• Communications of Mathematical Education
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• v.35 no.3
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• pp.213-231
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• 2021

This study is intended to investigate contents related to parents' perception and satisfaction level of school mathematics curriculum. Based on the results, this study intended to deduce implications for mathematics education in schools, child education, and parent education. According to the result of the survey, the more positively the parents perceived the value of the mathematics learning, the more positively the child perceived, and the higher the parent's participation rate in mathematics-related education was. In terms of perception of teaching and learning activities, it showed that the willingness to participate in educational programs was lower for the parents of middle and high school students than the parents of elementary school students and the parents of elementary school students also showed higher satisfaction level of school mathematics curriculum. parents have perceived the necessity of teaching and mathematics education to develop artificial intelligence or data analysis skills. It was also found that the parents of middle and high school students' participation experience in education had an effect on the satisfaction level of their children's math teacher's class preparedness. Parents perceived positively to how pragmatic mathematics curriculum can be and provided answers to what they wish in specific mathematics classes in learning methods and future mathematics learning. As this is for educational experts to consider much in-depth in the future, this study suggested the need for diverse parents' education related to mathematics including the expansion of mathematics education with parents' participation, the creation of a mathematics learning environment for future mathematics learning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.279-284
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• 2022

As a study of the blended learning method on design education through the blended learning method, I would like to propose that more advanced learner-led customized design education is possible. Understanding in face-to-face classes and advantages in non-face-to-face classes can be supplemented in an appropriate way in remote classes. Advanced artificial intelligence and big data technology can provide personalized and subdivided learning materials and effective learning methods tailored to learners' levels and interests based on quantified data in design classes. In this paper, it was proposed to maximize the efficiency of the class by applying a method that exceeds the limitations of time and space through the proposal of the A La Carte model (A La Carte). It is a remote class that can be heard anytime, anywhere, and it is also possible to bridge the educational quality and educational gap provided to students living in underprivileged areas. As the goal of fostering creative convergence-type future talents, it is changing with a rapid technological development speed. It is necessary to adapt to the change in learning methods in line with this. An analysis of the infographic virtual space design and construction process through the A La Carte model (A La Carte) proposal was presented. Rather than simply acquiring knowledge, it is expected that knowledge can be sorted, distinguished, learned, and easily reborn with its own knowledge.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.71-79
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• 2024

ChatGPT, an interactive artificial intelligence (AI) chatbot developed by Open AI in the U.S., gaining popularity with great repercussions around the world. Some academia are concerned that ChatGPT can be used by students for plagiarism, but ChatGPT is also widely used in a positive direction, such as being used to write marketing phrases or website phrases. There is also an opinion that ChatGPT could be a new future for "search," and some analysts say that the focus should be on fostering rather than excessive regulation. This study analyzed consciousness about ChatGPT for college students through a survey of their perception of ChatGPT. And, plagiarism inspection systems were prepared to establish an education support model using ChatGPT and ChatGPT. Based on this, a university education support model using ChatGPT was constructed. The education model using ChatGPT established an education model based on text, digital, and art, and then composed of detailed strategies necessary for the era of the 4th industrial revolution below it. In addition, it was configured to guide students to use ChatGPT within the permitted range by using the ChatGPT detection function provided by the plagiarism inspection system, after the instructor of the class determined the allowable range of content generated by ChatGPT according to the learning goal. By linking and utilizing ChatGPT and the plagiarism inspection system in this way, it is expected to prevent situations in which ChatGPT's excellent ability is abused in education.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.9-16
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• 2020

The purpose of this study is to understand the public needs for AI education actively promoted and supported by the current government. In doing so, 11 metropolitan news articles and Twitter posts regarding AI education that have been posted from January 1, 2018 to December 31, 2019 were collected. Then, word frequency analysis using TF(Term Frequency) method and LDA(Latent Dirichlet Allocation) method of topic modeling analysis were conducted. The topics of the news articles turn out to be a macroscopic policy support such as 'training female manpower in the AI field' and 'curriculum reform of university and K-12', whereas the topics of twitter delineate more detailed social perception on future society, such as future competencies and pedagogical methods, including 'coexistence with intelligent robots', 'coding education', and 'humane education competence development'. The findings are expected to be used to suggest the implications for the composition and management of AI curriculum as well as the basic framework of human resources development in the future industry.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.243-250
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• 2020

