• Asia-Pacific Journal of Business
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• v.3 no.1
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• pp.1-9
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• 2012

The Index of Learning Styles (ILS) instrument based on the Felder-Silverman Learning Style Model was used to determine distribution of learning styles of 125 South Korean business students enrolled in a South Korean institution of higher education. Results show that greater proportion of South Korean business students surveyed in this study prefer sensing over intuitive, visual over verbal, reflective over active, and global over sequential learning styles. The majority of business students have a balanced learning style in all four dimensions of the Felder-Silverman model. Among the students that do not have a balanced learning style, students with sensing, visual, reflective, and global learning styles dominate. Gender difference in learning style preference was not statistically significant for any of the four dimensions.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.42.1-42.1
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• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of Gifted/Talented Education
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• v.21 no.1
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• pp.19-38
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• 2011

This study aims to apply a teaching-learning model using science oriented UCC created by students themselves(4C model) in a gifted students' project learning and investigate its effects in science-related affective property by gender in order to use UCC systematically in a gifted students' project learning course. After conducting of gifted students' using UCC project learning, We surveyed gifted students' recognition about it. Nonparametric test has done with the results of before and after science-related affective property tests of them. As results, gifted students took an active part in 'using UCC project learning'. Girls are more active and interested than boys whereas more boys felt a difficulty in creating UCC than girls. Girls' creativity was improved and anxiety about science was decreased whereas the results of boys were not statistically significant. Therefore, 'using UCC project learning' is more effective than tha of boys to girls.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.306-312
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• 2008

In a goal-oriented dialogue, speaker's intention can be approximated by a semantic structure that consists of a pair of a speech act and a concept sequence. Therefore, it is very important to correctly identify the semantic structure of an utterance for implementing an intelligent dialogue system. In this paper, we propose a model to efficiently analyze the semantic structures based on an active teaming method. To reduce the burdens of high-level linguistic analysis, the proposed model only uses morphological features and previous semantic structures as input features. To improve the precisions of semantic structure analysis, the proposed model adopts CRFs(Conditional Random Fields), which show high performances in natural language processing, as an underlying statistical model. In the experiments in a schedule arrangement domain, we found that the proposed model shows similar performances(92.4% in speech act analysis and 89.8% in concept sequence analysis) to the previous models although it uses about a third of training data.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2265-2271
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• 1994

With the improvement of standard of living, requirement for comfortable and quiet environment has been increased and, therefore, there has been a many researches for active noise reduction to overcome the limit of passive control method. In this study, active noise control is performed in a duct system using intelligent control technique which needs not decide the coefficients of high order filter and the mathematical modeling of a system. Back propagation algorithm is applied as an intelligent control technique and control system is organized to exclude the error microphone and high speed operational device which are indispensable for conventional active noise control techniques. Furthermore, learning is performed by organizing acoustic feedback model, and the effect of the proposed control technique is verified via computer simulation and experiment of active noise control in a duct system.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.705-712
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• 2013

Active learning aims to improve the performance of a classification model by repeating the process to select the most helpful unlabeled data and include it to the training set through labelling by expert. In this paper, we propose a method for active learning based on hierarchical agglomerative clustering using Ward's linkage. The proposed method is able to construct a training set actively so as to include at least one sample from each cluster and also to reflect the total data distribution by expanding the existing training set. While most of existing active learning methods assume that an initial training set is given, the proposed method is applicable in both cases when an initial training data is given or not given. Experimental results show the superiority of the proposed method.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.32 no.3
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• pp.186-201
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• 2019

Objectives : The aim of this study is to develop clinical practice program using reflective journals in the department of Korean medicine ophthalomology & otolaryngology & dermatology. Methods : It was applied to clinical practice and considered the adequacy of the clinical practice program using reflective journal for students who complete the clinical practice. Result : Students are given high marks for self-directed learning and Korean medicine ophththalomology & otolaryngology & dermatology professional learning.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.247-253
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• 2021

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.2
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• pp.83-93
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• 2024

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

• Journal of The Korean Association of Information Education
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• v.21 no.5
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• pp.487-495
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• 2017

The flipped learning is an inverted teaching model where students learn the basic concepts using short videos at home and then come to class to enable effective practice and interactions among teachers and students. However, due to the students' lack of self-regulated competence, most students have difficulties of comprehending the instructional materials out of class by themselves. In this paper, we develop an interactive app for active flipped learning in the mathematics courses in the elementary schools. We examine the effectiveness of the active flipped learning on learners groups with different achievement levels in learning 4th grade mathematics concepts in the elementary schools. The pretest and posttest survey results show that the proposed flipped learning approach has better performance compared to the traditional flipped learning approach.