• Proceedings of the Korea Technology Innovation Society Conference
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• 2012.05a
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• pp.207-225
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• 2012

한국이 탈추격에 진입하였으며 이에 대응한 새로운 기술혁신전략이 필요하다고 제기되고 있는데, 본 연구에서는 이를 산업연관표에 기반한 산업별 생산방식 변화 등의 분석을 통하여 확인하며, 산업별 인력 구성의 변화를 중심으로 새로운 기술혁신전략을 모색하였다. 인력양성과 활용의 측면을 중심으로 한 학습과정(leaning process)이 R&D 등 혁신노력과 성공적으로 조응될 때 기대한 혁신성과가 실현될 수 있음을, 28산업분류 수준에서 대학 전공의 일에서의 유용성과 R&D투입간 조응성을 통하여 분석하고, 이러한 사항이 국가수준의 혁신정책에 통합되어야 함을 제시하였다.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.261-267
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• 2022

This study is a descriptive investigation study attempted to identify the factors influencing the self-regulated learning ability of the 4th grade students of the Department of Nursing who are preparing for the national nurse exam. Data were collected for two weeks from mid-November 2021 and 107 survey results were analyzed using the SPSS/WIN 21.0 program. As a result, ego-resilience (r=.376, p<.001), self-efficacy (r=.590, p<.001), self-esteem (r=.495, p<.001) was found to have a positive correlation with self-regulated learning ability, respectively. These factors significantly affected self-regulated learning ability(adj. R²=.411, F=25.684, p<.001). The results of this study can be used as basic data for developing programs and new teaching methods for nursing college students preparing for the national examination.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.375-383
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• 2020

The study was to examine the mediating effect of self-efficacy in learning in the relationship between the learning satisfaction and motivation to transfer of nursing students who received the psychiatric nursing convergence simulation practicum using standardized patients. Participants were 144 third grade nursing students. Data were analyzed descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficient analysis, and multiple regression following the Baron and Kenny's method and Sobel test for mediation. There were significant correlations between learning satisfaction and self-efficacy in learning(r=.686, p<.001), learning satisfaction and motivation to transfer(r=.633, p<.001) and self-efficacy in learning and motivation to transfer(r=.804, p<.001). Self-efficacy in learning showed partial mediating effects in the relationship between learning satisfaction and motivation to transfer(Z=7.63, p<.001). To increase the motivation to transfer, strategies to enhance the self-efficacy of nursing students are required.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.557-562
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• 2020

In the non-face-to-face learning environment forced into effect by the COVID-19 pandemic, online learning is being adopted as a major educational technique. Given the lack of research on how online learning patterns affect academic performance, this study focuses on the number and duration of online video learning sessions as a major factor based on midterm and final exams, and with a formative assessment for each type of learning. The correlation of the learning effects was analyzed. The analysis focused on computer programming subjects, which are among the most difficult liberal arts subjects for arts and science students at the university level. The analysis of cases of actual students showed no correlation among weekly formative assessments, the number of learning sessions, and the learning duration. On the other hand, the number of learning sessions (r=.39 p<0.05) and learning duration (r=.42 p<0.05) were correlated with the midterm and final exams. Elements, such as SMS text, bulletin board, and e-mail, were excluded from the analysis because not all students have access to them. Therefore, the results can be improved if future analysis of the students' learning patterns in a non-face-to-face lecture environment is performed considering more factors/elements and the learners' needs.

• Journal of Korean Public Health Nursing
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• v.26 no.3
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• pp.465-477
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• 2012

Purpose: The purpose of this study was to investigate the influence of breakfast characteristics of university students on their self-regulated learning, attentional control, and fatigue in order to provide-basic data for establishing desirable eating habits, self-regulated learning skills using attentional control, and advisable learning habits of university students. Method: The level of fatigue was estimated using the Visual analogue scale (VAS) and Critical flicker frequency (CFF). Attentional control was measured using the Attentional Control Questionnaire (ACQ) adapted by Yoon. Self-regulated learning was surveyed by the Self-Regulated Learning Test developed by Chung. Data from atotal of 142 university students were collected from November 30 to December 9, 2011. Result: 69% of the subjects skipped their breakfast. Attentional control has a negative correlation with fatigue (r=-.179, p=.033) and a positive correlation with self-regulated learning (r=.352, p<.001). The multiple regression model of self-regulated learning consists of attentional control (t=3.218, p=.002), commuting time (t=-3.076, p=.003), understanding the importance of breakfast (t=-2.413, p=.008), and skipping breakfast(t=-2.195, p=.030) and its R-square was 21.8%. Conclusion: Learning efficiency of university students should be improved by means of attentional control, which is related to self-regulated learning. Also, it is essential for university students to establish healthy lifestyles including regular eating habits and attentional control, in order to improve their self-regulated learning.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.281-290
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• 2020

The Vision-and-Language Navigation(VLN) task is a complex intelligence problem that requires both visual and language comprehension skills. In this paper, we propose a new learning model for visual-language navigation agents. The model adopts a hybrid learning that combines imitation learning based on demo data and reinforcement learning based on action reward. Therefore, this model can meet both problems of imitation learning that can be biased to the demo data and reinforcement learning with relatively low data efficiency. In addition, the proposed model uses a novel path-based reward function designed to solve the problem of existing goal-based reward functions. In this paper, we demonstrate the high performance of the proposed model through various experiments using both Matterport3D simulation environment and R2R benchmark dataset.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.332-340
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• 2019

This descriptive research study was undertaken to identify the degree of learning motivation, transfer climate, learning self-efficacy, and transfer motivation, and to correlate the variables, in nursing students receiving simulation-based education. The subjects of this study were 4th grade nursing students who completed a simulation course at a nursing university; data collected using the self-report questionnaire were analyzed using the SPSS 21.0 program. Our results indicate high values of learning motivation, transfer climate (including the lower variables supervisor's support, peer's support, and transfer opportunity), learning self-efficacy, and transfer motivation. Learning motivation, learning self-efficacy, and transfer motivation significantly differed with respect to social motivation for entering school (Z=6.04, p=0.049; Z=6.92, p=0.031; Z=9.16, p=0.010, respectively) and major satisfaction (Z=8.55, p=0.036; Z=12.55, p=0.006; Z=13.47, p=0.004, respectively). All these variables were positively correlated, especially transfer motivation with learning motivation, supervisor's support, peer's support, transfer opportunity, and learning self-efficacy. Taken together, the results of this study indicate a need to develop an effective simulation-based education program to encourage transfer motivation, as well as follow-up studies that verify the causal relationship between transfer motivation and related variables.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.572-578
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• 2008

We analyze the search space considered by the previously proposed R-CORE method for learning Bayesian network structures of large scale. Experimental analysis on the search space of the method is also shown. The R-CORE method reduces the search space considered for Bayesian network structures by recursively clustering the random variables and restricting the orders between clusters. We show the R-CORE method has a similar search space with the previous method in the worst case but has a much less search space in the average case. By considering much less search space in the average case, the R-CORE method shows less tendency of overfitting in learning Bayesian network structures compared to the previous method.