• Journal of Digital Convergence
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• v.16 no.7
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• pp.213-222
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• 2018

The purpose of the study is to promote the expansion of educational environment and students' competence through the application of smart learning. In G University in 2017, 118 freshmen in the department of Chemical-bio engineering who were taking Calculus I class were divided into 2 groups of experimental and control group. The study analyzed the effect of the various learning experience using educational technology and the interaction in the class through SNS on students' visual understanding and academic achievement. The result shows that the students' academic achievement and satisfaction in the experimental group were higher than those in the control group. This verifies the potential of smart learning in the field of mathematics in the tertiary level and suggests strategies for high quality smart learning.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1297-1304
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• 2018

Online education, commonly referred to as distance education, has developed rapidly. However, it is questionable whether such distance education has been applied to various educational fields and has achieved satisfactory results in terms of learning effect. One of the reasons for not maximizing the benefits of distance education is non-dynamicity in the production and application of educational content. Educational contents production is made up of collaborative work between the instructor who is the contents expert and the developer who is the production expert. For this reason, existing researches have also concentrated on the improvement of each educational effect. In this paper, we propose to replace a production expert from a developer to an instructor. At this time, the important point is that the educational contents produced by the instructor, who is a development non-expert, should still be able to be maintained with high-quality contents utilizing the characteristics of the web. For this purpose, the production system was developed based on open source to maintain the quality similar to the educational contents developed by the production expert. This will increase the effectiveness of education by applying the developed Smart-Blended Learning System to various educational sites.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.183-191
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• 2020

Many learners are encountering e-Learning contents with smart devices and contents provides are carefully observing learners' reuse intention and behavior. Therefore, this study investigated the effect of e-Learning content platform characteristics on reuse intention for 200 users with smartphone-based e-Learning experience. The results show that the characteristics affecting reuse intention are content quality, interactivity, and ubiquity. Moreover, for men, only interactivity affects reuse intention, and for women, ubiquity and content quality affect reuse intention. When using smartphone-based e-Learning for less than an hour a day, only content quality affects reuse intention. On the contrary, ubiquity, convenience, and interactivity influence reuse intention when learning for more than one hour. Our results suggest meaningful implications that how e-Learning companies change their smartphone-based platform business strategy and how they utilize its key factors.

• Smart Media Journal
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• v.12 no.4
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• pp.9-18
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• 2023

Steganography is a technology capable of protecting data by hiding the existence of data. Recently, with the development of deep learning technology, deep learning-based steganography are being developed. Deep learning can learn by analyzing high-dimensional features of data, so it can improve the performance and quality of steganography. In this paper, we investigated the research trend of image steganography based on deep learning.

• The Journal of Information Systems
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• v.28 no.4
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• pp.377-401
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• 2019

Purpose The purpose of this study is to investigate the effects of learning expectation and perceived knowledge sharing on user satisfaction and IS continuance in the Korean Army which is currently using the Regiments' Information System to help their Integrated Administration Management. Based on both the Information System(IS) Continuance Model and IS Success Model, this study also examine the role of system quality on user satisfaction. We develop a research model(structural equation model) and its hypotheses that learning expectation, perceived knowledge sharing, and system quality increase users' satisfaction, which leads to IS continuance. The effect of learning expectation on perceived knowledge sharing is also hypothesized. Design/methodology/approach Online Survey using e-mails was administered to test our research model and associated hypotheses. Among the 360 e-mail letters including our survey questionnaire, 285 responses were collected via e-mails. Meaningful 225 cases were analyzed for our study. SPSS Statistics 24.0 and SmartPLS 3.0 were used to analyze both measuremant test and hyotheses test by using the data set. Findings Survey results show that learning expectation(confirmation variable), learning expectation, perceived knowledge sharing(a perceived usefulness variable), and system quality(a system characteristic) each increases user satisfaction, which leads to IS continuance, under the control of the effect of habit to use information systems. Learning expectation also has a positive influence on perceived knowledge sharing. Theoretical and practical implications are presented.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.199-203
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• 2019

In this paper, we propose the efficient impulsive noise mitigation scheme for power line communication (PLC) systems in smart grid applications. The proposed scheme estimates the channel impulsive noise information of receiver by applying machine learning. Then, the estimated impulsive noise is updated in data base. In the modulator, the impulsive noise which reduces the PLC performance is effectively mitigated through proposed technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the conventional model. As a result, the proposed noise mitigation improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC systems for smart grid.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.51-55
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• 2021

In this paper, we propose the deep learning based interference cancellation scheme algorithm for visible light communication (VLC) systems in smart building. The proposed scheme estimates the channel noise information by applying a deep learning model. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the VLC performance is effectively removed through interference cancellation technique. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance. Consequently, the proposed interference cancellation with deep learning improves the signal quality of VLC systems by effectively removing the channel noise. The results of the paper can be applied to VLC for smart building and general communication systems.

• Smart Media Journal
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• v.13 no.3
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• pp.27-35
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• 2024

This study deals with a method of combining image generation using Semi Supervised Learning based on GAN (Generative Adversarial Network) and image classification using ResNet50. Through this, a new approach was proposed to obtain more accurate and diverse results by integrating image generation and classification. The generator and discriminator are trained to distinguish generated images from actual images, and image classification is performed using ResNet50. In the experimental results, it was confirmed that the quality of the generated images changes depending on the epoch, and through this, we aim to improve the accuracy of industrial accident prediction. In addition, we would like to present an efficient method to improve the quality of image generation and increase the accuracy of image classification through the combination of GAN and ResNet50.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.7-14
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• 2021

Purpose: To solve urban problems, empirical research is being actively conducted to implement a smart city based on various ICT technologies, and digital twin technology is needed to effectively implement a smart city. A digital twin is essential for the realization of a smart city. A digital twin is a virtual environment that intuitively visualizes multidimensional data in the real world based on 3D. Digital twin is implemented on the premise of the convergence of GIS and BIM, and in particular, a lot of time is invested in data pre-processing and labeling in the data construction process. In digital twin, data quality is prioritized for consistency with reality, but there is a limit to data inspection with the naked eye. Therefore, in order to improve the required time and quality of digital twin construction, it was attempted to detect a building using Mask R-CNN, a deep learning-based masking algorithm for aerial images. If the results of this study are advanced and used to build digital twin data, it is thought that a high-quality smart city can be realized.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.