• Journal of The Korean Association of Information Education
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• v.15 no.1
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• pp.137-146
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• 2011

The elementary school English Education Course consists of four integrated aspects of language (Listen, Speak, Read, Write) used to develop skills for daily communication in English. yet the 6th Grade English Education Curriculum focuses almost exclusively on sentence completion through copying and "fill-in-the-blank" exercises. Further, these activities are insufficient time to develop literacy skills. Additionally, the curriculum's emphasis on memorization within the written component is very time consuming for students, leading many to develop negative opinions of the written aspect of a comprehensive understanding of English. This thesis attempts to address each these problems through development of a web-based Learning Model for Cooperative Writing in English. The study resulted in three observations. First, this model overcame limitations of the current teaching model in schools. Second, students expressed more interest in the experimental model than in the current curriculum and standard pedagogical methods. Finally, the study demonstrated that improvement of English literacy is indeed possible using the model presented here.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.30-38
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• 2021

In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.789-795
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• 2020

Many deaths have been reported due to the worldwide pandemic of COVID-19. In order to prevent the further spread of COVID-19, it is necessary to quickly and accurately read images of suspected patients and take appropriate measures. To this end, this paper introduces a deep learning-based COVID-19 chest X-ray reading technique that can assist in image reading by providing medical staff whether a patient is infected. First of all, in order to learn the reading model, a sufficient dataset must be secured, but the currently provided COVID-19 open dataset does not have enough image data to ensure the accuracy of learning. Therefore, we solved the image data number imbalance problem that degrades AI learning performance by using a Stacked Generative Adversarial Network(StackGAN++). Next, the DenseNet-based classification model was trained using the augmented data set to develop the reading model. This classification model is a model for binary classification of normal chest X-ray and COVID-19 chest X-ray, and the performance of the model was evaluated using part of the actual image data as test data. Finally, the reliability of the model was secured by presenting the basis for judging the presence or absence of disease in the input image using Grad-CAM, one of the explainable artificial intelligence called XAI.

• Communications of Mathematical Education
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• v.37 no.1
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• pp.21-45
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• 2023

This study presents a machine learning program designed to identify impactful papers in the field of mathematics education. To achieve this objective, we examined the impact of papers from a scientific econometrics perspective, developed a mathematics education research network, and defined the impact of mathematics education research using PageRank, a network centrality index. We developed a machine learning model to determine the impact of mathematics education research and identified the journals with the highest percentage of impactful articles to be the Journal for Research in Mathematics Education (25.66%), Educational Studies in Mathematics (22.12%), Zentralblatt für Didaktik der Mathematik (8.46%), Journal of Mathematics Teacher Education (5.8%), and Journal of Mathematical Behaviour (5.51%). The results of the machine learning program were similar to the findings of previous studies that were read and evaluated qualitatively by experts in mathematics education. Significantly, the AI-assisted impact evaluation of mathematics education research, which typically requires significant human resources and time, was carried out efficiently in this study.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.2
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• pp.305-312
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• 2020

The Privacy Act stipulates that the privacy policy document, which is a privacy statement, should be disclosed in order to guarantee the rights of the information subjects, and the Fair Trade Commission considers the privacy policy as a condition and conducts an unfair review of the terms and conditions under the Terms and Conditions Control Act. However, the information subjects tend not to read personal information because it is complicated and difficult to understand. Simple and legible information processing policies will increase the probability of participating in online transactions, contributing to the increase in corporate sales and resolving the problem of information asymmetry between operators and information entities. In this study, complex personal information processing policies are analyzed using deep learning, and models are presented for acquiring simplified personal information processing policies that are highly readable by the information subjects. To present the model, the personal information processing policies of 258 domestic companies were established as data sets and analyzed using deep learning technology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.572-573
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• 2021

By optimizing the development of IoT-type thermal image-based photovoltaic fault detection equipment and interworking with drones using a drone with an intelligent path movement function, real-time analysis of the acquired image data facilitates fault reading of solar power plants. , design a system that can read out the failure of a solar panel using the image subtraction analysis technique and the presentation of the basic technology that can improve the power generation rate of the solar power plant and make an efficient maintenance model.

• Journal of The Korean Association For Science Education
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• v.32 no.8
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• pp.1281-1294
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• 2012

This paper describes the types of models that biology majors use and how they go about making their models in learning key concepts in biology such as the cell membrane, cytoskeleton and cell structure. Initially, a total of 44 biology students from all year levels enrolled in the second semester of calendar year 2008-2009 were asked to make their respective models of the cell membrane, cytoskeleton and cell structure. They were also asked to answer an open-ended questionnaire. Of the 44, only 20 (five from each year level) were randomly selected for a one-on-one interview. Results showed that the student-generated models from all year levels were mostly analogies, some textbook definitions and occasional drawings. In making their model, students first read the text; second, outline similarities in structure and function or both; and third, make the model. Data suggest that models are good diagnostic tools for identifying critical thinking skills of students. In this case, students mostly demonstrate the ability to recognize similarities in structure and function between the concept and their model. Some senior students demonstrated integration and reflective thinking in making their models. Thus, more opportunities for student-generated models must be available if students were to develop integration and reflective thinking in their models.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.145-156
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• 2022

Children are the foundation and future of this society and understanding their impressions and behaviors is very important and the child's behavioral problems are a burden on the family and society as well as have a bad impact on the development of the child, and the early diagnosis of these problems helps to solve or mitigate them, and in this research project we aim to understand and know the behaviors of children, through artificial intelligence algorithms that helped solve many complex problems in an automated system, By using this technique to read and analyze the behaviors and feelings of the child by reading the features of the child's face, the movement of the child's body, the method of the child's session and nervous emotions, and by analyzing these factors we can predict the feelings and behaviors of children from grief, tension, happiness and anger as well as determine whether this child has the autism spectrum or not. The scarcity of studies and the privacy of data and its scarcity on these behaviors and feelings limited researchers in the process of analysis and training to the model presented in a set of images, videos and audio recordings that can be connected, this model results in understanding the feelings of children and their behaviors and helps doctors and specialists to understand and know these behaviors and feelings.

• Journal of The Korean Association of Information Education
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• v.14 no.1
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• pp.1-12
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• 2010

For hearing-impaired students, it is known that reading ability is the most important means of communication. In the meanwhile, with recent development of wireless communication technologies, mobile devices are used in various education fields. The purpose of this study is to design and implement a mobile system to improve reading ability of hearing-impaired students. For this purpose, "Question Generation Strategy", known as one of the effective methods for improving reading ability, is adopted to make study contents. Also, 2Bi (Bilingual-Bicultural) Approach Model, an attractive model for improving reading ability of hearing-impaired students, is used. Characteristics of the proposed mobile system are as follows. First, the system is developed to let students learn written language usage through repetition and difference of two organically-related curriculums for hearing-impaired students. Second, study contents are made to increase sentence understanding ability using an activity that is to let students read articles, make questions and answer questions for themselves. Third, the proposed system is designed and implemented to allow students to choose study contents individually anytime anywhere depending on their study levels.