with data collected by existing investigated materials, actual measured values and results of many models. In addition, this work optimized the performance of it by various experiments and predict energy consumption of wired access networks. The performance evaluation of the regression model was achieved by well-knowned evaluation metrics.

• Convergence Security Journal
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• v.23 no.5
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• pp.81-89
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• 2023

Kubernetes is a representative open-source software for container orchestration, playing a crucial role in monitoring and managing resources allocated to containers. As container environments become prevalent, security threats targeting containers continue to rise, with resource exhaustion attacks being a prominent example. These attacks involve distributing malicious crypto-mining software in containerized form to hijack computing resources, thereby affecting the operation of the host and other containers that share resources. Previous research has focused on detecting resource depletion attacks, so technology to respond when attacks occur is lacking. This paper proposes a reinforcement learning-based dynamic resource management framework for detecting and responding to resource exhaustion attacks and malicious containers running in Kubernetes environments. To achieve this, we define the environment's state, actions, and rewards from the perspective of responding to resource exhaustion attacks using reinforcement learning. It is expected that the proposed methodology will contribute to establishing a robust defense against resource exhaustion attacks in container environments

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.353-353
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• 2022

본 연구에서는 CMIP5(The fifth phase of the Couple Model Intercomparison Project) 미래기후시나리오와 LSTM(Long Short-Term Memory) 모형 기반의 딥러닝 기법을 이용하여 하천유량 예측을 위한 최적 학습 기간을 제시하였다. 연구지역으로는 진안군(성산리) 지점을 선정하였다. 보정(2000~2002/2014~2015) 및 검증(2003~2005/2016~2017) 기간을 설정하여 연구지역의 실측 유량 자료와 LSTM 기반 모의유량을 비교한 결과, 전체적으로 모의값이 실측값을 잘 반영하는 것으로 나타났다. 또한, LSTM 모형의 장기간 예측 성능을 평가하기 위하여 LSTM 모형 기반 유량을 보정(2000~2015) 및 검증(2016~2019) 기간의 SWAT 기반 유량에 비교하였다. 비록 모의결과에일부 오차가 발생하였으나, LSTM 모형이 장기간의 하천유량을 잘 산정하는 것으로 나타났다. 검증 결과를 기반으로 2011년~2100년의 CMIP5 미래기후시나리오 기상자료를 이용하여 SWAT 기반 유량을 모의하였으며, 모의한 하천유량을 LSTM 모형의 학습자료로 사용하였다. 다양한 학습 시나리오을 적용하여 LSTM 및 SWAT 모형 기반의 하천유량을 모의하였으며, 최적 학습 기간을 제시하기 위하여 학습 시나리오별 LSTM/SWAT 기반 하천유량의 상관성 및 불확실성을 비교하였다. 비교 결과 학습 기간이 최소 30년 이상일때, 실측유량과 비교하여 LSTM 모형 기반 하천유량의 불확실성이 낮은 것으로 나타났다. 따라서 CMIP5 미래기후시나리오와 딥러닝 기반 LSTM 모형을 연계하여 미래 장기간의 일별 유량을 모의할 경우, 신뢰성 있는 LSTM 모형 기반 하천유량을 모의하기 위해서는 최소 30년 이상의 학습 기간이 필요할 것으로 판단된다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.39-60
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• 2014

In domestic university libraries, the difference between the knowledge resource collection activities on campus is depending on the size of the university, and their collection is concentrated on some types of digital resources. In recent years, the main universities in developed countries has developed actively in social openness and share activities of their knowledge resources, through the OA-based institutional repository, for the purpose of image improvement and competitiveness as a knowledge production base. This study examined ways to improve the relevant regulations in order to effectively collect and systematically manage the knowledge resources from graduate school, research institutes, center for teaching and learning, e-learning center, museum, press, a variety of campus organizations, so as to enhance the role of the library as the right manager of knowledge resources on campus. To this end, this study, considering the improvement of relevant regulations, investigates the operating situation of the library regulations of 176 universities and suggests necessary improvement methods in order to facilitate the digital legal deposit and expand its scope.

