• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.125-138
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• 2022

Companies want organizational members who take e-learning courses to enjoy the advantages of transcending time and space that e-learning has, but also want what they have learned to help the organization, the work they perform, or their future careers. In addition, while enjoying the effect of reducing education costs compared to offline education through e-learning, it is expected that executives and employees will apply the knowledge and skills learned to the field and perform tasks to achieve results. As COVID-19 continues, many education programs that have been conducted offline at corporate sites have been converted to e-learning, with a larger number of e-learning operations than in the past. This study was conducted based on the perception that learners' learning satisfaction is important for the successful operation of e-learning education, and that learners' own self-directed learning ability and self-determination are important as well as corporate efforts. As a result of the study, hypotheses 1-1, 1-2, 1-3-1, and 1-3-2 that the better the self-determination (autonomy, competence, full-time support, and peer support) is, the higher the learning satisfaction will be. Both Hypothesis 2-1 and Hypothesis 2-2 were adopted that the better self-directed learning (subjectivity, execution ability) is, the higher the learning satisfaction will increase. In conclusion, it is necessary to properly introduce the concepts of self-determination and self-directed learning in corporate education while operating with the corporate education system.

• The Journal of Information Technology
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• v.5 no.4
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• pp.89-97
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• 2002

A web based courseware was designed and implemented for teaching number 0 to 5 to students of first grade in elementary school. The leveled learning program, according to the difference of learning abilities between learners, provides them with the contents and problems of leveled learning. This study shows the proper feedback to learners after considering their answers and provides them the effective learning situation where children learn without any restrictions of time and space. Therefore, the leveled and differentiated learning has been realized and this courseware enhanced students' interests in learning and desire for achievement. Also, it led them who can attend just for a short time to attend continuously and to participate eagerly in learning.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.4
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• pp.648-661
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• 2011

The purpose of this study was to examine the school parent and student's perception of Practical Arts and Technology Home Economics and to suggest plan to school parent of multicultural and learning disability families. The subjects of this study were elementary and secondary teachers who teaching Practical Arts or Technology Home Economics. The instrument of this study was questionnaire including five sections: influence on students by Practical Arts and Technology Home Economics, interest in Practical Arts and Technology Home Economics to school parents, school support with learning ability improvement, school parents support with home in Practical Arts and Technology Home Economics, data form of school parents support with home in Practical Arts and Technology Home Economics. The findings of this study were as follows; Sociality development of students is influential by Practical Arts and Technology Home Economics. School parents and students believe important subjects in Practical Arts and Technology Home Economics. Teachers ask for support with learning ability improvement for practical training space and programs in Practical Arts and Technology Home Economics. School parents ask for support with learning ability improvement of programs of teaching learning method for home education on Practical Arts and Technology Home Economics.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.29-44
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• 2024

This research investigates the effectiveness of data augmentation techniques in the automated analysis of B-scan images from ground-penetrating radar (GPR) using deep learning. In spite of the growing interest in automating GPR data analysis and advancements in deep learning for image classification and object detection, many deep learning-based GPR data analysis studies have been limited by the availability of large, diverse GPR datasets. Data augmentation techniques are widely used in deep learning to improve model performance. In this study, we applied four data augmentation techniques (geometric transformation, color-space transformation, noise injection, and applying kernel filter) to the GPR datasets obtained from a testbed. A deep learning model for GPR data analysis was developed using three models (Faster R-CNN ResNet, SSD ResNet, and EfficientDet) based on transfer learning. It was found that data augmentation significantly enhances model performance across all cases, with the mAP and AR for the Faster R-CNN ResNet model increasing by approximately 4%, achieving a maximum mAP (Intersection over Union = 0.5:1.0) of 87.5% and maximum AR of 90.5%. These results highlight the importance of data augmentation in improving the robustness and accuracy of deep learning models for GPR B-scan analysis. The enhanced detection capabilities achieved through these techniques contribute to more reliable subsurface investigations in geotechnical engineering.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Journal of The Korean Association of Information Education
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• v.9 no.2
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• pp.309-318
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• 2005

