• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.139-146
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• 2022

Recently, solving industrial and social problems and creating new values based on big data and AI is being discussed as the main policy goal. The new government also set the digital platform government as a national task in order to achieve new value creation based on big data and AI. However, studies that summarize and diagnose discussions over the past five years are insufficient. Therefore, this study diagnoses the discussions over the past 5 years using the 4th industrial revolution as a keyword. After collecting news editorials from 2017 to 2022 by applying the text mining technique, 9 major topics were discovered. In conclusion, this study provided implications for the government's task to prepare for the future society.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• KNOM Review
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• v.22 no.3
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• pp.1-12
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• 2019

Recently, researching using big data and AI has emerged as a major issue in the ICT field. But, the size of big data for research is growing exponentially. In addition, users of data transmission of existing network method suggest that the problem the time taken to send and receive big data is slower than the time to copy and send the hard disk. Accordingly, researchers require dynamic and flexible network technology that can transmit data at high speed and accommodate various network structures. SDN/NFV technologies can be programming a network to provide a network suitable for the needs of users. It can easily solve the network's flexibility and security problems. Also, the problem with performing AI is that centralized data processing cannot guarantee real-time, and network delay occur when traffic increases. In order to solve this problem, the edge-computing technology, should be used which has moved away from the centralized method. In this paper, we investigate the concept and research trend of SDN, NFV, and edge-computing technologies, and analyze the trends of data central network technologies used by combining these three technologies.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.245-252
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• 2023

In this study, we analyzed elementary school teachers' perception and method of establishing a new subject according to their computing competency. First, we developed a survey on the need to establish a new elementary school informatics. We also collected data from 166 elementary school teachers. As a result of the analysis, opinions differed on the establishment of information subjects depending on teachers' computing competencies. Teachers also showed differences in the characteristics of their subjects, number of class hours, and methods of organizing classes. As the results, we found that in order to establish a new information subject, a plan must be prepared to raise awareness and the need for informatics subjects among teachers who have low computing compency or no major in computer-related fields. We hope that many elementary school teachers will recognize the necessity and importance of establishing a new information subject.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.17-27
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• 2024

Video frame interpolation is an important technique used in the field of video and media, as it increases the continuity of motion and enables smooth playback of videos. In the study of video frame interpolation using deep learning, Kernel Based Method captures local changes well, but has limitations in handling global changes. In this paper, we propose a new U-Net structure that applies feature map differentiation and two directions to focus on capturing major changes to generate intermediate frames more accurately while reducing the number of parameters. Experimental results show that the proposed structure outperforms the existing model by up to 0.3 in PSNR with about 61% fewer parameters on common datasets such as Vimeo, Middle-burry, and a new YouTube dataset. Code is available at https://github.com/Go-MinSeong/SF-AdaCoF.

• Convergence Security Journal
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• v.24 no.3
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• pp.33-39
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• 2024

The remarkable advancement of artificial intelligence technology is bringing innovative changes to the field of education. In particular, generative AI models like ChatGPT hold great potential in self-directed programming education due to their natural conversational abilities. This study analyzed the learning effects of using ChatGPT in Scratch classes for non-SW majors. Dividing the classes into those using ChatGPT and those not, and conducting the same evaluations and surveys for the ChatGPT-utilizing group, the results showed that ChatGPT significantly enhanced learning outcomes and the utility of ChatGPT was highly evaluated in advanced learning areas such as understanding Scratch's advanced features and algorithms. This study is significant as it empirically demonstrates the potential of generative AI like ChatGPT as an effective tool in programming education.

• Journal of Nutrition and Health
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• v.41 no.1
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• pp.100-110
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• 2008

The present study estimates intake levels of dietary fiber (DF) in Korean adults aged 20 and over, using a newly established dietary fiber database for 3,149 food items, as well as 24-hour recall method data from the 2001 Korean National Health and Nutrition Survey. Dietary fiber intake and food groups are analyzed by gender, age, and region. The average dietary fiber intake, per capita of Korea was estimated to be 12.24g/1,000kcal or 23.58g/day. Calorie-based dietary fiber intake for 20-49 year-old-Korean males, 20-29 year-old females, and the adults who resided in metropolitan areas was under the Adequate Intake for DF, 12 g/1,000 kcal. Further, the dietary fiber intake after adjusting energy intake in people over 75 year-old was estimated to be 75% of AI. Vegetables, cereals and fruits were three major sources of DF for Korean, making up approximately 75% of DF. Regarding the subjects of this study, major sources of dietary fiber were Kimchi and well-polished rice, which supplied 13.98% and 9.16% of total dietary fiber intake, respectively. The result of this study could contribute to the establishment of DRIs for dietary fiber, after adjusting energy intake for Korean aged 75 years and over. The beneficial health effects of DF and the necessity of nutritional education in this area should be continuously emphasized concerning 20-29 year-old people and metropolitan adults.

