• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.489-499
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• 2021

This paper proposes an arts and culture content creation tool powered by artificial intelligence. With the recent advances in technologies including artificial intelligence, there are active research activities on creating art and culture contents. However, it is still difficult and cumbersome for those who are not familiar with programming and artificial intelligence. In order to deal with the content creation with new technologies, we analyze related creation tools, services and technologies that process with raw visual and audio data, generate new media contents and visualize intermediate results. We then extract key requirements for a future creation tool for creators who are not familiar with programming and artificial intelligence. We finally introduce an intuitive and integrated content creation tool for end-users. We hope that this tool will allow creators to intuitively and creatively generate new media arts and culture contents based on not only understanding given data but also adopting new technologies.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.51-62
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• 2021

This study developed the factors capable of systematic/comprehensive evaluation of the task performance in order to strengthen the performance management of the professional career personnel invitation program (PCPIP). To this end, a performance evaluation framework was developed by analyzing existing project evaluation studies based on boundary theory and Kirkpatrick's four-level evaluation model. Afterwords, through two Delphi surveys, evaluation factors that can measure performance in terms of individual and invitation institutions of PCP were derived and validated. With this procedure, five evaluation factors were finally selected: adaptability, connectivity, clarity, compatibility, and expandability. This study has implications suggesting a performance evaluation factors capable of hybrid quantitative/qualitative evaluation for the performance management of PCPIP operated by National Research Foundation of Korea Research since 1994.

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.384-390
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• 2020

The reverberation effect on the sound when producing movies or VR contents is a very important factor in the realism and liveliness. The reverberation time depending the space is recommended in a standard called RT60(Reverberation Time 60 dB). In this paper, we propose a scene recognition technique for automatic reverberation editing. To this end, we devised a classification model that independently trains color images and predicted depth images in the same model. Indoor scene classification is limited only by training color information because of the similarity of internal structure. Deep learning based depth information extraction technology is used to use spatial depth information. Based on RT60, 10 scene classes were constructed and model training and evaluation were conducted. Finally, the proposed SCR + DNet (Scene Classification for Reverb + Depth Net) classifier achieves higher performance than conventional CNN classifiers with 92.4% accuracy.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.284-290
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• 2021

This study aimed to identify the efficiency of online non-face-to-face English classes to propose possibilities for expanding these types of classes in a sustainable way even in the post-COVID era. Moreover, the study suggests pedagogical implications for the directions that should be further explored for online non-face-to-face English classes. To this end, the study employed an online non-face-to-face English reading class using Zoom and investigated the effects of online teaching on college students' reading comprehension via two achievement tests. The study also analyzed learners' satisfaction or dissatisfaction with this online non-face-to-face English reading class (and their reasons) through a questionnaire. Ultimately, the study found that online non-face-to-face English reading classes have a positive effect on learners' reading comprehension learning. In addition, the reasons for learners' satisfaction with online non-face-to-face classes include systematic class progress, class quality, and efficiency of learning. Instructors must be aware of the need to expand online non-face-to-face classes, for which they will have to be thoroughly prepared in advance. Instructors will also need to implement efficient online class activities, organize classes systematically with detailed explanations, and provide quick and useful feedback.

• Journal of Korean Elementary Science Education
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• v.41 no.3
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• pp.521-537
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• 2022

State curiosity and state anxiety in the science learning have a great influence on academic performance and achievement. Since the levels of state curiosity and anxiety can change at any moment, it is essential to identify the levels of student's state curiosity and state anxiety throughout the course of science learning. Accordingly, we assessed the changes in state curiosity and anxiety levels sensed by 5th- and 6th-grade elementary school students depending on their exposure to the violation of expectation. To this end, we classified science learning into three situations: confronting a scientific task, checking the result, and learning science concepts. As a result, there was no significant difference in state curiosity level of the nVOE group who confronting the result consistent with their expectations in checking the result after confronting a scientific task, but the state curiosity level of the VOE group who facing violation of their expectation increased. In the VOE groups, there was no significant change in the state curiosity level of the VOE-R group who correctly inferred the reason for the result, but that of the VOE-FR group who could not correctly inferred increased. The state anxiety levels of the VOE and nVOE groups decreased after checking the result of the task. The state anxiety level also declined in the VOE-R group. In contrast, there was no significant change in state anxiety level of the VOE-FR group. In learning science concepts of the result after checking the result, the state curiosity of the VOE, nVOE, and VOE-FR group all faded. No significant change was observed in the state anxiety level of the nVOE group, whereas the VOE, VOE-R, and VOE-FR group presented a decreased state anxiety. This study discusses the educational implication of these findings and its outcomes are expected to broaden the understanding of emotional states of students in science learning.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.169-181
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• 2023

