• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1015-1029
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• 2021

Modern technology offers many ways to enhance teaching and learning that in turn promote the development of tools for educational activities both inside and outside the classroom. Many educational programs using the augmented reality (AR) technology are being widely used to provide supplementary learning materials for students. This paper describes the potential and challenges of using GeoGebra AR in mathematical studies, whereby students can view 3D geometric objects for a better understanding of their structure, and verifies the feasibility of its use based on experimental results. The GeoGebra software can be used to draw geometric objects, and 3D geometric objects can be viewed using AR software or AR applications on mobile phones or computer tablets. These could provide some of the required materials for mathematical education at high schools or universities. The use of the GeoGebra application for education in Laos will be particularly discussed in this paper.

• Smart Media Journal
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• v.10 no.2
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• pp.48-54
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• 2021

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians' policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.

• Smart Media Journal
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• v.11 no.5
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Smart Media Journal
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• v.12 no.3
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• pp.68-76
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• 2023

Emerging mobile edge computing (MEC) can be used in battery-constrained Internet of things (IoT). The execution latency of IoT applications can be improved by offloading computation-intensive tasks to an MEC server. Recently, the popularity of unmanned aerial vehicles (UAVs) has increased rapidly, and UAV-based MEC systems are receiving considerable attention. In this paper, we propose a dynamic computation offloading paradigm for UAV-based MEC systems, in which a UAV flies over an urban environment and provides edge services to IoT devices on the ground. Since most IoT devices are energy-constrained, we formulate our problem as a Markov decision process considering the energy level of the battery of each IoT device. We also use model-free Q-learning for time-critical tasks to maximize the system utility. According to our performance study, the proposed scheme can achieve desirable convergence properties and make intelligent offloading decisions.

• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• Journal of Korean Library and Information Science Society
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• v.31 no.1
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• pp.209-228
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• 2000

Information technology is affecting most areas of society in information society, schools are facing with the change of learning methods by computer and internet. The purpose of this study is to apply internet technology to school library, for maximizing the function of school library in information age. This study designed and implemented th ISLMC(Internet School Library Media Center) using the WWW, that gathers, processes and stores the useful information resources on the internet and effectively provides these to teachers, students and their parents of the learning activities.

• Broadcasting and Media Magazine
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• v.25 no.1
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• pp.75-82
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• 2020

디지털 트윈이나 증강현실, 가상현실, 자율주행 등과 같이 현실 좌표계의 위치를 다루거나 현실과 가상세계를 융합하는 기술들에 있어 측위 기술은 상당히 주요하게 작용한다. 측위 기술은 그 목적과 타겟 디바이스에 따라 매우 다양하게 존재하며, 기존 측위 기술들에 인공지능을 융합하여 정밀도와 측위 주기를 개선시키는 등 다양한 연구가 진행되고 있는 분야이다. 본 고에서는 기존의 다양한 측위 기술들의 동향과 인공지능을 융합하여 성능을 높인 사례들에 대해 설명한다.

• Broadcasting and Media Magazine
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• v.27 no.2
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• pp.19-25
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• 2022

본 고에서는 최근 컴퓨터 비전 분야에서 가장 활발히 연구되고 있는 분야 중에 하나인 자가 학습(Self-supervised Learning) 기술의 동향과 향후 방향성에 대해서 논의한다. 컴퓨터 비전 분야에서의 자가 학습 기술은 최근에 Contrastive Learning 기법을 활용하여 활발하게 연구되고 있는데, 이를 위한 좋은 Positive와 Negative를 어떻게 추출할까에 대한 고민으로 수많은 연구들이 진행되어 왔다. 본 고에서는 이러한 방향성에서 대표적인 몇 가지의 방법론에 대해서 논의하고 이의 한계점을 언급하며 컴퓨터 비전 분야에서 자가 학습 기법이 가야 할 방향성에 대해서 논의하고자 한다.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.109-123
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• 2023

Deep learning has made rapid progress in recent years and is affecting various fields and industries. The art field cannot be an exception, and in this paper, we would like to explore and experiment and analyze research fields that creatively generate 2D images in 3D from a visual arts and engineering perspective. To this end, the original image of the domestic artist is learned through GAN or Diffusion Models, and then converted into 3D using 3D conversion software and deep learning. And we compare the results with prior algorithms. After that, we will analyze the problems and improvements of 2D to 3D creative generation.