• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.1-13
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• 2023

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.177-182
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• 2016

In this paper, we propose the computational thinking education framework which provides three steps of computational thinking process and three kind of activities about computational thinking learning in class. The key idea of this framework is to provide the guidelines of designing activity steps of teaching and learning computational thinking in class using three axles of framework such as problem area, process of learning, and steps of computational thinking process. After designing a framework, we show that sample course of programming education program containing contents of Informatics subject in middle school by implementing our framework. Proposed framework and programming education program in middle school will be the good case study and guide to implement computational thinking concerned education programs in elementary, secondary, and universities.

• Hwankyungkyoyuk
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• v.14 no.2
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• pp.51-67
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• 2001

In this study we developed a framework to evaluate environmental education programs through lived experience in nature and the framework was applied to a neat case,'2001 Green Camp'. The framework consists of 4 items; goals and objectives, instructional planning, teaching and learning, methods and learning operation and environment. Learning outcomes such as changes to the levels of knowledge, attitude, participation and environmental sensitivity are not included in the evaluation framework but evaluated through direct questions to students. Two researchers observed and evaluated programs with the framework. This study reflected various perspectives of researchers, teachers, students and staff members.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.2
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• pp.12-16
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• 2012

This paper presents a learning framework that fits the digital age - an e-Friendly PLE. The learning framework is based on the theory of connectivism which asserts that knowledge and the learning of knowledge is distributive and is not located in any given place but rather consists of the network of connections formed from experiences and interactions with a knowing community, thus, the newly empowered learner is thinking and interacting in new ways. The framework's approach to learning is based on conversation and interaction, on sharing, creation and participation, on learning not as a separate activity, but rather as embedded in meaningful activities such as games or workflows. It sees learning as an active, personal inquiry, interpretation, and construction of meaning from prior knowledge and experience with one's actual environment.

• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.426-431
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• 2009

Recently, the application of E-learning is changing the method that is able to process the learning to learner more efficiently and conveniently. For this purpose, the application research of U-learning that is able to support the learning using mobile device such as PDA, NetBook, Tablet PC and so on is actively processing. But lots of U-learning framework is only considering the change to fit the exist learning contents the mobile device without SCORM that is able to support to make and process the learning contents by regular forms. In this paper, we proposed the learning contents interconnection of U-learning framework considering SCORM. For this purpose, we have to construct the learning by learning object and asset within SCORM. And this method can support learning information that was reconstructed it by learning contents to fit the mobile device as used the mobile device meta-data.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Computers and Concrete
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• v.32 no.3
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• pp.313-326
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• 2023

The seismic safety of the shear wall structure can be assessed through seismic fragility analysis, which requires high computational costs in estimating seismic demands. Accordingly, machine learning methods have been applied to such fragility analyses in recent years to reduce the numerical analysis cost, but it still remains a challenging task. Therefore, this study uses the ensemble machine learning method to present an improved framework for developing a more accurate seismic demand model than the existing ones. To this end, a rank-based selection method that enables determining an excellent model among several single machine learning models is presented. In addition, an index that can evaluate the degree of overfitting/underfitting of each model for the selection of an excellent single model is suggested. Furthermore, based on the selected single machine learning model, we propose a method to derive a more accurate ensemble model based on the bagging method. As a result, the seismic demand model for which the proposed framework is applied shows about 3-17% better prediction performance than the existing single machine learning models. Finally, the seismic fragility obtained from the proposed framework shows better accuracy than the existing fragility methods.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2989-3000
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• 1999

Until now, most Intelligent Tutoring Systems are lacking in the modularity, the extensibility of the system, and the flexibility in the dynamic environment due to the static exchanges of knowledge among modules. To overcome these flexibility in the dynamic due to the static exchanges of knowledge among modules. To overcome these problems, we will suggest, in this paper, a Distance Intelligent Tutoring Framework, called DELFOM, based on the multiagent to cope with the various and complicated learner's requests. We could make different types of learning systems by simply changing the contents of DELFOM External that is variant part of DELFOM. This framework, therefore, provides software reuse and the extensibility based on object-oriented paradigm. And we will propose two different distance learning systems using DELFOM. Therefore this framework gives the developer/the learner the effective and easy development/learning environment. DELFOM is implemented using CORBA and Java for the network transparency and platform independence.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1736-1739
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• 2015

Social learning encourages and enables learners with common interests to communicate and share knowledge with others through social networks. However, social learning suffers a barrier on communication among learners with various la nguage and culture background. Aiming to avoid this barrier, this paper proposes a framework of cross-language s ocial learning system which can involve more learners' participation on the web. With this framework, an illustrati ve example of task-oriented collaborative learning paradigm is elaborated. It is expected that our proposed system can stimulate more learners to share the learning resource for deep discussions as well as to promote the knowled ge innovation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.370-373
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• 2022

Recently, various deep learning framework models such as Tensorflow, Pytorch, Keras, etc. have appeared. In addition, CNN (Convolutional Neural Network) is applied to image recognition using frameworks such as Tensorflow, Pytorch, and Keras, and the optimization model in image classification is mainly used. In this paper, based on the results of training the CNN model with the Paitotchi and tensor flow frameworks most often used in the field of deep learning image recognition, the two frameworks are compared and analyzed for image analysis. Derived an optimized framework.