• Korean Journal of Food Science and Technology
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• v.34 no.5
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• pp.893-897
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• 2002

The chloroform layer from 75% ethanol extract of Dalbergia odorifera T.CHEN showed strong antioxidative activity on lard and palm oil as tested by Rancimat method. Antioxidative active compound isolated and identified by silica gel column chromatography, thin layer chromatography, mass spectrophotometer, $^1H-NMR$ and $^{13}C-NMR$ was identified as mucronulatol (3(R&S)-3,7-Dihydroxy-2',4-dimethoxyisoflavan). Results of Rancimat method revealed the induction period of Mucronulatol increased longer than those of synthetic antioxidant, BHA and BHT, at the same concentration. Mucronulatol combined with ${\delta}-tocopherol(200ppm)$, and with ascorbic acid (200 ppm) and citric acid (200 ppm) on lard and palm oil, respectively, showed strong synergistic effects.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.675-687
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• 2019

As interest in artificial intelligence increases, each university has been establishing a special graduate school with artificial intelligence major, and recently, the Korea government has established various support policies for artificial intelligence education. However, each university has a lot of difficulties because it has little experience in operating graduate education with the latest field of artificial intelligence and it is not easy to find experts. In this study, the response of graduate school students majoring in artificial intelligence was analyzed using IPA technique, and the direction of education of graduate school artificial intelligence major was searched. Among the 40 items surveyed by IPA, 12 items such as systematization of artificial intelligence curriculum, progress of class considering learning level, improvement of academic relations with guidance professors were extracted as items to be improved first. On the other hand, 8 items such as assistant capacity, and relationship with colleagues were overloaded, and twelve items such as instructor's lecture competency, appropriateness of educational contents, learner's artificial intelligence skills and knowledge, and attitude acquisition were to be maintained. In addition, eight items such as convergence education curriculum and diversity of education methods were all low in importance and performance. It is suggested that AI graduate school should be divided into two tracks(technical specialization, convergence expansion) by educational goal, and each track should be conducted by level-specific educational contents and methods suitable for student level. The curriculum should be elaborate and systematic to acquire AI knowledge, skills, and attitudes, and should have an individualized guidance system centered on excellent faculty members.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1922-1929
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• 2020

Objective: The present study aimed to survey seasonal changes in reproductive performance of local cows receiving artificial insemination (AI) in the Pursat province of Cambodia, a tropical country, to investigate if ambient conditions affect the reproductive performance of cows as to better understand the major problems regarding cattle production. Methods: The number of cows receiving AI, resultant number of calving, and calving rate were analyzed for those receiving the first AI from 2016 to 2017. The year was divided into three seasons: cool/dry (from November to February), hot/dry (from March to June), and wet (from July to October), based on the maximal temperature and rainfall in Pursat, to analyze the relationship between ambient conditions and the reproductive performance of cows. Body condition scores (BCS) and feeding schemes were also analyzed in these seasons. Results: The number of cows receiving AI was significantly higher in the cool/dry season than the wet season. The number of calving and calving rate were significantly higher in cows receiving AI in the cool/dry season compared with the hot/dry and wet seasons. The cows showed higher BCSs in the cool/dry season compared to the hot/dry and wet seasons probably due to the seasonal changes in the feeding schemes: these cows grazed on wild grasses in the cool/dry season but fed with a limited amount of grasses and straw in the hot/dry and wet seasons. Conclusion: The present study suggests that the low number of cows receiving AI, low number of calving, and low calving rate could be mainly due to poor body condition as a result of the poor feeding schemes during the hot/dry and wet seasons. The improvement of body condition by the refinement of feeding schemes may contribute to an increase in the reproductive performance in cows during the hot/dry and wet seasons in Cambodia.