• Journal of Digital Policy
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• v.3 no.1
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• pp.11-20
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• 2024

The intelligent information society is expected to drastically change our lives. The purpose of this content is to derive tasks in the field of media education for the elderly for the realization of digital inclusion in an intelligent information society. To this end, the vision, goals, strategies, and tasks of the intelligent information society were examined through the 6th National Informatization Basic Plan(2018~2022) and the 2022 Education Informatization White Paper(2022). In addition, the current status of the digital gap among the elderly classified as vulnerable groups was identified through the results of the 2022 Digital Information Gap Survey. In order to ease the digital information gap between the elderly in the intelligent information society, it is believed that the development of intelligent media education services using intelligent information technology, provision of media education services for the elderly through learning online service channels, and support for digital intelligent media education for the elderly are necessary.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.165-170
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• 2024

This study aims to verify the accuracy of the air quality management system in Yangju City using an artificial intelligence (AI) evaluation model. The consistency and reliability of fine dust data were assessed by comparing public data from the Ministry of Environment with data from Yangju City's air quality management system. To this end, we analyzed the completeness, uniqueness, validity, consistency, accuracy, and integrity of the data. Exploratory statistical analysis was employed to compare data consistency. The results of the AI-based data quality index evaluation revealed no statistically significant differences between the two datasets. Among AI-based algorithms, the random forest model demonstrated the highest predictive accuracy, with its performance evaluated through ROC curves and AUC. Notably, the random forest model was identified as a valuable tool for optimizing the air quality management system. This study confirms that the reliability and suitability of fine dust data can be effectively assessed using AI-based model performance evaluation, contributing to the advancement of air quality management strategies.

• Geophysics and Geophysical Exploration
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• v.27 no.3
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• pp.171-180
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• 2024

The processing of seismic data involves analyzing earthquake wave data to understand the internal structure and characteristics of the Earth, which requires high computational power. Recently, machine learning (ML) techniques have been introduced to address these challenges and have been utilized in various tasks such as noise reduction and velocity model construction. However, most studies have focused on specific seismic data processing tasks, limiting the full utilization of similar features and structures inherent in the datasets. In this study, we compared the efficacy of using receiver-wise time-series data ("receiver array") and synchronized receiver signals ("time array") from shotgathers for pretraining a Bidirectional Encoder Representations from Transformers (BERT) model. To this end, shotgather data generated from a synthetic model containing faults was used to perform noise reduction, velocity prediction, and fault detection tasks. In the task of random noise reduction, both the receiver and time arrays showed good performance. However, for tasks requiring the identification of spatial distributions, such as velocity estimation and fault detection, the results from the time array were superior.

• Journal of Yeungnam Medical Science
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• v.41 no.3
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• pp.213-219
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• 2024

Background: Aortic valve replacement (AVR) has recently been performed at many centers using a minimally invasive approach to reduce postoperative mortality, morbidity, and pain. Most previous reports on minimally invasive AVR (MiAVR) have mainly focused on aortic stenosis, and those exclusively dealing with aortic regurgitation (AR) are few. The purpose of this study was to investigate early surgical results and review our experience with patients with chronic severe AR who underwent AVR via right anterior mini-thoracotomy (RAT). Methods: Data were retrospectively collected in this single-center study. Eight patients who underwent RAT AVR between January 2020 and January 2024 were enrolled. Short-term outcomes, including the length of hospital stay, in-hospital mortality, postoperative complications, and echocardiographic data, were analyzed. Results: No in-hospital mortalities were observed. Postoperative atrial fibrillation occurred temporarily in three patients (37.5%). However, none required permanent pacemaker implantation or renal replacement therapy. The median values of ventilator time, length of intensive care unit stay, and hospital stay were 17 hours, 34.5 hours, and 9 days, respectively. Preoperative and postoperative measurements of left ventricular ejection fraction were similar. However, the left ventricular end systolic and diastolic diameters significantly decreased postoperatively from 42 mm to 35.5 mm (p=0.018) and 63 mm to 51 mm (p=0.012), respectively. Conclusion: MiAVR via RAT is a safe and reproducible procedure with acceptable morbidity and complication rates in patients with chronic severe AR. Despite some limitations such as a narrow surgical field and demanding learning curve, MiAVR is a competent method for AR.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.67-83
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• 2018

