• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.325-333
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• 2016

The development of smart technology has brought the conversion of closed traditional e-learning contents into open flexible smart learning contents consisting of learner-centered modules, without the constraints of time and space by use of smart devices from the uniformed and passive classroom between teachers and learners. It has been demanded an open, personalized and customized teaching and learning contents of smart education and training systems according to wide supply of various smart devices. In this paper, we discuss about the status of the smart teaching and learning systems and analyze the characteristics and structure of the web contents for smart education and training systems by use of smart devices. And we propose a method how to block web contents, to extract them, and adapt personalized segments of web contents by adaptive algorithm into smart learning devices. We extract blocks from the web contents based on the smart device information and the preference information of the learners from existing web contents without the hassle of learners environment. After specifying a block priority from the extracted web contents by the adaptive segment algorithm, it can be displayed directly to the screen to fit the individual learning progress of the learners.

• The Journal of Korean Physical Therapy
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• v.31 no.2
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• pp.88-93
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• 2019

Purpose: The aim of the study was to develop a Korean version of an ICF e-Learning tool (KICF e-Learning tool). Methods: The process of translation and adaptation of the ICF e-Learning tool was followed: two translators developed the Korean versions independently, and a consensus version of the translation was then produced. An expert committee, which was composed of five experts from physiotherapy, occupational therapy, speech pathology, and social welfare, reviewed the consensus Korean version to make a beta version of the tool. A field test was conducted to determine if the Korean version of the tool was easy to understand and suitable to use in ICF learning. Feedback from the field test were used for the final adaptation of the KICF e-Learning tool. Results: One-hundred and twenty-six volunteers (40 males and 76 females) were invited to examine the KICF e-Learning tool. The participants reported various levels of ICF knowledge from none to very good. Forty-eight participants reported no knowledge of ICF. The majority of participants (n=84) reported that Korean terms or expression in the tool were easy to understand and one-hundred fourteen participants would recommend the tool to another person. The Korean cases would be helpful for a Korean audience to study the ICF using the tool. Conclusion: The KICF e-Learning tool was developed and is ready for use by the public for the consistency of ICF education. On the other hand, development of an advanced module will be needed.

• Journal of information and communication convergence engineering
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• v.21 no.2
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• pp.139-144
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• 2023

Engineers prefer deep neural networks (DNNs) for solving computer vision problems. However, DNNs pose two major problems. First, neural networks require large amounts of well-labeled data for training. Second, the covariate shift problem is common in computer vision problems. Domain adaptation has been proposed to mitigate this problem. Recent work on adversarial-learning-based unsupervised domain adaptation (UDA) has explained transferability and enabled the model to learn robust features. Despite this advantage, current methods do not guarantee the distinguishability of the latent space unless they consider class-aware information of the target domain. Furthermore, source and target examples alone cannot efficiently extract domain-invariant features from the encoded spaces. To alleviate the problems of existing UDA methods, we propose the mixup regularization in adversarial discriminative domain adaptation (ADDA) method. We validated the effectiveness and generality of the proposed method by performing experiments under three adaptation scenarios: MNIST to USPS, SVHN to MNIST, and MNIST to MNIST-M.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.694-700
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• 2012

The purpose of this study is to analyze the factor giving effect to department selection, learning adaptation and attributional-style after entering school by selecting university students as targets and help high school students who will graduate soon and university students select department and direction. Those were analyzed by using 287 questionnaire data from June 1 to June 30, 2011. The study result revealed that 64.5% of students considered 'popularity and employment prospect' first when they select department. Generally, it was researched that when selecting department, 68.8% of women and 78.7% of health major considered 'popularity and employment prospect'(P<0.05, P<0.01). For learning adaptation and attribution trend of each major, health major showed that learning adaptation was high when motif was high and application score was high and for the relationship with attributional-style, health major showed higher internal attributional-style, showing significant difference(P<0.05). When synthesizing the results above, it is necessary to develop and use the program that can develop internal attribution trend of students on the basis of attributional-style. For planned and careful selection, it is necessary to perform synthetic consulting through direction search program that considers entrance period of middle school or high school, general affairs of university or direction guide to increase department or direction adaptation in the future.

