• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.309-318
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• 2012

Smart learning is a new trend in education following E-learning, U-Learning, and M-Learning. In June 2011, the Korean government announced the education policy on promoting smart learning, and presented the vision and the direction for the smart learning. However, it seems that the current government-directed education policy on smart learning has promoted the unconditional implementation of the policy without taking into consideration of a variety of factors, such as the reality of the classroom, educational environment, educators' competencies to use smart learning, and so on. The aims of this study are to examine the reality of the classroom and the educational environments for smart learning, and to take a survey on the elementary teachers' use of the smart learning. In addition, the study attempted to investigate the teachers' understanding of the various factors regarding the use of smart learning. On the basis of the results of the survey, the problems of implementing smart learning in the classroom were analyzed, and then some suggestions were made to pave the way for the more improved and systematic smart learning.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.125-132
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• 2012

In recent years, a variety of learning content construction utilizing the smart phone is coming. In this paper, we investigate on overall trends and movements in e-learning performance at University. And system developed a e-learning platform consisting of smart phone portal, learning management system(LMS), and learning content management system(LCMS). Throughout the experiment, each of the components of the e-learning were implemented. LMS was implemented more efficiently using a user profile evaluation system for qualification.

• Journal of Digital Convergence
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• v.19 no.1
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• pp.11-22
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• 2021

The COVID-19 pandemic has led to distance learning in primary and secondary school. Little has been known whether home ICTs environment is appropriate for the distance learning. This paper aims to assess the current state of ICTs environment at home for the distance learning of children. Using 2012 and 2019 Korean Media Panel Survey, we investigated the number of smart devices owned by households and found differences in ownership by household characteristics. The results showed that the majority of household owned more than one smart devices per child. However, the difference in the proportion of households with less than one device per child varied depending on whether smartphone was included in smart devices. These results imply that public intervention is needed to prevent educational inequality caused by the home ICTs environment for the distance learning.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1793-1802
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• 2017

In the recent years, IT and Network Technology has rapidly advanced environment in accordance with the needs of the times, the usage of the smart learning service is increasing. Smart learning is extended from e-learning which is limited concept of space and place. This system can be easily exposed to the various security threats due to characteristic of wireless service system. Therefore, this paper proposes the improvement methods of smart learning system security by use of faults analysis methods such as the FTA(Fault Tree Analysis) and FMECA(Failure Mode Effects and Criticality Analysis) utilizing the consolidated analysis method which maximized advantage and minimized disadvantage of each technique.

• Journal of the Korean Chemical Society
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• v.64 no.6
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• pp.360-370
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• 2020

This study explored the learning environment for promoting conceptual understanding, immersion, and situational interest in small group learning using augmented reality, according to the level of students' self-regulation. 95 ninth-grade students from a coed high school in Seoul participated in this study. Students were divided into a group of four and each group was randomly assigned to three learning environments that provide one marker and one smart device(1-1), two markers and two smart devices(2-2), and four markers and four smart devices(4-4) for a group. Small group learning using augmented reality was conducted for two class periods about the chemical bonding concept from the Integrated Science subject. Two-way ANOVA results revealed that students in the 4-4 learning environment scored significantly higher than those in the 1-1 or 2-2 learning environment in a conception test. Changes in the learning environment have affected students with a low level of self-regulation. In an immersion test, students in the 4-4 learning environment scored significantly higher than those in the 1-1 learning environment, and changes in the learning environment have affected students with a high level of self-regulation. As a result of situational interest test, students in the 4-4 and 2-2 learning environments scored significantly higher than those in the 1-1 learning environment, and changes in the learning environment have affected students with a low and a high level of self-regulation. Based on the results, the educational implications of the learning environment for promoting conceptual understanding, immersion, and situational interest in small group learning using augmented reality are discussed.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.173-178
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• 2020

As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.

• The Journal of Korean Association of Computer Education
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• v.16 no.6
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• pp.83-94
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• 2013

As exchanges and cooperation of political, economic, social, and cultural aspects among nations are recently vigorous, recognition of the global world has increased. In order to nurture the right talent in this era of globalization, transition into appropriate education for the global age beyond the state centered curriculum is required. Due to the recent explosive spread of smart devices, a new educational paradigm of smart training has emerged. Global learning can be effectively supported in the smart educational environment that entails brisk interactions, active participation and cooperation in a highly connected society. In this research, we suggest an instructional model for effectively supporting global learning in smart learning environments.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.4
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• pp.193-198
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• 2010

Smart phone systems such as Android and iPhone are spreading into the next generation's computing and the prediction that cell phone (especially smart phone) penetration rate will be higher than that of PC in the near future is predominant. In this paper, we designed and materialized an item pool system which has self directed learning function that enables learning with smart phone (the PC in the hand). Users can choose the way of studying and do the offered estimation depending on their levels with smart phone. It is materialized the studying environment which can be done immediately anytime and anywhere.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.41-48
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• 2021

A smart tuned mass damper (TMD) is widely studied for seismic response reduction of various structures. Control algorithm is the most important factor for control performance of a smart TMD. This study used a Deep Deterministic Policy Gradient (DDPG) among reinforcement learning techniques to develop a control algorithm for a smart TMD. A magnetorheological (MR) damper was used to make the smart TMD. A single mass model with the smart TMD was employed to make a reinforcement learning environment. Time history analysis simulations of the example structure subject to artificial seismic load were performed in the reinforcement learning process. Critic of policy network and actor of value network for DDPG agent were constructed. The action of DDPG agent was selected as the command voltage sent to the MR damper. Reward for the DDPG action was calculated by using displacement and velocity responses of the main mass. Groundhook control algorithm was used as a comparative control algorithm. After 10,000 episode training of the DDPG agent model with proper hyper-parameters, the semi-active control algorithm for control of seismic responses of the example structure with the smart TMD was developed. The simulation results presented that the developed DDPG model can provide effective control algorithms for smart TMD for reduction of seismic responses.