• Journal of Engineering Education Research
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• v.26 no.4
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• pp.36-46
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• 2023

This paper presents a case study and online-offline (hybrid) course structure for project-oriented community-based service learning in order to solve real-world problems for society. It examines social issues and conduct student projects to develop solutions that can generate sustainable value. This course helps students to use their major knowledge to assess and solve the problems faced by the local community. The outcomes of this course conducted via online lectures and offline project activities are discussed. The operation of this blended type of social problem-solving course is also stated.

• ETRI Journal
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• v.46 no.3
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.89-94
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• 2024

The main purpose of the study is to determine the main elements of the use of digital technologies for learning a foreign language in educational institutions. The era of digital technologies is a transition from the traditional format of working with information to a digital format. This is the era of the total domination of digital technologies. Digital technologies have gained an unprecedented rapid and general distribution. In recent years, all spheres of human life have already undergone the intervention of digital technologies. Therefore, it is precisely the educational industry that faces a difficult task - to move to a new level of education, where digital technologies will be actively used, allowing you to conveniently and quickly work in the information field for more effective learning and development. The study has limitations and they relate to the fact that the practical activities of the process of using digital technologies in the system of preparing the study of a foreign language were not taken into account.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.82.3-82.3
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• 2019

In this study, we generate future full disk magnetograms in 12, 24, 36 and 48 hours advance from SDO/HMI images using deep learning. To perform this generation, we apply the convolutional generative adversarial network (cGAN) algorithm to a series of SDO/HMI magnetograms. We use SDO/HMI data from 2011 to 2016 for training four models. The models make AI-generated images for 2017 HMI data and compare them with the actual HMI magnetograms for evaluation. The AI-generated images by each model are very similar to the actual images. The average correlation coefficient between the two images for about 600 data sets are about 0.85 for four models. We are examining hundreds of active regions for more detail comparison. In the future we will use pix2pix HD and video2video translation networks for image prediction.

• Journal of Korean Elementary Science Education
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• v.35 no.3
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• pp.326-335
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• 2016

The purpose of this study is to devise the concept of properties of the light on learning progressions for 3-6 grade students in the elementary school. For the purpose, this study creates a construct map of the properties of the light. Ordered multiple-choice items were developed in view of the construct map and presented to 200, 3~6 grade students in the elementary school. A partial credit model of Rasch model was used to analyze the results of those items, and the learning progressions was devised from the analysis results. The study can be summarized as follows: the construct modeling approach was used to devise the properties of light on learning progressions. As a result, the concept was selected for the core standards of construct map. Based on the construct map, the multiple-choice items were developed for students' conceptual understanding. The items were analyzed using the partial credit model, and the analysis findings showed that they were appropriate to assess students about their level of understanding of the properties of light. Finally, learning progressions were devised with the use of item analysis results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4317-4335
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• 2018

Semi-supervised multi-view latent space learning is gaining considerable popularity recently in many machine learning applications due to the high cost and difficulty to obtain the large amount of label information of data. Although some semi-supervised multi-view latent space learning methods have been presented, there is still much space for improvement: 1) How to learn latent discriminant intact feature representations by employing data of multiple views; 2) How to exploit the manifold structure of both labeled and unlabeled point in the learned latent intact space effectively. To address the above issues, we propose an approach called semi-supervised multi-view manifold discriminant intact space learning ($SM^2DIS$) for image classification in this paper. $SM^2DIS$ aims to seek a manifold discriminant intact space for data of different views by making use of both the discriminant information of labeled data and the manifold structure of both labeled and unlabeled data. Experimental results on MNIST, COIL-20, Multi-PIE, and Caltech-101 databases demonstrate the effectiveness and robustness of our proposed approach.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.525-531
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• 2012

Objective: The aim of this study is to investigate learnability of gestures-commands pair when people use gestures to control a device. Background: In vision-based gesture recognition system, selecting gesture-command pairing is critical for its usability in learning. Subjective preference and its agreement score, used in previous study(Lim et al., 2012) was used to group four gesture-command pairings. To quantify the learnability, two learning models, average time model and marginal time model, were used. Method: Two sets of eight gestures, total sixteen gestures were listed by agreement score and preference data. Fourteen participants divided into two groups, memorized each set of gesture-command pair and performed gesture. For a given command, time to recall the paired gesture was collected. Results: The average recall time for initial trials were differed by preference and agreement score as well as the learning rate R driven by the two learning models. Conclusion: Preference rate agreement score showed influence on learning of gesture-command pairs. Application: This study could be applied to any device considered to adopt gesture interaction system for device control.

• English Language & Literature Teaching
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• v.8 no.2
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• pp.151-176
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• 2003

Motivation and learning strategies, some of the important factors affecting language learning, have mostly been studied with reference to their relationship in terms of proficiency. This study investigated motivation and learning strategies and their relationship in order to find the inward predisposition of learners. Data was collected from 200 university students in Taejon and Chungnam province, Korea language learning strategies were measured by the Strategy Inventory for Language Learning(SILL), and motivation by the Attitude / Motivation Test Battery(AMTB), with adaptations for Koreans. The detailed analysis of the data Indicated that Korean university students were more motivated to learn English for a practical goal than a formal one. They had a strong willingness to learn but showed 'the tendency of the new generation' of choosing the easiest and most convenient ways in studying English in terms of motivational intensity and strategy use. Findings imply that there have to be some changes and improvements in the deep-rooted classroom teaching methods. A systematic device is needed to induce students to be autonomous learners, providing them with a variety of activities suitable for their purposes and levels, as in opportunities of contacting native speakers, multi-media language labs, the Internet etc.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.59-73
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• 2021

There frequently exists inadequacy regarding the number of boreholes installed along tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling geological characterization, they aim to detect specific object (e.g., water body and karst cave). There remains great motivation for the industry to develop a real-time identification technology relating complex geological conditions with the existing tunnelling parameters. This study explores the potential for the use of machine learning-based data driven approaches to identify the change in geology during tunnel excavation. Further, the feasibility for machine learning-based anomaly detection approaches to detect the development of clayey clogging is also assessed. The results of an application of the machine learning-based approaches to Xi'an Metro line 4 are presented in this paper where two tunnels buried in the water-rich sandy soils at depths of 12-14 m are excavated using a 6.288 m diameter EPB shield machine. A reasonable agreement with the measurements verifies their applicability towards widening the application horizon of machine learning-based approaches.

• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.