• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.147-153
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• 1995

In this paper, we propose the position- and size-independent handwritten on-line Korean character recognition system using multilayer neural networks which are trained with error back-propagation learning algorithm and the features of Hanguel consonants and vowels. Starting point, end point, and three vectors from starting point to end point of each stroke of characters inputted from mouse or tablet are applied as inputs of neural networks. If double consonants and vowels are separated by single consonants and vowels, all consonants and vowels have at most four strokes. Therefore, four neural networks learn the consonants and the vowels having each number of strokes. Also, we propose the algorithm of separating the consonants and vowels and constructing a character.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.1-3
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• 2022

최근 기존의 영상 압축 파이프라인 대신 신경망의 종단 간 학습을 통해 압축을 수행하는 알고리즘의 연구가 활발히 진행되고 있다. 본 논문은 종단 간 학습 기반 공간적 스케일러블 압축 기술을 제안한다. 보다 구체적으로 본 논문은 신경망의 각 계층에서 하위 계층의 학습된 특징 (feature)을 융합하여 상위 계층으로 전달하는 다중 스케일 특징 융합 (multi-scale feature fusion) 모듈을 도입해 상위 계층이 더욱 풍부한 특징 정보를 학습하고 계층 사이의 특징 중복성을 더욱 잘 제거할 수 있도록 한다. 기존 방법 대비 향상 계층(enhancement layer)에서 1.37%의 BD-rate가 향상된 결과를 볼 수 있다.

• Journal of Korean language education
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• v.29 no.4
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• pp.65-92
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• 2018

This study explored how a particular social media can be used to supplement elementary-level Korean language course in the US public university. The researchers administered a survey measuring students' patterns and habits of social media use. Based on the survey results, researchers designed six different types of learning materials and uploaded them regularly to Instagram throughout the semester. At the end of the semester, a survey was conducted to find out students' satisfactory level. From the 44 students' responses, the study found out that using Instagram 1) is more accessible to students than any other learning management system, 2) is fun and students are willing to participate, 3) increased the target language exposure and authentic language use, 4) increased interaction between teachers, students and even other native speakers, and 5) is helpful to improve listening and other language skills. The study closes with the suggestion for further experimental studies.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.9-16
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• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Information Systems Review
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• v.6 no.2
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• pp.161-179
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• 2004

The purpose of this study is to suggest practical experience to develop small sized e-Learning system. The system is designed to help lecturers can arrange interface, organize contents, submit examinations and assess learner's score with no professional computing skills. The system has three advantages. First, it reduced implementation period through the use of GUI. Second, it is ordered to be personalized to construct format of the whole interface. Third, it has operational convenience which can be implemented on PC based system. These personalized features are enabling Learning on Demand. Also, there is comparatively low cost and high effectiveness on e-Learning implementation which facilitating quick adoption of e-Learning in its lectures.

• Research in Mathematical Education
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• v.11 no.1
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• pp.75-85
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• 2007

This paper analyzes the difficulties with the learning of sequence and series of the second-year students who participated in a year long whole class at the university level. The research was carried out at the end of students' third semester. These students were randomly selected. They were applied to one paper and pencil test containing eight task items on sequence and series. In this study, qualitative method (case study design) was used to explore students' difficulties and misunderstandings in learning sequence and series. Students' responses to the questions were divided into three categories: These were "correct", "partial correct" and "false or no responses". Students' responses to the paper and pencil test were evaluated. The results show that students had difficulties and misunderstandings in series and sequence.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.77-81
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• 2020

Recently we have lots of Youtube contents and their influence. But Just a few Studies have announced Youtube content's effect. The purpose of this paper is to see if ASMR content, which is popular through Youtube recently, helps teenagers stabilize their minds and improve their learning abilities. To that end, a survey of teenagers found that ASMR content is very familiar to teenagers, and that 66.7 percent of teenagers use ASMR content for sleep and learning. About the change before and after watching, half of the respondents said they felt a positive difference in learning and psychological stability. As a result, ASMR is a significant content for teenagers with a specific purpose. Therefore, policies such as 'after-school' in terms of school education are proposed as alternatives rather than unilateral measures such as banning ASMR content to teenagers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.674-677
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• 2020

The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.197-207
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• 2022

The applications and user interfaces (UIs) of smart mobile devices are constantly diversifying. For example, deep learning can be an innovative solution to classify widgets in screen images for increasing convenience. To this end, the present research leverages captured images and the ReDraw dataset to write deep learning datasets for image classification purposes. First, as the validation for datasets using ResNet50 and EfficientNet, the experiments show that the dataset composed in this study is helpful for classification according to a widget's functionality. An implementation for widget detection and classification on RetinaNet and EfficientNet is then executed. Finally, the research suggests the Widg-C and Widg-D datasets-a deep learning dataset for identifying the widgets of smart devices-and implementing them for use with representative convolutional neural network models.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.190-195
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• 2024

This study aimed to investigate types of emotional experiences that college students undergo, particularly those affecting learning motivation and academic performance. To this end, six college students residing in Seoul, South Korea participated in a series of 'focus-group interview (FGI)' sessions in which in-depths discussions took place. The researcher attempted to draw the participant students' opinions and ideas as they made interactions with each other. Three participants were placed in each of two groups, and each group had approximately 90-minutes-long sessions. The results showed that positive emotions, such as joy and enthusiasm, can increase learning motivation and academic achievement, while negative emotions such as anxiety and stress can hinder them. The findings also highlight that students actively employ coping strategies to manage negative emotions. Moreover, the study underscores students' desire for improved emotional support from instructors, indicating a gap between their expectations and the actual emotional care provided in educational settings. Relevant issues are discussed for future suggestions.