This study was conducted to examine the remuneration curriculum of vocational ability development training teachers and instructors and to examine ways to apply micro credentials. To this end, the current status of the remuneration curriculum of vocational ability development training instructors and instructors at K University's Competency Education Development Institute, the characteristics of micro credentials, and the possibility of linking the remuneration curriculum to micro credentials are as follows. First, most of the recognition of digital certificates was positive for digital certificates such as digital credit, digital badge issuance, and recognition of the recognized qualification process of maintenance education when completing the training course. In addition, as a method of applying micro credentials to conservative education, various cases were proposed to benefit from conservative education, systematization and grading of the qualification process, and credit of the qualification process. Second, as an institutional supplement to enhance the utilization of conservative education using micro credentials, the need to expand NCS-based major conservative education, provide efficient learning contents and learning methods, and set minimum completion time. In addition, the most common response as a way to improve the understanding of teachers and instructors in vocational ability development training was the micro credential promotion plan. Third, in the role of conservative education institutions and vocational ability development training instructors and instructors, conservative education institutions mention maintaining educational quality the most, and active participation was the role of vocational ability development training instructors. Through this study, it is expected to establish a vocational training environment that can enhance expertise and provide a practical portfolio of practical competency history by linking the remuneration curriculum of vocational competency development training instructors and micro credentials.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.3
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• pp.38-47
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• 2020

This study analyzed the perceptions of teachers to establish the direction for managing the space outside an elementary school. Satisfaction with outdoor school spaces is influenced by the satisfaction with tree and flower plantation and outdoor rest spaces. This study found that the longer the working years of a teacher, the higher their awareness of the importance and necessity of outdoor spaces in the school. Respondents emphasized the lack of manpower and budget, as well as the indifference of the administration as hindrances to the management of outdoor spaces in the school. The outdoor space in the school should include a secure play area, plant education space, class practice spaces, and a rest area. Furthermore, the space outside the elementary school should support learning, playing, and resting. To this end, facilities such as benches, pergolas, outdoor classrooms, ecological ponds, farms, and flower beds should be provided. In an outdoor space, plants featured in textbooks, seasonal plants, and those that provide shade can be planted along with labels to provide information and thereby promote learning. The teachers expected that the management of the external space will have an educational and emotional effect on students. In response to the innovation of the school spaces, it is necessary to continuously manage the external spaces to achieve educational and emotional effects by organically connecting the outdoor spaces with the indoor space. For this purpose, it is required to provide support for securing budgets and manpower, and to introduce relevant policies.

• Journal of Industrial Convergence
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• v.20 no.5
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• pp.83-92
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• 2022

As cases of social and ethical problems caused by artificial intelligence technology have occurred, artificial intelligence ethics are drawing attention along with social interest in the risks and side effects of artificial intelligence. Artificial intelligence ethics should not just be known and felt, but should be actionable and practiced. Therefore, this study proposes an artificial intelligence ethics education model to strengthen the practical ability of artificial intelligence ethics. The artificial intelligence ethics education model derived educational goals and problem-solving processes using artificial intelligence through existing research analysis, applied teaching and learning methods to strengthen practical skills, and compared and analyzed the existing artificial intelligence education model. The artificial intelligence ethics education model proposed in this paper aims to cultivate computing thinking skills and strengthen the practical ability of artificial intelligence ethics. To this end, the problem-solving process using artificial intelligence was presented in six stages, and artificial intelligence ethical factors reflecting the characteristics of artificial intelligence were derived and applied to the problem-solving process. In addition, it was designed to unconsciously check the ethical standards of artificial intelligence through preand post-evaluation of artificial intelligence ethics and apply learner-centered education and learning methods to make learners' ethical practices a habit. The artificial intelligence ethics education model developed through this study is expected to be artificial intelligence education that leads to practice by developing computing thinking skills.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.