• Journal of Korea Game Society
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• v.21 no.3
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• pp.13-24
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• 2021

This paper shows the face rotation applicable to game character facial illustration. Existing studies limited data to human face data, required a large amount of data, and the synthesized results were not good. In this paper, the following method was introduced to solve the existing problems of existing studies. First, a 3D model with features of the input image was rotated and then rendered as a 2D image to construct a data set. Second, by designing GAN that can learn features of various poses from the data built through the 3D model, the input image can be synthesized at a desired pose. This paper presents the results of synthesizing the game character face illustration. From the synthesized result, it can be confirmed that the proposed method works well.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.538-547
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• 2022

Recently introduced image classification methods using Transformers show remarkable performance improvements over conventional neural network-based methods. In order to effectively consider regional features, research has been actively conducted on how to apply transformers by dividing image areas into multiple window areas, but learning of inter-window relationships is still insufficient. In this paper, to overcome this problem, we propose a transformer structure that can reflect the relationship between windows in learning. The proposed method computes the importance of each window region through compression and a fully connected layer based on self-attention operations for each window region. The calculated importance is scaled to each window area as a learned weight of the relationship between the window areas to re-calibrate the feature value. Experimental results show that the proposed method can effectively improve the performance of existing transformer-based methods.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.385-391
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• 2021

In this paper, a case of deep learning artificial neural network education was studied for artificial intelligence literacy education for preservice teachers and incumbent teachers. In addition, through the proposed educational case, we tried to explore the contents of artificial neural network principle education that elementary, middle and high school students can experience. To this end, first, an example of training on the principle of operation of an artificial neural network that recognizes two types of images is presented. And as an artificial neural network extension application education case, an artificial neural network education case for recognizing three types of images was presented. The number of output layers was changed according to the number of images to be recognized by the artificial neural network, and the cases implemented in a spreadsheet were divided and explained. In addition, in order to experience the operation results of the artificial neural network, we presented the educational contents to directly write the learning data necessary for the artificial neural network of the supervised learning method. In this paper, the implementation of the artificial neural network and the recognition test results are visually presented using a spreadsheet.

• Composites Research
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• v.36 no.6
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• pp.395-401
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• 2023

In this study, finite element modeling of unidirectional composite materials of the computed tomography (CT) was conducted using a supervised learning-based segmentation technique. Firstly, Micro-CT scan was performed to obtain the raw volume of unidirectional composite materials, providing microstructure information. From the CT volume images, actual microstructure of the cross-section of unidirectional composite materials was extracted by the labeling process. Then, a U-net deep learning model was trained with a small number of raw images as inputs and their labeled images as outputs to generate a segmentation model. Subsequently, most of remaining images were input to the trained U-net deep learning model to segment all raw volume for identifying complex microstructure, which was used for the generation of finite element model. Finally, the fiber volume fraction of the finite element model was compared with that of experimentally measured volume to validate the appropriateness of the proposed method.

• 한국정보교육학회:학술대회논문집
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• 2010.01a
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• pp.127-132
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• 2010

창의성은 21세기 지식기반 정보사회를 살아가는 현대인에게 정보홍수에서 새로운 정보를 재창조하는 중요한 능력이다. 그리고, 디지털미디어는 현대인들의 가장 빠르고 쉽게 생각이나 느낌을 표현하는 도구이다. 현 교육현장에서는 학습자의 창의성과 문제해결력 신장을 위해 디지털미디어를 활용한 이미지와 영상을 투입하고 있다. 하지만, 본 연구는 교수자의 입장에서 디지털미디어를 활용한 교수학습자료를 투입하는 것이 아닌, 학습자 스스로가 디지털미디어를 활용하여 자신의 생각을 창의성있게 표현하는데 중점을 두었다. 특히, 디지털미디어 중에서 디지털카메를 활용하여 초등학교 5학년 국어과에서 적합한 학습차시를 선택하여 학습자의 창의력 신장을 위한 수업을 설계하여 적용해 보기로 하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.349-352
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• 2009

최근 정보의 형태는 텍스트나 이미지 기반에서 벗어나 복합 멀티미디어, 즉 동영상 위주로 빠르게 이동하고 있다. 특히 사용자에 의해 제작되고 유통되는 동영상 UCC의 급격한 부상은 사용자의 정보 생산력과 정보 공유, 소비 형태를 능동적으로 변화시키고 있다. PC 뿐 아니라 IPTV에서도 주요 서비스 모델로 관심을 받는 동영상 UCC는 향후 지식 결부형 학습 콘텐츠로 옮아갈 것이라 예상되고 있으며 여기에는 수익 모델의 개발과 저작권 보호 이슈가 해결해야 할 선결 과제로 인식된다. 이에 본 논문은 방송 콘텐츠 제공 표준 기술인 TV-Anytime, 학습객체메타데이터인 LOM(Learning Object Metadata)을 기반으로 OSMU 동영상 UCC 학습 콘텐츠 서비스 모델을 위한 에디터를 설계하고 외부 콘텐츠 소스를 활용할 수 있는 콘텐츠 저작 시나리오에 기반한 메타데이터를 설계하였다. 이를 통해 사용자의 다양한 지식을 활용할 수 있는 UCC 학습 콘텐츠 서비스 모델 발굴과 콘텐츠의 확대 재생산에 있어서 적극적인 저작권 보호가 이루어질 것을 기대한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.779-782
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• 2003