The Korean film industry with significant increase every year exceeded the number of cumulative audiences of 200 million people in 2013 finally. However, starting from 2015 the Korean film industry entered a period of low growth and experienced a negative growth after all in 2016. To overcome such difficulty, stakeholders like production company, distribution company, multiplex have attempted to maximize the market returns using strategies of predicting change of market and of responding to such market change immediately. Since a film is classified as one of experiential products, it is not easy to predict a box office record and the initial number of audiences before the film is released. And also, the number of audiences fluctuates with a variety of factors after the film is released. So, the production company and distribution company try to be guaranteed the number of screens at the opining time of a newly released by multiplex chains. However, the multiplex chains tend to open the screening schedule during only a week and then determine the number of screening of the forthcoming week based on the box office record and the evaluation of audiences. Many previous researches have conducted to deal with the prediction of box office records of films. In the early stage, the researches attempted to identify factors affecting the box office record. And nowadays, many studies have tried to apply various analytic techniques to the factors identified previously in order to improve the accuracy of prediction and to explain the effect of each factor instead of identifying new factors affecting the box office record. However, most of previous researches have limitations in that they used the total number of audiences from the opening to the end as a target variable, and this makes it difficult to predict and respond to the demand of market which changes dynamically. Therefore, the purpose of this study is to predict the weekly number of audiences of a newly released film so that the stakeholder can flexibly and elastically respond to the change of the number of audiences in the film. To that end, we considered the factors used in the previous studies affecting box office and developed new factors not used in previous studies such as the order of opening of movies, dynamics of sales. Along with the comprehensive factors, we used the machine learning method such as Random Forest, Multi Layer Perception, Support Vector Machine, and Naive Bays, to predict the number of cumulative visitors from the first week after a film release to the third week. At the point of the first and the second week, we predicted the cumulative number of visitors of the forthcoming week for a released film. And at the point of the third week, we predict the total number of visitors of the film. In addition, we predicted the total number of cumulative visitors also at the point of the both first week and second week using the same factors. As a result, we found the accuracy of predicting the number of visitors at the forthcoming week was higher than that of predicting the total number of them in all of three weeks, and also the accuracy of the Random Forest was the highest among the machine learning methods we used. This study has implications in that this study 1) considered various factors comprehensively which affect the box office record and merely addressed by other previous researches such as the weekly rating of audiences after release, the weekly rank of the film after release, and the weekly sales share after release, and 2) tried to predict and respond to the demand of market which changes dynamically by suggesting models which predicts the weekly number of audiences of newly released films so that the stakeholders can flexibly and elastically respond to the change of the number of audiences in the film.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.531-538
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• 2023

Recently, many university classes have been changing from instructor-centered classes to learner-centered classes, and universities are trying to establish a new direction for university education, especially to foster talented people suitable for the Fourth Industrial Revolution. To this end, universities are presenting various competencies necessary for students and focusing on research on efficient education plans for each competency. Among them, creativity is considered the most important competency that students should obtain in universities. Developing a creative problem-solving-based subject where various majors gather to produce results while conducting creative team activities away from desk classes is considered a meaningful subject to cultivate capacities suitable for the requirements of the times. Therefore, this study purpose to develop creative problem-solving-based subjects and analyze the results of class progress. This creative problem-solving-based class is an Action Learning class for step-by-step idea development, which starts with a theoretical lecture for creative idea development and then consists of five stages of Action Learning. The tasks of action learning used in this class consisted of ceramic expression to increase the intimacy of the formed group and the group's collective expression, ideas in life to combine and compress individual ideas into one, environmental improvement programs around schools, and finally UCC on various topics. In the theoretical lecture conducted throughout the class, a class was conducted on Scientific Thinking for creative problem solving, and then a group-type action learning class was conducted sequentially. This Action Learnin process gradually increased the difficulty level and led to in-depth learning by increasing the level of difficulty step by step.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.19-26
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• 2018