In this paper, we propose an effective software education scheme for universities. The key idea of this software education scheme is to analyze software curriculum of QS world university rankings Top 10, SW-oriented university, and regional main national university. And based on the results, we propose five improvements for the effective SW education method of universities. The first is to enhance the adaptability of the industry by developing courses based on the SW developer's job analysis in the curriculum development process. Second, it is necessary to strengthen the curriculum of the 4th industrial revolution core technologies(cloud computing, big data, virtual/augmented reality, Internet of things, etc.) and integrate them with various fields such as medical, bio, sensor, human, and cognitive science. Third, programming language education should be included in software convergence course after basic syntax education to implement projects in various fields. In addition, the curriculum for developing system programming developers and back-end developers should be strengthened rather than application program developers. Fourth, it offers opportunities to participate in industrial projects by reinforcing courses such as capstone design and comprehensive design, which enables product-based self-directed learning. Fifth, it is necessary to develop university-specific curriculum based on local industry by reinforcing internship or industry-academic program that can acquire skills in local industry field.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.39-46
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• 2021

This study aims to empirically analyze the effect of CRM on the management performance of senior club market type senior job business. The purpose of this study was to provide basic data for the economic income creation of senior club market type senior job project. The results of this study are as follows: First, as a result of verifying the difference in management performance according to sociodemographic characteristics, there was a difference in age, academic background, and monthly average income. Second, the contact service and communication of senior club market type senior job business had a positive effect on the management performance. Based on the results of this study, the following suggestions are made. First, the database(DB) should be constructed reflecting the personal characteristics of consumers and the big data and artificial intelligence analysis should be utilized. Second, education using Internet environment such as YouTube and ZOOM should be strengthened and communication management should be strengthened based on information collected through customer monitoring.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Korean Journal of Applied Biomechanics
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• v.32 no.1
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• pp.1-8
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• 2022

Objective: This study proposes a methodology to analyze important variables that have a significant impact on the putting direction prediction using a machine learning-based putting direction prediction model trained with IMU sensor data. Method: Putting data were collected using an IMU sensor measuring 12 variables from 6 adult males in their 20s at K University who had no golf experience. The data was preprocessed so that it could be applied to machine learning, and a model was built using five machine learning algorithms. Finally, by comparing the performance of the built models, the model with the highest performance was selected as the proposed model, and then 12 variables of the IMU sensor were applied one by one to analyze important variables affecting the learning performance. Results: As a result of comparing the performance of five machine learning algorithms (K-NN, Naive Bayes, Decision Tree, Random Forest, and Light GBM), the prediction accuracy of the Light GBM-based prediction model was higher than that of other algorithms. Using the Light GBM algorithm, which had excellent performance, an experiment was performed to rank the importance of variables that affect the direction prediction of the model. Conclusion: Among the five machine learning algorithms, the algorithm that best predicts the putting direction was the Light GBM algorithm. When the model predicted the putting direction, the variable that had the greatest influence was the left-right inclination (Roll).

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.51-58
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• 2022

In this paper, we propose a decision tree-based machine learning model that leads to food exchange table renewal by classifying food groups through machine learning for existing food and food data found by web crawling. The food exchange table is the standard for food exchange intake when composing a diet such as diet and diet, as well as patients who need nutritional management. The food exchange table, which is the standard for the composition of the diet, takes a lot of manpower and time in the process of revision through the National Health and Nutrition Survey, making it difficult to quickly reflect food changes according to new foods or trends. Since the proposed technique classifies newly added foods based on the existing food group, it is possible to organize a rapid food exchange table reflecting the trend of food. As a result of classifying food into the proposed model in the study, the accuracy of the food group in the food exchange table was 97.45%, so this food classification model is expected to be highly utilized for the composition of a diet that suits your taste in hospitals and nursing homes.