• Journal of Convergence for Information Technology
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• v.10 no.5
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• pp.1-7
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• 2020

This paper proposes a link adaptation method for Underwater Internet of Things (IoT), which reduces power consumption of sensor nodes and improves the throughput of network in underwater IoT network. Adaptive Modulation and Coding (AMC) technique is one of link adaptation methods. AMC uses the strong correlation between Signal Noise Rate (SNR) and Bit Error Rate (BER), but it is difficult to apply in underwater IoT as it is. Therefore, we propose the machine learning based AMC technique for underwater environments. The proposed Modulation Coding and Scheme (MCS) prediction model predicts transmission method to achieve target BER value in underwater channel environment. It is realistically difficult to apply the predicted transmission method in real underwater communication in reality. Thus, this paper uses the high accuracy BER prediction model to measure the performance of MCS prediction model. Consequently, the proposed AMC technique confirmed the applicability of machine learning by increase the probability of communication success.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.469-479
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• 2020

This study was attemted to grasp the factors affecting the caring efficacy of senior nursing students. Data were collected from 173 nursing students at J university in G-do. Analysis was done using t-test, ANOVA, Pearson correlation coefficient, and Multiple regression with IBM SPSS WIN/25.0. Caring efficacy was positively correlated with clinical learning environment (r=.42, p<.001), field practice adaptation (r=.53, p<.001), nursing professionalism (r=.42, p<.001), and negatively correlated to clinical practice powerlessness (r=-.46, p<.001). The most influential factor on the subjects' caring efficacy was field practice adaptation (β=.330, p<.001), followed by nursing professionalism (β=.188, p=.005), clinical learning environment (β=.176, p=.015), introvert (β=-.146, p=.018), and extrovert (β=.134, p=.035). The explanatory power was 41.8%. Therefore, systematic nursing programs that can enhance caring efficacy are needed. Also, the following data can be utilized as basic data to help develop caring efficacy programs.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.306-314
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• 2000

In this paper we present a new approach to estimate link travel speed based on the hybrid neuro-fuzzy network. It combines the fuzzy ART algorithm for structure learning and the backpropagation algorithm for parameter adaptation. At first, the fuzzy ART algorithm partitions the input/output space using the training data set in order to construct initial neuro-fuzzy inference network. After the initial network topology is completed, a backpropagation learning scheme is applied to optimize parameters of fuzzy membership functions. An initial neuro-fuzzy network can be applicable to any other link where the probe car data are available. This can be realized by the network adaptation and add/modify module. In the network adaptation module, a CBR(Case-Based Reasoning) approach is used. Various experiments show that proposed methodology has better performance for estimating link travel speed comparing to the existing method.

• The Journal of Korean Academic Society of Nursing Education
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• v.14 no.1
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• pp.30-37
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• 2008

Purpose: This study proposed to examine the effects of a blended-learning orientation program executed for nursing students' clinical practice. Method: The participants were 61 nursing students in the experimental group and 57 in the control group. For the experimental group, a blended-learning orientation program was executed by e-learning (on-line) and lecture-led training (off-line) from two-week before the start of clinical practice in medical-surgical nursing. For the control group, orientation was given in the traditional lecture-led training by distributing printed materials before clinical practice. A pre-test was conducted on the experimental and control group before clinical practice, and a post-test was conducted after two-week of clinical practice in order to examine the effects of the orientation program. Results: After two-week of clinical practice, differences were observed between the experimental group and the control group in adaptation to clinical practice (F=10.242, p=.002), communication skills (F=4.305, p=.040) and clinical competence (F=6.823, p=.010). Conclusions: The blended-learning orientation program enhanced nursing students' adaptation to clinical practice, improved their communication skill and increased their clinical competence. Accordingly, it is recommended to develop and apply practical education using blended-learning in the area of nursing science.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.17-25
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• 2006

An intelligent game has been studied for some time, and the main purpose of the study was to win against human by enhancing game skills. But some commercial games rather focused on adaptation of the user's behavior in order to bring interests on the games. In this study, we are suggesting an adaptive reinforcement learning algorithm, which focuses on the adaptation of user behavior. We have designed and developed the Othello game, which provides large state spaces. The evaluation of the experiment was done by playing two reinforcement learning algorithms against Min-Max algorithm individually. And the results show that our approach is playing more improved learning rate, than the previous reinforcement learning algorithm.

• The Journal of the Korea Contents Association
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• v.13 no.8
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• pp.482-489
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• 2013

The purpose of this study was to identify the relationship of instructional design, perceived learning transfer, and satisfaction. The data were collected using questionnaire from the sample of 239 nursing students. The level of learning transfer was explained by introduction with learning context & providing guidance and initial attention. The level of learning transfer was explained by learning object with motivation, learning goal alignment, accessibility and feedback & adaptation. The level of program satisfaction was explained by introduction with learning context & providing guidance and initial attention. The level of program satisfaction was explained by learning object with motivation, presentation design, interaction availability, feedback & adaptation, learning goal alignment and contents quality. The findings serve as basic data to design e-Learning program to improve learning transfer and satisfaction.