In an effort to investigate the effects of chatbots on English listening and reading skills, 46 college students participated in the current study. Participants consisted of first-year students who enrolled in an English class at a university in South Korea. They were randomly divided into two groups: one experimental group (n=24) and one control group (n=22). During 16 weeks, the experimental group engaged in chats with a chatbot, named Elbot, while the control group did not. There were pre- and post-tests to confirm the effects of the chatbot usage. Major findings are as follows: First, participants in both groups significantly improved listening and reading skills. On the post-listening test, however, the experimental group showed more improvements. Their listening proficiency level improved from intermediate to advanced level after engaging in chat with the chatbot. Limitations and implications for theory and practice are discussed at the end.

• Korean Journal of Cognitive Science
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• v.27 no.2
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• pp.221-245
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• 2016

Peer assessment refers to having students, rather than the instructor, make assessments of one another's work. Peer assessment is often used as a tool to train writing skills or a tool to apply or extend learning in higher education. Park(2016) recently proposed a system which utilizes peer assessment as a part of preparatory activity for college courses. Before weekly class, students studied given material on their own, wrote a one page essay on a given question based on their reading, and assessed the essays of other students. In this study, the system was implemented in undergraduate courses at S University over 2 semesters and the results were analyzed. The reliability of weekly scores given by students was not very high, but the correlation was high between the cumulative scores given by students across weeks and the scores of the end of the term paper assessed by the instructor. Based on these findings, the possibility of utilizing the results of the peer assessments as part of the final grades was discussed.

• KIEAE Journal
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• v.11 no.6
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• pp.53-62
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• 2011

This study aimed at suggesting improvement of lighting environment of the coffee house using study place. To this end, a series of field investigation was conducted in four possible target coffee houses around university. The field measurement included measurement of general illuminance and tabletop illuminance, observing illumination condition, and status of artificial lighting. Also, on-site questionnaire survey was administrated to 80 users of field measurement targets about using characteristics of coffee house and user's subjective response on light environment. The results are summarized as follows: (1) According to questionnaire survey, most of users checked 'learning (study and reading)' in 'purpose of coffee house using', and 'slightly dark' was checked most in each subjective response (brightness on general space and on tabletop at daytime/night); (2) as results of measurements on general illuminance and on tabletop illuminance during daytime, only one coffee house was suitable for standard; (3) as results of measurements on illuminance during night, all target coffee houses were not met the standard; (4) as results of uniformity ratios, almost uniformities of general illuminance were not met the standard except one case. The common problems of lighting environment of coffee house were analyzed as lack of daylight illumination e.g. having low amount of sunshine from skylight, un-uniformity of insolation by floor plan and absence of window blind, and un-uniformity of artificial luminous intensity e.g. lack of the number or brightness of artificial lighting, using the indirect lighting, using only local lighting, and non-uniform arrangement of artificial lighting.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.607-612
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• 2017

The use of RaspberryPi in building an embedded system may be difficult for users in understanding the circuit and the hardware cost. This paper proposes a solution that can test the systems virtually. The solution consists of three elements; (i) editor, (ii) interpreter and (iii) simulator and provides nine full modules and also allows the users to configure/run/test their own circuits like real environment. The task of abstraction for modules through the actual circuit test was carried out on the basis of the data sheet and the specification provided by the manufacturer. If we can improve the level of quality of our solution, it can be useful in terms of cost reduction and easy learning. To achieve this end, the electrical physics engine, the level of interpreter that can be ported to the actual board, and a generalization of the simulation logic are required.