컴퓨터 관련기술의 눈부신 발전은 기술적인 분야뿐만 아니라 우리의 생활과 문화의 거의 모든 분야의 변화를 초래했다 그 중 하이퍼미디어 분야는 편이성과 오락성을 바탕으로 대중들에게 크게 어필하면서 전문가가 아닌 사용자에게도 컴퓨터에 대한 접근을 더욱 쉽게 유도하였다 이러한 하이퍼미디어의 가능성은 교육분야에도 광범위하게 적용되어 텍스트나 단순한 이미지만으로 학습하기 어려운 부분을 동영상과 사운드, 상호작용을 이용하여 보다 쉽고 편리하며 재미있게 학습할 수 있게 되었다. 본 연구에서 구현된 컴파일러 프론트 엔드 학습모듈은 이러한 하이퍼미디어의 장점을 최대한 살려 컴퓨터 관련학과 학생들에게도 난이도가 높은 '컴파일러'라는 과목을 보다 시각적이고 역동적인 화면과 사운드를 이용하여 학습효과를 극대화하는데 활용할 수 있을 것이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.637-639
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• 2021

인공지능 기반의 생활폐기물의 인식 및 선별에서, 선별 정확도의 저하는 인식 대상의 형태적 다양성과 학습데이터 부족 및 불균등성에 기인한다. 본 연구에서는 비전 인공지능 기반의 효과적인 폐기물 선별을 위한 인식 시스템 및 감독학습 기반의 인공지능 학습 기법을 제안한다. 생활폐기물 중 순환자원적 가치가 높은 CAN, PET, 그리고 이와 형상적으로 유사한 폐기물에 대해 본 연구에서 제안된 시스템에서 물체원형 및 훼손된 형태의 총 18 종 이미지 데이터를 대상으로, 감독학습기반의 인공지능 모델 제작에서 최적의 데이터 레이블링을 위한 분류체계를 제시한다.

• Journal of the Korean earth science society
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• v.42 no.1
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• pp.88-101
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• 2021

The purpose of this study was to qualitatively examine the meaning of students' drawings in outdoor classes and modeling-based learning cycles. Ten students were observed in a gifted education center in Seoul. Under the theme of the Hantan River, three outdoor classes and three modeling activities were conducted. Data were collected to document all student activities during field trips and classroom modeling activities using simultaneous video and audio recording and observation notes made by the researcher and students. Please note it is unclear what this citation refers to. If it is the previous sentence it should be placed within that sentence's punctuation. Hatisaru (2020) Ddrawing typess were classified by modifying the representations in a learning context in geological field trips. We used deductive content analysis to describe the drawing characteristics, including students writing. The results suggest that students have symbolic images that consist of geologic concepts, visual images that describe topographical features, and affective images that express students' emotion domains. The characteristics were classified into explanation, generality, elaboration, evidence, coherence, and state-of-mind. The characteristics and drawing types are consecutive in the modeling-based learning cycle and reflect the students' positive attitude and cognitive scientific domain. Drawing is a useful tool for reflecting students' thoughts and opinions in both outdoor class and classroom modeling activities. This study provides implications for emphasizing the importance of drawing activities.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.360-366
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• 2022

This paper presents a AI model structure for restoring Korean traditional palace photographs, which remain only black-and-white photographs, to color photographs using Pix2Pix, one of the adversarial generative neural network techniques. Pix2Pix consists of a combination of a synthetic image generator model and a discriminator model that determines whether a synthetic image is real or fake. This paper deals with an artificial intelligence model by adjusting a receptive field of the discriminator, and analyzes the results by considering the characteristics of the ancient palace photograph. The receptive field of Pix2Pix, which is used to restore black-and-white photographs, was commonly used in a fixed size, but a fixed size of receptive field is not suitable for a photograph which consisting with various change in an image. This paper observed the result of changing the size of the existing fixed a receptive field to identify the proper size of the discriminator that could reflect the characteristics of ancient palaces. In this experiment, the receptive field of the discriminator was adjusted based on the prepared ancient palace photos. This paper measure a loss of the model according to the change in a receptive field of the discriminator and check the results of restored photos using a well trained AI model from experiments.