• Journal of Creative Information Culture
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• v.7 no.2
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• pp.91-98
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• 2021

As the amount of time that elementary school students spend online increased due to Corona 19, the amount of posts, comments, and chats they write increased, and problems such as offending others' feelings or using swear words are occurring. Netiquette is being educated in elementary school, but training time is insufficient. In addition, it is difficult to expect changes in student behavior. So, technical support through natural language processing is needed. In this study, an experiment was conducted to automatically filter profanity sentences by applying them to a pre-trained language model on sentences written by elementary school students. In the experiment, chat details of elementary school 4-6 graders were collected on an online learning platform, and general sentences and profanity sentences were trained through a pre-learned language model. As a result of the experiment, as a result of classifying profanity sentences, it was analyzed that the precision was 75%. It has been shown that if the learning data is sufficiently supplemented, it can be sufficiently applied to the online platform used by elementary school students.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.891-897
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• 2022

In big data analysis, raw text data mostly exists in various unstructured data forms, so it becomes a structured data form that can be analyzed only after undergoing heuristic pre-processing and computer post-processing cleansing. Therefore, in this study, unnecessary elements are purified through pre-processing of the collected raw data in order to apply the wordcloud of R program, which is one of the text data analysis techniques, and stopwords are removed in the post-processing process. Then, a case study of wordcloud analysis was conducted, which calculates the frequency of occurrence of words and expresses words with high frequency as key issues. In this study, to improve the problems of the "nested stopword source code" method, which is the existing stopword processing method, using the word cloud technique of R, we propose the use of "general stopword corpus" and "user-defined stopword corpus" and conduct case analysis. The advantages and disadvantages of the proposed "unstructured data cleansing process model" are comparatively verified and presented, and the practical application of word cloud visualization analysis using the "proposed external corpus cleansing technique" is presented.

• Journal of the Korean Society of Food Culture
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• v.21 no.2
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• pp.187-192
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• 2006

This study is to investigate the recognition and preference of tofu food among general consumers and housewives in order to develope new tofu menu. The questionnaires are consisted of general questions, style of dining out, frequency of dining, health status, preference of tofu, reason for prefer tofu. A total of 262 questionnaires were analyzed for statistical analysis. The statistical analysis was completed using SAS program (Version 8.2) for descriptive analysis and ${\chi}^2\;-test$. Main results of this study were as follows: Most of the respondents prefer Korean food, 70% of the respondents are general consumers while 73.5% of the respondents are housewives. The frequency of dining out was 1-2 times per week. The two groups bought pre-cooked food one to two times per week. Fourity seven percent of the general consumers and 50% of housewives did not like the taste of tofu due to plain flavor. The respondents overall preferred many different ways to prepare tofu dishes. The results also indicated that tofu dishes are used as side-dishes. Thirty three percent of house wives had tofu with miso soup and pan-fried tofu, while 29.6% of the general consumers had soft tofu stew. 34% of the general consumers preferred stuffed tofu with shrimp, while 35.5% of the housewives liked it. 17% of the general consumers liked grilled tofu with crab meat sauce while only 14.5% of the housewives preferred the menu. Tofu teriyaki was preferred among 8.2% of the general consumers while 13.2% of the housewives liked tofu teriyaki.

• Korean Journal of Culture and Social Issue
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• v.18 no.3
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• pp.367-387
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• 2012

The purpose of the current study was to examine the effect of psychological factors on caregiver burden and depression among Korean spousal caregivers. 142 spousal caregivers (89 wives, 52 husbands) in Seoul and Incheon City were surveyed to determine the influence of objective factors of the care recipient, demographic of the caregiver, personality dimensions of extroversion and neuroticism of caregiver, social support, and pre-caregiving marital satisfaction on caregiver burden and depression of spousal caregivers. Hierarchical regression was used to determine the influence of the various factors on caregiver burden and depression. Finding suggest that care recipient's activities of daily living(ADL) and caregiver neuroticism predicted caregiver burden, whereas pre-caregiving martial satisfaction and caregiver neuroticism predicted depression. In particular, psychological factors were better predictors of caregiver burden and depression compared with objective factors. Based on the results, the implications, interventions, limitations and future directions for research were discussed about the psychological factors on spousal caregiving.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.833-841
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• 2023

