• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1527-1534
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• 2016

Online voltage stability monitoring using real-time measurements is one of the most important tasks in a smart grid system to maintain the grid stability. Loading margin is a good indicator for assessing the voltage stability level. This paper presents an Extreme Learning Machine (ELM) approach for estimation of voltage stability level under credible contingencies using real-time measurements from Phasor Measurement Units (PMUs). PMUs enable a much higher data sampling rate and provide synchronized measurements of real-time phasors of voltages and currents. Depth First (DF) algorithm is used for optimally placing the PMUs. To make the ELM approach applicable for a large scale power system problem, Mutual information (MI)-based feature selection is proposed to achieve the dimensionality reduction. MI-based feature selection reduces the number of network input features which reduces the network training time and improves the generalization capability. Voltage magnitudes and phase angles received from PMUs are fed as inputs to the ELM model. IEEE 30-bus test system is considered for demonstrating the effectiveness of the proposed methodology for estimating the voltage stability level under various loading conditions considering single line contingencies. Simulation results validate the suitability of the technique for fast and accurate online voltage stability assessment using PMU data.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.3
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• pp.344-355
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, Multilayer Perceptron (MLP) has been used in wide applications. But, it is known that Error Backpropagation (EBP) algorithm which MLP uses in learning has a defect that requires relatively long leaning time. Because learning data in pattern recognition contain abundant redundancies, in order to increase learning speed it is very effective to use online-based teaming methods, which update parameters of MLP pattern by pattern. Typical online EBP algorithm applies fixed learning rate for each update of parameters. Though a large amount of speedup with online EBP can be obtained by choosing an appropriate fixed rate, fixing the rate leads to the problem that the algorithm cannot respond effectively to different leaning phases as the phases change and the learning pattern areas vary. To solve this problem, this paper defines learning as three phases and proposes a Instant Learning by Varying Rate and Skipping (ILVRS) method to reflect only necessary patterns when learning phases change. The basic concept of ILVRS is as follows. To discriminate and use necessary patterns which change as learning proceeds, (1) ILVRS uses a variable learning rate which is an error calculated from each pattern and is suppressed within a proper range, and (2) ILVRS bypasses unnecessary patterns in loaming phases. In this paper, an experimentation is conducted for speaker verification as an application of pattern recognition, and the results are presented to verify the performance of ILVRS.

• Journal of Korean Academy of Nursing Administration
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• v.20 no.5
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• pp.567-575
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• 2014

Purpose: This study was performed to investigate the effects of a blended learning program on ethical values in undergraduate nursing students. Methods: This study was a one group pretest-posttest design. Seventy-one undergraduate nursing students who were taking a nursing ethics course at K University in S city were conveniently selected. The blended learning program was undertaken for 120 minutes one day weekly for 15 weeks. It consisted of case-based learning through an online method combined with problem-based learning offline. Scores for ethical value were measured using the ethical values scale. Results: The ethical values score increased significantly in the students after the blended learning (p=.004). Of the subgroup of ethical values human-life, relationship with collaborator, and nursing job scores increased significantly in students after the blended learning, respectively (p=.034; p<.001; p<.001), the score for area as relationship with nursing clients decreased significantly in the students after the blended learning (p<.001). Conclusion: The blended learning program was identified as an educational program which induces a positive effect on the development of ethical values in undergraduate nursing students, and in future it can be utilized in nursing ethics education.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.473-478
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• 2022

Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.437-447
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• 2010

The purpose of this thesis was to design and develop an effective e-PBL(Problem-Based Learning) for cultivating consciousness of information and communication ethics. The proposed e-PBL is based on PBL which is one of the constructivism teaching-learning theories. Online learning and face-to-face classes were systematically combined for achieving the teaching-learning goals. And the main module for online learning run on Moodle, an open source learning management system. To examine educational effectiveness of the proposed e-PBL, an experimental study was conducted through the education content and method to the subject of two class in the second-grade of university located in OO city. For experiment 60 students(treatment group=30, control group=30) are participated. And they were randomly assigned to one of ten subgroups, comprising of six students, respectively. The results of this study showed that the education using proposed e-PBL is more effective in cultivating consciousness of information and communication ethics and learners responded positively than the education using traditional face-to-face PBL learning method.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.382-395
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• 2017

Learning computational thinking is very important in programming education. Computational thinking refers to the problem solving ability based on the theories of computer science, indicating the importance of algorithm thinking. That is the reason for focusing on promoting creativity and improving the problem solving ability of the students in programming education. This paper commented the elements to consider for teachers when teaching computational thinking to elementary school students with online coding education website 'code.org' that helps beginners have easy programming experiences based on the characteristics of the website, and proposed the appropriate teaching methods.

• Human Ecology Research
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• v.61 no.1
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• pp.15-28
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• 2023

The aim of this study was to develop, implement, and evaluate the use of Artificial Intelligence in the prevention of violence among middle-school students. The sample for this study consisted of 20 first-year middle-school students who participated in theme selection activities in a free semester program as part of their home economics studies. The data for the study consisted of nine class observation logs, four group activity outputs, 30 class results, an online survey, and in-depth interviews with three students. A program called "R.U.OK" was developed by setting problematic situation for school violence prevention linked to the contents of the Home Economics Education(HEE) curriculum. After the program was implemented, the survey on the students' class satisfaction content elements, with AI-based learning activities and PBL and interest, displayed high points, with an average of 4.0 or higher. Our qualitative analysis produced four significant results. First, students' concerns about school violence had increased and they showed a change in attitude, having more empathy with friends and more interest in their surroundings. Second, digital and AI literacy had improved, and students' interest in digital media learning had increased. Third, there had been an improvement in problem-solving ability in terms of being able to think more critically and independently. Fourth, the results also demonstrated that there had been a positive effect on self-direction and an improved capacity for teamwork. This study was significant in demonstrating the effectiveness of a program for the prevention of school violence based on the use of digital technology in the educational environment.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.421-437
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• 2020

Problem/Project based learning(PBL) is a student-centered teaching method in which students collaboratively solve problems and reflect their experiences. According to the results of PBL study and the experiences of the authors in PBL instruction, this paper introduced the necessities, output and significance of learning process PBL evaluation method and sums up our PBL evaluation process. The issue of appropriate and fair evaluation has been raised in untact (non-contact) university mathematics education due to the novel coronavirus (COVID-19) of the year 2020. To this end, when we had the course on for the summer semester held at S University in the summer of 2020. To ensure the fairness in evaluation and to improve the quality of our college math education, the PBL evaluation method was fully adapted. As a result, most of the students who took the lecture have learned a wide range of related knowledge without a single exception, and students agreed it is an ideal, fair, rational, and effective evaluation method applicable to other online courses in the era of untact education. This case was summarized in detail and introduced in this paper.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.55-62
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• 2020

The purpose of this study was to review nursing ethics education program for nursing students in Korea. An integrative literature review was applied as a research method, and the study was conducted according to five steps of problem identification, literature search, data evaluation, data analysis, and presentation. Twelve studies were analyzed, and the educational content was about biomedical ethics and nursing ethics, and most of them were through subject classes. Teaching methods included case-based debates, discussions, action learning, online learning, and problem-based learning, including traditional lectures. Through education programs, there was a significant increase in biomedical ethics, ethical values, moral judgment, and moral sensitivity. Progressive and continuous nursing ethics education for nursing college students is required within the curriculum.

• Smart Media Journal
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• v.12 no.2
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• pp.36-41
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• 2023

With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.