• Journal of KIISE:Software and Applications
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• v.32 no.1
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• pp.50-61
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• 2005

With the wide spread use of multimedia device, the demand for the image processing engineers are increasing in various fields. However there are few engineers who can develop practical applications in the image processing area. To teach practical image processing techniques, we need a visual programming environment which can efficiently present the image processing theories and, at the same time, provide interactive experiments for the theory presented. In this paper, we propose a visual programming environment of the integrated environment for image processing. It consists of the theory presentation systems and experiment systems based on the visual programming environment. The theory presentation systems support multimedia data, web documents and powerpoint files. The proposed system provides an integrated environment for application development as well as education. The proposed system accumulates the teaching materials and exercise data and it manages, an ideal image processing education and research environment to students and instructors.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.207-212
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• 2004

This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Q-learning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents Communicate With Others Sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.167-177
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• 2024

In nursing education, there has been efforts for enhancing the quality, with a growing interest in the utilization of digital technologies. In clinical training of nursing curriculum, the emphasis on digital technology is pronounced, as it has the potential to offer learners effective and accessible educational experience while enabling the integrated management of individualized learning outcomes. This study developed a digital nursing education platform, allowing educators and learners to select functionalities based on the educational content and characteristics of the learning tools. Additionally, the user interface was designed to facilitate learners' accurate understanding and execution of assigned tasks and objectives. The detailed design and implementation process of the platform are elaborated and then the validation of its usefulness was provided based on feedback from ten educators who are responsible for diverse subjects. The high usability of the digital nursing practicum education platform was confirmed, with potential implications for significant improvements in learner performance. The potential of this digital platform is to lead to innovative shifts in educational methodologies within the field of integrative nursing education.

• Journal of Advanced Technology Convergence
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• v.3 no.3
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• pp.47-56
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• 2024

This study analyzes the field practicum experiences of adult learners in social welfare and discusses areas for improvement. The field practicum is an essential process and training course for becoming a social worker. Through the practicum experience, learners gain a valuable opportunity to apply theoretical knowledge in real-world settings and understand their future roles as prospective social workers. However, if the field practicum does not adequately reflect the characteristics of adult learners, it may be difficult to ensure a successful practicum experience. For adult learners to successfully complete their social welfare practicum, integrated and consistent support from both the university and practicum institutions is essential. In particular, the challenges adult learners may face, such as difficulties in time management, psychological stress, and the gap between theory and practice, must be addressed. Most importantly, thorough preparation before the practicum is necessary to ensure success.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.91-91
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• 2020

IPCC (Intergovernmental Panel on Climate Change) 5차 평가보고서에 의하면 최근 배출 온실가스의 양은 관측 이래 최고 수준이며 온실가스로 인한 기후변화는 인간계와 자연계에 광범위한 영향을 주고 있다고 보고하였다. 기후변화의 영향은 국제적으로 빙하 감소, 사막화, 해수면 상승 등 뚜렷하게 나타나고 있다. 이러한 기후변화에 대응하기 위해 온실가스 완화 정책과 동시에 새로운 기후변화 환경에 적응하는 것이 필요하다. 기후변화 적응이란 현재 나타나고 있거나 미래에 나타날 것으로 예상되는 기후변화의 파급효과와 영향에 대응할 수 있도록 하는 모든 행동이며 이를 위해서는 기후변화 영향분석이 수반되어야 한다. MOTIVE 연구단에서는 기후변화 적응대책 수립의 지원을 목표로 7개 부문(건강, 물관리, 농업, 산림, 생태, 해양, 수산)에서 "한국형 통합평가 모형"을 개발하고 있다. 각 부문에서 개발하는 프로세스 모델은 시스템에 대한 지식을 가진 상황에서 사용하면 신뢰할 수 있는 예측 결과를 얻을 수 있지만, 부문별 통합을 통한 영향 분석 시 타 분야에 대한 지식이 수반되어야 하는 어려움을 가진다. 이를 위해 본 연구에서는 시스템 내의 물리적 프로세스에 대한 요구 없이 입출력 데이터만을 이용하여 결과를 신속하게 추정하는 데이터 모델링(기계학습)을 이용하였다. 데이터 모델링을 위한 데이터는 다양한 자연 현상에 대한 BANPOL(수질 프로세스 모델) 분석을 통한 자료를 이용하여 학습 자료를 구축하였다. 즉, 데이터 모델링은 BANPOL 모델을 대리하는 메타모델이며, 낙동강 표준유역에 대한 유량 및 수질을 높은 상관성으로 추정하였다. 원 모델보다 정확도는 낮을 수 있으나 메타모델의 개발을 통한 웹 시스템을 개발하여 비전문가의 구동 및 신속한 기후 시나리오를 적용할 수 있는 환경을 개발하였다.