The study is to suggest the example of convergence capstone design operation in department of early childhood education at a college and intellectual property rights application and registration. Based on key experiences such as practical training, students derived ideas for solving problems across the field related to young child, and overlaps with existing intellectual property rights ideas were verified. Linkage with industry and engineering experts was established for mentoring, after going through a refinement process, it contains the process by which five teams among the winning works of the school competition achieved the result of patent application and registration. Through this, we revitalize convergence capstone design education that goes beyond a creative and practical competency-centered curriculum and is linked to the performance of securing intellectual property rights.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.935-940
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• 2023

Most text data collected through web scraping for artificial intelligence and big data analysis is generally large and unstructured, so a purification process is required for big data analysis. The process becomes structured data that can be analyzed through a heuristic pre-processing refining step and a post-processing machine refining step. Therefore, in this study, in the post-processing machine refining process, the Korean dictionary and the stopword dictionary are used to extract vocabularies for frequency analysis for word cloud analysis. In this process, "user-defined stopwords" are used to efficiently remove stopwords that were not removed. We propose a methodology for applying the "thesaurus" and examine the pros and cons of the proposed refining method through a case analysis using the "user-defined stop word thesaurus" technique proposed to complement the problems of the existing "stop word dictionary" method with R's word cloud technique. We present comparative verification and suggest the effectiveness of practical application of the proposed methodology.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.453-458
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• 2024

Optical Character Recognition is the technology that recognizes text in images and converts them into digital format. Deep learning-based OCR is being used in many industries with large quantities of recorded data due to its high recognition performance. To improve medical services, deep learning-based OCR was actively introduced by the medical industry. In this paper, we discussed trends in OCR engines and medical OCR and provided a roadmap for development of medical OCR. By using natural language processing on detected text data, current medical OCR has improved its recognition performance. However, there are limits to the recognition performance, especially for non-standard handwriting and modified text. To develop advanced medical OCR, databaseization of medical data, image pre-processing, and natural language processing are necessary.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.121-128
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• 2024

With the recent rise in the use of AI-based online translation tools, interest in their methods and effects on education has grown. This study involved 30 prospective elementary school teachers who completed an English writing task using an AI-based online translation tool. The study focused on assessing the impact of these tools on English writing skills and their practical applications. It examined the usability, educational value, and the advantages and disadvantages of the AI translation tool. Through data collected via writing tests, surveys, and interviews, the study revealed that the use of translation tools positively affects English writing skills. From the learners' perspective, these tools were perceived to provide support and convenience for learning. However, there was also recognition of the need for educational strategies to effectively use these tools, alongside concerns about methods to enhance the completeness or accuracy of translations and the potential for over-reliance on the tools. The study concluded that for effective utilization of translation tools, the implementation of educational strategies and the role of the teacher are crucial.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.217-225
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• 2024

This study explores the use of animation as a tool for both English learning and recognizing social problems. In addition, this study examines how topic-centered learning paired with animation affects the acquisition of English vocabulary and expressions specific to discussing social problems. To achieve these goals, the study used two animations, Zootopia and Luca, and focused specifically on discrimination and prejudice. Conversation analysis, discussion activities, and learning of vocabulary and expressions in context were conducted. To evaluate the research, pre-tests, post-tests, a questionnaire, and thinking notes containing learners' opinions were used. Pre- and post-tests were administered to determine the extent of improvement in students' vocabulary and expression learning, and they reveal a statistically significant difference between the two tests. A questionnaire and thinking notes were analyzed in order to understand learners' responses and attitudes toward the class, and the results demonstrate an overall satisfaction with this class using animation topics (81.8%). The data highlights three reasons for this satisfaction: developing an in-depth understanding of movies, enhanced awareness of social problems, and increased engagement through the use of animations. These findings highlight the importance of conducting an in-depth analysis of the targeted topic when using animation.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.639-644
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• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.