• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.565-574
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• 2024

ChatGPT is causing significant reverberations across all sectors of our society, and this holds true for the field of education as well. However, scholarly and societal discussions regarding ChatGPT in academic settings have primarily focused on issues such as plagiarism, with relatively limited research on the positive effects of utilizing generative AI. Additionally, amidst the educational crisis of the post-COVID era, there is a growing recognition of the need to enhance academic engagement. In light of these concerns, we investigated how academic engagement varies based on students' levels of ChatGPT literacy and examined whether students' academic confidence and perceived academic competence serve as mediators between ChatGPT literacy and academic engagement. An analysis using SPSS was conducted on the data collected from 406 college students. The results showed that ChatGPT literacy had a positive effect on academic engagement, and academic confidence mediated the relationship between ChatGPT literacy and academic engagement. Also, when the mediating effect of perceived academic competence was significant only when it was serially mediated. Based on these findings, we discussed the theoretical contributions of identifying the theoretical mechanism between ChatGPT literacy and academic engagement. In addition, practical implications regarding the importance of ChatGPT literacy education were described.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.9-18
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• 2020

The research objectives investigates main research topics and trends in the information and communication technology(ICT) field, Korea using LDA(Latent Dirichlet Allocation), one of the topic modeling techniques. The experimental dataset of ICT research and development(R&D) project of 5,200 was acquired through matching with the EZone system of IITP after downloading R&D project dataset from NTIS(National Science and Technology Information Service) during recent five years. Consequently, our finding was that the majority research topics were found as intelligent information technologies such as AI, big data, and IoT, and the main research trends was hyper realistic media. Finally, it is expected that the research results of topic modeling on the national R&D foundation dataset become the powerful information about establishment of planning and strategy of future's research and development in the ICT field.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.870-878
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• 2019

In this paper, we propose an Attention-based BLSTM for predicting the video compression standard of a video. Recently, in NLP, many researches have been studied to predict the next word of sentences, classify and translate sentences by their semantics using the structure of RNN, and they were commercialized as chatbots, AI speakers and translator applications, etc. LSTM is designed to solve the gradient vanishing problem in RNN, and is used in NLP. The proposed algorithm makes video compression standard prediction possible by applying BLSTM and Attention algorithm which focuses on the most important word in a sentence to a bitstream of a video, not an sentence of a natural language.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.5
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• pp.11-19
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• 2021

In this paper, we proposed an asymmetric multi-core processor scheduling scheme which is based on the mileage of each core. We considered a big-LITTLE multi-core processor structure, which consists of low power consuming LITTLE cores with general performance and high power consuming big cores with high performance. If a task needs to be processed, the processor decides a core type (big or LITTLE) to handle the task, and then investigate the core with the shortest mileage among unoccupied cores. Then assigns the task to the core. We developed a mileage-based balancing algorithm for asymmetric multi-core assignment and showed that the proposed scheduling scheme is more cost-effective compared to the traditional scheme from a management perspective. Simulation is also conducted for the purpose of performance evaluation of our proposed algorithm.

• The Research Journal of the Costume Culture
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• v.27 no.5
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• pp.449-460
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• 2019

Today, the fashion business environment of the 4.0 generation is changing based on fashion technology combined with advanced digital technologies such as AI (Artificial Intelligence), big data and IoT (Internet of Things). "Digital Transformation" means a fundamental change and innovation in a digital paradigm including corporate strategy, organization, communication, and business model, based on the utilization of digital technology. Thus, this study examines digital transformation strategies through the fashion brand Burberry. The study contents are as follows. First, it examines the theoretical concept of digital transformation and its utilization status. Second, it analyzes the characteristics of Burberry's digital transformation based on its strategies. For the research methodology, a literature review was performed on books and papers, aligning with case studies through websites, social media, and news articles. The result showed that first, Burberry has reset their main target to Millennials who actively use mobile and social media, and continues to communicate with them by utilizing digital strategy in the entire management. Second, Burberry is quickly delivering consistent brand identity to consumers by internally creating and providing social media-friendly content. Third, they have started real-time product sales and services by using IT to enhance access to brands and to lead consumers towards more active participation. In this study, Burberry's case shows that digital transformation can contribute to increased brand value and sales, keeping up with the changes in the digital paradigm. Therefore, the study suggests that digital transformation will serve as an important business strategy for fashion brands in the future.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.51-63
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• 2021

Recently, virtual communication has become a standard tool due to the outbreak of COVID-19. Likewise online communication is emerging as an essential competency. In this study, we aimed to develop a comprehensive and systematic digital literacy measurement tool reflecting the changes and needs of society. Construct variables were drawn by characterizing existing digital literacy measurement tools. Thirty-four items corresponding to the concept of each variable were developed. The developed measurement tool was then evaluated in the form of surveys from university students belonging to the digital native generation, and the reliability and validity were performed through exploratory and confirmatory factor analysis. The digital literacy measurement tool contained five sub-factors and twenty-five questions. In addition, hierarchical regression analysis was performed to verify the predictive validity of digital literacy sub-factors. Based on these findings, the implication of future research is discussed.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1355-1362
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• 2021

Recently, not only congenital hearing impairment, but also the number of people with hearing impairment due to acquired factors is increasing. The environment in which sign language can be learned is poor. Therefore, this study intends to present a sign language (sign language number/sign language text) evaluation system as a sign language learning assistance tool for sign language learners. Therefore, in this paper, sign language is captured as an image using OpenCV and Convolutional Neural Network (CNN). In addition, we study a system that recognizes sign language behavior using MediaPipe, converts the meaning of sign language into text-type data, and provides it to users. Through this, self-directed learning is possible so that learners who learn sign language can judge whether they are correct dez. Therefore, we develop a sign language learning assistance system that helps us learn sign language. The purpose is to propose a sign language learning assistance system as a way to support sign language learning, the main language of communication for the hearing impaired.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.226-234
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• 2022

With advances in autonomous vehicles and networked control, there is a growing interest in the consensus control of a multi-agents system to control multi-agents with distributed control beyond the control of a single agent. Since consensus control is a distributed control, it is bound to have delay in a practical system. In addition, it is often difficult to have a very accurate mathematical model for a system. Even though a reinforcement learning (RL) method was developed to deal with these issues, it often experiences slow convergence in the presence of large uncertainties. Thus, we propose a slide RL which combines the sliding mode control with RL to be robust to the uncertainties. The structure of a sliding mode control is introduced to the action in RL while an auxiliary sliding variable is included in the state information. Numerical simulation results show that the slide RL provides comparable performance to the model-based consensus control in the presence of unknown time-varying delay and disturbance while outperforming existing state-of-the-art RL-based consensus algorithms.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.125-134
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• 2022

This paper implements an AR manual for a vehicle that can be used even in the vehicle interior space where it is difficult to apply the augmentation method of AR content, which is mainly used, and applies a deep learning model to improve the augmentation matching between real space and virtual objects. Through deep learning, the logo of the steering wheel is recognized regardless of the position, angle, and inclination, and 3D interior space coordinates are generated based on this, and the virtual button is precisely augmented on the actual vehicle parts. Based on the same learning model, the function to recognize the main warning light symbols of the vehicle is also implemented to increase the functionality and usability as an AR manual for vehicles.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.