e-learning has been recently introduced in all educational domains and it has expanded rapidly in educational field. Blended learning, which has emerged with e-learning nowadays, is an exact example of a new paradigm. It has not only educational effects of traditional classroom learning, but it also has effects of e-learning which provides learner-centered classroom environment and removes barriers of time and space. This study looked into several e-learning contents evaluation criteria that were already studied, And arranged with the evaluation question item that can evaluate learning effectiveness of e-learning contents in elementary school through a questionnaire executed in elementary school teachers. And it used this evaluation question item and the study accomplishment results of an education ruler, and applied to learning effectiveness evaluation of e-learning contents. This paper will give future directions and assessment criteria of e-learning. Moreover, this thesis will provide theoretical and practical materials for developing e-learning contents to improve quality of blended learning.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.369-377
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• 2020

The purpose of this study is to find out the effect of e-learning quality and learner's usage motivation on e-learning satisfaction in Korea and China. In addition, by comparing and analyzing the factors influencing the satisfaction of learners between the two countries, this study aims to suggest the effective use of e-learning. This study surveyed Korean university students at Y and K universities in Gyeongsangbuk-do and Chinese university students at A university in Henan, China. As a result, for Korean university students, it is showed that learning time, learning space, learning process, usefulness, e-learning information quality, and service quality affect e-learning satisfaction. For Chinese university students, learning time, learning process and e-learning system quality, information quality, and service quality were found to affect e-learning satisfaction. Among them, service quality was an important factor influencing e-learning satisfaction in both countries, but the average score of each factor was very low. In the future, we discussed ways to improve service quality.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.215-222
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• 2019

The purpose of this study is to investigate the change of students' perceptions by comparing factors affecting e-learning intention before and after e-learning experience based on longitudinal data. For this purpose, a research study was conducted targeting university students at Y and K universities in Gyeongsangbuk-do. As a result, first, the convenience of learning time, the ease of learning process, and the usefulness of e-learning had an effect on e-learning intention regardless of the learning experience. Second, the convenience of the learning space was different in the perception before and after the learning experience, and it was found to affect the intention to use after the e-learning experience. Third, college students who have experienced e-learning have shown that the ease of learning process has the most influence on their intention to use. The results of this study can provide an effective direction of e-learning activation and provide basic data for longitudinal research on e-learning learners.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.60.2-60.2
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• 2021

In this study, we determinate the differential emission measure(DEM) of solar corona using three SDO/AIA EUV channel images and three AI-generated ones. To generate the AI-generated images, we apply a deep learning model based on multi-layer perceptrons by assuming that all pixels in solar EUV images are independent of one another. For the input data, we use three SDO/AIA EUV channels (171, 193, and 211). For the target data, we use other three SDO/AIA EUV channels (94, 131, and 335). We train the model using 358 pairs of SDO/AIA EUV images at every 00:00 UT in 2011. We use SDO/AIA pixels within 1.2 solar radii to consider not only the solar disk but also above the limb. We apply our model to several brightening patches and loops in SDO/AIA images for the determination of DEMs. Our main results from this study are as follows. First, our model successfully generates three solar EUV channel images using the other three channel images. Second, the noises in the AI-generated EUV channel images are greatly reduced compared to the original target ones. Third, the estimated DEMs using three SDO/AIA images and three AI-generated ones are similar to those using three SDO/AIA images and three stacked (50 frames) ones. These results imply that our deep learning model is able to analyze temperature response functions of SDO/AIA channel images, showing a sufficient possibility that AI-generated data can be used for multi-wavelength studies of various scientific fields. SDO: Solar Dynamics Observatory AIA: Atmospheric Imaging Assembly EUV: Extreme Ultra Violet DEM: Diffrential Emission Measure

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.