• Journal of Technology Innovation
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• v.5 no.2
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• pp.22-47
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• 1997

This paper suggests a model of Living & Learning Nation as a new ploicy framework. It is a combination of Living Nation and Learning Nation. Living Nation model takes the nation as a living entity composed of spirit, resource, and communication : it grows but healthy and balanced growth is needed, its organs are closely connected, it has a circulation system, the 'spirit' factor plays the central role, etc.. Learning Nation model is a national level version of learning organization concept. The model defines new perspectives on the objectives, span of means, and the role of government in S&T policy. Therefore, the model can be used to give new insights to policymakers of developing countries facing the knowledge-based economy.

• Journal of Korean Elementary Science Education
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• v.25 no.spc5
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• pp.548-555
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• 2007

In this paper, we suggested that those elementary school students who are gifted in science should be taught basic and fundamental concepts to solve applied problems. We developed a teaching model based on a lesson regarding the path that light takes when passing through a lens on the base of refraction of light. We applied the teaching model to scientifically-gifted elementary school students and analyzed the results. The teaching model is based on the circulation loaming model appropriate for learning such concepts. The problems were designed and applied in order to determine the students' level of concept skills held and also to develop new teaching tools to help their understanding of concepts. As a result, we confirmed that the students, who were unable to describe the path of the light before the course of instruction was given, were able to draw and explain the path of light passing trough lens by using the law of refraction following the instruction.

• Korean Circulation Journal
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• v.53 no.10
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• pp.677-689
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• 2023

Background and Objectives: There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients. Methods: We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features. Results: Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance. Conclusions: Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.46-55
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• 1997

This paper presents the development of intelligent performance management of computer communication networks for larger-scale integrated systems and the demonstration of its efficacy using computer simula- tion. The innermost core of the performance management is based on fuzzy set theory. This fuzzy perfor- mance manager has learning ability by using principles of neuro-fuzzy model, neuralnetwork, genetic algo- rithm(GA). Two types of performance managers are described in this paper. One is the Neuro-Fuzzy Per- formance Manager(NFPM) of which learning ability is based on the conventional gradient method, and the other is GA-based Neuro-Fuzzy Performance Manager(GNFPM)with its learning ability based on a genetic algorithm. These performance managers have been evaluated via discrete event simulation of a computer network.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.260-266
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• 1996

In this paper, we presents neural network identification and control of highly complicated nonlinear Left Ventricular Assist Device(LVAD) system with a pneumatically driven mock circulation system. Generally the LVAD system need to compensate nonlinearities. Hence, it is necessary to apply high performance control techniques. Fortunately, the neural network can be applied to control of a nonlinear dynamic system by learning capability. In this study, we identify the LVAD system with Neural Network Identification. Once the NNI has learned the dynamic model of LVAD system, the other network, called Neural Network Controller(NNC), is designed for control of a LVAD system. The ability and effectiveness of identifying and controlling a LVAD system using the proposed algorithm will be demonstrated by computer simulation.

• Korean Medical Education Review
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• v.16 no.3
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• pp.126-131
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• 2014

The purpose of this study is to analyze the medical education system of Korea and to propose a method of curriculum redesign. Although there have been many attempts by medical educators to improve the quality of medical education, the results have not been fruitful. First, there exists a limitation to the dualistic curriculum design based on Flexnerianism, and thus, this model does not provide an integrated experience to medical students. Therefore, we propose a unidimensional model for curriculum redesign. Second, it is impossible to promote excellence in medical education without solving the structural problems of teaching and learning, such as the teaching competency of the faculty, large-scale lectures, and team teaching systems. A curricular strategy that emphasizes mutual interaction and teaching accountability is necessary to promote meaningful learning. Third, the current clinical training system, the circulation model, provides incomplete training as well as a lack of sequence and articulation experiences. This system needs to be redesigned in a way that allows only those students who have mastered both the knowledge and the application of medical education to advance to the next step. Fourth, norm-referenced assessments of a medical college distort the learning process and create unconstructive system energy. A criterion-referenced assessment that values cooperation, independent study, and intrinsic motivation is more important for the reliability and validity of the assessment. Medical students should not focus on formative and informative learning. Medical colleges should investigate the multifaceted potential of the students and provide transformative learning to grow students into change agents. For this to take place, curriculum redesign-not new methods of medical education-is required.

• Journal of the Korean Home Economics Association
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• v.47 no.3
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• pp.67-77
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• 2009

This study developed a model economic education program for young children related to Korean seasonal customs. The model was developed via literature reviews and survey research concerning teachers’' recognition of ‘'early childhood economic education’', consultations with professionals in the field, and testing and modifying the program. The goals of the model program were: teaching children to understand basic economic concepts, helping children to develop an economic attitude that emphasizes interdependence, and acknowledging the importance of eco-friendly economic values. The model includes three educational areas and 21 content areas including understanding the concept of exchange, sharing and cooperation, and circulation with nature. This study also developed 37 early childhood economic education activities using the teaching and learning methods of experiencing nature, virtual experience, real life experience, and traditional games experience.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.29 no.2
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• pp.101-115
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• 2024

The ocean heatwave is emerging as a major issue due to global warming, posing a direct threat to marine ecosystems and humanity through decreased food resources and reduced carbon absorption capacity of the oceans. Consequently, the prediction of ocean heatwaves in the vicinity of the Korean Peninsula is becoming increasingly important for marine environmental monitoring and management. In this study, an LSTM model was developed to improve the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system of the Korean Peninsula Ocean Prediction System. Based on the results of ocean heatwave predictions for the Korean Peninsula conducted in 2023, as well as those generated by the LSTM model, the performance of heatwave predictions in the East Sea, Yellow Sea, and South Sea areas surrounding the Korean Peninsula was evaluated. The LSTM model developed in this study significantly improved the prediction performance of sea surface temperatures during periods of temperature increase in all three regions. However, its effectiveness in improving prediction performance during periods of temperature decrease or before temperature rise initiation was limited. This demonstrates the potential of the LSTM model to address the underestimated prediction of ocean heatwaves caused by the coarse vertical grid system during periods of enhanced stratification. It is anticipated that the utility of data-driven artificial intelligence models will expand in the future to improve the prediction performance of dynamical models or even replace them.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.122-129
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• 2020

The circumference of the body is not only an indicator in order to buy clothes in our life but an important factor which can increase the effectiveness healing properly after figuring out the shape of body in a hospital. There are several measurement tools and methods so as to know this, however, it spends a lot of time because of the method measured by hand for accurate identification, compared to the modern advanced societies. Also, the current equipments for automatic body scanning are not easy to use due to their big volume or high price generally. In this papers, OpenPose model which is a deep learning-based Skeleton Tracking is used in order to solve the problems previous methods have and for ease of application. It was researched to find joints and an approximation by applying the data of the deep camera via reference data of the measurement parts provided by the hospitals and to develop a program which is able to measure the circumference of the body lighter and easier by utilizing the elliptical circumference formula.