• Journal of Advanced Navigation Technology
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• v.13 no.6
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• pp.854-860
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• 2009

Ubiquitous is a new computing environment with IT technology and information communication, and appling various equipments likes PDA and application parts. Recently, user's using environment is changing to smart phone and is expanded learning tools to learner without educational environment. Thus, in this paper, we designed SCORM based meta-data to use smart phone. For this purpose, we made U-learning server and smart phone process server that is to handling with existence LMS and SCORM. To apply smart phones characteristics that have different ones each other, meta-data was able to have some resource information as like CPU, screen size and memory. The meta-data adapter could be process the characteristics.

• Journal of Korean Library and Information Science Society
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• v.44 no.2
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• pp.27-50
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• 2013

In this study, a smart Learning instruction model for school library was developed in terms of library instruction. Based on ADDIE model and ASSURE model, this model is organized considering the characteristics of school library, including facilities, materials, human resources, information problem solving process, collaborative teaching and blended learning, and utilizing smart devices. The entire procedure of this model is as follows: "establishment of instructional objectives${\rightarrow}$learner analysis${\rightarrow}$analyzing the learning environment${\rightarrow}$analyzing the learning task${\rightarrow}$instructional process design${\rightarrow}$developing instructional tool${\rightarrow}$instruction${\rightarrow}$evaluation". In addition, an instructional practice is provided for actual experience of smart Learning in school libraries.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.207-221
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• 2014

Education environment of modern society is rapidly changing along the usage of various device and development of contents. Learners of diverse age groups and genders are exposed in smart education environment. Thus in order to investigate effective smart education contents production, this study classified interactive types that affect learning satisfaction into CAI (Computer Assisted Instruction) based , NCS (Network Communication System) based , and NTS (New Technology System) based . Then we investigated how each interactive types affect immersion, utility, self-efficacy, practicality, and stimulation. The effects were measured according to the learner's gender and age. As the result, interactive types do affect smart education, where male had higher learning satisfaction for CAI based, game type, and wiki type while female had higher satisfaction for relationship establishment type and experience type. Also, for age group, the 10s preferred NTS based, 20~30s NCS based, and 40s and over CAI based interactive type. Thus, if satisfaction levels according to gender and age are considered when producing smart education contents, it may be possible to create educative contents that meet the dispositions of the learners.

• 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 Internet Computing and Services
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• v.14 no.6
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• pp.59-70
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• 2013

With advances in information and communication technologies, many innovative technologies have been developed. Those technologies are changing every aspect of our daily life. Especially smart technologies are changing our life dramatically. Smart devices such as tablet PCs and smart phones are used in education so that new concept called "smart learning" is created and used. Currently smart learning becomes popular in accordance with wide distribution of smart devices and smart contents in schools. In order to compare and check the current status and progress of individuals in smart environment, we need smart literacy standards. However, there has been only few works for smart literacy standards for teachers and students. Also, those standards need to be improved. The purpose of this paper is to develop smart literacy standards for teachers and students in smart learning environment. The proposed literacy standards are developed based on the existing ICT literacy standards. In this work, smart literacy standards consist of four main areas, smart education, smart knowledge, smart application, and smart ethics, respectively. For development of smart literacy, wide experts from teachers, professors, and researchers are selected and surveyed. Their responses are analyzed using through statistical analysis so that final smart literacy standards are obtained.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.17-29
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• 2017

IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.403-406
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• 2011

본 논문에서는 최근 등장한 스마트 환경에서 e-러닝에 대한 관심의 확산에 따라 e-러닝의 개념을 도구적 접근, 환경적 접근, 그리고 이론적 접근을 통해 살펴보았다. 이를 통해 제시된 스마트 환경에서 e-러닝의 개선방향을 제시한다. 이 같은 스마트 환경에서 e-러닝 개선방안을 기반으로 향후 개별적 수업상황에 적합한 구체적인 개발전략의 지속적 연구가 필요하다.

• Korean Journal of Childcare and Education
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• v.19 no.2
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• pp.61-85
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• 2023

Objective: This study aims to investigate whether science teaching attitude of early childhood teachers mediates the relationship between the professional learning environment of institutions and their intention to accept artificial intelligence (AI) technology, and whether the experience of using smart and digital devices moderates the effect of science teaching attitude. Methods: An online survey was conducted targeting 118 teachers with more than 1 year of experience in kindergarten and day care center settings. Descriptive statistical analysis, correlation analysis, and The Process macro model 4, 14 were performed using SPSS 27.0 and The Process macro 3.5. Results: First, the science teaching attitude of early childhood teachers served as a mediator between the professional learning environment of institutions and teachers' intention to accept AI technology. Second, the experience of using smart and digital devices was found to moderate the effect of teachers' science teaching attitude on their intention to accept AI technology. Conclusion/Implications: This results showed that an institutional environment that supports teachers' professionalism development and provides rich experience is crucial for promoting teachers' active acceptance of AI technology. The findings highlight the importance of creating a supportive institutional envionment for teacher's professional growth, enhancing science teaching attitudes, and facilitating the use of various devices.